010423TaniyaDubeyMRA ProjectM2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea3c9bca",
   "metadata": {},
   "source": [
    "# Marketing & Retail Analytics Project 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa383ba",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "446d5e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries to help with reading and manipulating data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Libraries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "\n",
    "# Command to tell Python to actually display the graphs\n",
    "%matplotlib inline\n",
    "\n",
    "# this library contains a large number of probability distributions as well as a growing library of statistical functions\n",
    "import scipy.stats as stats  \n",
    "\n",
    "# set working directory\n",
    "import os\n",
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\\\Dubey\\\\Downloads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd694c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dubey\\AppData\\Local\\Temp\\ipykernel_4652\\1765676117.py:2: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:99.9% !important; }<\\style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this two lines of code is just to increase the size of the display\n",
    "from IPython.core.display import display, HTML \n",
    "display(HTML('<style>.container { width:99.9% !important; }<\\style>'))\n",
    "\n",
    "# set column number to see all columns\n",
    "pd.set_option('display.max_columns', 35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ba2849",
   "metadata": {},
   "source": [
    "# Problem Statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f61e46",
   "metadata": {},
   "source": [
    "A Grocery Store shared the transactional data with you. Your job is to identify the most popular combos that can be suggested to the Grocery Store chain after a thorough analysis of the most commonly occurring sets of items in the customer orders. The Store doesn’t have any combo offers. Can you suggest the best combos & offers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d51966eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# READ DATASET\n",
    "df = pd.read_csv('dataset_group.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b189b17d",
   "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>Date</th>\n",
       "      <th>Order_id</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>pork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>sandwich bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>lunch meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>all- purpose</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Order_id        Product\n",
       "0  01-01-2018         1         yogurt\n",
       "1  01-01-2018         1           pork\n",
       "2  01-01-2018         1  sandwich bags\n",
       "3  01-01-2018         1     lunch meat\n",
       "4  01-01-2018         1   all- purpose"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TOP ROWs OF DATASET\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efd41adc",
   "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>Date</th>\n",
       "      <th>Order_id</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>25-02-2020</td>\n",
       "      <td>1138</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>25-02-2020</td>\n",
       "      <td>1138</td>\n",
       "      <td>paper towels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>laundry detergent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20640</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>shampoo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Order_id            Product\n",
       "20636  25-02-2020      1138               soda\n",
       "20637  25-02-2020      1138       paper towels\n",
       "20638  26-02-2020      1139               soda\n",
       "20639  26-02-2020      1139  laundry detergent\n",
       "20640  26-02-2020      1139            shampoo"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75735501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20641 entries, 0 to 20640\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Date      20641 non-null  object\n",
      " 1   Order_id  20641 non-null  int64 \n",
      " 2   Product   20641 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 483.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Data Type of Columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a08dba9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20641, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rows and Columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "196fbb74",
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order_id</th>\n",
       "      <td>20641.0</td>\n",
       "      <td>575.986289</td>\n",
       "      <td>328.557078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean         std  min    25%    50%    75%     max\n",
       "Order_id  20641.0  575.986289  328.557078  1.0  292.0  581.0  862.0  1139.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Summary\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "647b10b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Order_id    0\n",
       "Product     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nulls in data set\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "934dae4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4730"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate in data set\n",
    "df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a58dd93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == 'int64' or df[column].dtype == 'float64':\n",
    "        plt.figure(figsize=(25,6))\n",
    "        sns.boxplot(df[column], color='Salmon')\n",
    "        plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "beee8a4a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    if df[column].dtype == 'int64' or df[column].dtype == 'float64':\n",
    "        plt.figure(figsize=(20,8))\n",
    "        sns.distplot(df[column])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29c244d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}