Power BI + Python + Machine Learning Forecasting/01 - Revenue - Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdcc50a3",
   "metadata": {},
   "source": [
    "____\n",
    "____\n",
    "# Python Linear Regression - Revenue\n",
    "____\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c0929b",
   "metadata": {},
   "source": [
    "____\n",
    "## Imports and Setup\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9acf49cb",
   "metadata": {},
   "source": [
    "** Import modules **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74213fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5f7f37",
   "metadata": {},
   "source": [
    "** Import data **"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5789b280",
   "metadata": {},
   "source": [
    "# IGNORE for now because it's not working without an \"authorization token\" - their documentation doesn't show how to get one :(\n",
    "# Web scraper for getting violent crime data from FBI\n",
    "\n",
    "# Import modules\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initial variables\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Getting data from Bureau of FBI API\n",
    "url = \"https://api.usa.gov/crime/fbi/cde/estimate/national/\"\n",
    "parameters = {\n",
    "    \"api_key\": \"VnMdghFxLKrD7BHRJ8ZHm3GRYwQD9xI4BeBiu033\",\n",
    "    \"from\": \"2009\",\n",
    "    \"to\": \"2023\",\n",
    "}\n",
    "\n",
    "response = requests.get(url=url, params=parameters)\n",
    "response.raise_for_status()\n",
    "data = response.json()\n",
    "\n",
    "for d in data:\n",
    "    month = 1\n",
    "\n",
    "    for x in range(12):\n",
    "        date = str(month) + \"/1/\" + str(d[\"year\"])\n",
    "        dates.append(date)\n",
    "        values.append(float(d[\"violent_crime\"]))\n",
    "        month += 1\n",
    "\n",
    "# Make crime dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"Violent_Crime_Value\": values\n",
    "}\n",
    "\n",
    "df_crime = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4cbae32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Web scraper for getting price index data from US Bureau of Economic Analysis\n",
    "\n",
    "# Import modules\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initial variables\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Getting data from Bureau of Economic Analysis API\n",
    "url = \"https://apps.bea.gov/api/data\"\n",
    "parameters = {\n",
    "    \"UserID\": \"90974B6B-A7A5-4CBF-A6F9-E553BEEB0B25\",\n",
    "    \"method\": \"getdata\",\n",
    "    \"datasetname\": \"NIPA\",\n",
    "    \"ResultFormat\": \"JSON\",\n",
    "    \"Frequency\": \"Q\",\n",
    "    \"TableName\": \"T10104\",\n",
    "    \"Year\": \"ALL\"\n",
    "}\n",
    "\n",
    "response = requests.get(url=url, params=parameters)\n",
    "response.raise_for_status()\n",
    "data = response.json()[\"BEAAPI\"][\"Results\"][\"Data\"]\n",
    "\n",
    "for d in data:\n",
    "    if d[\"LineDescription\"] == \"Gross domestic product\":\n",
    "        quarter = d[\"TimePeriod\"][-1]\n",
    "        month = 0\n",
    "\n",
    "        if quarter == \"1\":\n",
    "            month = 1\n",
    "        elif quarter == \"2\":\n",
    "            month = 4\n",
    "        elif quarter == \"3\":\n",
    "            month = 7\n",
    "        elif quarter == \"4\":\n",
    "            month = 10\n",
    "\n",
    "        for x in range(3):\n",
    "            date = str(month) + \"/1/\" + d[\"TimePeriod\"][0:4]\n",
    "            dates.append(date)\n",
    "            values.append(float(d[\"DataValue\"]))\n",
    "            month += 1\n",
    "\n",
    "# Make price index dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"Price_Index_Value\": values\n",
    "}\n",
    "\n",
    "df_price_index = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fd1f6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Web scraper for getting GDP data from US Bureau of Economic Analysis\n",
    "\n",
    "# Import modules\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initial variables\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Getting data from Bureau of Economic Analysis API, 1 year at a time\n",
    "url = \"https://apps.bea.gov/api/data\"\n",
    "parameters = {\n",
    "    \"UserID\": \"90974B6B-A7A5-4CBF-A6F9-E553BEEB0B25\",\n",
    "    \"method\": \"getdata\",\n",
    "    \"datasetname\": \"GDPByIndustry\",\n",
    "    \"ResultFormat\": \"JSON\",\n",
    "    \"Frequency\": \"Q\",\n",
    "    \"Industry\": \"ALL\",\n",
    "    \"TableID\": \"1\",\n",
    "    \"Year\": \"ALL\"\n",
    "}\n",
    "\n",
    "response = requests.get(url=url, params=parameters)\n",
    "response.raise_for_status()\n",
    "data = response.json()[\"BEAAPI\"][\"Results\"][0][\"Data\"]\n",
    "\n",
    "for d in data:\n",
    "    if d[\"IndustrYDescription\"] == \"Gross domestic product\":\n",
    "        quarter = d[\"Quarter\"]\n",
    "        month = 0\n",
    "\n",
    "        if quarter == \"I\":\n",
    "            month = 1\n",
