retail_sales_forecast.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TprtqAhLn9w8"
      },
      "source": [
        "# IMPORT DATASET AND LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PVrKXCk4njhr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import zipfile\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S1soONkDu67P"
      },
      "outputs": [],
      "source": [
        "# import the csv files using pandas \n",
        "feature = pd.read_csv('Features_data_set.csv')\n",
        "sales = pd.read_csv('sales_data_set.csv')\n",
        "stores = pd.read_csv('stores_data_set.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zMFdrC3Z7RXP",
        "outputId": "4ad96651-1be2-4282-a768-512eaf6890a3"
      },
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Type</th>\n",
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            "text/plain": [
              "    Store Type    Size\n",
              "0       1    A  151315\n",
              "1       2    A  202307\n",
              "2       3    B   37392\n",
              "3       4    A  205863\n",
              "4       5    B   34875\n",
              "5       6    A  202505\n",
              "6       7    B   70713\n",
              "7       8    A  155078\n",
              "8       9    B  125833\n",
              "9      10    B  126512\n",
              "10     11    A  207499\n",
              "11     12    B  112238\n",
              "12     13    A  219622\n",
              "13     14    A  200898\n",
              "14     15    B  123737\n",
              "15     16    B   57197\n",
              "16     17    B   93188\n",
              "17     18    B  120653\n",
              "18     19    A  203819\n",
              "19     20    A  203742\n",
              "20     21    B  140167\n",
              "21     22    B  119557\n",
              "22     23    B  114533\n",
              "23     24    A  203819\n",
              "24     25    B  128107\n",
              "25     26    A  152513\n",
              "26     27    A  204184\n",
              "27     28    A  206302\n",
              "28     29    B   93638\n",
              "29     30    C   42988\n",
              "30     31    A  203750\n",
              "31     32    A  203007\n",
              "32     33    A   39690\n",
              "33     34    A  158114\n",
              "34     35    B  103681\n",
              "35     36    A   39910\n",
              "36     37    C   39910\n",
              "37     38    C   39690\n",
              "38     39    A  184109\n",
              "39     40    A  155083\n",
              "40     41    A  196321\n",
              "41     42    C   39690\n",
              "42     43    C   41062\n",
              "43     44    C   39910\n",
              "44     45    B  118221"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Let's explore the 3 dataframes\n",
        "# \"stores\" dataframe contains information related to the 45 stores such as type and size of store.\n",
        "\n",
        "stores"
      ]
    },
    {
      "cell_type": "code",
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        "id": "Za58hy8evyGP",
        "outputId": "215470c8-b75d-4de0-ad49-e3f43fe64076"
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            "text/plain": [
              "      Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
              "0         1  05/02/2010        42.31       2.572        NaN        NaN   \n",
              "1         1  12/02/2010        38.51       2.548        NaN        NaN   \n",
              "2         1  19/02/2010        39.93       2.514        NaN        NaN   \n",
              "3         1  26/02/2010        46.63       2.561        NaN        NaN   \n",
              "4         1  05/03/2010        46.50       2.625        NaN        NaN   \n",
              "...     ...         ...          ...         ...        ...        ...   \n",
              "8185     45  28/06/2013        76.05       3.639    4842.29     975.03   \n",
              "8186     45  05/07/2013        77.50       3.614    9090.48    2268.58   \n",
              "8187     45  12/07/2013        79.37       3.614    3789.94    1827.31   \n",
              "8188     45  19/07/2013        82.84       3.737    2961.49    1047.07   \n",
              "8189     45  26/07/2013        76.06       3.804     212.02     851.73   \n",
              "\n",
              "      MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
              "0           NaN        NaN        NaN  211.096358         8.106      False  \n",
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              "3           NaN        NaN        NaN  211.319643         8.106      False  \n",
              "4           NaN        NaN        NaN  211.350143         8.106      False  \n",
              "...         ...        ...        ...         ...           ...        ...  \n",
              "8185       3.00    2449.97    3169.69         NaN           NaN      False  \n",
              "8186     582.74    5797.47    1514.93         NaN           NaN      False  \n",
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              "8188     204.19     363.00    1059.46         NaN           NaN      False  \n",
              "8189       2.06      10.88    1864.57         NaN           NaN      False  \n",
              "\n",
              "[8190 rows x 12 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Let's explore the \"feature\" dataframe\n",
        "# Features dataframe contains additional data related to the store, department, and regional activity for the given dates.\n",
        "# Store: store number\n",
        "# Date: week\n",
        "# Temperature: average temperature in the region\n",
        "# Fuel_Price: cost of fuel in the region\n",
        "# MarkDown1-5: anonymized data related to promotional markdowns. \n",
        "# CPI: consumer price index\n",
        "# Unemployment: unemployment rate\n",
        "# IsHoliday: whether the week is a special holiday week or not\n",
        "\n",
        "feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "bIOr7xnD5IZn",
        "outputId": "901b09cd-f03f-4b07-e1b8-01aa818f7e84"
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      "outputs": [
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>1061.02</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>421568</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
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              "      <td>98</td>\n",
              "      <td>26/10/2012</td>\n",
              "      <td>1076.80</td>\n",
              "      <td>False</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept        Date  Weekly_Sales  IsHoliday\n",
              "0           1     1  05/02/2010      24924.50      False\n",
              "1           1     1  12/02/2010      46039.49       True\n",
              "2           1     1  19/02/2010      41595.55      False\n",
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              "4           1     1  05/03/2010      21827.90      False\n",
              "...       ...   ...         ...           ...        ...\n",
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              "421567     45    98  12/10/2012       1061.02      False\n",
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              "\n",
              "[421570 rows x 5 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "# Let's explore the \"sales\" dataframe\n",
        "# \"Sales\" dataframe contains historical sales data, which covers 2010-02-05 to 2012-11-01. \n",
        "# Store: store number\n",
        "# Dept: department number\n",
        "# Date: the week\n",
        "# Weekly_Sales: sales for the given department in the given store\n",
        "# IsHoliday: whether the week is a special holiday week\n",
        "\n",
        "sales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mDyVGRM7VHb"
      },
      "source": [
        "# EXPLORE INDIVIDUAL DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MnLmPzZQ7PkR"
      },
      "outputs": [],
      "source": [
        "# Change the datatype of 'date' column\n",
        "\n",
        "feature['Date'] = pd.to_datetime(feature['Date'])\n",
        "sales['Date'] = pd.to_datetime(sales['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "VHgTmLH38nCI",
        "outputId": "f2806c2a-5152-4212-ca60-d098f993aef5"
      },
      "outputs": [
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              "      <td>582.74</td>\n",
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              "      <td>45</td>\n",
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              "      <td>3.614</td>\n",
              "      <td>3789.94</td>\n",
              "      <td>1827.31</td>\n",
              "      <td>85.72</td>\n",
              "      <td>744.84</td>\n",
              "      <td>2150.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
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              "      <td>76.06</td>\n",
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              "      <td>1864.57</td>\n",
              "      <td>NaN</td>\n",
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              "      Store       Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
              "0         1 2010-05-02        42.31       2.572        NaN        NaN   \n",
              "1         1 2010-12-02        38.51       2.548        NaN        NaN   \n",
              "2         1 2010-02-19        39.93       2.514        NaN        NaN   \n",
              "3         1 2010-02-26        46.63       2.561        NaN        NaN   \n",
              "4         1 2010-05-03        46.50       2.625        NaN        NaN   \n",
              "...     ...        ...          ...         ...        ...        ...   \n",
              "8185     45 2013-06-28        76.05       3.639    4842.29     975.03   \n",
              "8186     45 2013-05-07        77.50       3.614    9090.48    2268.58   \n",
              "8187     45 2013-12-07        79.37       3.614    3789.94    1827.31   \n",
              "8188     45 2013-07-19        82.84       3.737    2961.49    1047.07   \n",
              "8189     45 2013-07-26        76.06       3.804     212.02     851.73   \n",
              "\n",
              "      MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
              "0           NaN        NaN        NaN  211.096358         8.106      False  \n",
              "1           NaN        NaN        NaN  211.242170         8.106       True  \n",
              "2           NaN        NaN        NaN  211.289143         8.106      False  \n",
              "3           NaN        NaN        NaN  211.319643         8.106      False  \n",
              "4           NaN        NaN        NaN  211.350143         8.106      False  \n",
              "...         ...        ...        ...         ...           ...        ...  \n",
              "8185       3.00    2449.97    3169.69         NaN           NaN      False  \n",
              "8186     582.74    5797.47    1514.93         NaN           NaN      False  \n",
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              "8188     204.19     363.00    1059.46         NaN           NaN      False  \n",
              "8189       2.06      10.88    1864.57         NaN           NaN      False  \n",
              "\n",
              "[8190 rows x 12 columns]"
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          },
          "execution_count": 7,
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        "feature"
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-05-03</td>\n",
              "      <td>21827.90</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421565</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-09-28</td>\n",
              "      <td>508.37</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421566</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-05-10</td>\n",
              "      <td>628.10</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421567</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-12-10</td>\n",
              "      <td>1061.02</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421568</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-10-19</td>\n",
              "      <td>760.01</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421569</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1076.80</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "0           1     1 2010-05-02      24924.50      False\n",
              "1           1     1 2010-12-02      46039.49       True\n",
              "2           1     1 2010-02-19      41595.55      False\n",
              "3           1     1 2010-02-26      19403.54      False\n",
              "4           1     1 2010-05-03      21827.90      False\n",
              "...       ...   ...        ...           ...        ...\n",
              "421565     45    98 2012-09-28        508.37      False\n",
              "421566     45    98 2012-05-10        628.10      False\n",
              "421567     45    98 2012-12-10       1061.02      False\n",
              "421568     45    98 2012-10-19        760.01      False\n",
              "421569     45    98 2012-10-26       1076.80      False\n",
              "\n",
              "[421570 rows x 5 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CnohmFqH4KU"
      },
      "source": [
        "# MERGE DATASET INTO ONE DATAFRAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tjgqVAdd7RXa",
        "outputId": "624c48d3-687d-4922-dd0f-2ec84e623a70"
      },
      "outputs": [
        {
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>46039.49</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2010-02-19</td>\n",
              "      <td>41595.55</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-26</td>\n",
              "      <td>19403.54</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-05-03</td>\n",
              "      <td>21827.90</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "0      1     1 2010-05-02      24924.50      False\n",
              "1      1     1 2010-12-02      46039.49       True\n",
              "2      1     1 2010-02-19      41595.55      False\n",
              "3      1     1 2010-02-26      19403.54      False\n",
              "4      1     1 2010-05-03      21827.90      False"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sales.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dsfr3iZn7RXa",
        "outputId": "4312af25-1098-4d96-ee62-b64b0df36011"
      },
      "outputs": [
        {
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Date</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>IsHoliday</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>38.51</td>\n",
              "      <td>2.548</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>39.93</td>\n",
              "      <td>2.514</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.289143</td>\n",
              "      <td>8.106</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-26</td>\n",
              "      <td>46.63</td>\n",
              "      <td>2.561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>8.106</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-05-03</td>\n",
              "      <td>46.50</td>\n",
              "      <td>2.625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.350143</td>\n",
              "      <td>8.106</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store       Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  MarkDown3  \\\n",
              "0      1 2010-05-02        42.31       2.572        NaN        NaN        NaN   \n",
              "1      1 2010-12-02        38.51       2.548        NaN        NaN        NaN   \n",
              "2      1 2010-02-19        39.93       2.514        NaN        NaN        NaN   \n",
              "3      1 2010-02-26        46.63       2.561        NaN        NaN        NaN   \n",
              "4      1 2010-05-03        46.50       2.625        NaN        NaN        NaN   \n",
              "\n",
              "   MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
              "0        NaN        NaN  211.096358         8.106      False  \n",
              "1        NaN        NaN  211.242170         8.106       True  \n",
              "2        NaN        NaN  211.289143         8.106      False  \n",
              "3        NaN        NaN  211.319643         8.106      False  \n",
              "4        NaN        NaN  211.350143         8.106      False  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "feature.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n-rQAQ5Q7RXa"
      },
      "outputs": [],
      "source": [
        "df = pd.merge(sales, feature, on = ['Store','Date','IsHoliday'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qNdkfpIN7RXb",
        "outputId": "cd862fe7-74ba-4028-bcfe-0ad695f8bea6"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Dept</th>\n",
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              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
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              "      <td>2010-05-02</td>\n",
              "      <td>32229.38</td>\n",
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              "      <td>2012-10-26</td>\n",
              "      <td>2487.80</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>45</td>\n",
              "      <td>94</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>5203.31</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
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              "      <td>False</td>\n",
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              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
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              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
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              "    <tr>\n",
              "      <th>421568</th>\n",
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              "      <td>97</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>6817.48</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421569</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1076.80</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
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              "      <td>58.08</td>\n",
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              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
              "0           1     1 2010-05-02      24924.50      False        42.31   \n",
              "1           1     2 2010-05-02      50605.27      False        42.31   \n",
              "2           1     3 2010-05-02      13740.12      False        42.31   \n",
