EDA Purchased Bikes WIth Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d09a639e",
   "metadata": {},
   "source": [
    "# BIKE BUYERS IN EUROPE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95cddbd",
   "metadata": {},
   "source": [
    "This dataset can be found in Kaggle.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b185455f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78f1319e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading in the dataset\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\5 PROJECTS\\2. DATA CLEANING AND MANIPULATION\\bike_buyers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed4a2081",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the numbers have 0 decimal points\n",
    "\n",
    "pd.set_option(\"display.float_format\",\"{:.0f}\".format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "abf3d523",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchased Bike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12496</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24107</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>3</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>43</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14177</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>5</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2-5 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24381</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>5-10 Miles</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25597</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID Marital Status  Gender  Income  Children        Education  \\\n",
       "0  12496        Married  Female   40000         1        Bachelors   \n",
       "1  24107        Married    Male   30000         3  Partial College   \n",
       "2  14177        Married    Male   80000         5  Partial College   \n",
       "3  24381         Single     NaN   70000         0        Bachelors   \n",
       "4  25597         Single    Male   30000         0        Bachelors   \n",
       "\n",
       "       Occupation Home Owner  Cars Commute Distance   Region  Age  \\\n",
       "0  Skilled Manual        Yes     0        0-1 Miles   Europe   42   \n",
       "1        Clerical        Yes     1        0-1 Miles   Europe   43   \n",
       "2    Professional         No     2        2-5 Miles   Europe   60   \n",
       "3    Professional        Yes     1       5-10 Miles  Pacific   41   \n",
       "4        Clerical         No     0        0-1 Miles   Europe   36   \n",
       "\n",
       "  Purchased Bike  \n",
       "0             No  \n",
       "1             No  \n",
       "2             No  \n",
       "3            Yes  \n",
       "4            Yes  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the first 5 rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf6c654b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ID                1000 non-null   int64  \n",
      " 1   Marital Status    993 non-null    object \n",
      " 2   Gender            989 non-null    object \n",
      " 3   Income            994 non-null    float64\n",
      " 4   Children          992 non-null    float64\n",
      " 5   Education         1000 non-null   object \n",
      " 6   Occupation        1000 non-null   object \n",
      " 7   Home Owner        996 non-null    object \n",
      " 8   Cars              991 non-null    float64\n",
      " 9   Commute Distance  1000 non-null   object \n",
      " 10  Region            1000 non-null   object \n",
      " 11  Age               992 non-null    float64\n",
      " 12  Purchased Bike    1000 non-null   object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 101.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting infomation on the number of rows, columns, memeory used and missin values\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7e8b697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                   0\n",
       "Marital Status       7\n",
       "Gender              11\n",
       "Income               6\n",
       "Children             8\n",
       "Education            0\n",
       "Occupation           0\n",
       "Home Owner           4\n",
       "Cars                 9\n",
       "Commute Distance     0\n",
       "Region               0\n",
       "Age                  8\n",
       "Purchased Bike       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see the number missing values in each columns and dropping them\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22ef69b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, (952, 13))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace = True), df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13a75c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.0, 89.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.min(), df.Age.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aea64a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAking a age range column in the dataset to categorise the age of the people\n",
    "\n",
    "df.loc[df[\"Age\"] <=40, \"Age Range\"] = \"Adult\"\n",
    "df.loc[df[\"Age\"].between(41,60), \"Age Range\"] = \"Older Adult\"\n",
    "df.loc[df[\"Age\"].between(61,90), \"Age Range\"] = \"Grands\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62cb4de8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchased Bike</th>\n",
       "      <th>Age Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12496</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>42</td>\n",
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       "      <th>1</th>\n",
       "      <td>24107</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>3</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>43</td>\n",
       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14177</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>5</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2-5 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25597</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13507</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>10000</td>\n",
       "      <td>2</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Miles</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID Marital Status  Gender  Income  Children        Education  \\\n",
       "0  12496        Married  Female   40000         1        Bachelors   \n",
       "1  24107        Married    Male   30000         3  Partial College   \n",
       "2  14177        Married    Male   80000         5  Partial College   \n",
       "4  25597         Single    Male   30000         0        Bachelors   \n",
       "5  13507        Married  Female   10000         2  Partial College   \n",
       "\n",
       "       Occupation Home Owner  Cars Commute Distance  Region  Age  \\\n",
       "0  Skilled Manual        Yes     0        0-1 Miles  Europe   42   \n",
       "1        Clerical        Yes     1        0-1 Miles  Europe   43   \n",
       "2    Professional         No     2        2-5 Miles  Europe   60   \n",
       "4        Clerical         No     0        0-1 Miles  Europe   36   \n",
       "5          Manual        Yes     0        1-2 Miles  Europe   50   \n",
       "\n",
       "  Purchased Bike    Age Range  \n",
       "0             No  Older Adult  \n",
       "1             No  Older Adult  \n",
       "2             No  Older Adult  \n",
       "4            Yes        Adult  \n",