    "        elif quarter == \"II\":\n",
    "            month = 4\n",
    "        elif quarter == \"III\":\n",
    "            month = 7\n",
    "        elif quarter == \"IV\":\n",
    "            month = 10\n",
    "\n",
    "        for x in range(3):\n",
    "            date = str(month) + \"/1/\" + d[\"Year\"]\n",
    "            dates.append(date)\n",
    "            values.append(float(d[\"DataValue\"]))\n",
    "            month += 1\n",
    "\n",
    "# Make GDP dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"GDP_Value\": values\n",
    "}\n",
    "\n",
    "df_gdp = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed955efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Web scraper for getting monthly gas prices from U.S. Energy Information Administration\n",
    "\n",
    "# Import modules\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initial variables\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Getting data from U.S. Energy Information Administration\n",
    "url = \"https://api.eia.gov/v2/petroleum/pri/gnd/data/?frequency=monthly&data[0]=value&facets[product][]=EPM0&facets[duoarea][]=NUS&start=1990-08&sort[0][column]=period&sort[0][direction]=desc&offset=0&length=5000&api_key=Bdgf7N1rGlQoLjcn00cdRrll3eM11ohy2I3TTJvE\"\n",
    "response = requests.get(url=url)\n",
    "response.raise_for_status()\n",
    "data = response.json()[\"response\"][\"data\"]\n",
    "\n",
    "for d in data:\n",
    "    month = str(int(d[\"period\"][-2::]))\n",
    "    year = str(int(d[\"period\"][0:4]))\n",
    "    date = month + \"/1/\" + year\n",
    "    dates.append(date)\n",
    "    values.append(float(d[\"value\"]))\n",
    "\n",
    "\n",
    "# Make gas prices dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"Gas_Price_Value\": values\n",
    "}\n",
    "\n",
    "df_gas = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a370818e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Web scraper for getting annual commercial carbon-dioxide emissions from U.S. Energy Information Administration\n",
    "\n",
    "# Import modules\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initial variables\n",
    "month = 1\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Getting data from U.S. Energy Information Administration\n",
    "url = \"https://api.eia.gov/v2/co2-emissions/co2-emissions-aggregates/data/?frequency=annual&data[0]=value&facets[stateId][]=US&facets[sectorId][]=CC&facets[fuelId][]=TO&start=1970&sort[0][column]=period&sort[0][direction]=desc&offset=0&length=5000&api_key=Bdgf7N1rGlQoLjcn00cdRrll3eM11ohy2I3TTJvE\"\n",
    "response = requests.get(url=url)\n",
    "response.raise_for_status()\n",
    "data = response.json()[\"response\"][\"data\"]\n",
    "\n",
    "for d in data:\n",
    "    year = str(int(d[\"period\"]))\n",
    "\n",
    "    for iterations in range(12):\n",
    "        dates.append(str(month) + \"/1/\" + str(year))\n",
    "        values.append(float(d[\"value\"]))\n",
    "\n",
    "        if month == 12:\n",
    "            month = 1\n",
    "        else:\n",
    "            month += 1\n",
    "\n",
    "# Make gas prices dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"Carbon_Dioxide_Emission_Value\": values\n",
    "}\n",
    "\n",
    "df_co2 = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6f51aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Web scraper for collecting USA Census Bureau population data\n",
    "\n",
    "# Import modules\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize variables\n",
    "year = 2020\n",
    "month = 1\n",
    "dates = []\n",
    "values = []\n",
    "\n",
    "# Make soup for web page\n",
    "response = requests.get(\n",
    "    \"https://www.census.gov/data/tables/time-series/dec/popchange-data-text.html\")\n",
    "wp = response.text\n",
    "soup = BeautifulSoup(wp, \"html.parser\")\n",
    "\n",
    "for row in soup.find_all('tr'):\n",
    "    list_td = [t for t in row.find_all(\"td\")]\n",
    "\n",
    "    for index in range(len(list_td)):\n",
    "        if list_td[index].get(\"id\") == \"POP_US\":\n",
    "            for x in range(index + 1, index + 12):\n",
    "                for iterations in range(120):\n",
    "                    dates.append(str(month) + \"/1/\" + str(year))\n",
    "                    values.append(list_td[x].string)\n",
    "\n",
    "                    if month == 12:\n",
    "                        month = 1\n",
    "                        year -= 1\n",
    "                    else:\n",
    "                        month += 1\n",
    "\n",
    "# Make population dictionary, assign to a DataFrame, convert to csv\n",
    "data_dict = {\n",
    "    \"Date\": dates,\n",
    "    \"Population_Value\": values\n",
    "}\n",
    "\n",
    "df_pop = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5f16b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert population value to type float\n",
    "df_pop['Population_Value'] = df_pop['Population_Value'].str.replace(',', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91c864e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull in Walmart historical revenue data\n",