              "3           1     4 2010-05-02      39954.04      False        42.31   \n",
              "4           1     5 2010-05-02      32229.38      False        42.31   \n",
              "...       ...   ...        ...           ...        ...          ...   \n",
              "421565     45    93 2012-10-26       2487.80      False        58.85   \n",
              "421566     45    94 2012-10-26       5203.31      False        58.85   \n",
              "421567     45    95 2012-10-26      56017.47      False        58.85   \n",
              "421568     45    97 2012-10-26       6817.48      False        58.85   \n",
              "421569     45    98 2012-10-26       1076.80      False        58.85   \n",
              "\n",
              "        Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
              "0            2.572        NaN        NaN        NaN        NaN        NaN   \n",
              "1            2.572        NaN        NaN        NaN        NaN        NaN   \n",
              "2            2.572        NaN        NaN        NaN        NaN        NaN   \n",
              "3            2.572        NaN        NaN        NaN        NaN        NaN   \n",
              "4            2.572        NaN        NaN        NaN        NaN        NaN   \n",
              "...            ...        ...        ...        ...        ...        ...   \n",
              "421565       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421566       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421567       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421568       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421569       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "\n",
              "               CPI  Unemployment  \n",
              "0       211.096358         8.106  \n",
              "1       211.096358         8.106  \n",
              "2       211.096358         8.106  \n",
              "3       211.096358         8.106  \n",
              "4       211.096358         8.106  \n",
              "...            ...           ...  \n",
              "421565  192.308899         8.667  \n",
              "421566  192.308899         8.667  \n",
              "421567  192.308899         8.667  \n",
              "421568  192.308899         8.667  \n",
              "421569  192.308899         8.667  \n",
              "\n",
              "[421570 rows x 14 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "txJVhNTG7RXb",
        "outputId": "3bc4343c-09d1-4982-ac93-0bb0a99c91af"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
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              "      <td>2010-05-02</td>\n",
              "      <td>13740.12</td>\n",
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              "      <td>2.572</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>32229.38</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
              "0      1     1 2010-05-02      24924.50      False        42.31       2.572   \n",
              "1      1     2 2010-05-02      50605.27      False        42.31       2.572   \n",
              "2      1     3 2010-05-02      13740.12      False        42.31       2.572   \n",
              "3      1     4 2010-05-02      39954.04      False        42.31       2.572   \n",
              "4      1     5 2010-05-02      32229.38      False        42.31       2.572   \n",
              "\n",
              "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
              "0        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "1        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "2        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "3        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "4        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "\n",
              "   Unemployment  \n",
              "0         8.106  \n",
              "1         8.106  \n",
              "2         8.106  \n",
              "3         8.106  \n",
              "4         8.106  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XClko0vb7RXb",
        "outputId": "94b7850d-6543-49cc-eb29-cac11ffe6a23"
      },
      "outputs": [
        {
          "data": {
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              "  <tbody>\n",
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              "   Store Type    Size\n",
              "0      1    A  151315\n",
              "1      2    A  202307\n",
              "2      3    B   37392\n",
              "3      4    A  205863\n",
              "4      5    B   34875"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stores.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWpxLB5E7RXb"
      },
      "outputs": [],
      "source": [
        "df = pd.merge(df, stores, on = ['Store'], how = 'left')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cowhzkih7RXc",
        "outputId": "ca5d77b9-5550-4b0d-b5e4-18f232318376"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Dept</th>\n",
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              "      <th>Weekly_Sales</th>\n",
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              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2010-05-02</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>39954.04</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>32229.38</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
              "0      1     1 2010-05-02      24924.50      False        42.31       2.572   \n",
              "1      1     2 2010-05-02      50605.27      False        42.31       2.572   \n",
              "2      1     3 2010-05-02      13740.12      False        42.31       2.572   \n",
              "3      1     4 2010-05-02      39954.04      False        42.31       2.572   \n",
              "4      1     5 2010-05-02      32229.38      False        42.31       2.572   \n",
              "\n",
              "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
              "0        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "1        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "2        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "3        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "4        NaN        NaN        NaN        NaN        NaN  211.096358   \n",
              "\n",
              "   Unemployment Type    Size  \n",
              "0         8.106    A  151315  \n",
              "1         8.106    A  151315  \n",
              "2         8.106    A  151315  \n",
              "3         8.106    A  151315  \n",
              "4         8.106    A  151315  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZLZ5HzT7RXc",
        "outputId": "57261ccd-2bc3-4f24-faf7-617bfa47ac13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['2010', '05', '02']"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = '2010-05-02'\n",
        "str(x).split('-')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNFI-ZAFb14P"
      },
      "source": [
        "# EXPLORE MERGED DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "44huAIZj7RXd",
        "outputId": "45472267-5f8d-4759-e16b-bf11101fbe94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(df.isnull(), cbar = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "Kt3eLBWgI4YY",
        "outputId": "5263c64b-3128-4c4e-cced-d305deb342ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Store                0\n",
              "Dept                 0\n",
              "Date                 0\n",
              "Weekly_Sales         0\n",
              "IsHoliday            0\n",
              "Temperature          0\n",
              "Fuel_Price           0\n",
              "MarkDown1       270889\n",
              "MarkDown2       310322\n",
              "MarkDown3       284479\n",
              "MarkDown4       286603\n",
              "MarkDown5       270138\n",
              "CPI                  0\n",
              "Unemployment         0\n",
              "Type                 0\n",
              "Size                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check the number of non-null values in the dataframe\n",
        "df.isnull().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lt3bPLLHJFTb"
      },
      "outputs": [],
      "source": [
        "# Fill up NaN elements with zeros\n",
        "df = df.fillna(0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ykoz3N5W7RXe",
        "outputId": "8e529bad-3d8d-4b08-aab1-48755fed4f04"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>24924.50</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>50605.27</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>13740.12</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>39954.04</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>32229.38</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421565</th>\n",
              "      <td>45</td>\n",
              "      <td>93</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>2487.80</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421566</th>\n",
              "      <td>45</td>\n",
              "      <td>94</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>5203.31</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421567</th>\n",
              "      <td>45</td>\n",
              "      <td>95</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>56017.47</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421568</th>\n",
              "      <td>45</td>\n",
              "      <td>97</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>6817.48</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421569</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1076.80</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
              "0           1     1 2010-05-02      24924.50      False        42.31   \n",
              "1           1     2 2010-05-02      50605.27      False        42.31   \n",
              "2           1     3 2010-05-02      13740.12      False        42.31   \n",
              "3           1     4 2010-05-02      39954.04      False        42.31   \n",
              "4           1     5 2010-05-02      32229.38      False        42.31   \n",
              "...       ...   ...        ...           ...        ...          ...   \n",
              "421565     45    93 2012-10-26       2487.80      False        58.85   \n",
              "421566     45    94 2012-10-26       5203.31      False        58.85   \n",
              "421567     45    95 2012-10-26      56017.47      False        58.85   \n",
              "421568     45    97 2012-10-26       6817.48      False        58.85   \n",
              "421569     45    98 2012-10-26       1076.80      False        58.85   \n",
              "\n",
              "        Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
              "0            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "1            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "2            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "3            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "4            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "...            ...        ...        ...        ...        ...        ...   \n",
              "421565       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421566       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421567       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421568       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421569       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "\n",
              "               CPI  Unemployment Type    Size  \n",
              "0       211.096358         8.106    A  151315  \n",
              "1       211.096358         8.106    A  151315  \n",
              "2       211.096358         8.106    A  151315  \n",
              "3       211.096358         8.106    A  151315  \n",
              "4       211.096358         8.106    A  151315  \n",
              "...            ...           ...  ...     ...  \n",
              "421565  192.308899         8.667    B  118221  \n",
              "421566  192.308899         8.667    B  118221  \n",
              "421567  192.308899         8.667    B  118221  \n",
              "421568  192.308899         8.667    B  118221  \n",
              "421569  192.308899         8.667    B  118221  \n",
              "\n",
              "[421570 rows x 16 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "qzaFDkCYLI4z",
        "outputId": "6df2ba56-ce48-457e-91ed-13dc0f49429a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.200546</td>\n",
              "      <td>44.260317</td>\n",
              "      <td>15981.258123</td>\n",
              "      <td>60.090059</td>\n",
              "      <td>3.361027</td>\n",
              "      <td>2590.074819</td>\n",
              "      <td>879.974298</td>\n",
              "      <td>468.087665</td>\n",
              "      <td>1083.132268</td>\n",
              "      <td>1662.772385</td>\n",
              "      <td>171.201947</td>\n",
              "      <td>7.960289</td>\n",
              "      <td>136727.915739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.785297</td>\n",
              "      <td>30.492054</td>\n",
              "      <td>22711.183519</td>\n",
              "      <td>18.447931</td>\n",
              "      <td>0.458515</td>\n",
              "      <td>6052.385934</td>\n",
              "      <td>5084.538801</td>\n",
              "      <td>5528.873453</td>\n",
              "      <td>3894.529945</td>\n",
              "      <td>4207.629321</td>\n",
              "      <td>39.159276</td>\n",
              "      <td>1.863296</td>\n",
              "      <td>60980.583328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-4988.940000</td>\n",
              "      <td>-2.060000</td>\n",
              "      <td>2.472000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-265.760000</td>\n",
              "      <td>-29.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>126.064000</td>\n",
              "      <td>3.879000</td>\n",
              "      <td>34875.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2079.650000</td>\n",
              "      <td>46.680000</td>\n",
              "      <td>2.933000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.022667</td>\n",
              "      <td>6.891000</td>\n",
              "      <td>93638.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>7612.030000</td>\n",
              "      <td>62.090000</td>\n",
              "      <td>3.452000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.318780</td>\n",
              "      <td>7.866000</td>\n",
              "      <td>140167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>20205.852500</td>\n",
              "      <td>74.280000</td>\n",
              "      <td>3.738000</td>\n",
              "      <td>2809.050000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>4.540000</td>\n",
              "      <td>425.290000</td>\n",
              "      <td>2168.040000</td>\n",
              "      <td>212.416993</td>\n",
              "      <td>8.572000</td>\n",
              "      <td>202505.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>693099.360000</td>\n",
              "      <td>100.140000</td>\n",
              "      <td>4.468000</td>\n",
              "      <td>88646.760000</td>\n",
              "      <td>104519.540000</td>\n",
              "      <td>141630.610000</td>\n",
              "      <td>67474.850000</td>\n",
              "      <td>108519.280000</td>\n",
              "      <td>227.232807</td>\n",
              "      <td>14.313000</td>\n",
              "      <td>219622.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Store           Dept   Weekly_Sales    Temperature  \\\n",
              "count  421570.000000  421570.000000  421570.000000  421570.000000   \n",
              "mean       22.200546      44.260317   15981.258123      60.090059   \n",
              "std        12.785297      30.492054   22711.183519      18.447931   \n",
              "min         1.000000       1.000000   -4988.940000      -2.060000   \n",
              "25%        11.000000      18.000000    2079.650000      46.680000   \n",
              "50%        22.000000      37.000000    7612.030000      62.090000   \n",
              "75%        33.000000      74.000000   20205.852500      74.280000   \n",
              "max        45.000000      99.000000  693099.360000     100.140000   \n",
              "\n",
              "          Fuel_Price      MarkDown1      MarkDown2      MarkDown3  \\\n",
              "count  421570.000000  421570.000000  421570.000000  421570.000000   \n",
              "mean        3.361027    2590.074819     879.974298     468.087665   \n",
              "std         0.458515    6052.385934    5084.538801    5528.873453   \n",
              "min         2.472000       0.000000    -265.760000     -29.100000   \n",
              "25%         2.933000       0.000000       0.000000       0.000000   \n",
              "50%         3.452000       0.000000       0.000000       0.000000   \n",
              "75%         3.738000    2809.050000       2.200000       4.540000   \n",
              "max         4.468000   88646.760000  104519.540000  141630.610000   \n",
              "\n",
              "           MarkDown4      MarkDown5            CPI   Unemployment  \\\n",
              "count  421570.000000  421570.000000  421570.000000  421570.000000   \n",
              "mean     1083.132268    1662.772385     171.201947       7.960289   \n",
              "std      3894.529945    4207.629321      39.159276       1.863296   \n",
              "min         0.000000       0.000000     126.064000       3.879000   \n",
              "25%         0.000000       0.000000     132.022667       6.891000   \n",
              "50%         0.000000       0.000000     182.318780       7.866000   \n",
              "75%       425.290000    2168.040000     212.416993       8.572000   \n",
              "max     67474.850000  108519.280000     227.232807      14.313000   \n",
              "\n",
              "                Size  \n",
              "count  421570.000000  \n",
              "mean   136727.915739  \n",
              "std     60980.583328  \n",
              "min     34875.000000  \n",
              "25%     93638.000000  \n",
              "50%    140167.000000  \n",
              "75%    202505.000000  \n",
              "max    219622.000000  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Statistical summary of the combined dataframe\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "xd-eVE5fMATZ",
        "outputId": "d25cea9d-b45b-4ecb-c4dc-04145dec9422"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check the number of duplicated entries in the dataframe\n",
