Decision_Tree.ipynb

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  "metadata": {
    "colab": {
      "name": "Decision_Tree.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOU6MwHpzicTF+xY6iG3dbK",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vamsiteja142/Decision_Tree_Regression/blob/master/Decision_Tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2TCCKb4VCxtp",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pedzGWrAjdBy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERug4an_Ax-6",
        "colab_type": "code",
        "outputId": "ad55acef-b7f1-4ae1-ed99-2fc99459199d",
        "colab": {
          "resources": {
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              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "upload = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9a3ef756-bc81-4258-8c1d-1481828b659b\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-9a3ef756-bc81-4258-8c1d-1481828b659b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving beer_data.csv to beer_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tes5nkZAreY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('beer_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XNaXN3d2CwXO",
        "colab": {}
      },
      "source": [
        "dataset['min_cellar_temperature'] = dataset['Cellar Temperature'].apply(lambda x : int(x.split('-')[0].strip()))\n",
        "dataset['max_cellar_temperature'] = dataset['Cellar Temperature'].apply(lambda x : int(x.split('-')[1].strip()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHzqe7QPAwqE",
        "colab_type": "code",
        "outputId": "8a40532e-10b4-4aac-ff38-4fcecf4a7f4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ABV</th>\n",
              "      <th>Ratings</th>\n",
              "      <th>Cellar Temperature</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.5</td>\n",
              "      <td>1</td>\n",
              "      <td>40-45</td>\n",
              "      <td>4.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.3</td>\n",
              "      <td>22</td>\n",
              "      <td>40-45</td>\n",
              "      <td>3.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>45-50</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>35-40</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.9</td>\n",
              "      <td>1</td>\n",
              "      <td>45-50</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ABV  Ratings Cellar Temperature  Score\n",
              "0  7.5        1              40-45   4.08\n",
              "1  5.3       22              40-45   3.82\n",
              "2  9.0        1              45-50   4.03\n",
              "3  4.6        1              35-40   4.00\n",
              "4  6.9        1              45-50   3.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rubdENRZDROj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6dbea46e-e113-4ec4-cbfc-75ccee328bde"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ABV', 'Ratings', 'Cellar Temperature', 'Score',\n",
              "       'min_cellar_temperature', 'max_cellar_temperature'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVXGz5mkDWXx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset['min_cellar_temperature'] = dataset['Cellar Temperature'].apply(lambda x : int(x.split('-')[0].strip()))\n",
        "dataset['max_cellar_temperature'] = dataset['Cellar Temperature'].apply(lambda x : int(x.split('-')[1].strip()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGh1-n8mB_wF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = dataset[['ABV', 'Ratings', \n",
        "       'min_cellar_temperature', 'max_cellar_temperature', 'Score']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rICqZI4DeWd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "12abe075-72a4-45ec-8115-8b6c79604b64"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>ABV</th>\n",
              "      <th>Ratings</th>\n",
              "      <th>min_cellar_temperature</th>\n",
              "      <th>max_cellar_temperature</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.5</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>4.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.3</td>\n",
              "      <td>22</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>3.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>50</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.9</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>50</td>\n",
              "      <td>3.75</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>1626</th>\n",
              "      <td>6.5</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>3.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1628</th>\n",
              "      <td>4.8</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>3.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1629</th>\n",
              "      <td>5.5</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>4.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1630</th>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>3.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1631 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ABV  Ratings  min_cellar_temperature  max_cellar_temperature  Score\n",
              "0     7.5        1                      40                      45   4.08\n",
              "1     5.3       22                      40                      45   3.82\n",