    "df_walmart = pd.read_csv('Walmart_Revenue_Data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406ae383",
   "metadata": {},
   "source": [
    "** Combine all datasets together **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0181a995",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df_walmart, df_pop).merge(df_price_index).merge(df_gdp).merge(df_gas).merge(df_co2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c8db82",
   "metadata": {},
   "source": [
    "** Fix NaN values, if any **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b60153f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Date</th>\n",
       "      <th>Revenue_Value</th>\n",
       "      <th>Population_Value</th>\n",
       "      <th>Price_Index_Value</th>\n",
       "      <th>GDP_Value</th>\n",
       "      <th>Gas_Price_Value</th>\n",
       "      <th>Carbon_Dioxide_Emission_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Date, Revenue_Value, Population_Value, Price_Index_Value, GDP_Value, Gas_Price_Value, Carbon_Dioxide_Emission_Value]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e61652d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Revenue_Value</th>\n",
       "      <th>Population_Value</th>\n",
       "      <th>Price_Index_Value</th>\n",
       "      <th>GDP_Value</th>\n",
       "      <th>Gas_Price_Value</th>\n",
       "      <th>Carbon_Dioxide_Emission_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/1/2009</td>\n",
       "      <td>93.471</td>\n",
       "      <td>308745538.0</td>\n",
       "      <td>95.001</td>\n",
       "      <td>14430.9</td>\n",
       "      <td>1.975</td>\n",
       "      <td>224.027557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/1/2009</td>\n",
       "      <td>93.471</td>\n",
       "      <td>308745538.0</td>\n",
       "      <td>95.001</td>\n",
       "      <td>14430.9</td>\n",
       "      <td>2.011</td>\n",
       "      <td>224.027557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/1/2009</td>\n",
       "      <td>93.471</td>\n",
       "      <td>308745538.0</td>\n",
       "      <td>94.870</td>\n",
       "      <td>14381.2</td>\n",
       "      <td>2.102</td>\n",
       "      <td>224.027557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/1/2009</td>\n",
       "      <td>100.082</td>\n",
       "      <td>308745538.0</td>\n",
       "      <td>94.870</td>\n",
       "      <td>14381.2</td>\n",
       "      <td>2.316</td>\n",
       "      <td>224.027557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6/1/2009</td>\n",
       "      <td>100.082</td>\n",
       "      <td>308745538.0</td>\n",
       "      <td>94.870</td>\n",
       "      <td>14381.2</td>\n",
       "      <td>2.681</td>\n",
       "      <td>224.027557</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>8/1/2020</td>\n",
       "      <td>134.700</td>\n",
       "      <td>331449281.0</td>\n",
       "      <td>114.032</td>\n",
       "      <td>21362.4</td>\n",
       "      <td>2.272</td>\n",
       "      <td>235.793882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>9/1/2020</td>\n",
       "      <td>134.700</td>\n",
       "      <td>331449281.0</td>\n",
       "      <td>114.032</td>\n",
       "      <td>21362.4</td>\n",
       "      <td>2.274</td>\n",
       "      <td>235.793882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>10/1/2020</td>\n",
       "      <td>134.700</td>\n",
       "      <td>331449281.0</td>\n",
       "      <td>114.744</td>\n",
       "      <td>21704.7</td>\n",
       "      <td>2.248</td>\n",
       "      <td>235.793882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>11/1/2020</td>\n",
       "      <td>152.100</td>\n",
       "      <td>331449281.0</td>\n",
       "      <td>114.744</td>\n",
       "      <td>21704.7</td>\n",
       "      <td>2.200</td>\n",
       "      <td>235.793882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>12/1/2020</td>\n",
       "      <td>152.100</td>\n",
       "      <td>331449281.0</td>\n",
       "      <td>114.744</td>\n",
       "      <td>21704.7</td>\n",
       "      <td>2.284</td>\n",
       "      <td>235.793882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Revenue_Value  Population_Value  Price_Index_Value  GDP_Value  \\\n",
       "0     2/1/2009         93.471       308745538.0             95.001    14430.9   \n",
       "1     3/1/2009         93.471       308745538.0             95.001    14430.9   \n",
       "2     4/1/2009         93.471       308745538.0             94.870    14381.2   \n",
       "3     5/1/2009        100.082       308745538.0             94.870    14381.2   \n",
       "4     6/1/2009        100.082       308745538.0             94.870    14381.2   \n",
       "..         ...            ...               ...                ...        ...   \n",
       "138   8/1/2020        134.700       331449281.0            114.032    21362.4   \n",
       "139   9/1/2020        134.700       331449281.0            114.032    21362.4   \n",