        "df.duplicated().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "Rnt5bS74kt8u",
        "outputId": "6405949a-989c-42d6-ff67-0fb0159bc8c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "A    215478\n",
              "B    163495\n",
              "C     42597\n",
              "Name: Type, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Type'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNt9-bNH7RXf"
      },
      "source": [
        "MINI CHALLENGE\n",
        "- Replace the \"IsHoliday\" with ones and zeros instead of True and False (characters with numbers)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qtOUcsuH7RXf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YiO_pgFYt8Bs"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ge5qx0J_Tes8"
      },
      "source": [
        "# PERFORM EXPLORATORY DATA ANALYSIS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TCuqUkdgW_Mg"
      },
      "outputs": [],
      "source": [
        "# Create pivot tables to understand the relationship in the data\n",
        "\n",
        "result = pd.pivot_table(df, values = 'Weekly_Sales', columns = ['Type'], index = ['Date', 'Store', 'Dept'],\n",
        "                    aggfunc= np.mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "PTJYRJX_XJHc",
        "outputId": "48250681-e893-44b9-f1a6-856abb909a4a"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2010-01-10</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
              "      <th>1</th>\n",
              "      <td>20094.19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45829.02</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9775.17</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34912.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>23381.38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2012-12-10</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">45</th>\n",
              "      <th>93</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2644.24</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>94</th>\n",
              "      <td>NaN</td>\n",
              "      <td>4041.28</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>95</th>\n",
              "      <td>NaN</td>\n",
              "      <td>49334.77</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6463.32</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1061.02</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Type                          A         B   C\n",
              "Date       Store Dept                        \n",
              "2010-01-10 1     1     20094.19       NaN NaN\n",
              "                 2     45829.02       NaN NaN\n",
              "                 3      9775.17       NaN NaN\n",
              "                 4     34912.45       NaN NaN\n",
              "                 5     23381.38       NaN NaN\n",
              "...                         ...       ...  ..\n",
              "2012-12-10 45    93         NaN   2644.24 NaN\n",
              "                 94         NaN   4041.28 NaN\n",
              "                 95         NaN  49334.77 NaN\n",
              "                 97         NaN   6463.32 NaN\n",
              "                 98         NaN   1061.02 NaN\n",
              "\n",
              "[421570 rows x 3 columns]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
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        "result"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "--LMFrw5XM-U",
        "outputId": "714bf8dd-5dc0-4d17-b165-665af0c24f34"
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      "outputs": [
        {
          "data": {
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              "      <th>Type</th>\n",
              "      <th>A</th>\n",
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              "      <th>count</th>\n",
              "      <td>215478.000000</td>\n",
              "      <td>163495.000000</td>\n",
              "      <td>42597.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20099.568043</td>\n",
              "      <td>12237.075977</td>\n",
              "      <td>9519.532538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>26423.457227</td>\n",
              "      <td>17203.668989</td>\n",
              "      <td>15985.351612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-4988.940000</td>\n",
              "      <td>-3924.000000</td>\n",
              "      <td>-379.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3315.090000</td>\n",
              "      <td>1927.055000</td>\n",
              "      <td>131.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10105.170000</td>\n",
              "      <td>6187.870000</td>\n",
              "      <td>1149.670000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>26357.180000</td>\n",
              "      <td>15353.740000</td>\n",
              "      <td>12695.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>474330.100000</td>\n",
              "      <td>693099.360000</td>\n",
              "      <td>112152.350000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Type               A              B              C\n",
              "count  215478.000000  163495.000000   42597.000000\n",
              "mean    20099.568043   12237.075977    9519.532538\n",
              "std     26423.457227   17203.668989   15985.351612\n",
              "min     -4988.940000   -3924.000000    -379.000000\n",
              "25%      3315.090000    1927.055000     131.990000\n",
              "50%     10105.170000    6187.870000    1149.670000\n",
              "75%     26357.180000   15353.740000   12695.010000\n",
              "max    474330.100000  693099.360000  112152.350000"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result.describe()\n",
        "# It can be seen that Type A stores have much higher sales than Type B and Type C"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S1brlIr-C15J"
      },
      "outputs": [],
      "source": [
        "\n",
        "result_md = pd.pivot_table(df, values = ['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'], columns = ['IsHoliday'], index = ['Date', 'Store','Dept'],\n",
        "                    aggfunc={'MarkDown1' : np.mean,'MarkDown2' : np.mean, 'MarkDown3' : np.mean, 'MarkDown4' : np.mean, 'MarkDown5' : np.mean})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 481
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        "id": "LWFBGF6yTWOV",
        "outputId": "8fadd2dc-1487-485b-a35d-f5a39eb152b6"
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>IsHoliday</th>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>5</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2012-12-10</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">45</th>\n",
              "      <th>93</th>\n",
              "      <td>1956.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>599.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3990.54</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1956.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>599.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3990.54</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1956.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>599.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3990.54</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1956.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>599.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3990.54</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1956.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>599.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3990.54</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      MarkDown1       MarkDown2       MarkDown3        \\\n",
              "IsHoliday                 False True      False True      False True    \n",
              "Date       Store Dept                                                   \n",
              "2010-01-10 1     1         0.00   NaN       0.0   NaN      0.00   NaN   \n",
              "                 2         0.00   NaN       0.0   NaN      0.00   NaN   \n",
              "                 3         0.00   NaN       0.0   NaN      0.00   NaN   \n",
              "                 4         0.00   NaN       0.0   NaN      0.00   NaN   \n",
              "                 5         0.00   NaN       0.0   NaN      0.00   NaN   \n",
              "...                         ...   ...       ...   ...       ...   ...   \n",
              "2012-12-10 45    93     1956.28   NaN       0.0   NaN      7.89   NaN   \n",
              "                 94     1956.28   NaN       0.0   NaN      7.89   NaN   \n",
              "                 95     1956.28   NaN       0.0   NaN      7.89   NaN   \n",
              "                 97     1956.28   NaN       0.0   NaN      7.89   NaN   \n",
              "                 98     1956.28   NaN       0.0   NaN      7.89   NaN   \n",
              "\n",
              "                      MarkDown4       MarkDown5        \n",
              "IsHoliday                 False True      False True   \n",
              "Date       Store Dept                                  \n",
              "2010-01-10 1     1         0.00   NaN      0.00   NaN  \n",
              "                 2         0.00   NaN      0.00   NaN  \n",
              "                 3         0.00   NaN      0.00   NaN  \n",
              "                 4         0.00   NaN      0.00   NaN  \n",
              "                 5         0.00   NaN      0.00   NaN  \n",
              "...                         ...   ...       ...   ...  \n",
              "2012-12-10 45    93      599.32   NaN   3990.54   NaN  \n",
              "                 94      599.32   NaN   3990.54   NaN  \n",
              "                 95      599.32   NaN   3990.54   NaN  \n",
              "                 97      599.32   NaN   3990.54   NaN  \n",
              "                 98      599.32   NaN   3990.54   NaN  \n",
              "\n",
              "[421570 rows x 10 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_md"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "eve23zqHXltj",
        "outputId": "f69da564-ddb7-43f6-ac89-d2da6e99a213"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "           IsHoliday\n",
              "MarkDown1  False        1.017371e+09\n",
              "           True         7.452684e+07\n",
              "MarkDown2  False        2.310619e+08\n",
              "           True         1.399088e+08\n",
              "MarkDown3  False        2.460332e+07\n",
              "           True         1.727284e+08\n",
              "MarkDown4  False        4.196331e+08\n",
              "           True         3.698298e+07\n",
              "MarkDown5  False        6.585670e+08\n",
              "           True         4.240793e+07\n",
              "dtype: float64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_md.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "M5bAr9iUReW4",
        "outputId": "7542f97c-bafa-462e-8e99-f60b10881793"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">MarkDown1</th>\n",
              "      <th colspan=\"2\" halign=\"left\">MarkDown2</th>\n",
              "      <th colspan=\"2\" halign=\"left\">MarkDown3</th>\n",
              "      <th colspan=\"2\" halign=\"left\">MarkDown4</th>\n",
              "      <th colspan=\"2\" halign=\"left\">MarkDown5</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>391909.000000</td>\n",
              "      <td>29661.000000</td>\n",
              "      <td>391909.000000</td>\n",
              "      <td>29661.000000</td>\n",
              "      <td>391909.000000</td>\n",
              "      <td>29661.000000</td>\n",
              "      <td>391909.000000</td>\n",
              "      <td>29661.000000</td>\n",
              "      <td>391909.000000</td>\n",
              "      <td>29661.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2595.936803</td>\n",
              "      <td>2512.620778</td>\n",
              "      <td>589.580546</td>\n",
              "      <td>4716.929394</td>\n",
              "      <td>62.778142</td>\n",
              "      <td>5823.417900</td>\n",
              "      <td>1070.741151</td>\n",
              "      <td>1246.855336</td>\n",
              "      <td>1680.408001</td>\n",
              "      <td>1429.754053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6123.402037</td>\n",
              "      <td>5020.047408</td>\n",
              "      <td>2984.163111</td>\n",
              "      <td>15295.329993</td>\n",
              "      <td>630.704594</td>\n",
              "      <td>19959.302249</td>\n",
              "      <td>3921.553070</td>\n",
              "      <td>3513.998030</td>\n",
              "      <td>4312.031598</td>\n",
              "      <td>2427.638027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-265.760000</td>\n",
              "      <td>-9.980000</td>\n",
              "      <td>-29.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2826.570000</td>\n",
              "      <td>2463.160000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>3.840000</td>\n",
              "      <td>66.080000</td>\n",
              "      <td>442.390000</td>\n",
              "      <td>319.190000</td>\n",
              "      <td>2182.320000</td>\n",
              "      <td>2069.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88646.760000</td>\n",
              "      <td>36778.650000</td>\n",
              "      <td>45971.430000</td>\n",
              "      <td>104519.540000</td>\n",
              "      <td>25959.980000</td>\n",
              "      <td>141630.610000</td>\n",
              "      <td>67474.850000</td>\n",
              "      <td>29483.810000</td>\n",
              "      <td>108519.280000</td>\n",
              "      <td>11100.550000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               MarkDown1                    MarkDown2                 \\\n",
              "IsHoliday          False         True           False          True    \n",
              "count      391909.000000  29661.000000  391909.000000   29661.000000   \n",
              "mean         2595.936803   2512.620778     589.580546    4716.929394   \n",
              "std          6123.402037   5020.047408    2984.163111   15295.329993   \n",
              "min             0.000000      0.000000    -265.760000      -9.980000   \n",
              "25%             0.000000      0.000000       0.000000       0.000000   \n",
              "50%             0.000000      0.000000       0.000000       0.000000   \n",
              "75%          2826.570000   2463.160000       0.500000      65.000000   \n",
              "max         88646.760000  36778.650000   45971.430000  104519.540000   \n",
              "\n",
              "               MarkDown3                     MarkDown4                \\\n",
              "IsHoliday          False          True           False         True    \n",
              "count      391909.000000   29661.000000  391909.000000  29661.000000   \n",
              "mean           62.778142    5823.417900    1070.741151   1246.855336   \n",
              "std           630.704594   19959.302249    3921.553070   3513.998030   \n",
              "min           -29.100000       0.000000       0.000000      0.000000   \n",
              "25%             0.000000       0.000000       0.000000      0.000000   \n",
              "50%             0.000000       0.000000       0.000000      0.000000   \n",
              "75%             3.840000      66.080000     442.390000    319.190000   \n",
              "max         25959.980000  141630.610000   67474.850000  29483.810000   \n",
              "\n",
              "               MarkDown5                \n",
              "IsHoliday          False         True   \n",
              "count      391909.000000  29661.000000  \n",
              "mean         1680.408001   1429.754053  \n",
              "std          4312.031598   2427.638027  \n",
              "min             0.000000      0.000000  \n",
              "25%             0.000000      0.000000  \n",
              "50%             0.000000      0.000000  \n",
              "75%          2182.320000   2069.300000  \n",
              "max        108519.280000  11100.550000  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_md.describe()\n",
        "# we can conclude that MarkDown2 and MarkDown3 have higher volume on holidays compared to that of regular days \n",
        "# while other MarkDowns don't show significant changes relating to holiday."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bZ9245EiRfyW"
      },
      "outputs": [],
      "source": [
        "corr_matrix = df.drop(columns = ['Store']).corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "dlFQ8uY5xKvu",
        "outputId": "efed44c6-5815-433a-9ae0-4052df66a383"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (16,16))\n",
        "sns.heatmap(corr_matrix, annot = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hN04qp97RXh"
      },
      "source": [
        "# PERFORM DATA VISUALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_EfzQSvZ7RXh",
        "outputId": "7b4419b2-30bd-414c-bcb3-8920bca8a47d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>24924.50</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>50605.27</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>13740.12</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>39954.04</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2010-05-02</td>\n",
              "      <td>32229.38</td>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
              "      <td>2.572</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>211.096358</td>\n",
              "      <td>8.106</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421565</th>\n",
              "      <td>45</td>\n",
              "      <td>93</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>2487.80</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421566</th>\n",
              "      <td>45</td>\n",
              "      <td>94</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>5203.31</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421567</th>\n",
              "      <td>45</td>\n",
              "      <td>95</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>56017.47</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421568</th>\n",
              "      <td>45</td>\n",
              "      <td>97</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>6817.48</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421569</th>\n",
              "      <td>45</td>\n",
              "      <td>98</td>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1076.80</td>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>B</td>\n",
