Marketing_Strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b32e57da",
   "metadata": {
    "id": "b32e57da"
   },
   "source": [
    "## Marketing Strategy Analysis\n",
    "#### Programming Script and Technical Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e349f98f",
   "metadata": {
    "id": "e349f98f"
   },
   "source": [
    "### Table of Content\n",
    "1. Introduction\n",
    "2. Data Loading and Quality \n",
    "3. Feature Additions and Engineering\n",
    "4. Exploratory Data Analysis and Statistical Analysis\n",
    "5. Final Recommendations (Optimal Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9049adfb",
   "metadata": {
    "id": "9049adfb"
   },
   "source": [
    "### 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b85926",
   "metadata": {
    "id": "30b85926"
   },
   "source": [
    "**Introduction**\n",
    "\n",
    "Welcome to the \"Marketing Campaign Analytics\" Python project, where our goal is to optimize marketing campaigns and create a data-driven marketing strategy using Python. This project utilizes machine learning models, specifically linear regression and correlation analysis, to identify the most effective marketing tactics that significantly impact sales. The ultimate objective is to generate an ROI table that can be presented to management for informed decision-making."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acddae8",
   "metadata": {},
   "source": [
    "**Background of the Dataset**\n",
    "\n",
    "We are working with data from three types of hospital accounts: big, medium, and small hospitals. The sales are categorized into organic sales and inorganic revenue generated through marketing strategies such as phone calls, emails, flyers, and sales visits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155d4100",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aee5a604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Pandas library for data manipulation and analysis\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the NumPy library for numerical operations and array manipulations\n",
    "import numpy as np\n",
    "\n",
    "# Importing the Seaborn library for data visualization based on Matplotlib, providing an aesthetically pleasing and informative statistical graphics\n",
    "import seaborn as sns\n",
    "\n",
    "# Importing the SciPy library for scientific and technical computing, including statistical analysis tools\n",
    "import scipy.stats\n",
    "\n",
    "import matplotlib.pyplot as plt  # Importing plt from matplotlib.pyplot\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579dcfc9",
   "metadata": {
    "id": "579dcfc9"
   },
   "source": [
    "### 2. Data Loading and Quality Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d14d249a",
   "metadata": {
    "id": "d14d249a"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('Campaign-Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6577e1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of the dataset:\n",
      "   Client ID      Client Type  Number of Customers  Montly Target  Zip Code  \\\n",
      "0  ID-987275  Medium Facility                 2800            125      1003   \n",
      "1  ID-987275  Medium Facility                 2800            125      1003   \n",
      "2  ID-987275  Medium Facility                 2800            125      1003   \n",
      "3  ID-987275  Medium Facility                 2800            125      1003   \n",
      "4  ID-987275  Medium Facility                 2800            125      1003   \n",
      "\n",
      "  Calendardate  Amount Collected  Unit Sold  Campaign (Email)  \\\n",
      "0   16-01-2014                 0          0               0.0   \n",
      "1   16-02-2014           3409460         24               0.0   \n",
      "2   18-03-2014          10228384         75               0.0   \n",
      "3   18-04-2014          17047304        123               0.0   \n",
      "4   19-05-2014          23866224        171               0.0   \n",
      "\n",
      "   Campaign (Flyer)  Campaign (Phone)  Sales Contact 1  Sales Contact 2  \\\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        3547500.0   \n",
      "4               0.0               0.0              0.0              0.0   \n",
      "\n",
      "   Sales Contact 3  Sales Contact 4  Sales Contact 5 Number of Competition  \n",
      "0              0.0              0.0              0.0                   Low  \n",
      "1              0.0              0.0         322500.0                   Low  \n",
      "2              0.0              0.0              0.0                   Low  \n",
      "3        1290000.0              0.0              0.0                   Low  \n",
      "4              0.0              0.0              0.0                   Low  \n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the dataset\n",
    "print(\"First 5 rows of the dataset:\")\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "44ec3f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset Information:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2976 entries, 0 to 2975\n",
      "Data columns (total 17 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Client ID              2976 non-null   object \n",
      " 1   Client Type            2976 non-null   object \n",
      " 2   Number of Customers    2976 non-null   int64  \n",
      " 3   Montly Target          2976 non-null   int64  \n",
      " 4   Zip Code               2976 non-null   int64  \n",
      " 5   Calendardate           2976 non-null   object \n",
      " 6   Amount Collected       2976 non-null   int64  \n",
      " 7   Unit Sold              2976 non-null   int64  \n",
      " 8   Campaign (Email)       2976 non-null   float64\n",
      " 9   Campaign (Flyer)       2976 non-null   float64\n",
      " 10  Campaign (Phone)       2976 non-null   float64\n",
      " 11  Sales Contact 1        2976 non-null   float64\n",
      " 12  Sales Contact 2        2976 non-null   float64\n",
      " 13  Sales Contact 3        2976 non-null   float64\n",
      " 14  Sales Contact 4        2976 non-null   float64\n",
      " 15  Sales Contact 5        2976 non-null   float64\n",
      " 16  Number of Competition  2976 non-null   object \n",
      "dtypes: float64(8), int64(5), object(4)\n",
      "memory usage: 395.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Display general information about the dataset\n",
    "print(\"\\nDataset Information:\")\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9844174b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9844174b",
    "outputId": "d7cabb68-e2be-464b-d382-4a88a0d6c22a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Client ID', 'Client Type', 'Number of Customers', 'Montly Target',\n",
      "       'Zip Code', 'Calendardate', 'Amount Collected', 'Unit Sold',\n",
      "       'Campaign (Email)', 'Campaign (Flyer)', 'Campaign (Phone)',\n",
      "       'Sales Contact 1', 'Sales Contact 2', 'Sales Contact 3',\n",
      "       'Sales Contact 4', 'Sales Contact 5', 'Number of Competition'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Displaying column names\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0f10e736",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "0f10e736",
    "outputId": "6c49430e-d4f7-4a65-a896-b739ee99d797"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Client ID</th>\n",
       "      <th>Client Type</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Montly Target</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Calendardate</th>\n",
       "      <th>Amount Collected</th>\n",
       "      <th>Unit Sold</th>\n",
       "      <th>Campaign (Email)</th>\n",
       "      <th>Campaign (Flyer)</th>\n",
       "      <th>Campaign (Phone)</th>\n",
       "      <th>Sales Contact 1</th>\n",
       "      <th>Sales Contact 2</th>\n",
       "      <th>Sales Contact 3</th>\n",
       "      <th>Sales Contact 4</th>\n",
       "      <th>Sales Contact 5</th>\n",
       "      <th>Number of Competition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID-987275</td>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID-987275</td>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>1003</td>\n",
       "      <td>16-02-2014</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>322500.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID-987275</td>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>1003</td>\n",
       "      <td>18-03-2014</td>\n",
       "      <td>10228384</td>\n",
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       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID-987275</td>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>1003</td>\n",
       "      <td>18-04-2014</td>\n",
       "      <td>17047304</td>\n",
       "      <td>123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3547500.0</td>\n",
       "      <td>1290000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID-987275</td>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>1003</td>\n",
       "      <td>19-05-2014</td>\n",
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      "text/plain": [
       "   Client ID      Client Type  Number of Customers  Montly Target  Zip Code  \\\n",
       "0  ID-987275  Medium Facility                 2800            125      1003   \n",
       "1  ID-987275  Medium Facility                 2800            125      1003   \n",
       "2  ID-987275  Medium Facility                 2800            125      1003   \n",
       "3  ID-987275  Medium Facility                 2800            125      1003   \n",
       "4  ID-987275  Medium Facility                 2800            125      1003   \n",
       "\n",
       "  Calendardate  Amount Collected  Unit Sold  Campaign (Email)  \\\n",
       "0   16-01-2014                 0          0               0.0   \n",
       "1   16-02-2014           3409460         24               0.0   \n",
       "2   18-03-2014          10228384         75               0.0   \n",
       "3   18-04-2014          17047304        123               0.0   \n",
       "4   19-05-2014          23866224        171               0.0   \n",
       "\n",
       "   Campaign (Flyer)  Campaign (Phone)  Sales Contact 1  Sales Contact 2  \\\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        3547500.0   \n",
       "4               0.0               0.0              0.0              0.0   \n",
       "\n",
       "   Sales Contact 3  Sales Contact 4  Sales Contact 5 Number of Competition  \n",
       "0              0.0              0.0              0.0                   Low  \n",
       "1              0.0              0.0         322500.0                   Low  \n",
       "2              0.0              0.0              0.0                   Low  \n",
       "3        1290000.0              0.0              0.0                   Low  \n",
       "4              0.0              0.0              0.0                   Low  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "80ea5fd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset Shape:\n",
      "(2976, 17)\n"
     ]
    }
   ],
   "source": [
    "# Display the shape of the dataset (number of rows and columns)\n",
    "print(\"\\nDataset Shape:\")\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5c609499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Descriptive Statistics:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Montly Target</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Amount Collected</th>\n",
       "      <th>Unit Sold</th>\n",
       "      <th>Campaign (Email)</th>\n",
       "      <th>Campaign (Flyer)</th>\n",
       "      <th>Campaign (Phone)</th>\n",
       "      <th>Sales Contact 1</th>\n",
       "      <th>Sales Contact 2</th>\n",
       "      <th>Sales Contact 3</th>\n",
       "      <th>Sales Contact 4</th>\n",
       "      <th>Sales Contact 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1456.935484</td>\n",
       "      <td>75.080645</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>1.700440e+07</td>\n",
       "      <td>121.464718</td>\n",
       "      <td>1.432850e+05</td>\n",
       "      <td>6.854186e+05</td>\n",
       "      <td>2.977743e+04</td>\n",
       "      <td>1.313407e+05</td>\n",
       "      <td>2.023319e+06</td>\n",
       "      <td>1.867487e+06</td>\n",
       "      <td>7.076361e+04</td>\n",
       "      <td>1.462954e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1669.848734</td>\n",