              "2     9.0        1                      45                      50   4.03\n",
              "3     4.6        1                      35                      40   4.00\n",
              "4     6.9        1                      45                      50   3.75\n",
              "...   ...      ...                     ...                     ...    ...\n",
              "1626  6.5        2                      40                      45   3.67\n",
              "1627  6.0        6                      40                      45   3.75\n",
              "1628  4.8        1                      35                      40   3.25\n",
              "1629  5.5        1                      40                      45   4.11\n",
              "1630  4.8        2                      40                      45   3.44\n",
              "\n",
              "[1631 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEdv02OwDfKl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1e083511-a477-4a85-8763-200d0bf1c6a5"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1631 entries, 0 to 1630\n",
            "Data columns (total 5 columns):\n",
            "ABV                       1631 non-null float64\n",
            "Ratings                   1631 non-null int64\n",
            "min_cellar_temperature    1631 non-null int64\n",
            "max_cellar_temperature    1631 non-null int64\n",
            "Score                     1631 non-null float64\n",
            "dtypes: float64(2), int64(3)\n",
            "memory usage: 63.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxF8cvB-DqEN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = dataset.iloc[:,:-1]\n",
        "y = dataset.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LF7117rGD1I8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d4c6ed65-e9b3-451e-e5ad-23627c03059a"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ABV</th>\n",
              "      <th>Ratings</th>\n",
              "      <th>min_cellar_temperature</th>\n",
              "      <th>max_cellar_temperature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.5</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.3</td>\n",
              "      <td>22</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.9</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ABV  Ratings  min_cellar_temperature  max_cellar_temperature\n",
              "0  7.5        1                      40                      45\n",
              "1  5.3       22                      40                      45\n",
              "2  9.0        1                      45                      50\n",
              "3  4.6        1                      35                      40\n",
              "4  6.9        1                      45                      50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vkuc2lmvD2Fs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2c700136-e5d3-4bab-9087-2dfb7d66a595"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4.08\n",
              "1    3.82\n",
              "2    4.03\n",
              "3    4.00\n",
              "4    3.75\n",
              "Name: Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AWaUV17D27n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "cab8ff69-468c-4b9d-e76f-425a24596763"
      },
      "source": [
        "x.info()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1631 entries, 0 to 1630\n",
            "Data columns (total 4 columns):\n",
            "ABV                       1631 non-null float64\n",
            "Ratings                   1631 non-null int64\n",
            "min_cellar_temperature    1631 non-null int64\n",
            "max_cellar_temperature    1631 non-null int64\n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 51.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLT8DihzD5Xk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = .2, random_state = 0) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rx_iBPeOEDbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr = DecisionTreeRegressor(criterion = 'mse')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imbzObb7EBgE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "9d49f7c6-61a6-462c-b0e6-e5bb829ca5bd"
      },
      "source": [
        "dtr.fit(x_train, y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uz_X0gGDEwa6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = dtr.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luJ6FjsAE1VR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c03b8f79-d7e0-463e-f547-5db84177f65a"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.89      , 0.        , 3.57      , 3.83      , 4.23      ,\n",
              "       3.70833333, 3.53      , 0.        , 3.47      , 3.575     ,\n",
              "       3.92      , 3.29      , 3.36333333, 3.55      , 3.98      ,\n",
              "       3.77142857, 3.86      , 4.02      , 3.515     , 3.9425    ,\n",
              "       3.91333333, 4.23      , 0.        , 4.38      , 4.01      ,\n",
              "       0.        , 3.83      , 4.1       , 3.658     , 4.01      ,\n",
              "       4.012     , 0.        , 3.92      , 0.        , 4.1       ,\n",
              "       2.88      , 3.65      , 3.98      , 3.7       , 3.06333333,\n",
              "       3.765     , 2.54      , 3.92      , 3.95      , 3.77416667,\n",
              "       3.99      , 4.33      , 3.        , 4.1       , 4.23      ,\n",