              "      <td>118221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
              "0           1     1 2010-05-02      24924.50      False        42.31   \n",
              "1           1     2 2010-05-02      50605.27      False        42.31   \n",
              "2           1     3 2010-05-02      13740.12      False        42.31   \n",
              "3           1     4 2010-05-02      39954.04      False        42.31   \n",
              "4           1     5 2010-05-02      32229.38      False        42.31   \n",
              "...       ...   ...        ...           ...        ...          ...   \n",
              "421565     45    93 2012-10-26       2487.80      False        58.85   \n",
              "421566     45    94 2012-10-26       5203.31      False        58.85   \n",
              "421567     45    95 2012-10-26      56017.47      False        58.85   \n",
              "421568     45    97 2012-10-26       6817.48      False        58.85   \n",
              "421569     45    98 2012-10-26       1076.80      False        58.85   \n",
              "\n",
              "        Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
              "0            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "1            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "2            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "3            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "4            2.572       0.00       0.00        0.0       0.00       0.00   \n",
              "...            ...        ...        ...        ...        ...        ...   \n",
              "421565       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421566       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421567       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421568       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "421569       3.882    4018.91      58.08      100.0     211.94     858.33   \n",
              "\n",
              "               CPI  Unemployment Type    Size  \n",
              "0       211.096358         8.106    A  151315  \n",
              "1       211.096358         8.106    A  151315  \n",
              "2       211.096358         8.106    A  151315  \n",
              "3       211.096358         8.106    A  151315  \n",
              "4       211.096358         8.106    A  151315  \n",
              "...            ...           ...  ...     ...  \n",
              "421565  192.308899         8.667    B  118221  \n",
              "421566  192.308899         8.667    B  118221  \n",
              "421567  192.308899         8.667    B  118221  \n",
              "421568  192.308899         8.667    B  118221  \n",
              "421569  192.308899         8.667    B  118221  \n",
              "\n",
              "[421570 rows x 16 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "CKo4wOfW7RXi",
        "outputId": "444938a2-c936-480d-8d22-eb1348fcf9d1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<string>:6: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
          ]
        },
        {
          "ename": "TypeError",
          "evalue": "numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_unsigned_subtract\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m    350\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m         \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigned_to_unsigned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: <class 'numpy.bool_'>",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-41cb5f2dd4da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mhist_frame\u001b[0;34m(data, column, by, grid, xlabelsize, xrot, ylabelsize, yrot, ax, sharex, sharey, figsize, layout, bins, backend, legend, **kwargs)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m     )\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/plotting/_matplotlib/hist.py\u001b[0m in \u001b[0;36mhist_frame\u001b[0;34m(data, column, by, grid, xlabelsize, xrot, ylabelsize, yrot, ax, sharex, sharey, figsize, layout, bins, legend, **kwds)\u001b[0m\n\u001b[1;32m    432\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcan_set_label\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1449\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m   6649\u001b[0m             \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6650\u001b[0m             \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6651\u001b[0;31m             \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6652\u001b[0m             \u001b[0mtops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6653\u001b[0m         \u001b[0mtops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m         \u001b[0;31m# Pre-compute histogram scaling factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 822\u001b[0;31m         \u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_equal_bins\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0m_unsigned_subtract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_edge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m         \u001b[0;31m# We iterate over blocks here for two reasons: the first is that for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_unsigned_subtract\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m    351\u001b[0m         \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigned_to_unsigned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubtract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0;31m# we know the inputs are integers, and we are deliberately casting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead."
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.hist(bins = 30, figsize = (20,20), color = 'r')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "3cRiy_jz7RXi",
        "outputId": "6c204e1d-b87a-4441-8ff1-2e476bc00b00"
      },
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "\"['month'] not in index\"",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-70643fe14f11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# holiday and sales\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Weekly sales and markdown #3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Weekly_Sales\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"IsHoliday\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"MarkDown1\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"MarkDown2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"MarkDown3\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"MarkDown4\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"MarkDown5\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Type\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"month\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2910\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2911\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2912\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2914\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1302\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m             \u001b[0;31m# we skip the warning on Categorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['month'] not in index\""
          ]
        }
      ],
      "source": [
        "# visualizing the relationship using pairplots\n",
        "# there is a relationship between markdown #1 and Markdown #4\n",
        "# holiday and sales \n",
        "# Weekly sales and markdown #3\n",
        "sns.pairplot(df[[\"Weekly_Sales\",\"IsHoliday\",\"MarkDown1\",\"MarkDown2\",\"MarkDown3\",\"MarkDown4\",\"MarkDown5\",\"Type\",\"month\"]], diag_kind = \"kde\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w5caPKS_7RXi"
      },
      "outputs": [],
      "source": [
        "df_type = df.groupby('Type').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "oZav6RvCkwCi",
        "outputId": "f943cd0c-922d-4791-d2cb-8bac7bc668df"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>21.736419</td>\n",
              "      <td>44.622156</td>\n",
              "      <td>20099.568043</td>\n",
              "      <td>0.070471</td>\n",
              "      <td>60.531945</td>\n",
              "      <td>3.343999</td>\n",
              "      <td>3102.403194</td>\n",
              "      <td>1083.216159</td>\n",
              "      <td>549.644930</td>\n",
              "      <td>1325.891281</td>\n",
              "      <td>2147.830168</td>\n",
              "      <td>174.408895</td>\n",
              "      <td>7.791595</td>\n",
              "      <td>182231.285486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>18.450417</td>\n",
              "      <td>43.112273</td>\n",
              "      <td>12237.075977</td>\n",
              "      <td>0.070412</td>\n",
              "      <td>57.562951</td>\n",
              "      <td>3.382523</td>\n",
              "      <td>2553.465968</td>\n",
              "      <td>827.500452</td>\n",
              "      <td>481.215226</td>\n",
              "      <td>1043.927675</td>\n",
              "      <td>1324.921913</td>\n",
              "      <td>167.176656</td>\n",
              "      <td>7.928836</td>\n",
              "      <td>101818.735827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>38.942015</td>\n",
              "      <td>46.836350</td>\n",
              "      <td>9519.532538</td>\n",
              "      <td>0.069582</td>\n",
              "      <td>67.554266</td>\n",
              "      <td>3.364654</td>\n",
              "      <td>138.960203</td>\n",
              "      <td>53.274338</td>\n",
              "      <td>5.142226</td>\n",
              "      <td>5.603993</td>\n",
              "      <td>505.826631</td>\n",
              "      <td>170.429314</td>\n",
              "      <td>8.934350</td>\n",
              "      <td>40535.725286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Store       Dept  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
              "Type                                                                           \n",
              "A     21.736419  44.622156  20099.568043   0.070471    60.531945    3.343999   \n",
              "B     18.450417  43.112273  12237.075977   0.070412    57.562951    3.382523   \n",
              "C     38.942015  46.836350   9519.532538   0.069582    67.554266    3.364654   \n",
              "\n",
              "        MarkDown1    MarkDown2   MarkDown3    MarkDown4    MarkDown5  \\\n",
              "Type                                                                   \n",
              "A     3102.403194  1083.216159  549.644930  1325.891281  2147.830168   \n",
              "B     2553.465968   827.500452  481.215226  1043.927675  1324.921913   \n",
              "C      138.960203    53.274338    5.142226     5.603993   505.826631   \n",
              "\n",
              "             CPI  Unemployment           Size  \n",
              "Type                                           \n",
              "A     174.408895      7.791595  182231.285486  \n",
              "B     167.176656      7.928836  101818.735827  \n",
              "C     170.429314      8.934350   40535.725286  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "104gl2UG7RXj",
        "outputId": "16c92aba-de99-424a-ee96-e6ced9ce5826"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Type', ylabel='Weekly_Sales'>"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x = df['Type'], y = df['Weekly_Sales'], data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "m40DoRw-kz_Z",
        "outputId": "98ba67b4-001a-4e44-8e34-19fb6411269c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
              "      <th>MarkDown3</th>\n",
              "      <th>MarkDown4</th>\n",
              "      <th>MarkDown5</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dept</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>19213.485088</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>2429.019322</td>\n",
              "      <td>818.872810</td>\n",
              "      <td>429.184037</td>\n",
              "      <td>1008.870435</td>\n",
              "      <td>1581.806813</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>130287.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>43607.020113</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>2429.019322</td>\n",
              "      <td>818.872810</td>\n",
              "      <td>429.184037</td>\n",
              "      <td>1008.870435</td>\n",
              "      <td>1581.806813</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>130287.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>11793.698516</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>2429.019322</td>\n",
              "      <td>818.872810</td>\n",
              "      <td>429.184037</td>\n",
              "      <td>1008.870435</td>\n",
              "      <td>1581.806813</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>130287.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>25974.630238</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>2429.019322</td>\n",
              "      <td>818.872810</td>\n",
              "      <td>429.184037</td>\n",
              "      <td>1008.870435</td>\n",
              "      <td>1581.806813</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>130287.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>22.757366</td>\n",
              "      <td>21365.583515</td>\n",
              "      <td>0.069797</td>\n",
              "      <td>60.559367</td>\n",
              "      <td>3.365397</td>\n",
              "      <td>2462.697233</td>\n",
              "      <td>830.226332</td>\n",
              "      <td>435.134596</td>\n",
              "      <td>1022.858240</td>\n",
              "      <td>1603.738276</td>\n",
              "      <td>171.109374</td>\n",
              "      <td>7.978057</td>\n",
              "      <td>131530.357964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>69824.423080</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>2429.019322</td>\n",
              "      <td>818.872810</td>\n",
              "      <td>429.184037</td>\n",
              "      <td>1008.870435</td>\n",
              "      <td>1581.806813</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>130287.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>23.258138</td>\n",
              "      <td>15210.942761</td>\n",
              "      <td>0.069839</td>\n",
              "      <td>61.539285</td>\n",
              "      <td>3.359920</td>\n",
              "      <td>2362.845647</td>\n",
              "      <td>820.762363</td>\n",
              "      <td>397.214137</td>\n",
              "      <td>999.452087</td>\n",
              "      <td>1660.599345</td>\n",
              "      <td>171.562686</td>\n",
              "      <td>7.849295</td>\n",
              "      <td>129282.010507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>23.357439</td>\n",
              "      <td>14255.576919</td>\n",
              "      <td>0.069767</td>\n",
              "      <td>60.490781</td>\n",
              "      <td>3.362418</td>\n",
              "      <td>2463.638764</td>\n",
              "      <td>833.096524</td>\n",
              "      <td>432.439341</td>\n",
              "      <td>1025.957821</td>\n",
              "      <td>1591.276367</td>\n",
              "      <td>170.445933</td>\n",
              "      <td>8.043564</td>\n",
              "      <td>130676.377190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>24.173920</td>\n",
              "      <td>6824.694889</td>\n",
              "      <td>0.071967</td>\n",
              "      <td>60.115942</td>\n",
              "      <td>3.372656</td>\n",
              "      <td>2569.994716</td>\n",
              "      <td>882.483088</td>\n",
              "      <td>467.655716</td>\n",
              "      <td>1074.883525</td>\n",
              "      <td>1678.390840</td>\n",
              "      <td>169.196443</td>\n",
              "      <td>8.111493</td>\n",
              "      <td>135266.091501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>21.438515</td>\n",
              "      <td>415.487065</td>\n",
              "      <td>0.110209</td>\n",
              "      <td>62.813596</td>\n",
              "      <td>3.592702</td>\n",
              "      <td>7741.403376</td>\n",
              "      <td>2164.573063</td>\n",
              "      <td>1734.841903</td>\n",
              "      <td>3897.476369</td>\n",
              "      <td>4526.868643</td>\n",
              "      <td>179.263692</td>\n",
              "      <td>7.428856</td>\n",
              "      <td>186622.052204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Store  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
              "Dept                                                                \n",
              "1     23.000000  19213.485088   0.069930    60.663782    3.358607   \n",
              "2     23.000000  43607.020113   0.069930    60.663782    3.358607   \n",
              "3     23.000000  11793.698516   0.069930    60.663782    3.358607   \n",
              "4     23.000000  25974.630238   0.069930    60.663782    3.358607   \n",
              "5     22.757366  21365.583515   0.069797    60.559367    3.365397   \n",
              "...         ...           ...        ...          ...         ...   \n",
              "95    23.000000  69824.423080   0.069930    60.663782    3.358607   \n",
              "96    23.258138  15210.942761   0.069839    61.539285    3.359920   \n",
              "97    23.357439  14255.576919   0.069767    60.490781    3.362418   \n",
              "98    24.173920   6824.694889   0.071967    60.115942    3.372656   \n",
              "99    21.438515    415.487065   0.110209    62.813596    3.592702   \n",
              "\n",
              "        MarkDown1    MarkDown2    MarkDown3    MarkDown4    MarkDown5  \\\n",
              "Dept                                                                    \n",
              "1     2429.019322   818.872810   429.184037  1008.870435  1581.806813   \n",
              "2     2429.019322   818.872810   429.184037  1008.870435  1581.806813   \n",
              "3     2429.019322   818.872810   429.184037  1008.870435  1581.806813   \n",
              "4     2429.019322   818.872810   429.184037  1008.870435  1581.806813   \n",
              "5     2462.697233   830.226332   435.134596  1022.858240  1603.738276   \n",
              "...           ...          ...          ...          ...          ...   \n",
              "95    2429.019322   818.872810   429.184037  1008.870435  1581.806813   \n",
              "96    2362.845647   820.762363   397.214137   999.452087  1660.599345   \n",
              "97    2463.638764   833.096524   432.439341  1025.957821  1591.276367   \n",
              "98    2569.994716   882.483088   467.655716  1074.883525  1678.390840   \n",