       "140  10/1/2020        134.700       331449281.0            114.744    21704.7   \n",
       "141  11/1/2020        152.100       331449281.0            114.744    21704.7   \n",
       "142  12/1/2020        152.100       331449281.0            114.744    21704.7   \n",
       "\n",
       "     Gas_Price_Value  Carbon_Dioxide_Emission_Value  \n",
       "0              1.975                     224.027557  \n",
       "1              2.011                     224.027557  \n",
       "2              2.102                     224.027557  \n",
       "3              2.316                     224.027557  \n",
       "4              2.681                     224.027557  \n",
       "..               ...                            ...  \n",
       "138            2.272                     235.793882  \n",
       "139            2.274                     235.793882  \n",
       "140            2.248                     235.793882  \n",
       "141            2.200                     235.793882  \n",
       "142            2.284                     235.793882  \n",
       "\n",
       "[143 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae0d3ab",
   "metadata": {},
   "source": [
    "** Update Date column to pandas datetime type **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "314cf324",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b18ecdf",
   "metadata": {},
   "source": [
    "** Make dummy variable for Date column **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9e7843e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# date_labels = ['Date']\n",
    "# df = pd.get_dummies(df, columns=date_labels, drop_first=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7dffda6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd38d37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79316e0f",
   "metadata": {},
   "source": [
    "____\n",
    "## Linear Regression\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9971e0a6",
   "metadata": {},
   "source": [
    "** Setup testing/training data for the linear regression model **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b49972e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Revenue_Value', 'Population_Value', 'Price_Index_Value',\n",
       "       'GDP_Value', 'Gas_Price_Value', 'Carbon_Dioxide_Emission_Value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4dede857",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Including Date as dummy variables\n",
    "# x = df.drop(['Revenue_Value'], axis=1)\n",
    "# y = df['Revenue_Value']\n",
    "\n",
    "x = df.drop(['Revenue_Value', \"Date\"], axis=1).values\n",
    "y = df['Revenue_Value'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c6148f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  2.24027557e+02]\n",
      " [3.08745538e+08 9.48700000e+01 1.43812000e+04 2.10200000e+00\n",
      "  2.24027557e+02]\n",
      " [3.08745538e+08 9.48700000e+01 1.43812000e+04 2.31600000e+00\n",
      "  2.24027557e+02]\n",
      " [3.08745538e+08 9.48700000e+01 1.43812000e+04 2.68100000e+00\n",
      "  2.24027557e+02]\n",
      " [3.08745538e+08 9.49280000e+01 1.44489000e+04 2.58200000e+00\n",
      "  2.24027557e+02]\n",
      " [3.08745538e+08 9.49280000e+01 1.44489000e+04 2.67000000e+00\n",
      "  2.24027557e+02]\n",
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      "  2.24027557e+02]\n",
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      "  2.21539444e+02]\n",
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      "  2.01679157e+02]\n",
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      "  2.01679157e+02]\n",
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      "  2.01679157e+02]\n",
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      "  2.01679157e+02]\n",
      " [3.31449281e+08 1.00304000e+02 1.63195000e+04 3.78000000e+00\n",
      "  2.01679157e+02]\n",
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      "  2.01679157e+02]\n",
      " [3.31449281e+08 1.00730000e+02 1.64204000e+04 3.81200000e+00\n",
      "  2.01679157e+02]\n",
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      "  2.23451960e+02]\n",
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      "  2.23451960e+02]\n",
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      "  2.23451960e+02]\n",
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      "  2.34806616e+02]\n",
      " [3.31449281e+08 1.02965000e+02 1.71443000e+04 3.60600000e+00\n",
      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.34806616e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.41913353e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
      " [3.31449281e+08 1.05576000e+02 1.86116000e+04 2.37100000e+00\n",
      "  2.33960463e+02]\n",
      " [3.31449281e+08 1.05576000e+02 1.86116000e+04 2.46700000e+00\n",
      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      "  2.33960463e+02]\n",
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      " [3.31449281e+08 1.07022000e+02 1.91482000e+04 2.41600000e+00\n",