       "5             No  Older Adult  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3e669f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the Commute Distance column\n",
    "\n",
    "df.rename(columns = {\"Commute Distance\":\"Commute Distance(Miles)\"}, inplace =True)\n",
    "\n",
    "df[\"Commute Distance(Miles)\"] = df[\"Commute Distance(Miles)\"].str.split(\" \").str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "198b3fa2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Mapping the commute distance from 1-5\n",
    "\n",
    "df[\"CD(Miles)_cat\"]= df[\"Commute Distance(Miles)\"].map({\"0-1\":1, \"1-2\": 2, \"2-5\": 3, \"5-10\": 4, \"10+\": 5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a38e29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping the Home Owner and Purchased Bike from 0-1. also creating new columns.\n",
    "\n",
    "df[\"HO_cat\"]= df[\"Home Owner\"].map({\"Yes\":1, \"No\": 0})\n",
    "df[\"PB_cat\"]= df[\"Purchased Bike\"].map({\"Yes\":1, \"No\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "596093ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Marital Status</th>\n",
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       "      <th>0</th>\n",
       "      <td>12496</td>\n",
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       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24107</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1</td>\n",
       "      <td>Europe</td>\n",
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       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14177</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>5</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25597</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13507</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>10000</td>\n",
       "      <td>2</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID Marital Status  Gender  Income  Children        Education  \\\n",
       "0  12496        Married  Female   40000         1        Bachelors   \n",
       "1  24107        Married    Male   30000         3  Partial College   \n",
       "2  14177        Married    Male   80000         5  Partial College   \n",
       "4  25597         Single    Male   30000         0        Bachelors   \n",
       "5  13507        Married  Female   10000         2  Partial College   \n",
       "\n",
       "       Occupation Home Owner  Cars Commute Distance(Miles)  Region  Age  \\\n",
       "0  Skilled Manual        Yes     0                     0-1  Europe   42   \n",
       "1        Clerical        Yes     1                     0-1  Europe   43   \n",
       "2    Professional         No     2                     2-5  Europe   60   \n",
       "4        Clerical         No     0                     0-1  Europe   36   \n",
       "5          Manual        Yes     0                     1-2  Europe   50   \n",
       "\n",
       "  Purchased Bike    Age Range  CD(Miles)_cat  HO_cat  PB_cat  \n",
       "0             No  Older Adult              1       1       0  \n",
       "1             No  Older Adult              1       1       0  \n",
       "2             No  Older Adult              3       0       0  \n",
       "4            Yes        Adult              1       0       1  \n",
       "5             No  Older Adult              2       1       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6316148b",
   "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>ID</th>\n",
       "      <th>Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Age</th>\n",
       "      <th>CD(Miles)_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>994</td>\n",
       "      <td>992</td>\n",
       "      <td>991</td>\n",
       "      <td>992</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19966</td>\n",
       "      <td>56268</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5347</td>\n",
       "      <td>31068</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15291</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19744</td>\n",
       "      <td>60000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24471</td>\n",
       "      <td>70000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29447</td>\n",
       "      <td>170000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Income  Children  Cars  Age  CD(Miles)_cat\n",
       "count  1000     994       992   991  992           1000\n",
       "mean  19966   56268         2     1   44              3\n",
       "std    5347   31068         2     1   11              1\n",
       "min   11000   10000         0     0   25              1\n",
       "25%   15291   30000         0     1   35              1\n",
       "50%   19744   60000         2     1   43              2\n",
       "75%   24471   70000         3     2   52              4\n",
       "max   29447  170000         5     4   89              5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the overall statistic of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04b112c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21567e83",
   "metadata": {},
   "source": [
    "There is a corellation between\n",
    "1. Age and Children\n",
    "2. Income and Car owners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c41239",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0a7f6f22",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc8f782",
   "metadata": {},
   "source": [
    "1. how many people bought bike\n",
    "1. who buy the most bikes m or f\n",
    "2. who buy the most bikes are married or single\n",
    "3. buyer by the income\n",
    "4. which profession buy the most bikes\n",
    "5. do people that commute for 5-10 miles need more bikes\n",
    "6. age range that use the bike the most\n",
    "7. range of the buyer tha own their own homes\n",
    "8. car owners with the most bought bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9edf8e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Marital Status', 'Gender', 'Income', 'Children', 'Education',\n",
       "       'Occupation', 'Home Owner', 'Cars', 'Commute Distance(Miles)', 'Region',\n",
       "       'Age', 'Purchased Bike', 'Age Range', 'CD(Miles)_cat', 'HO_cat',\n",
       "       'PB_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9be05326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     496\n",
       "Yes    456\n",
       "Name: Purchased Bike, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.how many people actually bought bike\n",
    "\n",
    "df['Purchased Bike'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "283b5ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Purchased Bike', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Purchased Bike',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28053e3",
   "metadata": {},
   "source": [
    "Only 456 people in the dataset bought the bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c2d02ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9ec1da8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  Purchased Bike\n",
       "Female  No                242\n",
       "        Yes               231\n",
       "Male    No                254\n",
       "        Yes               225\n",
       "Name: Purchased Bike, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. who buy the most bikes m or f\n",
    "\n",
    "df1 = df.groupby('Gender')['Purchased Bike'].value_counts()\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b5b9602",