              "       4.11      , 3.575     , 3.96      , 3.85      , 4.08      ,\n",
              "       3.765     , 0.        , 3.97727273, 3.875     , 3.29      ,\n",
              "       4.        , 4.315     , 3.94      , 0.        , 3.84      ,\n",
              "       3.67      , 4.05      , 3.4       , 3.55      , 3.76      ,\n",
              "       3.96      , 3.81      , 3.412     , 4.05      , 3.8       ,\n",
              "       3.97      , 3.735     , 2.47      , 4.245     , 3.66      ,\n",
              "       4.1       , 4.06      , 4.18      , 3.35      , 3.67      ,\n",
              "       3.36      , 3.77416667, 0.        , 3.99      , 3.8       ,\n",
              "       3.6       , 3.06333333, 0.        , 3.99      , 0.        ,\n",
              "       4.09      , 3.11      , 3.36      , 4.02      , 3.        ,\n",
              "       3.606     , 3.4       , 3.815     , 3.9225    , 3.7875    ,\n",
              "       4.27      , 4.115     , 3.7       , 3.83      , 3.97727273,\n",
              "       3.61      , 3.77416667, 4.3       , 3.66      , 0.        ,\n",
              "       3.86      , 3.93      , 3.67      , 3.77055556, 3.915     ,\n",
              "       4.19      , 0.        , 3.96      , 3.7875    , 2.94      ,\n",
              "       3.28      , 3.62      , 3.55      , 3.862     , 2.94      ,\n",
              "       3.87      , 0.        , 3.65      , 4.01      , 3.87      ,\n",
              "       3.9       , 3.72333333, 3.7       , 3.70714286, 3.96      ,\n",
              "       3.85      , 3.73      , 3.96      , 0.        , 4.01      ,\n",
              "       1.88      , 4.38      , 4.        , 4.08      , 3.565     ,\n",
              "       1.89      , 0.        , 0.        , 3.45333333, 0.        ,\n",
              "       4.02      , 0.        , 0.        , 4.08      , 3.94      ,\n",
              "       4.        , 3.658     , 2.66      , 3.77      , 3.7       ,\n",
              "       3.948     , 3.575     , 3.67      , 3.88      , 3.83      ,\n",
              "       3.68      , 3.5       , 3.93      , 3.85      , 3.54666667,\n",
              "       4.02      , 4.22      , 3.99      , 2.73      , 3.97      ,\n",
              "       3.665     , 3.70833333, 3.75      , 4.0325    , 0.        ,\n",
              "       3.75      , 3.52      , 3.63      , 3.92      , 3.78      ,\n",
              "       0.        , 3.75      , 3.23333333, 3.93      , 0.        ,\n",
              "       4.085     , 3.52      , 3.75      , 3.99      , 3.915     ,\n",
              "       4.13      , 3.83      , 4.        , 2.94      , 4.28      ,\n",
              "       3.5       , 3.75      , 0.        , 3.85666667, 0.        ,\n",
              "       3.54      , 3.65      , 3.70833333, 0.        , 4.05      ,\n",
              "       4.31      , 0.        , 0.        , 3.77142857, 3.52      ,\n",
              "       3.7875    , 3.55      , 3.66      , 0.        , 3.38      ,\n",
              "       0.        , 3.83      , 3.745     , 3.95      , 4.205     ,\n",
              "       0.        , 3.13      , 3.70714286, 0.        , 0.        ,\n",
              "       3.88      , 3.89      , 3.61      , 4.29      , 3.97      ,\n",
              "       3.78      , 3.634     , 3.68      , 3.9       , 3.83      ,\n",
              "       4.23      , 0.        , 3.32666667, 3.75      , 3.86      ,\n",
              "       4.18      , 3.41      , 3.48      , 0.        , 3.4       ,\n",
              "       3.83      , 3.66      , 3.72      , 3.32      , 4.23      ,\n",
              "       3.7       , 0.        , 3.73      , 4.08      , 2.96      ,\n",
              "       3.95      , 3.384     , 4.        , 2.82      , 4.245     ,\n",
              "       0.        , 4.        , 3.69      , 3.77055556, 3.57      ,\n",
              "       3.83      , 3.        , 3.90571429, 4.64      , 3.82      ,\n",
              "       4.55      , 3.77      , 3.99      , 3.        , 3.59375   ,\n",
              "       3.68      , 0.        , 3.64      , 3.51      , 3.49      ,\n",
              "       4.        , 0.        , 0.        , 4.23      , 3.61      ,\n",
              "       3.745     , 3.63      , 3.68      , 4.08      , 0.        ,\n",
              "       4.22      , 3.384     , 0.        , 4.23      , 4.08      ,\n",
              "       3.69      , 3.29      , 4.46      , 3.11      , 3.67      ,\n",
              "       4.15      , 3.59375   , 3.        , 4.24      , 3.77416667,\n",
              "       3.384     , 3.64      , 3.01      , 3.606     , 1.9       ,\n",
              "       3.81      , 3.88      , 3.78714286, 3.61      , 0.        ,\n",
              "       3.93      , 4.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQ2tvwP9E2f5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  def rmsle(y_test, y_pred):\n",
        "    error = np.square(np.log10(y_test + 1) - np.log10(y_pred + 1)).mean() **.5\n",
        "    score = 1 - error\n",
        "    return score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvXZWU4xFXZP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e26f425c-141a-4988-8c86-5f92a568ffc5"
      },
      "source": [
        "print(rmsle(y_test, y_pred))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9448220259147109\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4TlB0cuYFcJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}