              "99    7741.403376  2164.573063  1734.841903  3897.476369  4526.868643   \n",
              "\n",
              "             CPI  Unemployment           Size  \n",
              "Dept                                           \n",
              "1     171.578394      7.999151  130287.600000  \n",
              "2     171.578394      7.999151  130287.600000  \n",
              "3     171.578394      7.999151  130287.600000  \n",
              "4     171.578394      7.999151  130287.600000  \n",
              "5     171.109374      7.978057  131530.357964  \n",
              "...          ...           ...            ...  \n",
              "95    171.578394      7.999151  130287.600000  \n",
              "96    171.562686      7.849295  129282.010507  \n",
              "97    170.445933      8.043564  130676.377190  \n",
              "98    169.196443      8.111493  135266.091501  \n",
              "99    179.263692      7.428856  186622.052204  \n",
              "\n",
              "[81 rows x 13 columns]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# df_dept = df.drop(columns = ['Store','Type','IsHoliday','Temperature','Fuel_Price','CPI','Unemployment','Size','month'])\n",
        "df_dept = df.groupby('Dept').mean()\n",
        "df_dept"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "16nzPWcIJDbY",
        "outputId": "c13b4895-b421-4fc7-f952-c34caa31ef92"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "df_dept['Weekly_Sales'].plot(kind = 'barh', color = 'r', width = 0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "TG1SiYP_VWCP",
        "outputId": "d6145dfa-6e23-4974-d172-3fa2db5fa91c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "df_dept['MarkDown1'].plot(kind = 'barh', color = 'blue', width = 0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "jCZ93S8nWKdR",
        "outputId": "85ef5a71-3d26-497a-8eb1-1f6c66a97a4a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "\n",
        "df_dept['MarkDown2'].plot(kind = 'barh', color = 'yellow', width = 0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "ciTfxcDZWKyT",
        "outputId": "f4b73766-dd54-454d-c8e8-b8c5ba27db25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "\n",
        "df_dept['MarkDown3'].plot(kind = 'barh', color = 'black', width = 0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "04IbCeM6WK6A",
        "outputId": "516a7e1c-4d18-4a14-d65a-5860abe0fbe8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "\n",
        "df_dept['MarkDown4'].plot(kind = 'barh', color = 'orange', width = 0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "IWQVY3-vehyC",
        "outputId": "288e53c5-bbd2-4725-bae5-e6141c32e95f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Dept'>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (14,16))\n",
        "\n",
        "df_dept['MarkDown5'].plot(kind = 'barh', color = 'brown', width = 0.9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8keNDrDg6-4"
      },
      "source": [
        "- We can conclude that departments that have poor weekly sales have been assigned high number of markdowns. Let's explore this in more details\n",
        "- Example: check out store 77 and 99 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "_pr8ZOw1opRj",
        "outputId": "24faa877-1e04-4de3-e6d3-0d05bbcbc949"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dept\n",
              "47      -7.682554\n",
              "43       1.193333\n",
              "78       7.296638\n",
              "39      11.123750\n",
              "51      21.931729\n",
              "45      23.211586\n",
              "54     108.305985\n",
              "77     328.961800\n",
              "60     347.370229\n",
              "99     415.487065\n",
              "28     618.085116\n",
              "59     694.463564\n",
              "48    1344.893576\n",
              "27    1583.437727\n",
              "19    1654.815030\n",
              "52    1928.356252\n",
              "41    1965.559998\n",
              "36    2022.571061\n",
              "85    2264.359407\n",
              "31    2339.440287\n",
              "50    2658.897010\n",
              "35    2921.044946\n",
              "37    3111.076193\n",
              "83    3383.349838\n",
              "58    3702.907419\n",
              "56    3833.706211\n",
              "30    4118.197208\n",
              "12    4175.397021\n",
              "44    4651.729658\n",
              "6     4747.856188\n",
              "Name: Weekly_Sales, dtype: float64"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Sort by weekly sales\n",
        "df_dept_sale = df_dept.sort_values(by = ['Weekly_Sales'], ascending = True)\n",
        "df_dept_sale['Weekly_Sales'][:30]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tvo6z45aBZ8"
      },
      "source": [
        "# PREPARE THE DATA BEFORE TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VvoT7ZIiyXFM"
      },
      "outputs": [],
      "source": [
        "# Drop the date\n",
        "df_target = df['Weekly_Sales']\n",
        "df_final = df.drop(columns = ['Weekly_Sales', 'Date'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M0fCbj6x7RXl"
      },
      "outputs": [],
      "source": [
        "df_final = pd.get_dummies(df_final, columns = ['Type', 'Store', 'Dept'], drop_first = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "LIslmuN89I3z",
        "outputId": "69562dc8-b1a1-4640-837f-791f6a1cf314",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(421570, 137)"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ezlGKAOm7RXm",
        "outputId": "5e6ba16d-0f33-4a60-ab57-009261dc2d2d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(421570,)"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_target.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jVuRyFk7RXm",
        "outputId": "ef53496d-c7e1-4bcf-eb62-b14b1681568c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Fuel_Price</th>\n",
              "      <th>MarkDown1</th>\n",
              "      <th>MarkDown2</th>\n",
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              "      <th>...</th>\n",
              "      <th>Dept_90</th>\n",
              "      <th>Dept_91</th>\n",
              "      <th>Dept_92</th>\n",
              "      <th>Dept_93</th>\n",
              "      <th>Dept_94</th>\n",
              "      <th>Dept_95</th>\n",
              "      <th>Dept_96</th>\n",
              "      <th>Dept_97</th>\n",
              "      <th>Dept_98</th>\n",
              "      <th>Dept_99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
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              "      <td>False</td>\n",
              "      <td>42.31</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>211.096358</td>\n",
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              "      <td>...</td>\n",
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              "      <th>421565</th>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
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              "      <th>421566</th>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421567</th>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>421568</th>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421569</th>\n",
              "      <td>False</td>\n",
              "      <td>58.85</td>\n",
              "      <td>3.882</td>\n",
              "      <td>4018.91</td>\n",
              "      <td>58.08</td>\n",
              "      <td>100.0</td>\n",
              "      <td>211.94</td>\n",
              "      <td>858.33</td>\n",
              "      <td>192.308899</td>\n",
              "      <td>8.667</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>421570 rows × 137 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        IsHoliday  Temperature  Fuel_Price  MarkDown1  MarkDown2  MarkDown3  \\\n",
              "0           False        42.31       2.572       0.00       0.00        0.0   \n",
              "1           False        42.31       2.572       0.00       0.00        0.0   \n",
              "2           False        42.31       2.572       0.00       0.00        0.0   \n",
              "3           False        42.31       2.572       0.00       0.00        0.0   \n",
              "4           False        42.31       2.572       0.00       0.00        0.0   \n",
              "...           ...          ...         ...        ...        ...        ...   \n",
              "421565      False        58.85       3.882    4018.91      58.08      100.0   \n",
              "421566      False        58.85       3.882    4018.91      58.08      100.0   \n",
              "421567      False        58.85       3.882    4018.91      58.08      100.0   \n",
              "421568      False        58.85       3.882    4018.91      58.08      100.0   \n",
              "421569      False        58.85       3.882    4018.91      58.08      100.0   \n",
              "\n",
              "        MarkDown4  MarkDown5         CPI  Unemployment  ...  Dept_90  Dept_91  \\\n",
              "0            0.00       0.00  211.096358         8.106  ...        0        0   \n",
              "1            0.00       0.00  211.096358         8.106  ...        0        0   \n",
              "2            0.00       0.00  211.096358         8.106  ...        0        0   \n",
              "3            0.00       0.00  211.096358         8.106  ...        0        0   \n",
              "4            0.00       0.00  211.096358         8.106  ...        0        0   \n",
              "...           ...        ...         ...           ...  ...      ...      ...   \n",
              "421565     211.94     858.33  192.308899         8.667  ...        0        0   \n",
              "421566     211.94     858.33  192.308899         8.667  ...        0        0   \n",
              "421567     211.94     858.33  192.308899         8.667  ...        0        0   \n",
              "421568     211.94     858.33  192.308899         8.667  ...        0        0   \n",
              "421569     211.94     858.33  192.308899         8.667  ...        0        0   \n",
              "\n",
              "        Dept_92  Dept_93  Dept_94  Dept_95  Dept_96  Dept_97  Dept_98  Dept_99  \n",
              "0             0        0        0        0        0        0        0        0  \n",
              "1             0        0        0        0        0        0        0        0  \n",
              "2             0        0        0        0        0        0        0        0  \n",
              "3             0        0        0        0        0        0        0        0  \n",
              "4             0        0        0        0        0        0        0        0  \n",
              "...         ...      ...      ...      ...      ...      ...      ...      ...  \n",
              "421565        0        1        0        0        0        0        0        0  \n",
              "421566        0        0        1        0        0        0        0        0  \n",
              "421567        0        0        0        1        0        0        0        0  \n",
              "421568        0        0        0        0        0        1        0        0  \n",
              "421569        0        0        0        0        0        0        1        0  \n",
              "\n",
              "[421570 rows x 137 columns]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Juaapkdk9cp1"
      },
      "outputs": [],
      "source": [
        "X = np.array(df_final).astype('float32')\n",
        "y = np.array(df_target).astype('float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "_oFDmIpm9oHK",
        "outputId": "e04c9840-ae1c-43b2-af1a-4ad99be61630"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(421570, 1)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# reshaping the array from (421570,) to (421570, 1)\n",
        "y = y.reshape(-1,1)\n",
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PeGmxIOnakg4"
      },
      "outputs": [],
      "source": [
        "# scaling the data before feeding the model\n",
        "# from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# scaler_x = StandardScaler()\n",
        "# X = scaler_x.fit_transform(X)\n",
        "\n",
        "# scaler_y = StandardScaler()\n",
        "# y = scaler_y.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8EoSltN41fV"
      },
      "outputs": [],
      "source": [
        "# spliting the data in to test and train sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15)\n",
        "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size = 0.5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YbO-w03B7RXn",
        "outputId": "26752901-1cd2-4629-e72c-a3364a7c1e22"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.   , 91.05 ,  3.575, ...,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   , 76.91 ,  2.784, ...,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   , 39.   ,  3.751, ...,  0.   ,  0.   ,  0.   ],\n",
              "       ...,\n",
              "       [ 0.   , 85.8  ,  3.554, ...,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   , 74.36 ,  3.827, ...,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   , 81.47 ,  3.523, ...,  0.   ,  0.   ,  0.   ]],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70B5K6wz_m_Q"
      },
      "source": [
        "# TRAIN XGBOOST REGRESSOR IN LOCAL MODE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ykhKvvy7RXo",
        "outputId": "51fc0e1a-c5ce-4dd0-ef5a-30470fddb7ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting xgboost\n",
            "  Downloading xgboost-1.3.3-py3-none-manylinux2010_x86_64.whl (157.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 157.5 MB 24 kB/s s eta 0:00:01\n",
            "\u001b[?25hRequirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from xgboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from xgboost) (1.5.3)\n",
            "Installing collected packages: xgboost\n",
            "Successfully installed xgboost-1.3.3\n"
          ]
        }
      ],
      "source": [
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MvM5lnPu7RXo",
        "outputId": "4608c153-af03-452f-fc73-9a8809e21ae5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
              "             importance_type='gain', interaction_constraints='',\n",
              "             learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "             n_estimators=100, n_jobs=2, num_parallel_tree=1, random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
              "             tree_method='exact', validate_parameters=1, verbosity=None)"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train an XGBoost regressor model \n",
        "\n",
        "import xgboost as xgb\n",
        "\n",
        "\n",
        "model = xgb.XGBRegressor(objective ='reg:squarederror', learning_rate = 0.1, max_depth = 5, n_estimators = 100)\n",
        "\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "S6IVsrhHpokq",
        "outputId": "b9b89512-0100-41fe-b1ed-bed4ad013bd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy : 0.8192406043997631\n"
          ]
        }
      ],
      "source": [
        "# predict the score of the trained model using the testing dataset\n",
        "\n",
        "result = model.score(X_test, y_test)\n",
        "\n",
        "print(\"Accuracy : {}\".format(result))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hf5kflaP7RXp"
      },
      "outputs": [],
      "source": [
        "# make predictions on the test data\n",
        "\n",
        "y_predict = model.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f8ytWDJs7RXp",
        "outputId": "2d5a04a1-8f67-4c0e-81d1-1bdf4f2fd5cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE = 9779.869 \n",
            "MSE = 95645850.0 \n",
            "MAE = 6435.3916 \n",
            "R2 = 0.8192406043997631 \n",
            "Adjusted R2 = 0.8184539450224686\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from math import sqrt\n",
        "k = X_test.shape[1]\n",
        "n = len(X_test)\n",
        "RMSE = float(format(np.sqrt(mean_squared_error(y_test, y_predict)),'.3f'))\n",
        "MSE = mean_squared_error(y_test, y_predict)\n",
        "MAE = mean_absolute_error(y_test, y_predict)\n",
        "r2 = r2_score(y_test, y_predict)\n",
        "adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)\n",
        "\n",
        "print('RMSE =',RMSE, '\\nMSE =',MSE, '\\nMAE =',MAE, '\\nR2 =', r2, '\\nAdjusted R2 =', adj_r2) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqsOm-3r7RXq"
      },
      "source": [
        "MINI CHALLENGE\n",
        "- Retrain the model with less 'max_depth'\n",
        "- Comment on the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JNSj30947RXq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4h4s6Zr7RXq"
      },
      "source": [
        "# TRAIN XGBOOST USING SAGEMAKER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i_pRZIV67RXq"
      },
      "outputs": [],
      "source": [
        "# Convert the array into dataframe in a way that target variable is set as the first column and followed by feature columns\n",
        "# This is because sagemaker built-in algorithm expects the data in this format.\n",
        "\n",
        "train_data = pd.DataFrame({'Target': y_train[:,0]})\n",
        "for i in range(X_train.shape[1]):\n",
        "    train_data[i] = X_train[:,i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-aZVEPm7RXq",
        "outputId": "3848a960-0e28-439c-ae9f-b85c42018641"
      },
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Target</th>\n",
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              "      <th>1</th>\n",
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              "      <td>10045.030273</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>8695.830078</td>\n",
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              "      <td>141.300781</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gmNivV1j7RXq"
      },
      "outputs": [],
      "source": [
        "val_data = pd.DataFrame({'Target':y_val[:,0]})\n",
        "for i in range(X_val.shape[1]):\n",
        "    val_data[i] = X_val[:,i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X0GlPKjv7RXq",
        "outputId": "2929c5a2-6d28-4d94-cba0-f3e7fb58498b"
      },
      "outputs": [
        {
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>73200.062500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.439999</td>\n",
              "      <td>3.157</td>\n",
              "      <td>5107.290039</td>\n",
              "      <td>32305.300781</td>\n",
              "      <td>144.660004</td>\n",
              "      <td>530.549988</td>\n",
              "      <td>6004.189941</td>\n",
              "      <td>223.192307</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 138 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Target    0          1      2            3             4           5  \\\n",