      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
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      "  2.35691580e+02]\n",
      " [3.31449281e+08 1.08639000e+02 1.98948000e+04 2.62100000e+00\n",
      "  2.35691580e+02]\n",
      " [3.31449281e+08 1.08639000e+02 1.98948000e+04 2.67800000e+00\n",
      "  2.35691580e+02]\n",
      " [3.31449281e+08 1.08639000e+02 1.98948000e+04 2.59400000e+00\n",
      "  2.35691580e+02]\n",
      " [3.31449281e+08 1.09365000e+02 2.01555000e+04 2.67100000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.09365000e+02 2.01555000e+04 2.70500000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.09365000e+02 2.01555000e+04 2.70900000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10176000e+02 2.04702000e+04 2.87300000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10176000e+02 2.04702000e+04 2.98700000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10176000e+02 2.04702000e+04 2.97000000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10680000e+02 2.06873000e+04 2.92800000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10680000e+02 2.06873000e+04 2.91400000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.10680000e+02 2.06873000e+04 2.91500000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.11155000e+02 2.08193000e+04 2.94300000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.11155000e+02 2.08193000e+04 2.73600000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.11155000e+02 2.08193000e+04 2.45700000e+00\n",
      "  2.56398355e+02]\n",
      " [3.31449281e+08 1.11560000e+02 2.10131000e+04 2.33800000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.11560000e+02 2.10131000e+04 2.39300000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.11560000e+02 2.10131000e+04 2.59400000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12184000e+02 2.12724000e+04 2.88100000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12184000e+02 2.12724000e+04 2.94600000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12184000e+02 2.12724000e+04 2.80400000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12558000e+02 2.15318000e+04 2.82300000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12558000e+02 2.15318000e+04 2.70700000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12558000e+02 2.15318000e+04 2.68100000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12910000e+02 2.17065000e+04 2.72400000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12910000e+02 2.17065000e+04 2.69300000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.12910000e+02 2.17065000e+04 2.64500000e+00\n",
      "  2.57365241e+02]\n",
      " [3.31449281e+08 1.13427000e+02 2.15380000e+04 2.63600000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.13427000e+02 2.15380000e+04 2.53300000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.13427000e+02 2.15380000e+04 2.32900000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.13053000e+02 1.96367000e+04 1.93800000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.13053000e+02 1.96367000e+04 1.96100000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.13053000e+02 1.96367000e+04 2.17000000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14032000e+02 2.13624000e+04 2.27200000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14032000e+02 2.13624000e+04 2.27200000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14032000e+02 2.13624000e+04 2.27400000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14744000e+02 2.17047000e+04 2.24800000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14744000e+02 2.17047000e+04 2.20000000e+00\n",
      "  2.35793882e+02]\n",
      " [3.31449281e+08 1.14744000e+02 2.17047000e+04 2.28400000e+00\n",
      "  2.35793882e+02]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46f1c1ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 93.471  93.471  93.471 100.082 100.082 100.082  98.667  98.667  98.667\n",
      " 112.8   112.8   112.8    99.1    99.1    99.1   103.    103.    103.\n",
      " 101.2   101.2   101.2   115.6   115.6   115.6   103.4   103.4   103.4\n",
      " 108.6   108.6   108.6   109.5   109.5   109.5   122.3   122.3   122.3\n",
      " 112.3   112.3   112.3   113.5   113.5   113.5   113.2   113.2   113.2\n",
      " 127.1   127.1   127.1   113.4   113.4   113.4   116.2   116.2   116.2\n",
      " 114.9   114.9   114.9   128.8   128.8   128.8   114.2   114.2   114.2\n",
      " 119.3   119.3   119.3   118.1   118.1   118.1   130.7   130.7   130.7\n",
      " 114.8   114.8   114.8   120.2   120.2   120.2   117.4   117.4   117.4\n",
      " 129.7   129.7   129.7   115.9   115.9   115.9   120.9   120.9   120.9\n",
      " 118.2   118.2   118.2   130.9   130.9   130.9   117.5   117.5   117.5\n",
      " 123.4   123.4   123.4   123.2   123.2   123.2   136.3   136.3   136.3\n",
      " 122.7   122.7   122.7   128.    128.    128.    124.9   124.9   124.9\n",
      " 138.8   138.8   138.8   123.9   123.9   123.9   130.4   130.4   130.4\n",
      " 128.    128.    128.    141.7   141.7   141.7   134.6   134.6   134.6\n",