       "      <td>87.042114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.025803e+07</td>\n",
       "      <td>216.413984</td>\n",
       "      <td>7.230452e+05</td>\n",
       "      <td>1.727587e+06</td>\n",
       "      <td>3.832134e+05</td>\n",
       "      <td>1.034882e+06</td>\n",
       "      <td>3.396991e+06</td>\n",
       "      <td>3.271349e+06</td>\n",
       "      <td>3.869872e+05</td>\n",
       "      <td>8.905955e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>-2.216150e+07</td>\n",
       "      <td>-63.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>960.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>3.409460e+06</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>3.225000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2090.000000</td>\n",
       "      <td>101.250000</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>2.045676e+07</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.148285e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.580000e+06</td>\n",
       "      <td>2.580000e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9840.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>2.079771e+08</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1.144673e+07</td>\n",
       "      <td>1.359395e+07</td>\n",
       "      <td>9.617380e+06</td>\n",
       "      <td>2.902500e+07</td>\n",
       "      <td>3.063750e+07</td>\n",
       "      <td>2.612250e+07</td>\n",
       "      <td>4.837500e+06</td>\n",
       "      <td>1.612500e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Customers  Montly Target  Zip Code  Amount Collected  \\\n",
       "count          2976.000000    2976.000000    2976.0      2.976000e+03   \n",
       "mean           1456.935484      75.080645    1003.0      1.700440e+07   \n",
       "std            1669.848734      87.042114       0.0      3.025803e+07   \n",
       "min               0.000000       5.000000    1003.0     -2.216150e+07   \n",
       "25%             240.000000      10.000000    1003.0      0.000000e+00   \n",
       "50%             960.000000      47.500000    1003.0      3.409460e+06   \n",
       "75%            2090.000000     101.250000    1003.0      2.045676e+07   \n",
       "max            9840.000000     510.000000    1003.0      2.079771e+08   \n",
       "\n",
       "         Unit Sold  Campaign (Email)  Campaign (Flyer)  Campaign (Phone)  \\\n",
       "count  2976.000000      2.976000e+03      2.976000e+03      2.976000e+03   \n",
       "mean    121.464718      1.432850e+05      6.854186e+05      2.977743e+04   \n",
       "std     216.413984      7.230452e+05      1.727587e+06      3.832134e+05   \n",
       "min     -63.000000      0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "25%       0.000000      0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "50%      24.000000      0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "75%     147.000000      0.000000e+00      8.148285e+04      0.000000e+00   \n",
       "max    1500.000000      1.144673e+07      1.359395e+07      9.617380e+06   \n",
       "\n",
       "       Sales Contact 1  Sales Contact 2  Sales Contact 3  Sales Contact 4  \\\n",
       "count     2.976000e+03     2.976000e+03     2.976000e+03     2.976000e+03   \n",
       "mean      1.313407e+05     2.023319e+06     1.867487e+06     7.076361e+04   \n",
       "std       1.034882e+06     3.396991e+06     3.271349e+06     3.869872e+05   \n",
       "min       0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "25%       0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "50%       0.000000e+00     6.450000e+05     3.225000e+05     0.000000e+00   \n",
       "75%       0.000000e+00     2.580000e+06     2.580000e+06     0.000000e+00   \n",
       "max       2.902500e+07     3.063750e+07     2.612250e+07     4.837500e+06   \n",
       "\n",
       "       Sales Contact 5  \n",
       "count     2.976000e+03  \n",
       "mean      1.462954e+04  \n",
       "std       8.905955e+04  \n",
       "min       0.000000e+00  \n",
       "25%       0.000000e+00  \n",
       "50%       0.000000e+00  \n",
       "75%       0.000000e+00  \n",
       "max       1.612500e+06  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display basic statistics of numerical columns\n",
    "print(\"\\nDescriptive Statistics:\")\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e73334da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Categorical Columns:\n",
      "Client Type: ['Medium Facility' 'Large Facility ' 'Small Facility ' 'Private Facility']\n",
      "Number of Competition: ['Low' 'High']\n"
     ]
    }
   ],
   "source": [
    "# Display unique values only for 'Client Type' and 'Number of Competition' columns\n",
    "print(\"\\nUnique Values in Categorical Columns:\")\n",
    "for column in ['Client Type', 'Number of Competition']:\n",
    "    print(f\"{column}: {data[column].unique()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "09ea07d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing Values:\n",
      "Client ID                0\n",
      "Client Type              0\n",
      "Number of Customers      0\n",
      "Montly Target            0\n",
      "Zip Code                 0\n",
      "Calendardate             0\n",
      "Amount Collected         0\n",
      "Unit Sold                0\n",
      "Campaign (Email)         0\n",
      "Campaign (Flyer)         0\n",
      "Campaign (Phone)         0\n",
      "Sales Contact 1          0\n",
      "Sales Contact 2          0\n",
      "Sales Contact 3          0\n",
      "Sales Contact 4          0\n",
      "Sales Contact 5          0\n",
      "Number of Competition    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Display missing values in the dataset\n",
    "print(\"\\nMissing Values:\")\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd8ded9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6802e5b4",
   "metadata": {
    "id": "6802e5b4"
   },
   "source": [
    "### 3. Feature Additions and Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c21d91",
   "metadata": {},
   "source": [
    "In this project, \"Amount Collected\" serves as the target variable, while the independent variables consist of \"Campaign (Email),\" \"Campaign (Flyer),\" \"Campaign (Phone),\" \"Sales Contact 1,\" \"Sales Contact 2,\" \"Sales Contact 3,\" \"Sales Contact 4,\" and \"Sales Contact 5.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d13a6f6d",
   "metadata": {
    "id": "d13a6f6d"
   },
   "outputs": [],
   "source": [
    "## Creation of Additional Features \n",
    "# Convert 'Calendardate' to datetime with dayfirst=True\n",
    "#data['Calendardate'] = pd.to_datetime(data['Calendardate'], dayfirst=True)\n",
    "\n",
    "# Create additional features for month and year\n",
    "#data['Calendar_Month'] = data['Calendardate'].dt.month\n",
    "#data['Calendar_Year'] = data['Calendardate'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d75169ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Client ID' column\n",
    "data.drop('Client ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "85f080ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Zip Code' column\n",
    "data.drop('Zip Code', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "eaa9b5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Calendardate' column\n",
    "data.drop('Calendardate', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5c698301",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding for 'Client Type' and 'Number of Competition'\n",
    "#label_encoder = LabelEncoder()\n",
    "#data['Client Type'] = label_encoder.fit_transform(data['Client Type'])\n",
    "#data['Number of Competition'] = label_encoder.fit_transform(data['Number of Competition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79283402",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c7ff8ec9",
   "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>Client Type</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Montly Target</th>\n",
       "      <th>Amount Collected</th>\n",
       "      <th>Unit Sold</th>\n",
       "      <th>Campaign (Email)</th>\n",
       "      <th>Campaign (Flyer)</th>\n",
       "      <th>Campaign (Phone)</th>\n",
       "      <th>Sales Contact 1</th>\n",
       "      <th>Sales Contact 2</th>\n",
       "      <th>Sales Contact 3</th>\n",
       "      <th>Sales Contact 4</th>\n",
       "      <th>Sales Contact 5</th>\n",
       "      <th>Number of Competition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>3409460</td>\n",
       "      <td>24</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>322500.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>10228384</td>\n",
       "      <td>75</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>17047304</td>\n",
       "      <td>123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3547500.0</td>\n",
       "      <td>1290000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>23866224</td>\n",
       "      <td>171</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Client Type  Number of Customers  Montly Target  Amount Collected  \\\n",
       "0  Medium Facility                 2800            125                 0   \n",
       "1  Medium Facility                 2800            125           3409460   \n",
       "2  Medium Facility                 2800            125          10228384   \n",
       "3  Medium Facility                 2800            125          17047304   \n",
       "4  Medium Facility                 2800            125          23866224   \n",
       "\n",
       "   Unit Sold  Campaign (Email)  Campaign (Flyer)  Campaign (Phone)  \\\n",
       "0          0               0.0               0.0               0.0   \n",
       "1         24               0.0               0.0               0.0   \n",
       "2         75               0.0               0.0               0.0   \n",
       "3        123               0.0               0.0               0.0   \n",
       "4        171               0.0               0.0               0.0   \n",
       "\n",
       "   Sales Contact 1  Sales Contact 2  Sales Contact 3  Sales Contact 4  \\\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        3547500.0        1290000.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   Sales Contact 5 Number of Competition  \n",
       "0              0.0                   Low  \n",
       "1         322500.0                   Low  \n",
       "2              0.0                   Low  \n",
       "3              0.0                   Low  \n",
       "4              0.0                   Low  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "98cd6177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2976, 14)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "2f523cac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2976 entries, 0 to 2975\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Client Type            2976 non-null   object \n",
      " 1   Number of Customers    2976 non-null   int64  \n",
      " 2   Montly Target          2976 non-null   int64  \n",
      " 3   Amount Collected       2976 non-null   int64  \n",
      " 4   Unit Sold              2976 non-null   int64  \n",
      " 5   Campaign (Email)       2976 non-null   float64\n",
      " 6   Campaign (Flyer)       2976 non-null   float64\n",
      " 7   Campaign (Phone)       2976 non-null   float64\n",
      " 8   Sales Contact 1        2976 non-null   float64\n",
      " 9   Sales Contact 2        2976 non-null   float64\n",
      " 10  Sales Contact 3        2976 non-null   float64\n",
      " 11  Sales Contact 4        2976 non-null   float64\n",
      " 12  Sales Contact 5        2976 non-null   float64\n",
      " 13  Number of Competition  2976 non-null   object \n",
      "dtypes: float64(8), int64(4), object(2)\n",
      "memory usage: 325.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2c9bae",
   "metadata": {
    "id": "9c2c9bae"
   },
   "source": [
    "### 4. Exploratory Data Analysis and Statistical Analysis\n",
    "\n",
    "\n",
    "<b> 4.1 Exploring and Understanding basics data </b>\n",
    "\n",
    "1. Distribution of Data across different accounts\n",
    "2. Difference of Sales in Account Types (Using Categorical Mean)\n",
    "\n",
    "<b> 4.2 Statistical Analysis - Answering the Questions</b>\n",
    "1. Impact of Marketing Strategy on Sales (Using Correlation, Regression and Decision Tree)\n",