              "0     70.000000  0.0  74.690002  2.860     0.000000      0.000000    0.000000   \n",
              "1     83.339996  0.0  67.790001  3.524     0.000000      0.000000    0.000000   \n",
              "2   5162.040039  1.0  28.139999  2.771     0.000000      0.000000    0.000000   \n",
              "3    898.780029  0.0  50.820000  3.583     0.000000      0.000000    0.000000   \n",
              "4  73200.062500  0.0  54.439999  3.157  5107.290039  32305.300781  144.660004   \n",
              "\n",
              "            6            7           8  ...  127  128  129  130  131  132  \\\n",
              "0    0.000000     0.000000  132.724838  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "1    0.000000     0.000000  206.673309  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "2    0.000000     0.000000  131.586609  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "3    0.000000     0.000000  210.117065  ...  0.0  0.0  0.0  1.0  0.0  0.0   \n",
              "4  530.549988  6004.189941  223.192307  ...  0.0  0.0  0.0  0.0  0.0  1.0   \n",
              "\n",
              "   133  134  135  136  \n",
              "0  0.0  0.0  0.0  0.0  \n",
              "1  0.0  0.0  0.0  0.0  \n",
              "2  0.0  0.0  0.0  0.0  \n",
              "3  0.0  0.0  0.0  0.0  \n",
              "4  0.0  0.0  0.0  0.0  \n",
              "\n",
              "[5 rows x 138 columns]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y1ihZcfV7RXr",
        "outputId": "d630d091-311d-42d0-84e0-9b1e745d25a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(31618, 138)"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ZDsK5uJ7RXr"
      },
      "outputs": [],
      "source": [
        "# save train_data and validation_data as csv files.\n",
        "\n",
        "train_data.to_csv('train.csv', header = False, index = False)\n",
        "val_data.to_csv('validation.csv', header = False, index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uyaxKnUM7RXr"
      },
      "outputs": [],
      "source": [
        "# Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python\n",
        "# Boto3 allows Python developer to write software that makes use of services like Amazon S3 and Amazon EC2\n",
        "\n",
        "import sagemaker\n",
        "import boto3\n",
        "from sagemaker import Session\n",
        "\n",
        "# Let's create a Sagemaker session\n",
        "sagemaker_session = sagemaker.Session()\n",
        "bucket = Session().default_bucket() \n",
        "prefix = 'XGBoost-Regressor'\n",
        "key = 'XGBoost-Regressor'\n",
        "#Roles give learning and hosting access to the data\n",
        "#This is specified while opening the sagemakers instance in \"Create an IAM role\"\n",
        "role = sagemaker.get_execution_role()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gfjPV4xa7RXr",
        "outputId": "877dae0f-478e-4f3f-8458-81b2315752f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "arn:aws:iam::542063182511:role/service-role/AmazonSageMaker-ExecutionRole-20191104T033920\n"
          ]
        }
      ],
      "source": [
        "print(role)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpnxwjII7RXr",
        "outputId": "9f17084a-36cd-4f0a-9614-9c06b74cfed7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "uploaded training data location: s3://sagemaker-us-east-2-542063182511/XGBoost-Regressor/train/XGBoost-Regressor\n"
          ]
        }
      ],
      "source": [
        "# read the data from csv file and then upload the data to s3 bucket\n",
        "import os\n",
        "with open('train.csv','rb') as f:\n",
        "    # The following code uploads the data into S3 bucket to be accessed later for training\n",
        "    boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(f)\n",
        "\n",
        "# Let's print out the training data location in s3\n",
        "s3_train_data = 's3://{}/{}/train/{}'.format(bucket, prefix, key)\n",
        "print('uploaded training data location: {}'.format(s3_train_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6TaTd9A17RXt",
        "outputId": "0af29223-45a4-4175-c3a3-7f7339f212e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "uploaded validation data location: s3://sagemaker-us-east-2-542063182511/XGBoost-Regressor/validation/XGBoost-Regressor\n"
          ]
        }
      ],
      "source": [
        "# read the data from csv file and then upload the data to s3 bucket\n",
        "\n",
        "with open('validation.csv','rb') as f:\n",
        "    # The following code uploads the data into S3 bucket to be accessed later for training\n",
        "\n",
        "    boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'validation', key)).upload_fileobj(f)\n",
        "# Let's print out the validation data location in s3\n",
        "s3_validation_data = 's3://{}/{}/validation/{}'.format(bucket, prefix, key)\n",
        "print('uploaded validation data location: {}'.format(s3_validation_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4FMSV9bo7RXt",
        "outputId": "989b172f-556a-4b5b-ad90-fe5d25948f7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "training artifacts will be uploaded to: s3://sagemaker-us-east-2-542063182511/XGBoost-Regressor/output\n"
          ]
        }
      ],
      "source": [
        "# creates output placeholder in S3 bucket to store the output\n",
        "\n",
        "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
        "print('training artifacts will be uploaded to: {}'.format(output_location))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PU4cUwQ37RXt",
        "outputId": "ca92fcd4-183c-4376-f4b4-ee02165566ee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The method get_image_uri has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "# This code is used to get the training container of sagemaker built-in algorithms\n",
        "# all we have to do is to specify the name of the algorithm, that we want to use\n",
        "\n",
        "# Let's obtain a reference to the XGBoost container image\n",
        "# Note that all regression models are named estimators\n",
        "# You don't have to specify (hardcode) the region, get_image_uri will get the current region name using boto3.Session\n",
        "\n",
        "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
        "\n",
        "container = get_image_uri(boto3.Session().region_name, 'xgboost','0.90-2') # Latest version of XGboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oztat4EB7RXt",
        "outputId": "7cd83898-3416-46c0-9f74-34398bcdf669"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "train_instance_count has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n",
            "train_instance_type has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "# Specify the type of instance that we would like to use for training \n",
        "# output path and sagemaker session into the Estimator. \n",
        "# We can also specify how many instances we would like to use for training\n",
        "\n",
        "# Recall that XGBoost works by combining an ensemble of weak models to generate accurate/robust results. \n",
        "# The weak models are randomized to avoid overfitting\n",
        "\n",
        "# num_round: The number of rounds to run the training.\n",
        "\n",
        "\n",
        "# Alpha: L1 regularization term on weights. Increasing this value makes models more conservative.\n",
        "\n",
        "# colsample_by_tree: fraction of features that will be used to train each tree.\n",
        "\n",
        "# eta: Step size shrinkage used in updates to prevent overfitting. \n",
        "# After each boosting step, eta parameter shrinks the feature weights to make the boosting process more conservative.\n",
        "\n",
        "\n",
        "Xgboost_regressor1 = sagemaker.estimator.Estimator(container,\n",
        "                                       role, \n",
        "                                       train_instance_count = 1, \n",
        "                                       train_instance_type = 'ml.m5.2xlarge',\n",
        "                                       output_path = output_location,\n",
        "                                       sagemaker_session = sagemaker_session)\n",
        "\n",
        "#We can tune the hyper-parameters to improve the performance of the model\n",
        "\n",
        "Xgboost_regressor1.set_hyperparameters(max_depth = 10,\n",
        "                           objective = 'reg:linear',\n",
        "                           colsample_bytree = 0.3,\n",
        "                           alpha = 10,\n",
        "                           eta = 0.1,\n",
        "                           num_round = 100\n",
        "                           )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jEP74ORW7RXu",
        "outputId": "7176fd30-6ca5-43ea-8815-0f90958422bc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The class sagemaker.session.s3_input has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n",
            "The class sagemaker.session.s3_input has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2021-03-25 17:45:10 Starting - Starting the training job...\n",
            "2021-03-25 17:45:27 Starting - Launching requested ML instancesProfilerReport-1616694310: InProgress\n",
            ".........\n",
            "2021-03-25 17:46:56 Starting - Preparing the instances for training......ProfilerReport-1616694310: Error\n",
            ".........\n",
            "2021-03-25 17:49:39 Downloading - Downloading input data\n",
            "2021-03-25 17:49:39 Training - Training image download completed. Training in progress..\u001b[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n",
            "\u001b[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:linear to Json.\u001b[0m\n",
            "\u001b[34mReturning the value itself\u001b[0m\n",
            "\u001b[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n",
            "\u001b[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34m[17:49:42] 358334x137 matrix with 49091758 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34m[17:49:43] 31618x137 matrix with 4331666 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
            "\u001b[34mINFO:root:Single node training.\u001b[0m\n",
            "\u001b[34mINFO:root:Train matrix has 358334 rows\u001b[0m\n",
            "\u001b[34mINFO:root:Validation matrix has 31618 rows\u001b[0m\n",
            "\u001b[34m[17:49:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\u001b[0m\n",
            "\u001b[34m[0]#011train-rmse:26735.3#011validation-rmse:26788.9\u001b[0m\n",
            "\u001b[34m[1]#011train-rmse:25600.2#011validation-rmse:25657.7\u001b[0m\n",
            "\u001b[34m[2]#011train-rmse:24691.8#011validation-rmse:24751.3\u001b[0m\n",
            "\u001b[34m[3]#011train-rmse:23662.7#011validation-rmse:23727.3\u001b[0m\n",
            "\u001b[34m[4]#011train-rmse:22736.2#011validation-rmse:22808.9\u001b[0m\n",
            "\u001b[34m[5]#011train-rmse:22063.1#011validation-rmse:22158.3\u001b[0m\n",
            "\u001b[34m[6]#011train-rmse:21138.4#011validation-rmse:21247.6\u001b[0m\n",
            "\u001b[34m[7]#011train-rmse:20568.6#011validation-rmse:20678.7\u001b[0m\n",
            "\u001b[34m[8]#011train-rmse:19777.8#011validation-rmse:19885.2\u001b[0m\n",
            "\u001b[34m[9]#011train-rmse:19354.4#011validation-rmse:19464.1\u001b[0m\n",
            "\u001b[34m[10]#011train-rmse:19052#011validation-rmse:19165.5\u001b[0m\n",
            "\u001b[34m[11]#011train-rmse:18572.5#011validation-rmse:18686.1\u001b[0m\n",
            "\u001b[34m[12]#011train-rmse:18374.9#011validation-rmse:18490.6\u001b[0m\n",
            "\u001b[34m[13]#011train-rmse:18055.1#011validation-rmse:18173.9\u001b[0m\n",
            "\u001b[34m[14]#011train-rmse:17604.2#011validation-rmse:17721.7\u001b[0m\n",
            "\u001b[34m[15]#011train-rmse:17357.6#011validation-rmse:17472.2\u001b[0m\n",
            "\u001b[34m[16]#011train-rmse:17146.6#011validation-rmse:17267.9\u001b[0m\n",
            "\u001b[34m[17]#011train-rmse:17024.6#011validation-rmse:17144.9\u001b[0m\n",
            "\u001b[34m[18]#011train-rmse:16870.6#011validation-rmse:16994.5\u001b[0m\n",
            "\u001b[34m[19]#011train-rmse:16579.3#011validation-rmse:16697.1\u001b[0m\n",
            "\u001b[34m[20]#011train-rmse:16242.5#011validation-rmse:16365.7\u001b[0m\n",
            "\u001b[34m[21]#011train-rmse:15886#011validation-rmse:16020.3\u001b[0m\n",
            "\u001b[34m[22]#011train-rmse:15482.7#011validation-rmse:15612.4\u001b[0m\n",
            "\u001b[34m[23]#011train-rmse:15336.7#011validation-rmse:15467.4\u001b[0m\n",
            "\u001b[34m[24]#011train-rmse:15062.1#011validation-rmse:15201.5\u001b[0m\n",
            "\u001b[34m[25]#011train-rmse:14800#011validation-rmse:14940.4\u001b[0m\n",
            "\u001b[34m[26]#011train-rmse:14660.6#011validation-rmse:14806.9\u001b[0m\n",
            "\u001b[34m[27]#011train-rmse:14567.9#011validation-rmse:14715.6\u001b[0m\n",
            "\u001b[34m[28]#011train-rmse:14320.4#011validation-rmse:14475.6\u001b[0m\n",
            "\u001b[34m[29]#011train-rmse:14210.8#011validation-rmse:14372.1\u001b[0m\n",
            "\u001b[34m[30]#011train-rmse:14038.1#011validation-rmse:14216.1\u001b[0m\n",
            "\u001b[34m[31]#011train-rmse:13717.1#011validation-rmse:13928\u001b[0m\n",
            "\u001b[34m[32]#011train-rmse:13662.7#011validation-rmse:13874.2\u001b[0m\n",
            "\u001b[34m[33]#011train-rmse:13512.1#011validation-rmse:13722.2\u001b[0m\n",
            "\u001b[34m[34]#011train-rmse:13170.7#011validation-rmse:13381\u001b[0m\n",
            "\u001b[34m[35]#011train-rmse:13001.8#011validation-rmse:13215.6\u001b[0m\n",
            "\u001b[34m[36]#011train-rmse:12893.1#011validation-rmse:13105.1\u001b[0m\n",
            "\u001b[34m[37]#011train-rmse:12765.5#011validation-rmse:12983\u001b[0m\n",
            "\u001b[34m[38]#011train-rmse:12483#011validation-rmse:12710.4\u001b[0m\n",
            "\u001b[34m[39]#011train-rmse:12239.9#011validation-rmse:12490.2\u001b[0m\n",
            "\u001b[34m[40]#011train-rmse:12158.3#011validation-rmse:12410.5\u001b[0m\n",
            "\u001b[34m[41]#011train-rmse:12019.8#011validation-rmse:12270.1\u001b[0m\n",
            "\u001b[34m[42]#011train-rmse:11929.9#011validation-rmse:12188.8\u001b[0m\n",
            "\u001b[34m[43]#011train-rmse:11566.6#011validation-rmse:11840.3\u001b[0m\n",
            "\u001b[34m[44]#011train-rmse:11517.3#011validation-rmse:11790.8\u001b[0m\n",
            "\u001b[34m[45]#011train-rmse:11470.6#011validation-rmse:11746.9\u001b[0m\n",
            "\u001b[34m[46]#011train-rmse:11298.4#011validation-rmse:11586.1\u001b[0m\n",
            "\u001b[34m[47]#011train-rmse:11165.1#011validation-rmse:11447.8\u001b[0m\n",
            "\u001b[34m[48]#011train-rmse:10936.7#011validation-rmse:11227.8\u001b[0m\n",
            "\u001b[34m[49]#011train-rmse:10750.2#011validation-rmse:11065.7\u001b[0m\n",
            "\u001b[34m[50]#011train-rmse:10588#011validation-rmse:10908\u001b[0m\n",
            "\u001b[34m[51]#011train-rmse:10458#011validation-rmse:10782.9\u001b[0m\n",
            "\u001b[34m[52]#011train-rmse:10318.4#011validation-rmse:10658.4\u001b[0m\n",
            "\u001b[34m[53]#011train-rmse:10148#011validation-rmse:10517.9\u001b[0m\n",
            "\u001b[34m[54]#011train-rmse:10118.5#011validation-rmse:10488.5\u001b[0m\n",
            "\u001b[34m[55]#011train-rmse:10040.7#011validation-rmse:10421.4\u001b[0m\n",
            "\u001b[34m[56]#011train-rmse:9886.65#011validation-rmse:10282\u001b[0m\n",
            "\u001b[34m[57]#011train-rmse:9754.12#011validation-rmse:10170.4\u001b[0m\n",
            "\u001b[34m[58]#011train-rmse:9642.31#011validation-rmse:10073.4\u001b[0m\n",
            "\u001b[34m[59]#011train-rmse:9611.77#011validation-rmse:10043.3\u001b[0m\n",
            "\u001b[34m[60]#011train-rmse:9524.63#011validation-rmse:9960.17\u001b[0m\n",
            "\u001b[34m[61]#011train-rmse:9448.91#011validation-rmse:9891.84\u001b[0m\n",
            "\u001b[34m[62]#011train-rmse:9403.89#011validation-rmse:9849.97\u001b[0m\n",
            "\u001b[34m[63]#011train-rmse:9334.95#011validation-rmse:9783.49\u001b[0m\n",
            "\u001b[34m[64]#011train-rmse:9250.86#011validation-rmse:9704.48\u001b[0m\n",
            "\u001b[34m[65]#011train-rmse:9147.58#011validation-rmse:9617.35\u001b[0m\n",
            "\u001b[34m[66]#011train-rmse:9122.84#011validation-rmse:9592.82\u001b[0m\n",
            "\u001b[34m[67]#011train-rmse:9090.01#011validation-rmse:9560.05\u001b[0m\n",
            "\u001b[34m[68]#011train-rmse:9033.96#011validation-rmse:9516.16\u001b[0m\n",
            "\u001b[34m[69]#011train-rmse:8938.9#011validation-rmse:9437.42\u001b[0m\n",
            "\u001b[34m[70]#011train-rmse:8871.94#011validation-rmse:9377.31\u001b[0m\n",
            "\u001b[34m[71]#011train-rmse:8740.4#011validation-rmse:9251.42\u001b[0m\n",
            "\u001b[34m[72]#011train-rmse:8556#011validation-rmse:9081.7\u001b[0m\n",
            "\u001b[34m[73]#011train-rmse:8453.9#011validation-rmse:8993.3\u001b[0m\n",
            "\u001b[34m[74]#011train-rmse:8304.43#011validation-rmse:8859.01\u001b[0m\n",
            "\u001b[34m[75]#011train-rmse:8208.02#011validation-rmse:8771.17\u001b[0m\n",
            "\u001b[34m[76]#011train-rmse:8179.8#011validation-rmse:8745.86\u001b[0m\n",
            "\u001b[34m[77]#011train-rmse:8153.07#011validation-rmse:8715.11\u001b[0m\n",
            "\u001b[34m[78]#011train-rmse:8092.56#011validation-rmse:8649.19\u001b[0m\n",
            "\u001b[34m[79]#011train-rmse:8047.98#011validation-rmse:8611.58\u001b[0m\n",
            "\u001b[34m[80]#011train-rmse:7998.25#011validation-rmse:8562.26\u001b[0m\n",
            "\u001b[34m[81]#011train-rmse:7959.45#011validation-rmse:8527.08\u001b[0m\n",
            "\u001b[34m[82]#011train-rmse:7927.72#011validation-rmse:8496.88\u001b[0m\n",
            "\u001b[34m[83]#011train-rmse:7865.56#011validation-rmse:8448.2\u001b[0m\n",
            "\u001b[34m[84]#011train-rmse:7816.54#011validation-rmse:8401.86\u001b[0m\n",
            "\u001b[34m[85]#011train-rmse:7747.3#011validation-rmse:8338.58\u001b[0m\n",
            "\u001b[34m[86]#011train-rmse:7711.16#011validation-rmse:8308.26\u001b[0m\n",
            "\u001b[34m[87]#011train-rmse:7656.64#011validation-rmse:8259.38\u001b[0m\n",
            "\u001b[34m[88]#011train-rmse:7611.04#011validation-rmse:8220.03\u001b[0m\n",
            "\u001b[34m[89]#011train-rmse:7549.18#011validation-rmse:8156.86\u001b[0m\n",
            "\u001b[34m[90]#011train-rmse:7485.37#011validation-rmse:8099.35\u001b[0m\n",
            "\u001b[34m[91]#011train-rmse:7453.62#011validation-rmse:8070.77\u001b[0m\n",
            "\u001b[34m[92]#011train-rmse:7385.48#011validation-rmse:8013.74\u001b[0m\n",
            "\u001b[34m[93]#011train-rmse:7335.87#011validation-rmse:7972.2\u001b[0m\n",
