6.using_seaborn_from_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207513eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "business understandading and problem statement\n",
    "data understanding\n",
    "data preparation\n",
    "modeling \n",
    "evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef656fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b44847f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=pd.read_csv(\"CardioGoodFitness-1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6272bdf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TM195</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40932</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TM195</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35247</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>TM498</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38658</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>TM798</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>99601</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TM195</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>46617</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>TM798</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>75946</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>TM195</td>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>46617</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>TM498</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>50028</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TM195</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>45480</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>TM798</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>49801</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "32    TM195   25  Female         16     Partnered      2        2   40932   \n",
       "4     TM195   20    Male         13     Partnered      4        2   35247   \n",
       "88    TM498   23    Male         14     Partnered      3        3   38658   \n",
       "169   TM798   30    Male         18     Partnered      5        5   99601   \n",
       "26    TM195   24  Female         16        Single      4        3   46617   \n",
       "155   TM798   25    Male         18     Partnered      6        5   75946   \n",
       "44    TM195   28  Female         14     Partnered      2        3   46617   \n",
       "96    TM498   24  Female         16        Single      3        3   50028   \n",
       "28    TM195   25    Male         14     Partnered      2        3   45480   \n",
       "149   TM798   24    Male         16        Single      5        5   49801   \n",
       "\n",
       "     Miles  \n",
       "32      47  \n",
       "4       47  \n",
       "88      85  \n",
       "169    150  \n",
       "26      75  \n",
       "155    240  \n",
       "44      56  \n",
       "96     106  \n",
       "28      56  \n",
       "149    160  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74a5f1a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "942dd42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          object\n",
       "Age               int64\n",
       "Gender           object\n",
       "Education         int64\n",
       "MaritalStatus    object\n",
       "Usage             int64\n",
       "Fitness           int64\n",
       "Income            int64\n",
       "Miles             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "066353bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Product', 'Age', 'Gender', 'Education', 'MaritalStatus', 'Usage',\n",
       "       'Fitness', 'Income', 'Miles'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c062fb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of     Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "0     TM195   18    Male         14        Single      3        4   29562   \n",
       "1     TM195   19    Male         15        Single      2        3   31836   \n",
       "2     TM195   19  Female         14     Partnered      4        3   30699   \n",
       "3     TM195   19    Male         12        Single      3        3   32973   \n",
       "4     TM195   20    Male         13     Partnered      4        2   35247   \n",
       "..      ...  ...     ...        ...           ...    ...      ...     ...   \n",
       "175   TM798   40    Male         21        Single      6        5   83416   \n",
       "176   TM798   42    Male         18        Single      5        4   89641   \n",
       "177   TM798   45    Male         16        Single      5        5   90886   \n",
       "178   TM798   47    Male         18     Partnered      4        5  104581   \n",
       "179   TM798   48    Male         18     Partnered      4        5   95508   \n",
       "\n",
       "     Miles  \n",
       "0      112  \n",
       "1       75  \n",
       "2       66  \n",
       "3       85  \n",
       "4       47  \n",
       "..     ...  \n",
       "175    200  \n",
       "176    200  \n",
       "177    160  \n",
       "178    120  \n",
       "179    180  \n",
       "\n",
       "[180 rows x 9 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59700ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          0\n",
       "Age              0\n",
       "Gender           0\n",
       "Education        0\n",
       "MaritalStatus    0\n",
       "Usage            0\n",
       "Fitness          0\n",
       "Income           0\n",
       "Miles            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbb6c3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.788889</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.943498</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   Education       Usage     Fitness         Income  \\\n",
       "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
       "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
       "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
       "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
       "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
       "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
       "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
       "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
       "\n",
       "            Miles  \n",
       "count  180.000000  \n",
       "mean   103.194444  \n",
       "std     51.863605  \n",
       "min     21.000000  \n",
       "25%     66.000000  \n",
       "50%     94.000000  \n",
       "75%    114.750000  \n",
       "max    360.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32ce90e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Age\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4667d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Education'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Education\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59c30e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Usage'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Usage\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c77ceb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fitness'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Fitness\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ab241b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Income'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Income\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6ac6ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Miles'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Miles\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fefc5c56",