    "2. Impact of Competition on Sales\n",
    "3. How different types of client can have different strategies (Broken down Question 1 and Question 2 based on Account Type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84978c66",
   "metadata": {
    "id": "84978c66"
   },
   "source": [
    "### 4.2 Impact of Marketing Strategy on Sales "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5d13cb",
   "metadata": {
    "id": "2a5d13cb"
   },
   "source": [
    "#### Understanding of distrubtions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "XPS-IXlR9oJd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XPS-IXlR9oJd",
    "outputId": "b121c800-2e6e-4917-a5a4-73090b6d6e84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage Distribution of Client Type:\n",
      "Client Type\n",
      "Large Facility      46\n",
      "Small Facility      28\n",
      "Medium Facility     17\n",
      "Private Facility     9\n",
      "Name: proportion, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "# Percentage distribution of 'Client Type'\n",
    "percentage_distribution = data['Client Type'].value_counts(normalize=True) * 100\n",
    "percentage_distribution = percentage_distribution.round(0).astype(int)  # Round to 0 decimal places\n",
    "print(\"Percentage Distribution of Client Type:\")\n",
    "print(percentage_distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a18a18",
   "metadata": {},
   "source": [
    "The result indicates that among the various client types, 46% are classified as Large Facility, 28% as Small Facility, 17% as Medium Facility, and 9% as Private Facility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2d462719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of each category in 'Client Type' as whole numbers:\n",
      "Client Type\n",
      "Large Facility      136896\n",
      "Small Facility       83328\n",
      "Medium Facility      50592\n",
      "Private Facility     26784\n",
      "Name: proportion, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "# Display the count of each category in 'Client Type' as whole numbers\n",
    "whole_numbers_counts = (percentage_distribution * len(data)).round().astype(int)\n",
    "print(\"Counts of each category in 'Client Type' as whole numbers:\")\n",
    "print(whole_numbers_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6742ab51",
   "metadata": {},
   "source": [
    "The counts of each category in the 'Client Type' variable are as follows: 136,896 instances of Large Facility, 83,328 instances of Small Facility, 50,592 instances of Medium Facility, and 26,784 instances of Private Facility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "AT3FZvZ61Gl-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "AT3FZvZ61Gl-",
    "outputId": "895f16f0-8c61-44df-8f04-d1759ce76771"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Tabulation of 'Number of Competition' and 'Client Type':\n"
     ]
    },
    {
     "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>Client Type</th>\n",
       "      <th>Large Facility</th>\n",
       "      <th>Medium Facility</th>\n",
       "      <th>Private Facility</th>\n",
       "      <th>Small Facility</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Competition</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>High</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Client Type            Large Facility   Medium Facility  Private Facility  \\\n",
       "Number of Competition                                                       \n",
       "High                          0.166667         0.166667          0.166667   \n",
       "Low                           0.833333         0.833333          0.833333   \n",
       "\n",
       "Client Type            Small Facility        All  \n",
       "Number of Competition                             \n",
       "High                          0.166667  0.166667  \n",
       "Low                           0.833333  0.833333  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crosstabulation of 'Number of Competition' and 'Client Type' with column percentages and totals\n",
    "cross_table = pd.crosstab(data['Number of Competition'], data['Client Type'], margins=True, normalize='columns')\n",
    "# Display the cross-tabulation\n",
    "print(\"Cross-Tabulation of 'Number of Competition' and 'Client Type':\")\n",
    "cross_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1fdbe5",
   "metadata": {},
   "source": [
    "The cross-tabulation table displays the distribution of 'Client Type' categories across different levels of 'Number of Competition.' The values represent the proportions of each 'Client Type' within the specified 'Number of Competition' levels. For instance, in the 'High' competition category, each 'Client Type'—Large Facility, Medium Facility, Private Facility, and Small Facility—constitutes 16.67% of the total instances within that competition level. In contrast, in the 'Low' competition category, each 'Client Type' represents 83.33% of the total instances within that competition level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9f4ae362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General Statistics:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Number of Customers</th>\n",
       "      <th>Montly Target</th>\n",
       "      <th>Amount Collected</th>\n",
       "      <th>Unit Sold</th>\n",
       "      <th>Campaign (Email)</th>\n",
       "      <th>Campaign (Flyer)</th>\n",
       "      <th>Campaign (Phone)</th>\n",
       "      <th>Sales Contact 1</th>\n",
       "      <th>Sales Contact 2</th>\n",
       "      <th>Sales Contact 3</th>\n",
       "      <th>Sales Contact 4</th>\n",
       "      <th>Sales Contact 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2976.000000</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "      <td>2.976000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1456.935484</td>\n",
       "      <td>75.080645</td>\n",
       "      <td>1.700440e+07</td>\n",
       "      <td>121.464718</td>\n",
       "      <td>1.432850e+05</td>\n",
       "      <td>6.854186e+05</td>\n",
       "      <td>2.977743e+04</td>\n",
       "      <td>1.313407e+05</td>\n",
       "      <td>2.023319e+06</td>\n",
       "      <td>1.867487e+06</td>\n",
       "      <td>7.076361e+04</td>\n",
       "      <td>1.462954e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1669.848734</td>\n",
       "      <td>87.042114</td>\n",
       "      <td>3.025803e+07</td>\n",
       "      <td>216.413984</td>\n",
       "      <td>7.230452e+05</td>\n",
       "      <td>1.727587e+06</td>\n",
       "      <td>3.832134e+05</td>\n",
       "      <td>1.034882e+06</td>\n",
       "      <td>3.396991e+06</td>\n",
       "      <td>3.271349e+06</td>\n",
       "      <td>3.869872e+05</td>\n",
       "      <td>8.905955e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-2.216150e+07</td>\n",
       "      <td>-63.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>960.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.409460e+06</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>3.225000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2090.000000</td>\n",
       "      <td>101.250000</td>\n",
       "      <td>2.045676e+07</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.148285e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.580000e+06</td>\n",
       "      <td>2.580000e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9840.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>2.079771e+08</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1.144673e+07</td>\n",
       "      <td>1.359395e+07</td>\n",
       "      <td>9.617380e+06</td>\n",
       "      <td>2.902500e+07</td>\n",
       "      <td>3.063750e+07</td>\n",
       "      <td>2.612250e+07</td>\n",
       "      <td>4.837500e+06</td>\n",
       "      <td>1.612500e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Customers  Montly Target  Amount Collected    Unit Sold  \\\n",
       "count          2976.000000    2976.000000      2.976000e+03  2976.000000   \n",
       "mean           1456.935484      75.080645      1.700440e+07   121.464718   \n",
       "std            1669.848734      87.042114      3.025803e+07   216.413984   \n",
       "min               0.000000       5.000000     -2.216150e+07   -63.000000   \n",
       "25%             240.000000      10.000000      0.000000e+00     0.000000   \n",
       "50%             960.000000      47.500000      3.409460e+06    24.000000   \n",
       "75%            2090.000000     101.250000      2.045676e+07   147.000000   \n",
       "max            9840.000000     510.000000      2.079771e+08  1500.000000   \n",
       "\n",
       "       Campaign (Email)  Campaign (Flyer)  Campaign (Phone)  Sales Contact 1  \\\n",
       "count      2.976000e+03      2.976000e+03      2.976000e+03     2.976000e+03   \n",
       "mean       1.432850e+05      6.854186e+05      2.977743e+04     1.313407e+05   \n",
       "std        7.230452e+05      1.727587e+06      3.832134e+05     1.034882e+06   \n",
       "min        0.000000e+00      0.000000e+00      0.000000e+00     0.000000e+00   \n",
       "25%        0.000000e+00      0.000000e+00      0.000000e+00     0.000000e+00   \n",
       "50%        0.000000e+00      0.000000e+00      0.000000e+00     0.000000e+00   \n",
       "75%        0.000000e+00      8.148285e+04      0.000000e+00     0.000000e+00   \n",
       "max        1.144673e+07      1.359395e+07      9.617380e+06     2.902500e+07   \n",
       "\n",
       "       Sales Contact 2  Sales Contact 3  Sales Contact 4  Sales Contact 5  \n",
       "count     2.976000e+03     2.976000e+03     2.976000e+03     2.976000e+03  \n",
       "mean      2.023319e+06     1.867487e+06     7.076361e+04     1.462954e+04  \n",
       "std       3.396991e+06     3.271349e+06     3.869872e+05     8.905955e+04  \n",
       "min       0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00  \n",
       "25%       0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00  \n",
       "50%       6.450000e+05     3.225000e+05     0.000000e+00     0.000000e+00  \n",
       "75%       2.580000e+06     2.580000e+06     0.000000e+00     0.000000e+00  \n",
       "max       3.063750e+07     2.612250e+07     4.837500e+06     1.612500e+06  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display general statistics\n",
    "print(\"General Statistics:\")\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "0c6a3c2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x2000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Numerical Features\n",
    "numerical_features = data.select_dtypes(include=[np.number]).columns\n",
    "\n",
    "# Calculate the number of rows and columns needed for subplots\n",
    "num_features = len(numerical_features)\n",
    "num_rows = (num_features - 1) // 3 + 1\n",
    "num_cols = min(num_features, 3)\n",
    "\n",
    "# Create a figure with subplots\n",
    "plt.figure(figsize=(15, 5 * num_rows))\n",
    "for i, feature in enumerate(numerical_features, 1):\n",
    "    plt.subplot(num_rows, num_cols, i)\n",
    "    \n",
    "    # Plot histogram with KDE\n",
    "    sns.histplot(data[feature], kde=True)\n",
    "    \n",
    "    # Add title to each subplot\n",
    "    plt.title(f'Distribution of {feature}')\n",
    "\n",
    "# Adjust layout for better visualization\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "378d29e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x2000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots for Numerical Features\n",
    "plt.figure(figsize=(15, 5 * num_rows))\n",
    "for i, feature in enumerate(numerical_features, 1):\n",
    "    plt.subplot(num_rows, num_cols, i)\n",
    "    \n",
    "    # Plot boxplot\n",
    "    sns.boxplot(x=data[feature])\n",
    "    \n",
    "    # Add title to each subplot\n",
    "    plt.title(f'Boxplot of {feature}')\n",
    "\n",
    "# Adjust layout for better visualization\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "954bad3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Client Type', 'Number of Customers', 'Montly Target',\n",
      "       'Amount Collected', 'Unit Sold', 'Campaign (Email)', 'Campaign (Flyer)',\n",
      "       'Campaign (Phone)', 'Sales Contact 1', 'Sales Contact 2',\n",
      "       'Sales Contact 3', 'Sales Contact 4', 'Sales Contact 5',\n",
      "       'Number of Competition'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ad8be577",