            "\u001b[34m[94]#011train-rmse:7291.9#011validation-rmse:7933.53\u001b[0m\n",
            "\u001b[34m[95]#011train-rmse:7251.62#011validation-rmse:7898.28\u001b[0m\n",
            "\u001b[34m[96]#011train-rmse:7220.39#011validation-rmse:7870.49\u001b[0m\n",
            "\u001b[34m[97]#011train-rmse:7196.69#011validation-rmse:7848.74\u001b[0m\n",
            "\u001b[34m[98]#011train-rmse:7168.67#011validation-rmse:7822.57\u001b[0m\n",
            "\u001b[34m[99]#011train-rmse:7126.33#011validation-rmse:7786.37\u001b[0m\n",
            "\n",
            "2021-03-25 17:50:24 Uploading - Uploading generated training model\n",
            "2021-03-25 17:50:24 Completed - Training job completed\n",
            "Training seconds: 66\n",
            "Billable seconds: 66\n"
          ]
        }
      ],
      "source": [
        "# Creating \"train\", \"validation\" channels to feed in the model\n",
        "# Source: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html\n",
        "\n",
        "train_input = sagemaker.session.s3_input(s3_data = s3_train_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
        "valid_input = sagemaker.session.s3_input(s3_data = s3_validation_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
        "\n",
        "\n",
        "data_channels = {'train': train_input,'validation': valid_input}\n",
        "\n",
        "\n",
        "Xgboost_regressor1.fit(data_channels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdwtHZmj7RXu"
      },
      "source": [
        "# DEPLOY THE MODEL TO MAKE PREDICTIONS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLkRCaWo7RXu",
        "outputId": "53ab85db-632d-4765-ba98-b72674734074"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-------------!"
          ]
        }
      ],
      "source": [
        "# Deploy the model to perform inference \n",
        "\n",
        "Xgboost_regressor = Xgboost_regressor1.deploy(initial_instance_count = 1, instance_type = 'ml.m5.2xlarge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-j9VnzMC7RXu"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Content type over-rides the data that will be passed to the deployed model, since the deployed model expects data\n",
        "in text/csv format, we specify this as content -type.\n",
        "\n",
        "Serializer accepts a single argument, the input data, and returns a sequence of bytes in the specified content\n",
        "type\n",
        "\n",
        "Reference: https://sagemaker.readthedocs.io/en/stable/predictors.html\n",
        "'''\n",
        "from sagemaker.predictor import csv_serializer, json_deserializer\n",
        "\n",
        "\n",
        "Xgboost_regressor.serializer = csv_serializer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mc0uQsTp7RXv",
        "outputId": "87249900-29eb-4b04-9967-5ac98c9fe91d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.0000000e+00, 8.3940002e+01, 3.5940001e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       2.1849796e+02, 6.2969999e+00, 1.5507800e+05, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "       0.0000000e+00], dtype=float32)"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shape()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HdSntjaL7RXv",
        "outputId": "773bca26-3238-41ea-b016-c88cf81416ae"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The csv_serializer has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "# making prediction\n",
        "\n",
        "predictions1 = Xgboost_regressor.predict(X_test[0:10000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7dfXMso7RXv",
        "outputId": "6c3cf354-01a4-424f-ec36-761ff6637183"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The csv_serializer has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "predictions2 = Xgboost_regressor.predict(X_test[10000:20000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oo0B53SJ7RXv",
        "outputId": "3bad869a-1157-45d2-c70e-383d8dfdddd4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The csv_serializer has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "predictions3 = Xgboost_regressor.predict(X_test[20000:30000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IChKR_LJ7RXv",
        "outputId": "0df7338c-ecbe-4d84-829d-fd991d3aae20"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The csv_serializer has been renamed in sagemaker>=2.\n",
            "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
          ]
        }
      ],
      "source": [
        "predictions4 = Xgboost_regressor.predict(X_test[30000:31618])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hbzNHRbM7RXv",
        "outputId": "0f436009-ff27-4f52-dcc2-074920558986"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gl0Qm-lY7RXw"
      },
      "outputs": [],
      "source": [
        "# custom code to convert the values in bytes format to array\n",
        "\n",
        "def bytes_2_array(x):\n",
        "    \n",
        "    # makes entire prediction as string and splits based on ','\n",
        "    l = str(x).split(',')\n",
        "    \n",
        "    # Since the first element contains unwanted characters like (b,',') we remove them\n",
        "    l[0] = l[0][2:]\n",
        "    #same-thing as above remove the unwanted last character (')\n",
        "    l[-1] = l[-1][:-1]\n",
        "    \n",
        "    # iterating through the list of strings and converting them into float type\n",
        "    for i in range(len(l)):\n",
        "        l[i] = float(l[i])\n",
        "        \n",
        "    # converting the list into array\n",
        "    l = np.array(l).astype('float32')\n",
        "    \n",
        "    # reshape one-dimensional array to two-dimensional array\n",
        "    return l.reshape(-1,1)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6DBXM2kL7RXw"
      },
      "outputs": [],
      "source": [
        "predicted_values_1 = bytes_2_array(predictions1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E7QieKlY7RXw",
        "outputId": "d08062c7-e462-4cc8-eaec-9d5ab07734f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values_1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fND_5gfU7RXw",
        "outputId": "6f713272-6220-452e-caab-68bce39daeba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values_2 = bytes_2_array(predictions2)\n",
        "predicted_values_2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ezrw_T0E7RXw",
        "outputId": "a62bb548-3282-4c84-e96f-de6c9f41838a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values_3 = bytes_2_array(predictions3)\n",
        "predicted_values_3.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kN9tagR37RXw",
        "outputId": "7fd99872-6293-465e-a900-f91fa834173e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1618, 1)"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values_4 = bytes_2_array(predictions4)\n",
        "predicted_values_4.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jaSV5E7C7RXx"
      },
      "outputs": [],
      "source": [
        "predicted_values = np.concatenate((predicted_values_1, predicted_values_2, predicted_values_3, predicted_values_4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xACol2sC7RXx",
        "outputId": "04795d85-d5d7-415e-e3a5-b37c667bd15e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(31618, 1)"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NYBZG1Qd7RXx",
        "outputId": "3a485713-3356-4468-ca16-ba01d1a51e2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE = 7492.593 \n",
            "MSE = 56138950.0 \n",
            "MAE = 4353.634 \n",
            "R2 = 0.8939039998714412 \n",
            "Adjusted R2 = 0.8934422733143379\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from math import sqrt\n",
        "k = X_test.shape[1]\n",
        "n = len(X_test)\n",
        "RMSE = float(format(np.sqrt(mean_squared_error(y_test, predicted_values)),'.3f'))\n",
        "MSE = mean_squared_error(y_test, predicted_values)\n",
        "MAE = mean_absolute_error(y_test, predicted_values)\n",
        "r2 = r2_score(y_test, predicted_values)\n",
        "adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)\n",
        "\n",
        "print('RMSE =',RMSE, '\\nMSE =',MSE, '\\nMAE =',MAE, '\\nR2 =', r2, '\\nAdjusted R2 =', adj_r2) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pq67UODr7RXx"
      },
      "outputs": [],
      "source": [
        "# Delete the end-point\n",
        "\n",
        "Xgboost_regressor.delete_endpoint()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUWQxt7V7RXx"
      },
      "source": [
        "# PERFORM HYPERPARAMETERS OPTIMIZATION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysILZIIN7RXy"
      },
      "source": [
        "# TRAIN THE MODEL WITH BEST PARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VjolKMCW7RXy"
      },
      "outputs": [],
      "source": [
        "# We have pass in the container, the type of instance that we would like to use for training \n",
        "# output path and sagemaker session into the Estimator. \n",
        "# We can also specify how many instances we would like to use for training\n",
        "\n",
        "Xgboost_regressor = sagemaker.estimator.Estimator(container,\n",
        "                                       role, \n",
        "                                       train_instance_count=1, \n",
        "                                       train_instance_type='ml.m4.xlarge',\n",
        "                                       output_path=output_location,\n",
        "                                       sagemaker_session=sagemaker_session)\n",
        "\n",
        "# We can tune the hyper-parameters to improve the performance of the model\n",
        "Xgboost_regressor.set_hyperparameters(max_depth=25,\n",
        "                           objective='reg:linear',\n",
        "                           colsample_bytree = 0.3913546819101119,\n",
        "                           alpha = 1.0994354985124635,\n",
        "                           eta = 0.23848185159806115,\n",
        "                           num_round = 237\n",
        "                           )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGkPE6xj7RXy",
        "outputId": "87c24fd3-d51a-4bc9-ab7d-867ed9d528e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2020-05-22 07:36:14 Starting - Starting the training job...\n",
            "2020-05-22 07:36:16 Starting - Launching requested ML instances.........\n",
            "2020-05-22 07:38:00 Starting - Preparing the instances for training......\n",
            "2020-05-22 07:38:55 Downloading - Downloading input data......\n",
            "2020-05-22 07:40:05 Training - Downloading the training image...\n",
            "2020-05-22 07:40:25 Training - Training image download completed. Training in progress.\u001b[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n",
            "\u001b[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:linear to Json.\u001b[0m\n",
            "\u001b[34mReturning the value itself\u001b[0m\n",
            "\u001b[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n",
            "\u001b[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34m[07:40:30] 358334x138 matrix with 49450092 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
            "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
            "\u001b[34m[07:40:30] 31618x138 matrix with 4363284 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
            "\u001b[34mINFO:root:Single node training.\u001b[0m\n",
            "\u001b[34mINFO:root:Train matrix has 358334 rows\u001b[0m\n",
            "\u001b[34mINFO:root:Validation matrix has 31618 rows\u001b[0m\n",
            "\u001b[34m[07:40:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\u001b[0m\n",
            "\u001b[34m[0]#011train-rmse:23594.8#011validation-rmse:23998.1\u001b[0m\n",
            "\u001b[34m[1]#011train-rmse:20656.5#011validation-rmse:21082.6\u001b[0m\n",
            "\u001b[34m[2]#011train-rmse:18682.7#011validation-rmse:19104.8\u001b[0m\n",
            "\u001b[34m[3]#011train-rmse:17031.4#011validation-rmse:17482.3\u001b[0m\n",
            "\u001b[34m[4]#011train-rmse:15932.2#011validation-rmse:16478.1\u001b[0m\n",
            "\u001b[34m[5]#011train-rmse:14796.8#011validation-rmse:15381.3\u001b[0m\n",
            "\u001b[34m[6]#011train-rmse:13329.9#011validation-rmse:13952.4\u001b[0m\n",
            "\u001b[34m[7]#011train-rmse:12066.5#011validation-rmse:12701.5\u001b[0m\n",
            "\u001b[34m[8]#011train-rmse:11245.9#011validation-rmse:11951.5\u001b[0m\n",
            "\u001b[34m[9]#011train-rmse:10597.6#011validation-rmse:11320.9\u001b[0m\n",
            "\u001b[34m[10]#011train-rmse:9916.77#011validation-rmse:10674.2\u001b[0m\n",
            "\u001b[34m[11]#011train-rmse:8991.53#011validation-rmse:9769.16\u001b[0m\n",
            "\u001b[34m[12]#011train-rmse:8452.89#011validation-rmse:9262.17\u001b[0m\n",
            "\u001b[34m[13]#011train-rmse:7861.46#011validation-rmse:8705.27\u001b[0m\n",
            "\u001b[34m[14]#011train-rmse:7355.17#011validation-rmse:8198.73\u001b[0m\n",
            "\u001b[34m[15]#011train-rmse:6817.53#011validation-rmse:7733.56\u001b[0m\n",
            "\u001b[34m[16]#011train-rmse:6494#011validation-rmse:7481.07\u001b[0m\n",
            "\u001b[34m[17]#011train-rmse:6348.54#011validation-rmse:7343.02\u001b[0m\n",
            "\u001b[34m[18]#011train-rmse:6126.29#011validation-rmse:7182.62\u001b[0m\n",
            "\u001b[34m[19]#011train-rmse:5998.95#011validation-rmse:7074.79\u001b[0m\n",
            "\u001b[34m[20]#011train-rmse:5790.78#011validation-rmse:6910.43\u001b[0m\n",
            "\u001b[34m[21]#011train-rmse:5666.01#011validation-rmse:6806.1\u001b[0m\n",
            "\u001b[34m[22]#011train-rmse:5388.56#011validation-rmse:6585.96\u001b[0m\n",
            "\u001b[34m[23]#011train-rmse:5056.26#011validation-rmse:6319.91\u001b[0m\n",
            "\u001b[34m[24]#011train-rmse:4928.01#011validation-rmse:6232.05\u001b[0m\n",
            "\u001b[34m[25]#011train-rmse:4762.47#011validation-rmse:6089.43\u001b[0m\n",
            "\u001b[34m[26]#011train-rmse:4651.49#011validation-rmse:6005.34\u001b[0m\n",
            "\u001b[34m[27]#011train-rmse:4591.26#011validation-rmse:5960.33\u001b[0m\n",
            "\u001b[34m[28]#011train-rmse:4393.76#011validation-rmse:5822.35\u001b[0m\n",
            "\u001b[34m[29]#011train-rmse:4319.47#011validation-rmse:5773.31\u001b[0m\n",
            "\u001b[34m[30]#011train-rmse:4240.99#011validation-rmse:5715.93\u001b[0m\n",
            "\u001b[34m[31]#011train-rmse:4122.15#011validation-rmse:5655.87\u001b[0m\n",
            "\u001b[34m[32]#011train-rmse:4066.85#011validation-rmse:5621.26\u001b[0m\n",
            "\u001b[34m[33]#011train-rmse:4003.88#011validation-rmse:5579.53\u001b[0m\n",
            "\u001b[34m[34]#011train-rmse:3853.33#011validation-rmse:5490.73\u001b[0m\n",
            "\u001b[34m[35]#011train-rmse:3815.17#011validation-rmse:5463.84\u001b[0m\n",
            "\u001b[34m[36]#011train-rmse:3793.72#011validation-rmse:5448.97\u001b[0m\n",
            "\u001b[34m[37]#011train-rmse:3710.71#011validation-rmse:5397.09\u001b[0m\n",
            "\u001b[34m[38]#011train-rmse:3672.58#011validation-rmse:5369.46\u001b[0m\n",
            "\u001b[34m[39]#011train-rmse:3645.36#011validation-rmse:5349.52\u001b[0m\n",
            "\u001b[34m[40]#011train-rmse:3445.38#011validation-rmse:5261.57\u001b[0m\n",
            "\u001b[34m[41]#011train-rmse:3397.83#011validation-rmse:5232.4\u001b[0m\n",
            "\u001b[34m[42]#011train-rmse:3351.12#011validation-rmse:5202.98\u001b[0m\n",
            "\u001b[34m[43]#011train-rmse:3201.92#011validation-rmse:5121.08\u001b[0m\n",
            "\u001b[34m[44]#011train-rmse:3182.63#011validation-rmse:5110.65\u001b[0m\n",
            "\u001b[34m[45]#011train-rmse:3091.38#011validation-rmse:5071.69\u001b[0m\n",
            "\u001b[34m[46]#011train-rmse:3035.31#011validation-rmse:5038.59\u001b[0m\n",
            "\u001b[34m[47]#011train-rmse:2972.59#011validation-rmse:5001.45\u001b[0m\n",
            "\u001b[34m[48]#011train-rmse:2859.99#011validation-rmse:4971.2\u001b[0m\n",
            "\u001b[34m[49]#011train-rmse:2803.35#011validation-rmse:4932.38\u001b[0m\n",
            "\u001b[34m[50]#011train-rmse:2735.41#011validation-rmse:4909.41\u001b[0m\n",
            "\u001b[34m[51]#011train-rmse:2653.77#011validation-rmse:4883\u001b[0m\n",
            "\u001b[34m[52]#011train-rmse:2624.6#011validation-rmse:4868.6\u001b[0m\n",
            "\u001b[34m[53]#011train-rmse:2596.57#011validation-rmse:4850.86\u001b[0m\n",
            "\u001b[34m[54]#011train-rmse:2577.77#011validation-rmse:4838.25\u001b[0m\n",
            "\u001b[34m[55]#011train-rmse:2547.1#011validation-rmse:4822.26\u001b[0m\n",
            "\u001b[34m[56]#011train-rmse:2473.07#011validation-rmse:4792.76\u001b[0m\n",
            "\u001b[34m[57]#011train-rmse:2456.47#011validation-rmse:4778.35\u001b[0m\n",
            "\u001b[34m[58]#011train-rmse:2417.79#011validation-rmse:4760.83\u001b[0m\n",
            "\u001b[34m[59]#011train-rmse:2392.86#011validation-rmse:4745.33\u001b[0m\n",
            "\u001b[34m[60]#011train-rmse:2339.87#011validation-rmse:4725.77\u001b[0m\n",
            "\u001b[34m[61]#011train-rmse:2323.01#011validation-rmse:4720.16\u001b[0m\n",
            "\u001b[34m[62]#011train-rmse:2291.35#011validation-rmse:4702.38\u001b[0m\n",
            "\u001b[34m[63]#011train-rmse:2253#011validation-rmse:4684.85\u001b[0m\n",
            "\u001b[34m[64]#011train-rmse:2236.12#011validation-rmse:4677.61\u001b[0m\n",
            "\u001b[34m[65]#011train-rmse:2225.14#011validation-rmse:4670.57\u001b[0m\n",
            "\u001b[34m[66]#011train-rmse:2211.16#011validation-rmse:4661.37\u001b[0m\n",
            "\u001b[34m[67]#011train-rmse:2207.72#011validation-rmse:4659.82\u001b[0m\n",
            "\u001b[34m[68]#011train-rmse:2201.6#011validation-rmse:4657.8\u001b[0m\n",
            "\u001b[34m[69]#011train-rmse:2184.24#011validation-rmse:4649.82\u001b[0m\n",
            "\u001b[34m[70]#011train-rmse:2169.01#011validation-rmse:4644.6\u001b[0m\n",
            "\u001b[34m[71]#011train-rmse:2161.16#011validation-rmse:4641.94\u001b[0m\n",
            "\u001b[34m[72]#011train-rmse:2143.12#011validation-rmse:4632.13\u001b[0m\n",
            "\u001b[34m[73]#011train-rmse:2127.7#011validation-rmse:4626.43\u001b[0m\n",
            "\u001b[34m[74]#011train-rmse:2063.68#011validation-rmse:4604.62\u001b[0m\n",
            "\u001b[34m[75]#011train-rmse:2046.12#011validation-rmse:4596.6\u001b[0m\n",
            "\u001b[34m[76]#011train-rmse:2038.11#011validation-rmse:4592.22\u001b[0m\n",
            "\u001b[34m[77]#011train-rmse:2034.29#011validation-rmse:4590.96\u001b[0m\n",
            "\u001b[34m[78]#011train-rmse:2030.03#011validation-rmse:4587.42\u001b[0m\n",
            "\u001b[34m[79]#011train-rmse:2024.78#011validation-rmse:4585.25\u001b[0m\n",
            "\u001b[34m[80]#011train-rmse:2017.67#011validation-rmse:4579.63\u001b[0m\n",