   "metadata": {},
   "source": [
    "231 Female bought more bikes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bcd87204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status  Purchased Bike\n",
       "Married         No                295\n",
       "                Yes               223\n",
       "Single          Yes               233\n",
       "                No                201\n",
       "Name: Purchased Bike, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. who buy the most bikes are married or single\n",
    "\n",
    "df2 = df.groupby('Marital Status')['Purchased Bike'].value_counts()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b909ddce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.barh(y = 'Purchased Bike', rot = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76156c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c00e04b4",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5e7997d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Occupation      Purchased Bike\n",
       "Professional    Yes               144\n",
       "Skilled Manual  No                132\n",
       "Professional    No                119\n",
       "Skilled Manual  Yes               110\n",
       "Management      No                 97\n",
       "Clerical        No                 86\n",
       "                Yes                83\n",
       "Management      Yes                69\n",
       "Manual          No                 62\n",
       "                Yes                50\n",
       "Name: Purchased Bike, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.which profession buy the most bikes\n",
    "df3 = df.groupby('Occupation')['Purchased Bike'].value_counts().sort_values(ascending = False)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f7eac4cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot.barh(y = 'Occupation', x= 'Purchased Bike')\n",
    "plt.title('Profession That Bought The Most Bikes')\n",
    "ylable = ()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d9b8a8ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Commute Distance(Miles)  Purchased Bike\n",
       "0-1                      Yes               187\n",
       "                         No                152\n",
       "1-2                      No                 88\n",
       "                         Yes                76\n",
       "10+                      No                 78\n",
       "                         Yes                30\n",
       "2-5                      Yes                92\n",
       "                         No                 67\n",
       "5-10                     No                111\n",
       "                         Yes                71\n",
       "Name: Purchased Bike, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5. do people that commute for 5-10 miles need more bikes\n",
    "df2 = df.groupby('Commute Distance(Miles)')['Purchased Bike'].value_counts()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "71d66bba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Commute Distance(Miles),Purchased Bike'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.barh(y = 'Commute Distance(Miles)', x= 'Purchased Bike')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9fc21ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fad852b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Marital Status', 'Gender', 'Income', 'Children', 'Education',\n",
       "       'Occupation', 'Home Owner', 'Cars', 'Commute Distance(Miles)', 'Region',\n",
       "       'Age', 'Purchased Bike', 'Age Range', 'CD(Miles)_cat', 'HO_cat',\n",
       "       'PB_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6. age range that bought the bike the most\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6f910c2c",
   "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>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance(Miles)</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchased Bike</th>\n",
       "      <th>Age Range</th>\n",
       "      <th>CD(Miles)_cat</th>\n",
       "      <th>HO_cat</th>\n",
       "      <th>PB_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25597</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19364</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22173</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>30000</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1-2</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>54</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25323</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>35</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Adult</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23542</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000</td>\n",
       "      <td>1</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>45</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Older Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Marital Status  Gender  Income  Children        Education  \\\n",
       "4   25597         Single    Male   30000         0        Bachelors   \n",
       "7   19364        Married    Male   40000         1        Bachelors   \n",
       "10  22173        Married  Female   30000         3      High School   \n",
       "13  25323        Married    Male   40000         2  Partial College   \n",
       "14  23542         Single    Male   60000         1  Partial College   \n",
       "\n",
       "        Occupation Home Owner  Cars Commute Distance(Miles)   Region  Age  \\\n",
       "4         Clerical         No     0                     0-1   Europe   36   \n",
       "7   Skilled Manual        Yes     0                     0-1   Europe   43   \n",
       "10  Skilled Manual         No     2                     1-2  Pacific   54   \n",
       "13        Clerical        Yes     1                     1-2   Europe   35   \n",
       "14  Skilled Manual         No     1                     0-1  Pacific   45   \n",
       "\n",
       "   Purchased Bike    Age Range  CD(Miles)_cat  HO_cat  PB_cat  \n",
       "4             Yes        Adult              1       0       1  \n",
       "7             Yes  Older Adult              1       1       1  \n",
       "10            Yes  Older Adult              2       0       1  \n",
       "13            Yes        Adult              2       1       1  \n",
       "14            Yes  Older Adult              1       0       1  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df[df.PB_cat==1]\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3e6d7aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age Range', ylabel='count'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Age Range', data = df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d80ff1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Home Owner', ylabel='count'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#7. range of the buyer tha own their own homes\n",
    "sns.countplot(x = 'Home Owner', data = df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dff2ad4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f6351419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cars', ylabel='count'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#8. car owners with the most bought bikes\n",
    "sns.countplot(x = 'Cars', data = df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e1740f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}