   "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>Client Type</th>\n",
       "      <th>Number of Customers</th>\n",
       "      <th>Montly Target</th>\n",
       "      <th>Amount Collected</th>\n",
       "      <th>Unit Sold</th>\n",
       "      <th>Campaign (Email)</th>\n",
       "      <th>Campaign (Flyer)</th>\n",
       "      <th>Campaign (Phone)</th>\n",
       "      <th>Sales Contact 1</th>\n",
       "      <th>Sales Contact 2</th>\n",
       "      <th>Sales Contact 3</th>\n",
       "      <th>Sales Contact 4</th>\n",
       "      <th>Sales Contact 5</th>\n",
       "      <th>Number of Competition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>3409460</td>\n",
       "      <td>24</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>322500.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>10228384</td>\n",
       "      <td>75</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>17047304</td>\n",
       "      <td>123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3547500.0</td>\n",
       "      <td>1290000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Medium Facility</td>\n",
       "      <td>2800</td>\n",
       "      <td>125</td>\n",
       "      <td>23866224</td>\n",
       "      <td>171</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Client Type  Number of Customers  Montly Target  Amount Collected  \\\n",
       "0  Medium Facility                 2800            125                 0   \n",
       "1  Medium Facility                 2800            125           3409460   \n",
       "2  Medium Facility                 2800            125          10228384   \n",
       "3  Medium Facility                 2800            125          17047304   \n",
       "4  Medium Facility                 2800            125          23866224   \n",
       "\n",
       "   Unit Sold  Campaign (Email)  Campaign (Flyer)  Campaign (Phone)  \\\n",
       "0          0               0.0               0.0               0.0   \n",
       "1         24               0.0               0.0               0.0   \n",
       "2         75               0.0               0.0               0.0   \n",
       "3        123               0.0               0.0               0.0   \n",
       "4        171               0.0               0.0               0.0   \n",
       "\n",
       "   Sales Contact 1  Sales Contact 2  Sales Contact 3  Sales Contact 4  \\\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        3547500.0        1290000.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   Sales Contact 5 Number of Competition  \n",
       "0              0.0                   Low  \n",
       "1         322500.0                   Low  \n",
       "2              0.0                   Low  \n",
       "3              0.0                   Low  \n",
       "4              0.0                   Low  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "57a46fb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2976 entries, 0 to 2975\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Client Type            2976 non-null   object \n",
      " 1   Number of Customers    2976 non-null   int64  \n",
      " 2   Montly Target          2976 non-null   int64  \n",
      " 3   Amount Collected       2976 non-null   int64  \n",
      " 4   Unit Sold              2976 non-null   int64  \n",
      " 5   Campaign (Email)       2976 non-null   float64\n",
      " 6   Campaign (Flyer)       2976 non-null   float64\n",
      " 7   Campaign (Phone)       2976 non-null   float64\n",
      " 8   Sales Contact 1        2976 non-null   float64\n",
      " 9   Sales Contact 2        2976 non-null   float64\n",
      " 10  Sales Contact 3        2976 non-null   float64\n",
      " 11  Sales Contact 4        2976 non-null   float64\n",
      " 12  Sales Contact 5        2976 non-null   float64\n",
      " 13  Number of Competition  2976 non-null   object \n",
      "dtypes: float64(8), int64(4), object(2)\n",
      "memory usage: 325.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "MLr6O-TkyLnl",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520
    },
    "id": "MLr6O-TkyLnl",
    "outputId": "1ec35bdd-8d8e-42b9-c04a-3cf53f2eec49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with 'Amount Collected':\n",
      "Number of Customers    0.607496\n",
      "Montly Target          0.608204\n",
      "Amount Collected       1.000000\n",
      "Unit Sold              0.997515\n",
      "Campaign (Email)       0.248235\n",
      "Campaign (Flyer)       0.444337\n",
      "Campaign (Phone)       0.034858\n",
      "Sales Contact 1        0.277478\n",
      "Sales Contact 2        0.552112\n",
      "Sales Contact 3        0.357887\n",
      "Sales Contact 4        0.236165\n",
      "Sales Contact 5        0.095795\n",
      "Name: Amount Collected, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Selecting numeric columns only\n",
    "numeric_columns = data.select_dtypes(include=np.number)\n",
    "\n",
    "# Calculating the correlation for the 'Amount Collected'\n",
    "correlation_amount_collected = numeric_columns.corr()['Amount Collected']\n",
    "\n",
    "# Displaying the correlation results\n",
    "print(\"Correlation with 'Amount Collected':\")\n",
    "print(correlation_amount_collected)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pgHWmXVr8J7u",
   "metadata": {
    "id": "pgHWmXVr8J7u"
   },
   "source": [
    "Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "25c3720a",
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    "colab": {
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     "height": 335
    },
    "id": "25c3720a",
    "outputId": "45270e77-2b8d-4be2-ce14-8e054844a8c3",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_e5e9d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e5e9d_level0_col0\" class=\"col_heading level0 col0\" >Impacting Variable</th>\n",
       "      <th id=\"T_e5e9d_level0_col1\" class=\"col_heading level0 col1\" >Degree of Linear Impact (Correlation)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n",
       "      <td id=\"T_e5e9d_row0_col0\" class=\"data row0 col0\" >Sales Contact 2</td>\n",
       "      <td id=\"T_e5e9d_row0_col1\" class=\"data row0 col1\" >0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_e5e9d_row1_col0\" class=\"data row1 col0\" >Campaign (Flyer)</td>\n",
       "      <td id=\"T_e5e9d_row1_col1\" class=\"data row1 col1\" >0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
       "      <td id=\"T_e5e9d_row2_col0\" class=\"data row2 col0\" >Sales Contact 3</td>\n",
       "      <td id=\"T_e5e9d_row2_col1\" class=\"data row2 col1\" >0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_e5e9d_row3_col0\" class=\"data row3 col0\" >Sales Contact 1</td>\n",
       "      <td id=\"T_e5e9d_row3_col1\" class=\"data row3 col1\" >0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row4\" class=\"row_heading level0 row4\" >1</th>\n",
       "      <td id=\"T_e5e9d_row4_col0\" class=\"data row4 col0\" >Campaign (Email)</td>\n",
       "      <td id=\"T_e5e9d_row4_col1\" class=\"data row4 col1\" >0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row5\" class=\"row_heading level0 row5\" >7</th>\n",
       "      <td id=\"T_e5e9d_row5_col0\" class=\"data row5 col0\" >Sales Contact 4</td>\n",
       "      <td id=\"T_e5e9d_row5_col1\" class=\"data row5 col1\" >0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row6\" class=\"row_heading level0 row6\" >8</th>\n",
       "      <td id=\"T_e5e9d_row6_col0\" class=\"data row6 col0\" >Sales Contact 5</td>\n",
       "      <td id=\"T_e5e9d_row6_col1\" class=\"data row6 col1\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e9d_level0_row7\" class=\"row_heading level0 row7\" >3</th>\n",
       "      <td id=\"T_e5e9d_row7_col0\" class=\"data row7 col0\" >Campaign (Phone)</td>\n",
       "      <td id=\"T_e5e9d_row7_col1\" class=\"data row7 col1\" >0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13f13befb50>"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Consolidated Strategy for Targeting\n",
    "import seaborn as sns\n",
    "\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "\n",
    "correlation_analysis = pd.DataFrame(data[['Amount Collected',\n",
    "                                           'Campaign (Email)', 'Campaign (Flyer)', 'Campaign (Phone)',\n",
    "                                           'Sales Contact 1', 'Sales Contact 2', 'Sales Contact 3',\n",
    "                                           'Sales Contact 4', 'Sales Contact 5']].corr()['Amount Collected']).reset_index()\n",
    "\n",
    "correlation_analysis.columns = ['Impacting Variable', 'Degree of Linear Impact (Correlation)']\n",
    "correlation_analysis = correlation_analysis[correlation_analysis['Impacting Variable'] != 'Amount Collected']\n",
    "correlation_analysis = correlation_analysis.sort_values('Degree of Linear Impact (Correlation)', ascending=False)\n",
    "\n",
    "# Use the format method to set precision\n",
    "correlation_analysis.style.background_gradient(cmap=cm).format({'Degree of Linear Impact (Correlation)': '{:.2f}'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56279f3f",
   "metadata": {},
   "source": [
    "The correlation results between the amount collected/sales and different marketing strategies reveal the degree and direction of the linear relationship between these variables. Here's an interpretation of the correlation coefficients:\n",
    "\n",
    "1. **Sales Contact 2 (0.55):**\n",
    "   - There is a moderate positive correlation (0.55) between the variable \"Sales Contact 2\" and the amount collected/sales. This suggests that as the involvement or impact of Sales Contact 2 increases, there is a corresponding positive increase in the amount collected.\n",
    "\n",
    "2. **Campaign (Flyer) (0.44):**\n",
    "   - A positive correlation of 0.44 exists between the \"Campaign (Flyer)\" variable and the amount collected/sales. This indicates that a stronger emphasis on flyer campaigns is associated with a higher amount collected.\n",
    "\n",
    "3. **Sales Contact 3 (0.36):**\n",
    "   - There is a positive correlation of 0.36 between \"Sales Contact 3\" and the amount collected/sales. An increase in the influence of Sales Contact 3 is linked to a positive impact on the amount collected.\n",
    "\n",
    "4. **Sales Contact 1 (0.28):**\n",
    "   - The variable \"Sales Contact 1\" has a positive correlation of 0.28 with the amount collected/sales. This suggests that a higher engagement of Sales Contact 1 is associated with an increase in the amount collected.\n",
    "\n",
    "5. **Campaign (Email) (0.25):**\n",
    "   - A positive correlation of 0.25 exists between the \"Campaign (Email)\" variable and the amount collected/sales. This implies that a stronger focus on email campaigns is correlated with a higher amount collected.\n",
    "\n",
    "6. **Sales Contact 4 (0.24):**\n",
    "   - There is a positive correlation of 0.24 between \"Sales Contact 4\" and the amount collected/sales. An increase in the influence of Sales Contact 4 is associated with a positive impact on the amount collected.\n",
    "\n",
    "7. **Sales Contact 5 (0.10):**\n",
    "   - The variable \"Sales Contact 5\" has a positive correlation of 0.10 with the amount collected/sales. Although the correlation is weaker compared to other variables, it still suggests a positive relationship.\n",
    "\n",
    "8. **Campaign (Phone) (0.03):**\n",
    "   - The correlation between the \"Campaign (Phone)\" variable and the amount collected/sales is weak (0.03). This indicates a minimal linear relationship between phone campaigns and the amount collected.\n",
    "\n",
    "In summary, these correlation coefficients provide insights into the strength and direction of the linear association between each marketing strategy variable and the amount collected/sales. Positive correlations signify a positive relationship, while the magnitude of the correlation indicates the degree of impact."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da606b7",
   "metadata": {
    "id": "0da606b7"
   },
   "source": [
    "#### Market Strategy Impact on Sales (Broken by different account type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b3ba879c",
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    "id": "b3ba879c",
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   },
   "outputs": [