            "\u001b[34m[81]#011train-rmse:2010.95#011validation-rmse:4576.27\u001b[0m\n",
            "\u001b[34m[82]#011train-rmse:2007.81#011validation-rmse:4575.1\u001b[0m\n",
            "\u001b[34m[83]#011train-rmse:2001.55#011validation-rmse:4571.38\u001b[0m\n",
            "\u001b[34m[84]#011train-rmse:1993.05#011validation-rmse:4566.56\u001b[0m\n",
            "\u001b[34m[85]#011train-rmse:1981.48#011validation-rmse:4562.87\u001b[0m\n",
            "\u001b[34m[86]#011train-rmse:1962.35#011validation-rmse:4551.31\u001b[0m\n",
            "\u001b[34m[87]#011train-rmse:1949.92#011validation-rmse:4544.56\u001b[0m\n",
            "\u001b[34m[88]#011train-rmse:1940.38#011validation-rmse:4541.44\u001b[0m\n",
            "\u001b[34m[89]#011train-rmse:1938.09#011validation-rmse:4539.52\u001b[0m\n",
            "\u001b[34m[90]#011train-rmse:1916.32#011validation-rmse:4532.4\u001b[0m\n",
            "\u001b[34m[91]#011train-rmse:1911.54#011validation-rmse:4530.51\u001b[0m\n",
            "\u001b[34m[92]#011train-rmse:1888.65#011validation-rmse:4525.51\u001b[0m\n",
            "\u001b[34m[93]#011train-rmse:1883.06#011validation-rmse:4523.43\u001b[0m\n",
            "\u001b[34m[94]#011train-rmse:1876.99#011validation-rmse:4520.03\u001b[0m\n",
            "\u001b[34m[95]#011train-rmse:1856.7#011validation-rmse:4512.71\u001b[0m\n",
            "\u001b[34m[96]#011train-rmse:1839.65#011validation-rmse:4512.33\u001b[0m\n",
            "\u001b[34m[97]#011train-rmse:1829.57#011validation-rmse:4509.58\u001b[0m\n",
            "\u001b[34m[98]#011train-rmse:1818.01#011validation-rmse:4506.78\u001b[0m\n",
            "\u001b[34m[99]#011train-rmse:1814.08#011validation-rmse:4505.07\u001b[0m\n",
            "\u001b[34m[100]#011train-rmse:1804.55#011validation-rmse:4498.66\u001b[0m\n",
            "\u001b[34m[101]#011train-rmse:1801.91#011validation-rmse:4497.67\u001b[0m\n",
            "\u001b[34m[102]#011train-rmse:1799.61#011validation-rmse:4497.35\u001b[0m\n",
            "\u001b[34m[103]#011train-rmse:1795.93#011validation-rmse:4495.77\u001b[0m\n",
            "\u001b[34m[104]#011train-rmse:1787.05#011validation-rmse:4492.41\u001b[0m\n",
            "\u001b[34m[105]#011train-rmse:1781.53#011validation-rmse:4491.57\u001b[0m\n",
            "\u001b[34m[106]#011train-rmse:1776.68#011validation-rmse:4489.9\u001b[0m\n",
            "\u001b[34m[107]#011train-rmse:1774.37#011validation-rmse:4488.43\u001b[0m\n",
            "\u001b[34m[108]#011train-rmse:1766.53#011validation-rmse:4486.16\u001b[0m\n",
            "\u001b[34m[109]#011train-rmse:1764.51#011validation-rmse:4485.25\u001b[0m\n",
            "\u001b[34m[110]#011train-rmse:1757.95#011validation-rmse:4482.22\u001b[0m\n",
            "\u001b[34m[111]#011train-rmse:1744.13#011validation-rmse:4476.74\u001b[0m\n",
            "\u001b[34m[112]#011train-rmse:1737.69#011validation-rmse:4476.4\u001b[0m\n",
            "\u001b[34m[113]#011train-rmse:1736.77#011validation-rmse:4475.89\u001b[0m\n",
            "\u001b[34m[114]#011train-rmse:1729.58#011validation-rmse:4475.18\u001b[0m\n",
            "\u001b[34m[115]#011train-rmse:1723.23#011validation-rmse:4473.42\u001b[0m\n",
            "\u001b[34m[116]#011train-rmse:1711#011validation-rmse:4469.16\u001b[0m\n",
            "\u001b[34m[117]#011train-rmse:1705.75#011validation-rmse:4466.94\u001b[0m\n",
            "\u001b[34m[118]#011train-rmse:1689.81#011validation-rmse:4459.63\u001b[0m\n",
            "\u001b[34m[119]#011train-rmse:1687.47#011validation-rmse:4458.79\u001b[0m\n",
            "\u001b[34m[120]#011train-rmse:1682.91#011validation-rmse:4457.29\u001b[0m\n",
            "\u001b[34m[121]#011train-rmse:1670.91#011validation-rmse:4452.36\u001b[0m\n",
            "\u001b[34m[122]#011train-rmse:1663.69#011validation-rmse:4450.26\u001b[0m\n",
            "\u001b[34m[123]#011train-rmse:1662.21#011validation-rmse:4449.77\u001b[0m\n",
            "\u001b[34m[124]#011train-rmse:1660.21#011validation-rmse:4449.35\u001b[0m\n",
            "\u001b[34m[125]#011train-rmse:1654.06#011validation-rmse:4444.8\u001b[0m\n",
            "\u001b[34m[126]#011train-rmse:1652.63#011validation-rmse:4444.44\u001b[0m\n",
            "\u001b[34m[127]#011train-rmse:1636.88#011validation-rmse:4438.62\u001b[0m\n",
            "\u001b[34m[128]#011train-rmse:1633.7#011validation-rmse:4435.16\u001b[0m\n",
            "\u001b[34m[129]#011train-rmse:1630.44#011validation-rmse:4434.53\u001b[0m\n",
            "\u001b[34m[130]#011train-rmse:1629.15#011validation-rmse:4434.21\u001b[0m\n",
            "\u001b[34m[131]#011train-rmse:1625.49#011validation-rmse:4433.17\u001b[0m\n",
            "\u001b[34m[132]#011train-rmse:1619.08#011validation-rmse:4429.25\u001b[0m\n",
            "\u001b[34m[133]#011train-rmse:1613.56#011validation-rmse:4426.43\u001b[0m\n",
            "\u001b[34m[134]#011train-rmse:1611.77#011validation-rmse:4425.62\u001b[0m\n",
            "\u001b[34m[135]#011train-rmse:1607.26#011validation-rmse:4423.1\u001b[0m\n",
            "\u001b[34m[136]#011train-rmse:1605.81#011validation-rmse:4422.41\u001b[0m\n",
            "\u001b[34m[137]#011train-rmse:1603.84#011validation-rmse:4419.71\u001b[0m\n",
            "\u001b[34m[138]#011train-rmse:1595.83#011validation-rmse:4416.7\u001b[0m\n",
            "\u001b[34m[139]#011train-rmse:1587.58#011validation-rmse:4413.7\u001b[0m\n",
            "\u001b[34m[140]#011train-rmse:1584.74#011validation-rmse:4411.65\u001b[0m\n",
            "\u001b[34m[141]#011train-rmse:1581.07#011validation-rmse:4410.2\u001b[0m\n",
            "\u001b[34m[142]#011train-rmse:1579.6#011validation-rmse:4409.46\u001b[0m\n",
            "\u001b[34m[143]#011train-rmse:1576.24#011validation-rmse:4408.03\u001b[0m\n",
            "\u001b[34m[144]#011train-rmse:1574.4#011validation-rmse:4408.49\u001b[0m\n",
            "\u001b[34m[145]#011train-rmse:1571.06#011validation-rmse:4407.52\u001b[0m\n",
            "\u001b[34m[146]#011train-rmse:1565.32#011validation-rmse:4405.31\u001b[0m\n",
            "\u001b[34m[147]#011train-rmse:1562.33#011validation-rmse:4404.65\u001b[0m\n",
            "\u001b[34m[148]#011train-rmse:1559.64#011validation-rmse:4404.33\u001b[0m\n",
            "\u001b[34m[149]#011train-rmse:1556.47#011validation-rmse:4403.07\u001b[0m\n",
            "\u001b[34m[150]#011train-rmse:1552.3#011validation-rmse:4401.14\u001b[0m\n",
            "\u001b[34m[151]#011train-rmse:1550.62#011validation-rmse:4400.6\u001b[0m\n",
            "\u001b[34m[152]#011train-rmse:1549.17#011validation-rmse:4400.22\u001b[0m\n",
            "\u001b[34m[153]#011train-rmse:1545.39#011validation-rmse:4398.25\u001b[0m\n",
            "\u001b[34m[154]#011train-rmse:1540.23#011validation-rmse:4397.51\u001b[0m\n",
            "\u001b[34m[155]#011train-rmse:1539.18#011validation-rmse:4396.97\u001b[0m\n",
            "\u001b[34m[156]#011train-rmse:1536.19#011validation-rmse:4396.33\u001b[0m\n",
            "\u001b[34m[157]#011train-rmse:1534.8#011validation-rmse:4395.84\u001b[0m\n",
            "\u001b[34m[158]#011train-rmse:1531.47#011validation-rmse:4394.89\u001b[0m\n",
            "\u001b[34m[159]#011train-rmse:1529.69#011validation-rmse:4394.51\u001b[0m\n",
            "\u001b[34m[160]#011train-rmse:1526.83#011validation-rmse:4394.16\u001b[0m\n",
            "\u001b[34m[161]#011train-rmse:1525.22#011validation-rmse:4393.53\u001b[0m\n",
            "\u001b[34m[162]#011train-rmse:1522.59#011validation-rmse:4392.77\u001b[0m\n",
            "\u001b[34m[163]#011train-rmse:1521.95#011validation-rmse:4393.04\u001b[0m\n",
            "\u001b[34m[164]#011train-rmse:1517.94#011validation-rmse:4390.2\u001b[0m\n",
            "\u001b[34m[165]#011train-rmse:1514.47#011validation-rmse:4389.07\u001b[0m\n",
            "\u001b[34m[166]#011train-rmse:1512.56#011validation-rmse:4388.72\u001b[0m\n",
            "\u001b[34m[167]#011train-rmse:1510.95#011validation-rmse:4388.07\u001b[0m\n",
            "\u001b[34m[168]#011train-rmse:1510.2#011validation-rmse:4387.69\u001b[0m\n",
            "\u001b[34m[169]#011train-rmse:1507.5#011validation-rmse:4387.15\u001b[0m\n",
            "\u001b[34m[170]#011train-rmse:1506.21#011validation-rmse:4386.67\u001b[0m\n",
            "\u001b[34m[171]#011train-rmse:1503.33#011validation-rmse:4386.41\u001b[0m\n",
            "\u001b[34m[172]#011train-rmse:1501.37#011validation-rmse:4385.94\u001b[0m\n",
            "\u001b[34m[173]#011train-rmse:1500.48#011validation-rmse:4385.9\u001b[0m\n",
            "\u001b[34m[174]#011train-rmse:1499.74#011validation-rmse:4385.44\u001b[0m\n",
            "\u001b[34m[175]#011train-rmse:1498.71#011validation-rmse:4385.31\u001b[0m\n",
            "\u001b[34m[176]#011train-rmse:1496.09#011validation-rmse:4385.13\u001b[0m\n",
            "\u001b[34m[177]#011train-rmse:1494.99#011validation-rmse:4384.98\u001b[0m\n",
            "\u001b[34m[178]#011train-rmse:1493.47#011validation-rmse:4384.61\u001b[0m\n",
            "\u001b[34m[179]#011train-rmse:1491.61#011validation-rmse:4384.17\u001b[0m\n",
            "\u001b[34m[180]#011train-rmse:1488.86#011validation-rmse:4383.77\u001b[0m\n",
            "\u001b[34m[181]#011train-rmse:1486.49#011validation-rmse:4383.58\u001b[0m\n",
            "\u001b[34m[182]#011train-rmse:1484.38#011validation-rmse:4383.04\u001b[0m\n",
            "\u001b[34m[183]#011train-rmse:1482.58#011validation-rmse:4382.75\u001b[0m\n",
            "\u001b[34m[184]#011train-rmse:1480.73#011validation-rmse:4383.17\u001b[0m\n",
            "\u001b[34m[185]#011train-rmse:1477.99#011validation-rmse:4382.22\u001b[0m\n",
            "\u001b[34m[186]#011train-rmse:1475.37#011validation-rmse:4381.77\u001b[0m\n",
            "\u001b[34m[187]#011train-rmse:1474.92#011validation-rmse:4381.56\u001b[0m\n",
            "\u001b[34m[188]#011train-rmse:1472.83#011validation-rmse:4380.58\u001b[0m\n",
            "\u001b[34m[189]#011train-rmse:1471.05#011validation-rmse:4380.07\u001b[0m\n",
            "\u001b[34m[190]#011train-rmse:1469.2#011validation-rmse:4379.63\u001b[0m\n",
            "\u001b[34m[191]#011train-rmse:1466.97#011validation-rmse:4379.56\u001b[0m\n",
            "\u001b[34m[192]#011train-rmse:1465.93#011validation-rmse:4379.17\u001b[0m\n",
            "\u001b[34m[193]#011train-rmse:1465.29#011validation-rmse:4379.15\u001b[0m\n",
            "\u001b[34m[194]#011train-rmse:1464.38#011validation-rmse:4378.72\u001b[0m\n",
            "\u001b[34m[195]#011train-rmse:1462.37#011validation-rmse:4378.24\u001b[0m\n",
            "\u001b[34m[196]#011train-rmse:1461.63#011validation-rmse:4378.03\u001b[0m\n",
            "\u001b[34m[197]#011train-rmse:1459.3#011validation-rmse:4377.52\u001b[0m\n",
            "\u001b[34m[198]#011train-rmse:1457.56#011validation-rmse:4377.18\u001b[0m\n",
            "\u001b[34m[199]#011train-rmse:1456.25#011validation-rmse:4377.86\u001b[0m\n",
            "\u001b[34m[200]#011train-rmse:1454.67#011validation-rmse:4377.79\u001b[0m\n",
            "\u001b[34m[201]#011train-rmse:1453.48#011validation-rmse:4377.51\u001b[0m\n",
            "\u001b[34m[202]#011train-rmse:1452.14#011validation-rmse:4377.61\u001b[0m\n",
            "\u001b[34m[203]#011train-rmse:1450.12#011validation-rmse:4377.21\u001b[0m\n",
            "\u001b[34m[204]#011train-rmse:1449.62#011validation-rmse:4377.03\u001b[0m\n",
            "\u001b[34m[205]#011train-rmse:1448.31#011validation-rmse:4376.79\u001b[0m\n",
            "\u001b[34m[206]#011train-rmse:1446.67#011validation-rmse:4376.92\u001b[0m\n",
            "\u001b[34m[207]#011train-rmse:1445.4#011validation-rmse:4376.66\u001b[0m\n",
            "\u001b[34m[208]#011train-rmse:1444.48#011validation-rmse:4376.35\u001b[0m\n",
            "\u001b[34m[209]#011train-rmse:1443.29#011validation-rmse:4376.07\u001b[0m\n",
            "\u001b[34m[210]#011train-rmse:1442.22#011validation-rmse:4375.64\u001b[0m\n",
            "\u001b[34m[211]#011train-rmse:1441.51#011validation-rmse:4375.23\u001b[0m\n",
            "\u001b[34m[212]#011train-rmse:1440.41#011validation-rmse:4375.15\u001b[0m\n",
            "\u001b[34m[213]#011train-rmse:1439.17#011validation-rmse:4374.93\u001b[0m\n",
            "\u001b[34m[214]#011train-rmse:1438.3#011validation-rmse:4374.48\u001b[0m\n",
            "\u001b[34m[215]#011train-rmse:1437.4#011validation-rmse:4374.12\u001b[0m\n",
            "\u001b[34m[216]#011train-rmse:1436.8#011validation-rmse:4373.85\u001b[0m\n",
            "\u001b[34m[217]#011train-rmse:1435.87#011validation-rmse:4373.82\u001b[0m\n",
            "\u001b[34m[218]#011train-rmse:1434.87#011validation-rmse:4373.68\u001b[0m\n",
            "\u001b[34m[219]#011train-rmse:1433.13#011validation-rmse:4373.97\u001b[0m\n",
            "\u001b[34m[220]#011train-rmse:1432.31#011validation-rmse:4373.82\u001b[0m\n",
            "\u001b[34m[221]#011train-rmse:1431.24#011validation-rmse:4374.02\u001b[0m\n",
            "\u001b[34m[222]#011train-rmse:1430.17#011validation-rmse:4374.35\u001b[0m\n",
            "\u001b[34m[223]#011train-rmse:1429.13#011validation-rmse:4374.45\u001b[0m\n",
            "\u001b[34m[224]#011train-rmse:1428.22#011validation-rmse:4373.81\u001b[0m\n",
            "\u001b[34m[225]#011train-rmse:1426.62#011validation-rmse:4373.66\u001b[0m\n",
            "\u001b[34m[226]#011train-rmse:1425.76#011validation-rmse:4373.7\u001b[0m\n",
            "\u001b[34m[227]#011train-rmse:1424.88#011validation-rmse:4373.55\u001b[0m\n",
            "\u001b[34m[228]#011train-rmse:1422.98#011validation-rmse:4373.02\u001b[0m\n",
            "\u001b[34m[229]#011train-rmse:1422.57#011validation-rmse:4372.94\u001b[0m\n",
            "\u001b[34m[230]#011train-rmse:1422.01#011validation-rmse:4373.11\u001b[0m\n",
            "\u001b[34m[231]#011train-rmse:1420.86#011validation-rmse:4372.74\u001b[0m\n",
            "\u001b[34m[232]#011train-rmse:1419.94#011validation-rmse:4372.67\u001b[0m\n",
            "\u001b[34m[233]#011train-rmse:1418.28#011validation-rmse:4373.21\u001b[0m\n",
            "\u001b[34m[234]#011train-rmse:1416.95#011validation-rmse:4373.01\u001b[0m\n",
            "\u001b[34m[235]#011train-rmse:1415.74#011validation-rmse:4373.04\u001b[0m\n",
            "\u001b[34m[236]#011train-rmse:1415.09#011validation-rmse:4372.92\u001b[0m\n",
            "\n",
            "2020-05-22 07:49:24 Uploading - Uploading generated training model\n",
            "2020-05-22 07:50:22 Completed - Training job completed\n",
            "Training seconds: 687\n",
            "Billable seconds: 687\n"
          ]
        }
      ],
      "source": [
        "train_input = sagemaker.session.s3_input(s3_data = s3_train_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
        "valid_input = sagemaker.session.s3_input(s3_data = s3_validation_data, content_type='csv',s3_data_type = 'S3Prefix')\n",
        "data_channels = {'train': train_input,'validation': valid_input}\n",
        "Xgboost_regressor.fit(data_channels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "42Oags847RXy",
        "outputId": "1e50679d-13ec-4c0d-ce69-69ce08fea35a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "---------------!"
          ]
        }
      ],
      "source": [
        "# Deploying the model to perform inference\n",
        "\n",
        "Xgboost_regressor = Xgboost_regressor.deploy(initial_instance_count = 1,\n",
        "                                          instance_type = 'ml.m4.xlarge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pN83FKBa7RXz"
      },
      "outputs": [],
      "source": [
        "from sagemaker.predictor import csv_serializer, json_deserializer\n",
        "\n",
        "# Xgboost_regressor.content_type = 'text/csv'\n",
        "Xgboost_regressor.serializer = csv_serializer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00YLZ6Fi7RXz"
      },
      "outputs": [],
      "source": [
        "# Try to make inference with the entire testing dataset (Crashes!)\n",
        "predictions = Xgboost_regressor.predict(X_test)\n",
        "predicted_values = bytes_2_array(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O-QGvXK_7RXz"
      },
      "outputs": [],
      "source": [
        "predictions1 = Xgboost_regressor.predict(X_test[0:10000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8aF-3JMj7RX0",
        "outputId": "20ba25ae-445e-4690-814e-a88b86990d79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 197,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_values_1 = bytes_2_array(predictions1)\n",
        "predicted_values_1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2KxwF0jr7RX0",
        "outputId": "0169474e-0daa-4d0d-b059-540755b0c386"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 198,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions2 = Xgboost_regressor.predict(X_test[10000:20000])\n",
        "predicted_values_2 = bytes_2_array(predictions2)\n",
        "predicted_values_2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kiBDHTr_7RX1",
        "outputId": "d3843bab-07c7-49b4-9bbd-3ddb3b49ecba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "execution_count": 199,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions3 = Xgboost_regressor.predict(X_test[20000:30000])\n",
        "predicted_values_3 = bytes_2_array(predictions3)\n",
        "predicted_values_3.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QUo_3tdb7RX1",
        "outputId": "87025f68-a512-4299-b300-004152725d14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1618, 1)"
            ]
          },
          "execution_count": 200,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions4 = Xgboost_regressor.predict(X_test[30000:31618])\n",
        "predicted_values_4 = bytes_2_array(predictions4)\n",
        "predicted_values_4.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7bDPLSyq7RX2"
      },
      "outputs": [],
      "source": [
        "predicted_values = np.concatenate((predicted_values_1, predicted_values_2, predicted_values_3, predicted_values_4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SNvWasaV7RX2",
        "outputId": "f6971978-7614-470a-b679-23877cb6c2d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE = 4266.012 \n",
            "MSE = 18198860.0 \n",
            "MAE = 1811.6404 \n",
            "R2 = 0.9638345632190437 \n",
            "Adjusted R2 = 0.9636760184661681\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from math import sqrt\n",
        "k = X_test.shape[1]\n",
        "n = len(X_test)\n",
        "RMSE = float(format(np.sqrt(mean_squared_error(y_test, predicted_values)),'.3f'))\n",
        "MSE = mean_squared_error(y_test, predicted_values)\n",
        "MAE = mean_absolute_error(y_test, predicted_values)\n",
        "r2 = r2_score(y_test, predicted_values)\n",
        "adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)\n",
        "\n",
        "print('RMSE =',RMSE, '\\nMSE =',MSE, '\\nMAE =',MAE, '\\nR2 =', r2, '\\nAdjusted R2 =', adj_r2) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bf3tKHNT7RX2"
      },
      "outputs": [],
      "source": [
        "# Delete the end-point\n",
        "\n",
        "Xgboost_regressor.delete_endpoint()"
      ]
    }
  ],
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