      " 137.7   137.7   137.7   134.7   134.7   134.7   152.1   152.1  ]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a53b9475",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9433db",
   "metadata": {},
   "source": [
    "** Training the data **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "77a9aad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "199bcc65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c24d2634",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.10765764e-08 -1.59386729e+00  8.08170466e-03  3.11413612e+00\n",
      "  1.76707108e-02]\n"
     ]
    }
   ],
   "source": [
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18e724c",
   "metadata": {},
   "source": [
    "** Predicting the testing data **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d338a54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92f20dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax = sns.scatterplot(data=None, x=y_test, y=predictions)\n",
    "ax.set(xlabel = 'Actual', ylabel = 'Predictions')\n",
    "ax2 = sns.regplot(data=None, x=y_test, y=predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "55eb78ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x28ee84f9490>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=y_test, y=predictions, data=None, kind='reg', joint_kws={'line_kws':{'color':'orange'}})\n",
    "g.set_axis_labels('Actual', 'Predictions', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46b310f",
   "metadata": {},
   "source": [
    "** Model evaluation **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "78dccb6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4.254507141010568\n",
      "MSE: 29.566566134653527\n",
      "RMSE: 5.43751470201723\n",
      "r2 score:  0.672089048580301\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))\n",
    "print('r2 score: ', r2_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa3c17c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28ee55d67f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=None, x=y_test - predictions, kde=True, bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe2a3052",
   "metadata": {},
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.107658e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.593867e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.081705e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.114136e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.767071e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "0  8.107658e-08\n",
       "1 -1.593867e+00\n",
       "2  8.081705e-03\n",
       "3  3.114136e+00\n",
       "4  1.767071e-02"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coeff_df = pd.DataFrame(lm.coef_, x.columns, columns=['Coefficient'])\n",
    "coeff_df = pd.DataFrame(lm.coef_)\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec7808ca",
   "metadata": {},
   "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>Actual Value</th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122.300</td>\n",
       "      <td>112.389812</td>\n",
       "      <td>9.910188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120.200</td>\n",
       "      <td>120.933660</td>\n",
       "      <td>-0.733660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136.300</td>\n",
       "      <td>126.867896</td>\n",
       "      <td>9.432104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119.300</td>\n",
       "      <td>120.218255</td>\n",
       "      <td>-0.918255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114.200</td>\n",
       "      <td>116.818638</td>\n",
       "      <td>-2.618638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>118.100</td>\n",
       "      <td>119.582971</td>\n",
       "      <td>-1.482971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117.500</td>\n",
       "      <td>122.922459</td>\n",
       "      <td>-5.422459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130.700</td>\n",
       "      <td>118.830177</td>\n",
       "      <td>11.869823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120.900</td>\n",
       "      <td>121.391200</td>\n",
       "      <td>-0.491200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120.900</td>\n",
       "      <td>120.953383</td>\n",
       "      <td>-0.053383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>114.800</td>\n",
       "      <td>119.413939</td>\n",
       "      <td>-4.613939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>103.000</td>\n",
       "      <td>105.857392</td>\n",
       "      <td>-2.857392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>109.500</td>\n",
       "      <td>111.715506</td>\n",
       "      <td>-2.215506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>103.400</td>\n",
       "      <td>111.106668</td>\n",
       "      <td>-7.706668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>93.471</td>\n",
       "      <td>100.585037</td>\n",
       "      <td>-7.114037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>141.700</td>\n",
       "      <td>135.393401</td>\n",