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_78248_row25_col2 {\n",
       "  background-color: #65b265;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_78248_row26_col2 {\n",
       "  background-color: #72b872;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_78248_row27_col2 {\n",
       "  background-color: #76ba76;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_78248_row28_col2 {\n",
       "  background-color: #99cb99;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_78248_row29_col2 {\n",
       "  background-color: #bfdebf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_78248_row30_col2 {\n",
       "  background-color: #e0eee0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_78248\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_78248_level0_col0\" class=\"col_heading level0 col0\" >Client Type</th>\n",
       "      <th id=\"T_78248_level0_col1\" class=\"col_heading level0 col1\" >Variable Impact on Sales</th>\n",
       "      <th id=\"T_78248_level0_col2\" class=\"col_heading level0 col2\" >Impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_78248_row0_col0\" class=\"data row0 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row0_col1\" class=\"data row0 col1\" >Sales Contact 2</td>\n",
       "      <td id=\"T_78248_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_78248_row1_col0\" class=\"data row1 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row1_col1\" class=\"data row1 col1\" >Sales Contact 3</td>\n",
       "      <td id=\"T_78248_row1_col2\" class=\"data row1 col2\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_78248_row2_col0\" class=\"data row2 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row2_col1\" class=\"data row2 col1\" >Campaign (Email)</td>\n",
       "      <td id=\"T_78248_row2_col2\" class=\"data row2 col2\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_78248_row3_col0\" class=\"data row3 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row3_col1\" class=\"data row3 col1\" >Campaign (Flyer)</td>\n",
       "      <td id=\"T_78248_row3_col2\" class=\"data row3 col2\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_78248_row4_col0\" class=\"data row4 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row4_col1\" class=\"data row4 col1\" >Sales Contact 4</td>\n",
       "      <td id=\"T_78248_row4_col2\" class=\"data row4 col2\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_78248_row5_col0\" class=\"data row5 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row5_col1\" class=\"data row5 col1\" >Sales Contact 5</td>\n",
       "      <td id=\"T_78248_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_78248_row6_col0\" class=\"data row6 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row6_col1\" class=\"data row6 col1\" >Sales Contact 1</td>\n",
       "      <td id=\"T_78248_row6_col2\" class=\"data row6 col2\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_78248_row7_col0\" class=\"data row7 col0\" >Small Facility </td>\n",
       "      <td id=\"T_78248_row7_col1\" class=\"data row7 col1\" >Campaign (Phone)</td>\n",
       "      <td id=\"T_78248_row7_col2\" class=\"data row7 col2\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_78248_row8_col0\" class=\"data row8 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row8_col1\" class=\"data row8 col1\" >Sales Contact 2</td>\n",
       "      <td id=\"T_78248_row8_col2\" class=\"data row8 col2\" >0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_78248_row9_col0\" class=\"data row9 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row9_col1\" class=\"data row9 col1\" >Campaign (Flyer)</td>\n",
       "      <td id=\"T_78248_row9_col2\" class=\"data row9 col2\" >0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_78248_row10_col0\" class=\"data row10 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row10_col1\" class=\"data row10 col1\" >Sales Contact 3</td>\n",
       "      <td id=\"T_78248_row10_col2\" class=\"data row10 col2\" >0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_78248_row11_col0\" class=\"data row11 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row11_col1\" class=\"data row11 col1\" >Sales Contact 5</td>\n",
       "      <td id=\"T_78248_row11_col2\" class=\"data row11 col2\" >0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_78248_row12_col0\" class=\"data row12 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row12_col1\" class=\"data row12 col1\" >Sales Contact 4</td>\n",
       "      <td id=\"T_78248_row12_col2\" class=\"data row12 col2\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_78248_row13_col0\" class=\"data row13 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row13_col1\" class=\"data row13 col1\" >Campaign (Phone)</td>\n",
       "      <td id=\"T_78248_row13_col2\" class=\"data row13 col2\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_78248_row14_col0\" class=\"data row14 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row14_col1\" class=\"data row14 col1\" >Sales Contact 1</td>\n",
       "      <td id=\"T_78248_row14_col2\" class=\"data row14 col2\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_78248_row15_col0\" class=\"data row15 col0\" >Private Facility</td>\n",
       "      <td id=\"T_78248_row15_col1\" class=\"data row15 col1\" >Campaign (Email)</td>\n",
       "      <td id=\"T_78248_row15_col2\" class=\"data row15 col2\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_78248_row16_col0\" class=\"data row16 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row16_col1\" class=\"data row16 col1\" >Sales Contact 2</td>\n",
       "      <td id=\"T_78248_row16_col2\" class=\"data row16 col2\" >0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_78248_row17_col0\" class=\"data row17 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row17_col1\" class=\"data row17 col1\" >Campaign (Flyer)</td>\n",
       "      <td id=\"T_78248_row17_col2\" class=\"data row17 col2\" >0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_78248_row18_col0\" class=\"data row18 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row18_col1\" class=\"data row18 col1\" >Sales Contact 1</td>\n",
       "      <td id=\"T_78248_row18_col2\" class=\"data row18 col2\" >0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_78248_row19_col0\" class=\"data row19 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row19_col1\" class=\"data row19 col1\" >Campaign (Email)</td>\n",
       "      <td id=\"T_78248_row19_col2\" class=\"data row19 col2\" >0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_78248_row20_col0\" class=\"data row20 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row20_col1\" class=\"data row20 col1\" >Sales Contact 3</td>\n",
       "      <td id=\"T_78248_row20_col2\" class=\"data row20 col2\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_78248_row21_col0\" class=\"data row21 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row21_col1\" class=\"data row21 col1\" >Sales Contact 4</td>\n",
       "      <td id=\"T_78248_row21_col2\" class=\"data row21 col2\" >0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_78248_row22_col0\" class=\"data row22 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row22_col1\" class=\"data row22 col1\" >Sales Contact 5</td>\n",
       "      <td id=\"T_78248_row22_col2\" class=\"data row22 col2\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_78248_row23_col0\" class=\"data row23 col0\" >Medium Facility</td>\n",
       "      <td id=\"T_78248_row23_col1\" class=\"data row23 col1\" >Campaign (Phone)</td>\n",
       "      <td id=\"T_78248_row23_col2\" class=\"data row23 col2\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_78248_row24_col0\" class=\"data row24 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row24_col1\" class=\"data row24 col1\" >Sales Contact 2</td>\n",
       "      <td id=\"T_78248_row24_col2\" class=\"data row24 col2\" >0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_78248_row25_col0\" class=\"data row25 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row25_col1\" class=\"data row25 col1\" >Campaign (Flyer)</td>\n",
       "      <td id=\"T_78248_row25_col2\" class=\"data row25 col2\" >0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_78248_row26_col0\" class=\"data row26 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row26_col1\" class=\"data row26 col1\" >Sales Contact 1</td>\n",
       "      <td id=\"T_78248_row26_col2\" class=\"data row26 col2\" >0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_78248_row27_col0\" class=\"data row27 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row27_col1\" class=\"data row27 col1\" >Sales Contact 4</td>\n",
       "      <td id=\"T_78248_row27_col2\" class=\"data row27 col2\" >0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_78248_row28_col0\" class=\"data row28 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row28_col1\" class=\"data row28 col1\" >Sales Contact 3</td>\n",
       "      <td id=\"T_78248_row28_col2\" class=\"data row28 col2\" >0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_78248_row29_col0\" class=\"data row29 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row29_col1\" class=\"data row29 col1\" >Campaign (Email)</td>\n",
       "      <td id=\"T_78248_row29_col2\" class=\"data row29 col2\" >0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_78248_row30_col0\" class=\"data row30 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row30_col1\" class=\"data row30 col1\" >Campaign (Phone)</td>\n",
       "      <td id=\"T_78248_row30_col2\" class=\"data row30 col2\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_78248_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_78248_row31_col0\" class=\"data row31 col0\" >Large Facility </td>\n",
       "      <td id=\"T_78248_row31_col1\" class=\"data row31 col1\" >Sales Contact 5</td>\n",
       "      <td id=\"T_78248_row31_col2\" class=\"data row31 col2\" >-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13f114d0bd0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "\n",
    "correlation_analysis = pd.DataFrame(data.groupby('Client Type')[['Amount Collected',\n",
    "                                                                 'Campaign (Email)', 'Campaign (Flyer)', 'Campaign (Phone)',\n",
    "                                                                 'Sales Contact 1', 'Sales Contact 2', 'Sales Contact 3',\n",
    "                                                                 'Sales Contact 4', 'Sales Contact 5']].corr()['Amount Collected']).reset_index()\n",
    "\n",
    "correlation_analysis = correlation_analysis.sort_values(['Client Type', 'Amount Collected'], ascending=False)\n",
    "correlation_analysis.columns = ['Client Type', 'Variable Impact on Sales', 'Impact']\n",
    "correlation_analysis = correlation_analysis[correlation_analysis['Variable Impact on Sales'] != 'Amount Collected'].reset_index(drop=True)\n",
    "\n",
    "# Use the format method to set precision\n",
    "correlation_analysis.style.background_gradient(cmap=cm).format({'Impact': '{:.2f}'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78400da",
   "metadata": {
    "id": "e78400da"
   },
   "source": [
    "#### Regression Analysis (Market Sales and Strategies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8bd89a80",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8bd89a80",
    "outputId": "bb5df4e1-27b5-483e-cf59-fbb129063a96",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Amount_Collected   R-squared:                       0.480\n",
      "Model:                            OLS   Adj. R-squared:                  0.478\n",
      "Method:                 Least Squares   F-statistic:                     342.1\n",
      "Date:                Sat, 16 Dec 2023   Prob (F-statistic):               0.00\n",
      "Time:                        18:20:50   Log-Likelihood:                -54512.\n",
      "No. Observations:                2976   AIC:                         1.090e+05\n",
      "Df Residuals:                    2967   BIC:                         1.091e+05\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept        1.481e+06   5.12e+05      2.891      0.004    4.77e+05    2.49e+06\n",
      "Campaign_Email      0.7932      0.597      1.329      0.184      -0.377       1.963\n",
      "Campaign_Flyer      3.3376      0.260     12.831      0.000       2.828       3.848\n",
      "Campaign_Phone      0.0734      1.053      0.070      0.944      -1.991       2.137\n",