   "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>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.788889</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.943498</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   Education       Usage     Fitness         Income  \\\n",
       "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
       "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
       "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
       "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
       "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
       "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
       "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
       "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
       "\n",
       "            Miles  \n",
       "count  180.000000  \n",
       "mean   103.194444  \n",
       "std     51.863605  \n",
       "min     21.000000  \n",
       "25%     66.000000  \n",
       "50%     94.000000  \n",
       "75%    114.750000  \n",
       "max    360.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92331f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3670b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(mydata[\"Age\"])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22588e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata.hist(figsize=(10,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d82906d1",
   "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>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</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>Product</th>\n",
       "      <td>180</td>\n",
       "      <td>3</td>\n",
       "      <td>TM195</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.788889</td>\n",
       "      <td>6.943498</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>180</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>180</td>\n",
       "      <td>2</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usage</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fitness</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>29562.0</td>\n",
       "      <td>44058.75</td>\n",
       "      <td>50596.5</td>\n",
       "      <td>58668.0</td>\n",
       "      <td>104581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miles</th>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.194444</td>\n",
       "      <td>51.863605</td>\n",
       "      <td>21.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>114.75</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count unique        top freq          mean           std  \\\n",
       "Product          180      3      TM195   80           NaN           NaN   \n",
       "Age            180.0    NaN        NaN  NaN     28.788889      6.943498   \n",
       "Gender           180      2       Male  104           NaN           NaN   \n",
       "Education      180.0    NaN        NaN  NaN     15.572222      1.617055   \n",
       "MaritalStatus    180      2  Partnered  107           NaN           NaN   \n",
       "Usage          180.0    NaN        NaN  NaN      3.455556      1.084797   \n",
       "Fitness        180.0    NaN        NaN  NaN      3.311111      0.958869   \n",
       "Income         180.0    NaN        NaN  NaN  53719.577778  16506.684226   \n",
       "Miles          180.0    NaN        NaN  NaN    103.194444     51.863605   \n",
       "\n",
       "                   min       25%      50%      75%       max  \n",
       "Product            NaN       NaN      NaN      NaN       NaN  \n",
       "Age               18.0      24.0     26.0     33.0      50.0  \n",
       "Gender             NaN       NaN      NaN      NaN       NaN  \n",
       "Education         12.0      14.0     16.0     16.0      21.0  \n",
       "MaritalStatus      NaN       NaN      NaN      NaN       NaN  \n",
       "Usage              2.0       3.0      3.0      4.0       7.0  \n",
       "Fitness            1.0       3.0      3.0      4.0       5.0  \n",
       "Income         29562.0  44058.75  50596.5  58668.0  104581.0  \n",
       "Miles             21.0      66.0     94.0   114.75     360.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1329960d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='MaritalStatus', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"MaritalStatus\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b6fe01ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Product\",hue=\"Gender\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a99a0837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Age\",hue=\"Fitness\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8031dfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product', ylabel='Age'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Product\",y=\"Age\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c7faff9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'MartialStatus'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mProduct\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMartialStatus\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmydata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda\\lib\\site-packages\\seaborn\\categorical.py:2943\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2940\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2941\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 2943\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_CountPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2944\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2945\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrorbar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2946\u001b[0m \u001b[43m    \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2947\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\n\u001b[0;32m   2948\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2950\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda\\lib\\site-packages\\seaborn\\categorical.py:1530\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m   1526\u001b[0m              estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m   1527\u001b[0m              orient, color, palette, saturation, width,\n\u001b[0;32m   1528\u001b[0m              errcolor, errwidth, capsize, dodge):\n\u001b[0;32m   1529\u001b[0m     \u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1530\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1531\u001b[0m \u001b[43m                             \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1532\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m   1533\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, errorbar, n_boot, seed)\n",
      "File \u001b[1;32m~\\anaconda\\lib\\site-packages\\seaborn\\categorical.py:541\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    539\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    540\u001b[0m         err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret input \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 541\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m    543\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[0;32m    544\u001b[0m orient \u001b[38;5;241m=\u001b[39m infer_orient(\n\u001b[0;32m    545\u001b[0m     x, y, orient, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequire_numeric\n\u001b[0;32m    546\u001b[0m )\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'MartialStatus'"
     ]
    }
   ],
   "source": [
    "sns.countplot(x=\"Product\",hue=\"MartialStatus\",data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "288fa7de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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