       "      <td>6.306599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116.200</td>\n",
       "      <td>116.485733</td>\n",
       "      <td>-0.285733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>113.500</td>\n",
       "      <td>113.472416</td>\n",
       "      <td>0.027584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>113.400</td>\n",
       "      <td>115.573485</td>\n",
       "      <td>-2.173485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>123.200</td>\n",
       "      <td>126.951977</td>\n",
       "      <td>-3.751977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>118.200</td>\n",
       "      <td>121.201237</td>\n",
       "      <td>-3.001237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>113.400</td>\n",
       "      <td>115.793446</td>\n",
       "      <td>-2.393446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>116.200</td>\n",
       "      <td>115.688708</td>\n",
       "      <td>0.511292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>115.900</td>\n",
       "      <td>120.171734</td>\n",
       "      <td>-4.271734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>114.900</td>\n",
       "      <td>116.308228</td>\n",
       "      <td>-1.408228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>118.100</td>\n",
       "      <td>119.633625</td>\n",
       "      <td>-1.533625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>113.200</td>\n",
       "      <td>114.755440</td>\n",
       "      <td>-1.555440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>119.300</td>\n",
       "      <td>118.954514</td>\n",
       "      <td>0.345486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>113.200</td>\n",
       "      <td>114.350603</td>\n",
       "      <td>-1.150603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>128.000</td>\n",
       "      <td>134.586167</td>\n",
       "      <td>-6.586167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>109.500</td>\n",
       "      <td>112.586002</td>\n",
       "      <td>-3.086002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>128.000</td>\n",
       "      <td>131.425589</td>\n",
       "      <td>-3.425589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>115.600</td>\n",
       "      <td>107.481232</td>\n",
       "      <td>8.118768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>115.600</td>\n",
       "      <td>107.901640</td>\n",
       "      <td>7.698360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>130.400</td>\n",
       "      <td>134.947407</td>\n",
       "      <td>-4.547407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>117.500</td>\n",
       "      <td>122.857062</td>\n",
       "      <td>-5.357062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>99.100</td>\n",
       "      <td>104.945699</td>\n",
       "      <td>-5.845699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>118.100</td>\n",
       "      <td>119.835216</td>\n",
       "      <td>-1.735216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>128.800</td>\n",
       "      <td>116.304518</td>\n",
       "      <td>12.495482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>138.800</td>\n",
       "      <td>130.835750</td>\n",
       "      <td>7.964250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>118.200</td>\n",
       "      <td>121.335145</td>\n",
       "      <td>-3.135145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>129.700</td>\n",
       "      <td>118.855664</td>\n",
       "      <td>10.844336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>115.600</td>\n",
       "      <td>109.652366</td>\n",
       "      <td>5.947634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual Value  Predictions  Difference\n",
       "0        122.300   112.389812    9.910188\n",
       "1        120.200   120.933660   -0.733660\n",
       "2        136.300   126.867896    9.432104\n",
       "3        119.300   120.218255   -0.918255\n",
       "4        114.200   116.818638   -2.618638\n",
       "5        118.100   119.582971   -1.482971\n",
       "6        117.500   122.922459   -5.422459\n",
       "7        130.700   118.830177   11.869823\n",
       "8        120.900   121.391200   -0.491200\n",
       "9        120.900   120.953383   -0.053383\n",
       "10       114.800   119.413939   -4.613939\n",
       "11       103.000   105.857392   -2.857392\n",
       "12       109.500   111.715506   -2.215506\n",
       "13       103.400   111.106668   -7.706668\n",
       "14        93.471   100.585037   -7.114037\n",
       "15       141.700   135.393401    6.306599\n",
       "16       116.200   116.485733   -0.285733\n",
       "17       113.500   113.472416    0.027584\n",
       "18       113.400   115.573485   -2.173485\n",
       "19       123.200   126.951977   -3.751977\n",
       "20       118.200   121.201237   -3.001237\n",
       "21       113.400   115.793446   -2.393446\n",
       "22       116.200   115.688708    0.511292\n",
       "23       115.900   120.171734   -4.271734\n",
       "24       114.900   116.308228   -1.408228\n",