      "Sales_Contact_1     4.2368      0.415     10.207      0.000       3.423       5.051\n",
      "Sales_Contact_2     3.6382      0.129     28.155      0.000       3.385       3.892\n",
      "Sales_Contact_3     2.3432      0.131     17.925      0.000       2.087       2.600\n",
      "Sales_Contact_4    10.9478      1.060     10.331      0.000       8.870      13.026\n",
      "Sales_Contact_5     3.5078      4.549      0.771      0.441      -5.412      12.428\n",
      "==============================================================================\n",
      "Omnibus:                     1099.749   Durbin-Watson:                   0.624\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             7733.226\n",
      "Skew:                           1.578   Prob(JB):                         0.00\n",
      "Kurtosis:                      10.239   Cond. No.                     5.89e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.89e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Replace spaces, '(' and ')' in column names\n",
    "data.columns = [mystring.replace(\" \", \"_\") for mystring in data.columns]\n",
    "data.columns = [mystring.replace(\"(\", \"\") for mystring in data.columns]\n",
    "data.columns = [mystring.replace(\")\", \"\") for mystring in data.columns]\n",
    "\n",
    "# Use the formula with the updated column names\n",
    "formula = 'Amount_Collected ~ Campaign_Email + Campaign_Flyer + Campaign_Phone + \\\n",
    "       Sales_Contact_1 + Sales_Contact_2 + Sales_Contact_3 + Sales_Contact_4 + Sales_Contact_5'\n",
    "\n",
    "results = smf.ols(formula, data=data).fit()\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "66feffdb",
   "metadata": {
    "id": "66feffdb"
   },
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "\n",
    "\n",
    "# Use StringIO to wrap the HTML string\n",
    "html_str = results.summary().tables[1].as_html()\n",
    "df = pd.read_html(StringIO(html_str), header=0, index_col=0)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d3bd76c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "3d3bd76c",
    "outputId": "9dfcb27e-c1fa-4f03-ed3d-c98a70723743"
   },
   "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>index</th>\n",
       "      <th>coef</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1.481000e+06</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Campaign_Flyer</td>\n",
       "      <td>3.337600e+00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sales_Contact_1</td>\n",
       "      <td>4.236800e+00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sales_Contact_2</td>\n",
       "      <td>3.638200e+00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sales_Contact_3</td>\n",
       "      <td>2.343200e+00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sales_Contact_4</td>\n",
       "      <td>1.094780e+01</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index          coef  P>|t|\n",
       "0        Intercept  1.481000e+06  0.004\n",
       "2   Campaign_Flyer  3.337600e+00  0.000\n",
       "4  Sales_Contact_1  4.236800e+00  0.000\n",
       "5  Sales_Contact_2  3.638200e+00  0.000\n",
       "6  Sales_Contact_3  2.343200e+00  0.000\n",
       "7  Sales_Contact_4  1.094780e+01  0.000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.reset_index()\n",
    "df=df[df['P>|t|']<0.05][['index','coef','P>|t|']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8975b164",
   "metadata": {
    "id": "8975b164"
   },
   "source": [
    "#### Regression Analysis (Market Sales and Strategies) - Broken for different client types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c89eb1bd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c89eb1bd",
    "outputId": "a65f96f1-aadc-45d1-8ccd-f4f7b7d6aaab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\4242996024.py:16: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  summary_df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Private Facility\n",
      "          Variable  Coefficient (Impact)      Account Type\n",
      "5  Sales_Contact_2                6.6223  Private Facility\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\4242996024.py:16: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  summary_df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small Facility \n",
      "          Variable  Coefficient (Impact)     Account Type\n",
      "5  Sales_Contact_2          8.101000e-01  Small Facility \n",
      "3   Campaign_Phone          6.449000e-07  Small Facility \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\4242996024.py:16: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  summary_df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medium Facility\n",
      "          Variable  Coefficient (Impact)     Account Type\n",
      "2   Campaign_Flyer                4.1059  Medium Facility\n",
      "5  Sales_Contact_2                3.5778  Medium Facility\n",
      "4  Sales_Contact_1                3.1365  Medium Facility\n",
      "6  Sales_Contact_3                2.1174  Medium Facility\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Large Facility \n",
      "          Variable  Coefficient (Impact)     Account Type\n",
      "4  Sales_Contact_1               11.6731  Large Facility \n",
      "7  Sales_Contact_4               10.6145  Large Facility \n",
      "5  Sales_Contact_2                4.0031  Large Facility \n",
      "2   Campaign_Flyer                2.7204  Large Facility \n",
      "6  Sales_Contact_3                2.0316  Large Facility \n",
      "3   Campaign_Phone               -3.5361  Large Facility \n",
      "Consolidated Summary:\n",
      "          Variable  Coefficient (Impact)      Account Type\n",
      "5  Sales_Contact_2          6.622300e+00  Private Facility\n",
      "5  Sales_Contact_2          8.101000e-01   Small Facility \n",
      "3   Campaign_Phone          6.449000e-07   Small Facility \n",
      "2   Campaign_Flyer          4.105900e+00   Medium Facility\n",
      "5  Sales_Contact_2          3.577800e+00   Medium Facility\n",
      "4  Sales_Contact_1          3.136500e+00   Medium Facility\n",
      "6  Sales_Contact_3          2.117400e+00   Medium Facility\n",
      "4  Sales_Contact_1          1.167310e+01   Large Facility \n",
      "7  Sales_Contact_4          1.061450e+01   Large Facility \n",
      "5  Sales_Contact_2          4.003100e+00   Large Facility \n",
      "2   Campaign_Flyer          2.720400e+00   Large Facility \n",
      "6  Sales_Contact_3          2.031600e+00   Large Facility \n",
      "3   Campaign_Phone         -3.536100e+00   Large Facility \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\4242996024.py:16: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  summary_df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Initialize an empty DataFrame for consolidated summary\n",
    "consolidated_summary = pd.DataFrame()\n",
    "\n",
    "for acctype in set(data['Client_Type']):\n",
    "    temp_data = data[data['Client_Type'] == acctype].copy()\n",
    "\n",
    "    # Fit the regression model\n",
    "    results = smf.ols('Amount_Collected ~ Campaign_Email + Campaign_Flyer + Campaign_Phone + \\\n",
    "           Sales_Contact_1 + Sales_Contact_2 + Sales_Contact_3 + Sales_Contact_4 + Sales_Contact_5', data=temp_data).fit()\n",
    "\n",
    "    # Extract relevant information from the summary\n",
    "    summary_df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n",
    "    summary_df = summary_df[summary_df['P>|t|'] < 0.05][['index', 'coef']]\n",
    "    summary_df.columns = ['Variable', 'Coefficient (Impact)']\n",
    "    summary_df['Account Type'] = acctype\n",
    "    summary_df = summary_df.sort_values('Coefficient (Impact)', ascending=False)\n",
    "    summary_df = summary_df[summary_df['Variable'] != 'Intercept']\n",
    "\n",
    "    print(acctype)\n",
    "    print(summary_df)\n",
    "\n",
    "    # Concatenate DataFrames to create a consolidated summary\n",
    "    consolidated_summary = pd.concat([consolidated_summary, summary_df])\n",
    "\n",
    "# Display consolidated summary\n",
    "print(\"Consolidated Summary:\")\n",
    "print(consolidated_summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1836964f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1836964f",
    "outputId": "9a4d4157-c232-4cec-9684-1691321ee47b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Private Facility\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\45952304.py:17: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Amount_Collected   R-squared:                       0.346\n",
      "Model:                            OLS   Adj. R-squared:                  0.326\n",
      "Method:                 Least Squares   F-statistic:                     16.88\n",
      "Date:                Sat, 16 Dec 2023   Prob (F-statistic):           4.63e-20\n",
      "Time:                        18:23:09   Log-Likelihood:                -4650.8\n",
      "No. Observations:                 264   AIC:                             9320.\n",
      "Df Residuals:                     255   BIC:                             9352.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept        4.439e+05    8.3e+05      0.535      0.593   -1.19e+06    2.08e+06\n",
      "Campaign_Email      6.6806     17.292      0.386      0.700     -27.374      40.735\n",
      "Campaign_Flyer      1.6661      1.125      1.482      0.140      -0.548       3.881\n",
      "Campaign_Phone      7.8827     10.196      0.773      0.440     -12.197      27.962\n",
      "Sales_Contact_1   -88.9282     47.497     -1.872      0.062    -182.465       4.608\n",
      "Sales_Contact_2     6.6223      0.718      9.220      0.000       5.208       8.037\n",
      "Sales_Contact_3    -0.7264      0.893     -0.814      0.417      -2.485       1.032\n",
      "Sales_Contact_4    19.4954     19.966      0.976      0.330     -19.824      58.815\n",
      "Sales_Contact_5    21.7693     13.600      1.601      0.111      -5.013      48.552\n",
      "==============================================================================\n",
      "Omnibus:                      274.961   Durbin-Watson:                   1.110\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9920.407\n",
      "Skew:                           4.276   Prob(JB):                         0.00\n",
      "Kurtosis:                      31.788   Cond. No.                     1.79e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.79e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Small Facility \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\45952304.py:17: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Amount_Collected   R-squared:                       0.054\n",
      "Model:                            OLS   Adj. R-squared:                  0.046\n",
      "Method:                 Least Squares   F-statistic:                     6.829\n",
      "Date:                Sat, 16 Dec 2023   Prob (F-statistic):           6.63e-08\n",
      "Time:                        18:23:10   Log-Likelihood:                -14125.\n",
      "No. Observations:                 840   AIC:                         2.827e+04\n",
      "Df Residuals:                     832   BIC:                         2.830e+04\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept        8.789e+05   2.07e+05      4.252      0.000    4.73e+05    1.28e+06\n",
      "Campaign_Email      1.8882      1.344      1.405      0.160      -0.750       4.526\n",
      "Campaign_Flyer      0.0753      0.268      0.281      0.778      -0.450       0.600\n",
      "Campaign_Phone   6.449e-07   1.52e-07      4.252      0.000    3.47e-07    9.43e-07\n",
      "Sales_Contact_1    -0.9764      1.655     -0.590      0.555      -4.224       2.271\n",
      "Sales_Contact_2     0.8101      0.130      6.218      0.000       0.554       1.066\n",
      "Sales_Contact_3     0.3277      0.198      1.656      0.098      -0.061       0.716\n",
      "Sales_Contact_4     0.4837      4.427      0.109      0.913      -8.206       9.173\n",
      "Sales_Contact_5    -2.6243      7.996     -0.328      0.743     -18.319      13.070\n",
      "==============================================================================\n",
      "Omnibus:                      985.034   Durbin-Watson:                   1.006\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            97146.151\n",
      "Skew:                           5.770   Prob(JB):                         0.00\n",
      "Kurtosis:                      54.405   Cond. No.                     6.65e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 4.57e-19. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n",
      "Medium Facility\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\45952304.py:17: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Amount_Collected   R-squared:                       0.437\n",
      "Model:                            OLS   Adj. R-squared:                  0.428\n",
      "Method:                 Least Squares   F-statistic:                     48.00\n",
      "Date:                Sat, 16 Dec 2023   Prob (F-statistic):           4.13e-57\n",
      "Time:                        18:23:11   Log-Likelihood:                -9461.9\n",
      "No. Observations:                 504   AIC:                         1.894e+04\n",
      "Df Residuals:                     495   BIC:                         1.898e+04\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept        5.682e+06   2.53e+06      2.241      0.025    7.01e+05    1.07e+07\n",
      "Campaign_Email      1.1260      1.287      0.875      0.382      -1.403       3.655\n",
      "Campaign_Flyer      4.1059      0.702      5.851      0.000       2.727       5.485\n",
      "Campaign_Phone      2.3077      3.014      0.766      0.444      -3.615       8.230\n",
      "Sales_Contact_1     3.1365      0.732      4.284      0.000       1.698       4.575\n",
      "Sales_Contact_2     3.5778      0.305     11.717      0.000       2.978       4.178\n",
      "Sales_Contact_3     2.1174      0.295      7.168      0.000       1.537       2.698\n",
      "Sales_Contact_4    -7.4136      4.497     -1.649      0.100     -16.250       1.422\n",
      "Sales_Contact_5     8.2368     10.845      0.760      0.448     -13.071      29.544\n",
      "==============================================================================\n",
      "Omnibus:                      103.101   Durbin-Watson:                   0.592\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              197.732\n",
      "Skew:                           1.144   Prob(JB):                     1.16e-43\n",
      "Kurtosis:                       5.045   Cond. No.                     1.44e+07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.44e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Large Facility \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return _pandas_is_categorical_dtype(dt)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_8828\\45952304.py:17: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Amount_Collected   R-squared:                       0.371\n",
      "Model:                            OLS   Adj. R-squared:                  0.367\n",
      "Method:                 Least Squares   F-statistic:                     100.2\n",
      "Date:                Sat, 16 Dec 2023   Prob (F-statistic):          4.64e-131\n",
      "Time:                        18:23:11   Log-Likelihood:                -25075.\n",
      "No. Observations:                1368   AIC:                         5.017e+04\n",
      "Df Residuals:                    1359   BIC:                         5.021e+04\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept        2.812e+06   9.29e+05      3.026      0.003    9.89e+05    4.63e+06\n",
      "Campaign_Email      0.7886      0.936      0.842      0.400      -1.048       2.625\n",
      "Campaign_Flyer      2.7204      0.352      7.739      0.000       2.031       3.410\n",
      "Campaign_Phone     -3.5361      1.333     -2.653      0.008      -6.151      -0.921\n",
      "Sales_Contact_1    11.6731      1.246      9.368      0.000       9.229      14.117\n",
      "Sales_Contact_2     4.0031      0.239     16.718      0.000       3.533       4.473\n",
      "Sales_Contact_3     2.0316      0.246      8.266      0.000       1.549       2.514\n",
      "Sales_Contact_4    10.6145      1.260      8.423      0.000       8.143      13.086\n",
      "Sales_Contact_5    -3.6385      6.703     -0.543      0.587     -16.789       9.512\n",
      "==============================================================================\n",
      "Omnibus:                      334.998   Durbin-Watson:                   0.655\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1353.896\n",
      "Skew:                           1.121   Prob(JB):                    1.01e-294\n",
      "Kurtosis:                       7.327   Cond. No.                     6.22e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.22e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Consolidated Summary:\n",
      "          Variable  Coefficient (Impact)      Account Type\n",
      "5  Sales_Contact_2          6.622300e+00  Private Facility\n",
      "5  Sales_Contact_2          8.101000e-01   Small Facility \n",
      "3   Campaign_Phone          6.449000e-07   Small Facility \n",
      "2   Campaign_Flyer          4.105900e+00   Medium Facility\n",
      "5  Sales_Contact_2          3.577800e+00   Medium Facility\n",
      "4  Sales_Contact_1          3.136500e+00   Medium Facility\n",
      "6  Sales_Contact_3          2.117400e+00   Medium Facility\n",
      "4  Sales_Contact_1          1.167310e+01   Large Facility \n",
      "7  Sales_Contact_4          1.061450e+01   Large Facility \n",
      "5  Sales_Contact_2          4.003100e+00   Large Facility \n",
      "2   Campaign_Flyer          2.720400e+00   Large Facility \n",
      "6  Sales_Contact_3          2.031600e+00   Large Facility \n",
      "3   Campaign_Phone         -3.536100e+00   Large Facility \n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize an empty DataFrame for consolidated summary\n",
    "consolidated_summary = pd.DataFrame()\n",
    "\n",
    "for acctype in set(data['Client_Type']):\n",
    "    print(acctype)\n",
    "    temp_data = data[data['Client_Type'] == acctype].copy()\n",
    "\n",
    "    # Fit the regression model\n",
    "    results = smf.ols('Amount_Collected ~ Campaign_Email + Campaign_Flyer + Campaign_Phone + \\\n",
    "           Sales_Contact_1 + Sales_Contact_2 + Sales_Contact_3 + Sales_Contact_4 + Sales_Contact_5', data=temp_data).fit()\n",
    "\n",
    "    # Extract relevant information from the summary\n",
    "    df = pd.read_html(results.summary().tables[1].as_html(), header=0, index_col=0)[0].reset_index()\n",
    "    df = df[df['P>|t|'] < 0.05][['index', 'coef']]\n",
    "    df.columns = ['Variable', 'Coefficient (Impact)']\n",
    "    df['Account Type'] = acctype\n",
    "    df = df.sort_values('Coefficient (Impact)', ascending=False)\n",
    "    df = df[df['Variable'] != 'Intercept']\n",
    "\n",
    "    consolidated_summary = pd.concat([consolidated_summary, df])\n",
    "\n",
    "    print(results.summary())\n",
    "\n",
    "# Display consolidated summary\n",
    "print(\"Consolidated Summary:\")\n",
    "print(consolidated_summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0954dab",
   "metadata": {
    "id": "e0954dab"
   },
   "source": [
    "### 5. Final Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530bf294",
   "metadata": {
    "id": "530bf294"
   },
   "source": [
    "Using the below table we can use the coefficent to see how much return we can derive from each dollar we spend, here we can clearly see that for different account type different Campaigns and Different Sales Contact are effective with different extend. \n",
    "\n",
    "<b>Case Explanation - Medium Facility </b><br>\n",
    "For Example Medium Facility shows decent results with Flyer Campiagns and each dollar spend return 4 dollars on average. Sales Contact 2 is highly effective followed by Sales Contact 1 and Sales Contact 3. Else all other strategy shows no impact can be dropped to save cost. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "s3-UKuRHOe2t",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "s3-UKuRHOe2t",
    "outputId": "64a44047-6962-4b64-acef-06463b70d425"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\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>Variable</th>\n",
       "      <th>Coefficient (Impact)</th>\n",
       "      <th>Account Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sales_Contact_2</td>\n",
       "      <td>6.622300e+00</td>\n",
       "      <td>Private Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sales_Contact_2</td>\n",
       "      <td>8.101000e-01</td>\n",
       "      <td>Small Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Campaign_Phone</td>\n",
       "      <td>6.449000e-07</td>\n",
       "      <td>Small Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Campaign_Flyer</td>\n",
       "      <td>4.105900e+00</td>\n",
       "      <td>Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sales_Contact_2</td>\n",
       "      <td>3.577800e+00</td>\n",
       "      <td>Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sales_Contact_1</td>\n",
       "      <td>3.136500e+00</td>\n",
       "      <td>Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sales_Contact_3</td>\n",
       "      <td>2.117400e+00</td>\n",
       "      <td>Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sales_Contact_1</td>\n",
       "      <td>1.167310e+01</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sales_Contact_4</td>\n",
       "      <td>1.061450e+01</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sales_Contact_2</td>\n",
       "      <td>4.003100e+00</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Campaign_Flyer</td>\n",
       "      <td>2.720400e+00</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sales_Contact_3</td>\n",
       "      <td>2.031600e+00</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Campaign_Phone</td>\n",
       "      <td>-3.536100e+00</td>\n",
       "      <td>Large Facility</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Variable  Coefficient (Impact)      Account Type\n",
       "5  Sales_Contact_2          6.622300e+00  Private Facility\n",
       "5  Sales_Contact_2          8.101000e-01   Small Facility \n",
       "3   Campaign_Phone          6.449000e-07   Small Facility \n",
       "2   Campaign_Flyer          4.105900e+00   Medium Facility\n",
       "5  Sales_Contact_2          3.577800e+00   Medium Facility\n",
       "4  Sales_Contact_1          3.136500e+00   Medium Facility\n",
       "6  Sales_Contact_3          2.117400e+00   Medium Facility\n",
       "4  Sales_Contact_1          1.167310e+01   Large Facility \n",
       "7  Sales_Contact_4          1.061450e+01   Large Facility \n",
       "5  Sales_Contact_2          4.003100e+00   Large Facility \n",
       "2   Campaign_Flyer          2.720400e+00   Large Facility \n",
       "6  Sales_Contact_3          2.031600e+00   Large Facility \n",
       "3   Campaign_Phone         -3.536100e+00   Large Facility "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consolidated_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822e5923",
   "metadata": {},
   "source": [
    "# Return on Investment (ROI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0711b5eb",
   "metadata": {},
   "source": [
    "Return on Investment (ROI) is a measure of the profitability of an investment. In the context of this analysis, the \"Return on Investment\" column represents the regression coefficient for each significant variable in a regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ea4d864e",
   "metadata": {
    "id": "ea4d864e"
   },
   "outputs": [],
   "source": [
    "consolidated_summary.reset_index(inplace=True)\n",
    "consolidated_summary.drop('index',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1-h0HuRzNqp3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "1-h0HuRzNqp3",
    "outputId": "edd317b3-4d1d-4b48-e2d4-dece7821011c"
   },
   "outputs": [
    {
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       "</style>\n",
       "<table id=\"T_dc4a7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dc4a7_level0_col0\" class=\"col_heading level0 col0\" >Variable</th>\n",
       "      <th id=\"T_dc4a7_level0_col1\" class=\"col_heading level0 col1\" >Return on Investment</th>\n",
       "      <th id=\"T_dc4a7_level0_col2\" class=\"col_heading level0 col2\" >Account Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_dc4a7_row0_col0\" class=\"data row0 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_dc4a7_row0_col1\" class=\"data row0 col1\" >6.600000</td>\n",