       "25       118.100   119.633625   -1.533625\n",
       "26       113.200   114.755440   -1.555440\n",
       "27       119.300   118.954514    0.345486\n",
       "28       113.200   114.350603   -1.150603\n",
       "29       128.000   134.586167   -6.586167\n",
       "30       109.500   112.586002   -3.086002\n",
       "31       128.000   131.425589   -3.425589\n",
       "32       115.600   107.481232    8.118768\n",
       "33       115.600   107.901640    7.698360\n",
       "34       130.400   134.947407   -4.547407\n",
       "35       117.500   122.857062   -5.357062\n",
       "36        99.100   104.945699   -5.845699\n",
       "37       118.100   119.835216   -1.735216\n",
       "38       128.800   116.304518   12.495482\n",
       "39       138.800   130.835750    7.964250\n",
       "40       118.200   121.335145   -3.135145\n",
       "41       129.700   118.855664   10.844336\n",
       "42       115.600   109.652366    5.947634"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "differences = pd.DataFrame({'Actual Value': y_test, 'Predictions': predictions, 'Difference': y_test - predictions})\n",
    "differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1ac66e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Revenue_Value  Price_Index_Value  GDP_Value  \\\n",
      "Revenue_Value                       1.000000           0.859023   0.838942   \n",
      "Price_Index_Value                   0.859023           1.000000   0.990656   \n",
      "GDP_Value                           0.838942           0.990656   1.000000   \n",
      "Gas_Price_Value                    -0.282172          -0.401395  -0.412298   \n",
      "Carbon_Dioxide_Emission_Value       0.524051           0.734586   0.769992   \n",
      "Population_Value                    0.630287           0.633901   0.611783   \n",
      "\n",
      "                               Gas_Price_Value  Carbon_Dioxide_Emission_Value  \\\n",
      "Revenue_Value                        -0.282172                       0.524051   \n",
      "Price_Index_Value                    -0.401395                       0.734586   \n",
      "GDP_Value                            -0.412298                       0.769992   \n",
      "Gas_Price_Value                       1.000000                      -0.476595   \n",
      "Carbon_Dioxide_Emission_Value        -0.476595                       1.000000   \n",
      "Population_Value                      0.188647                       0.287851   \n",
      "\n",
      "                               Population_Value  \n",
      "Revenue_Value                          0.630287  \n",
      "Price_Index_Value                      0.633901  \n",
      "GDP_Value                              0.611783  \n",
      "Gas_Price_Value                        0.188647  \n",
      "Carbon_Dioxide_Emission_Value          0.287851  \n",
      "Population_Value                       1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Visualize independent variables correlation\n",
    "features = [\"Revenue_Value\", \"Price_Index_Value\", \"GDP_Value\", \"Gas_Price_Value\", \"Carbon_Dioxide_Emission_Value\", \"Population_Value\"]\n",
    "\n",
    "corr_matrix = df.loc[:,features].corr()\n",
    "print(corr_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "53d803b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "21c1c6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since Violent_Crime_Value has the lowest correlation coefficient to Revenue, try taking this out of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b610fc33",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[156].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\GARRET~1.BEC\\AppData\\Local\\Temp/ipykernel_14956/3328496678.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Predict future revenue values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m156\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    236\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m         \"\"\"\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    218\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 220\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'coo'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    221\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[0;32m    222\u001b[0m                                dense_output=True) + self.intercept_\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    692\u001b[0m             \u001b[1;31m# If input is 1D raise error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 694\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    695\u001b[0m                     \u001b[1;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    696\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[156].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "# Predict future revenue values\n",
    "lm.predict([156])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dfb3ea9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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