       "      <td id=\"T_dc4a7_row0_col2\" class=\"data row0 col2\" >Private Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_dc4a7_row1_col0\" class=\"data row1 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_dc4a7_row1_col1\" class=\"data row1 col1\" >0.800000</td>\n",
       "      <td id=\"T_dc4a7_row1_col2\" class=\"data row1 col2\" >Small Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_dc4a7_row2_col0\" class=\"data row2 col0\" >Campaign_Phone</td>\n",
       "      <td id=\"T_dc4a7_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
       "      <td id=\"T_dc4a7_row2_col2\" class=\"data row2 col2\" >Small Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_dc4a7_row3_col0\" class=\"data row3 col0\" >Campaign_Flyer</td>\n",
       "      <td id=\"T_dc4a7_row3_col1\" class=\"data row3 col1\" >4.100000</td>\n",
       "      <td id=\"T_dc4a7_row3_col2\" class=\"data row3 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_dc4a7_row4_col0\" class=\"data row4 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_dc4a7_row4_col1\" class=\"data row4 col1\" >3.600000</td>\n",
       "      <td id=\"T_dc4a7_row4_col2\" class=\"data row4 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_dc4a7_row5_col0\" class=\"data row5 col0\" >Sales_Contact_1</td>\n",
       "      <td id=\"T_dc4a7_row5_col1\" class=\"data row5 col1\" >3.100000</td>\n",
       "      <td id=\"T_dc4a7_row5_col2\" class=\"data row5 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_dc4a7_row6_col0\" class=\"data row6 col0\" >Sales_Contact_3</td>\n",
       "      <td id=\"T_dc4a7_row6_col1\" class=\"data row6 col1\" >2.100000</td>\n",
       "      <td id=\"T_dc4a7_row6_col2\" class=\"data row6 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_dc4a7_row7_col0\" class=\"data row7 col0\" >Sales_Contact_1</td>\n",
       "      <td id=\"T_dc4a7_row7_col1\" class=\"data row7 col1\" >11.700000</td>\n",
       "      <td id=\"T_dc4a7_row7_col2\" class=\"data row7 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_dc4a7_row8_col0\" class=\"data row8 col0\" >Sales_Contact_4</td>\n",
       "      <td id=\"T_dc4a7_row8_col1\" class=\"data row8 col1\" >10.600000</td>\n",
       "      <td id=\"T_dc4a7_row8_col2\" class=\"data row8 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_dc4a7_row9_col0\" class=\"data row9 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_dc4a7_row9_col1\" class=\"data row9 col1\" >4.000000</td>\n",
       "      <td id=\"T_dc4a7_row9_col2\" class=\"data row9 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_dc4a7_row10_col0\" class=\"data row10 col0\" >Campaign_Flyer</td>\n",
       "      <td id=\"T_dc4a7_row10_col1\" class=\"data row10 col1\" >2.700000</td>\n",
       "      <td id=\"T_dc4a7_row10_col2\" class=\"data row10 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_dc4a7_row11_col0\" class=\"data row11 col0\" >Sales_Contact_3</td>\n",
       "      <td id=\"T_dc4a7_row11_col1\" class=\"data row11 col1\" >2.000000</td>\n",
       "      <td id=\"T_dc4a7_row11_col2\" class=\"data row11 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dc4a7_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_dc4a7_row12_col0\" class=\"data row12 col0\" >Campaign_Phone</td>\n",
       "      <td id=\"T_dc4a7_row12_col1\" class=\"data row12 col1\" >-3.500000</td>\n",
       "      <td id=\"T_dc4a7_row12_col2\" class=\"data row12 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13f13f3a190>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consolidated_summary.columns = ['Variable','Return on Investment','Account Type']\n",
    "consolidated_summary['Return on Investment']= consolidated_summary['Return on Investment'].apply(lambda x: round(x,1))\n",
    "consolidated_summary.style.background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "mUnfYiZKPyV5",
   "metadata": {
    "id": "mUnfYiZKPyV5"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "pbB87e4CRp2f",
   "metadata": {
    "id": "pbB87e4CRp2f"
   },
   "outputs": [],
   "source": [
    "def format(x):\n",
    "        return \"${:.1f}\".format(x)\n",
    "consolidated_summary['Return on Investment']  = consolidated_summary['Return on Investment'].apply(format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "KYCgua_ASEOx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "KYCgua_ASEOx",
    "outputId": "eccfd3e9-cfeb-4b67-99f3-0133662d7067"
   },
   "outputs": [
    {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_43687_level0_col0\" class=\"col_heading level0 col0\" >Variable</th>\n",
       "      <th id=\"T_43687_level0_col1\" class=\"col_heading level0 col1\" >Return on Investment</th>\n",
       "      <th id=\"T_43687_level0_col2\" class=\"col_heading level0 col2\" >Account Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_43687_row0_col0\" class=\"data row0 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_43687_row0_col1\" class=\"data row0 col1\" >$6.6</td>\n",
       "      <td id=\"T_43687_row0_col2\" class=\"data row0 col2\" >Private Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_43687_row1_col0\" class=\"data row1 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_43687_row1_col1\" class=\"data row1 col1\" >$0.8</td>\n",
       "      <td id=\"T_43687_row1_col2\" class=\"data row1 col2\" >Small Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_43687_row2_col0\" class=\"data row2 col0\" >Campaign_Phone</td>\n",
       "      <td id=\"T_43687_row2_col1\" class=\"data row2 col1\" >$0.0</td>\n",
       "      <td id=\"T_43687_row2_col2\" class=\"data row2 col2\" >Small Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_43687_row3_col0\" class=\"data row3 col0\" >Campaign_Flyer</td>\n",
       "      <td id=\"T_43687_row3_col1\" class=\"data row3 col1\" >$4.1</td>\n",
       "      <td id=\"T_43687_row3_col2\" class=\"data row3 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_43687_row4_col0\" class=\"data row4 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_43687_row4_col1\" class=\"data row4 col1\" >$3.6</td>\n",
       "      <td id=\"T_43687_row4_col2\" class=\"data row4 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_43687_row5_col0\" class=\"data row5 col0\" >Sales_Contact_1</td>\n",
       "      <td id=\"T_43687_row5_col1\" class=\"data row5 col1\" >$3.1</td>\n",
       "      <td id=\"T_43687_row5_col2\" class=\"data row5 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_43687_row6_col0\" class=\"data row6 col0\" >Sales_Contact_3</td>\n",
       "      <td id=\"T_43687_row6_col1\" class=\"data row6 col1\" >$2.1</td>\n",
       "      <td id=\"T_43687_row6_col2\" class=\"data row6 col2\" >Medium Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_43687_row7_col0\" class=\"data row7 col0\" >Sales_Contact_1</td>\n",
       "      <td id=\"T_43687_row7_col1\" class=\"data row7 col1\" >$11.7</td>\n",
       "      <td id=\"T_43687_row7_col2\" class=\"data row7 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_43687_row8_col0\" class=\"data row8 col0\" >Sales_Contact_4</td>\n",
       "      <td id=\"T_43687_row8_col1\" class=\"data row8 col1\" >$10.6</td>\n",
       "      <td id=\"T_43687_row8_col2\" class=\"data row8 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_43687_row9_col0\" class=\"data row9 col0\" >Sales_Contact_2</td>\n",
       "      <td id=\"T_43687_row9_col1\" class=\"data row9 col1\" >$4.0</td>\n",
       "      <td id=\"T_43687_row9_col2\" class=\"data row9 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_43687_row10_col0\" class=\"data row10 col0\" >Campaign_Flyer</td>\n",
       "      <td id=\"T_43687_row10_col1\" class=\"data row10 col1\" >$2.7</td>\n",
       "      <td id=\"T_43687_row10_col2\" class=\"data row10 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_43687_row11_col0\" class=\"data row11 col0\" >Sales_Contact_3</td>\n",
       "      <td id=\"T_43687_row11_col1\" class=\"data row11 col1\" >$2.0</td>\n",
       "      <td id=\"T_43687_row11_col2\" class=\"data row11 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_43687_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_43687_row12_col0\" class=\"data row12 col0\" >Campaign_Phone</td>\n",
       "      <td id=\"T_43687_row12_col1\" class=\"data row12 col1\" >$-3.5</td>\n",
       "      <td id=\"T_43687_row12_col2\" class=\"data row12 col2\" >Large Facility </td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13f1a8891d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consolidated_summary.columns = ['Variable','Return on Investment','Account Type']\n",
    "consolidated_summary.style.background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b98561f",
   "metadata": {},
   "source": [
    "The regression coefficients for different variables within distinct facility types provide insights into the estimated impact on Return on Investment (ROI) for a one-unit change in each independent variable, while keeping other variables constant. Here's a breakdown of the coefficients:\n",
    "\n",
    "**Sales_Contact_2 in Private Facility:**\n",
    "For a unit increase in Sales_Contact_2 within Private Facility, the Return on Investment is $6.6.\n",
    "\n",
    "**Sales_Contact_2 in Small Facility:**\n",
    "A unit increase in Sales_Contact_2 within Small Facility corresponds to a Return on Investment of $0.8.\n",
    "\n",
    "**Campaign_Phone in Small Facility:**\n",
    "In Small Facility, a unit increase in Campaign_Phone results in a negligible Return on Investment of $0.0.\n",
    "\n",
    "**Campaign_Flyer in Medium Facility:**\n",
    "For Medium Facility, a unit increase in Campaign_Flyer is associated with a Return on Investment of $4.1.\n",
    "\n",
    "**Sales_Contact_2 in Medium Facility:**\n",
    "In Medium Facility, a unit increase in Sales_Contact_2 yields a Return on Investment of $3.6.\n",
    "\n",
    "**Sales_Contact_1 in Medium Facility:**\n",
    "A unit increase in Sales_Contact_1 within Medium Facility corresponds to a Return on Investment of $3.1.\n",
    "\n",
    "**Sales_Contact_3 in Medium Facility:**\n",
    "In Medium Facility, a unit increase in Sales_Contact_3 results in a Return on Investment of $2.1.\n",
    "\n",
    "**Sales_Contact_1 in Large Facility:**\n",
    "For Large Facility, a unit increase in Sales_Contact_1 is associated with a substantial Return on Investment of $11.7.\n",
    "\n",
    "**Sales_Contact_4 in Large Facility:**\n",
    "In Large Facility, a unit increase in Sales_Contact_4 yields a significant Return on Investment of $10.6.\n",
    "\n",
    "**Sales_Contact_2 in Large Facility:**\n",
    "For Large Facility, a unit increase in Sales_Contact_2 corresponds to a Return on Investment of $4.0.\n",
    "\n",
    "**Campaign_Flyer in Large Facility:**\n",
    "In Large Facility, a unit increase in Campaign_Flyer results in a Return on Investment of $2.7.\n",
    "\n",
    "**Sales_Contact_3 in Large Facility:**\n",
    "For Large Facility, a unit increase in Sales_Contact_3 yields a Return on Investment of $2.0.\n",
    "\n",
    "**Campaign_Phone in Large Facility:**\n",
    "In Large Facility, a unit increase in Campaign_Phone is associated with a negative Return on Investment of $-3.5.\n",
    "\n",
    "These coefficients help interpret the direction and magnitude of the impact each variable has on ROI, providing valuable information for decision-making and understanding the factors influencing investment returns. Positive coefficients indicate a positive impact, while negative coefficients suggest a negative impact. The larger the coefficient, the stronger the influence on ROI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "wP5PJGD5E0i3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "wP5PJGD5E0i3",
    "outputId": "bec963ad-853f-4c0e-b3e7-80dd5264a30d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='consolidated_summary.csv' target='_blank'>consolidated_summary.csv</a><br>"
      ],
      "text/plain": [
       "C:\\Users\\DELL\\Documents\\Projects and Datasets\\Optimize Marketing Campaigns\\consolidated_summary.csv"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import FileLink\n",
    "\n",
    "# Save the CSV file\n",
    "consolidated_summary.to_csv('consolidated_summary.csv')\n",
    "\n",
    "# Create a link to download the file\n",
    "FileLink('consolidated_summary.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a978469",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "colab": {
   "collapsed_sections": [],
   "name": "Marketing Strategy.ipynb",
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   "language": "python",
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