diff --git "a/notebook/eda.ipynb" "b/notebook/eda.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebook/eda.ipynb"
@@ -0,0 +1,968 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAuthor       : Rupesh Garsondiya\\ngithub       : @Rupeshgarsondiya\\nEmail        : rupeshgarsondiya.edu@gmail.com\\nOrganization : L.J University\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "Author       : Rupesh Garsondiya\n",
+    "github       : @Rupeshgarsondiya\n",
+    "Email        : rupeshgarsondiya.edu@gmail.com\n",
+    "Organization : L.J University\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import required library\n",
+    "import random\n",
+    "import pandas as pd # type: ignore\n",
+    "import numpy as np # type: ignore\n",
+    "import matplotlib.pyplot as plt # type: ignore\n",
+    "import seaborn as sns # type: ignore\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create pandas dataframe\n",
+    "df = pd.read_csv('/home/rupeshgarsondiya/workstation/lab/Project-1/Data/user_behavior_dataset.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop('User ID',axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data Overview:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Device Model</th>\n",
+       "      <th>Operating System</th>\n",
+       "      <th>App Usage Time (min/day)</th>\n",
+       "      <th>Screen On Time (hours/day)</th>\n",
+       "      <th>Battery Drain (mAh/day)</th>\n",
+       "      <th>Number of Apps Installed</th>\n",
+       "      <th>Data Usage (MB/day)</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>User Behavior Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>291</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>1474</td>\n",
+       "      <td>46</td>\n",
+       "      <td>827</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>OnePlus 9</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>1116</td>\n",
+       "      <td>32</td>\n",
+       "      <td>320</td>\n",
+       "      <td>41</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>iPhone 12</td>\n",
+       "      <td>iOS</td>\n",
+       "      <td>444</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1873</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1093</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>267</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>1740</td>\n",
+       "      <td>45</td>\n",
+       "      <td>791</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Samsung Galaxy S21</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>138</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>889</td>\n",
+       "      <td>25</td>\n",
+       "      <td>323</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>Xiaomi Mi 11</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>320</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>52</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>561</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>2547</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>461</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1324</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Xiaomi Mi 11</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>971</td>\n",
+       "      <td>32</td>\n",
+       "      <td>431</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>Samsung Galaxy S21</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>357</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>2289</td>\n",
+       "      <td>74</td>\n",
+       "      <td>1242</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Device Model Operating System  App Usage Time (min/day)  \\\n",
+       "91       Google Pixel 5          Android                       291   \n",
+       "197           OnePlus 9          Android                       151   \n",
+       "484           iPhone 12              iOS                       444   \n",
+       "508      Google Pixel 5          Android                       267   \n",
+       "47   Samsung Galaxy S21          Android                       138   \n",
+       "131        Xiaomi Mi 11          Android                       320   \n",
+       "371      Google Pixel 5          Android                       561   \n",
+       "333      Google Pixel 5          Android                       461   \n",
+       "199        Xiaomi Mi 11          Android                       126   \n",
+       "408  Samsung Galaxy S21          Android                       357   \n",
+       "\n",
+       "     Screen On Time (hours/day)  Battery Drain (mAh/day)  \\\n",
+       "91                          4.1                     1474   \n",
+       "197                         3.7                     1116   \n",
+       "484                         6.0                     1873   \n",
+       "508                         5.9                     1740   \n",
+       "47                          3.6                      889   \n",
+       "131                         7.2                     2056   \n",
+       "371                        10.6                     2547   \n",
+       "333                         7.5                     2001   \n",
+       "199                         2.8                      971   \n",
+       "408                         7.8                     2289   \n",
+       "\n",
+       "     Number of Apps Installed  Data Usage (MB/day)  Age  Gender  \\\n",
+       "91                         46                  827   32  Female   \n",
+       "197                        32                  320   41  Female   \n",
+       "484                        61                 1093   39  Female   \n",
+       "508                        45                  791   22    Male   \n",
+       "47                         25                  323   27  Female   \n",
+       "131                        69                 1226   52  Female   \n",
+       "371                        86                 1823   22    Male   \n",
+       "333                        69                 1324   34  Female   \n",
+       "199                        32                  431   35  Female   \n",
+       "408                        74                 1242   22  Female   \n",
+       "\n",
+       "     User Behavior Class  \n",
+       "91                     3  \n",
+       "197                    2  \n",
+       "484                    4  \n",
+       "508                    3  \n",
+       "47                     2  \n",
+       "131                    4  \n",
+       "371                    5  \n",
+       "333                    4  \n",
+       "199                    2  \n",
+       "408                    4  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data overview\n",
+    "print(\"Data Overview:\") \n",
+    "df.sample(10) #  show random 10 rows of the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 700 entries, 0 to 699\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column                      Non-Null Count  Dtype  \n",
+      "---  ------                      --------------  -----  \n",
+      " 0   Device Model                700 non-null    object \n",
+      " 1   Operating System            700 non-null    object \n",
+      " 2   App Usage Time (min/day)    700 non-null    int64  \n",
+      " 3   Screen On Time (hours/day)  700 non-null    float64\n",
+      " 4   Battery Drain (mAh/day)     700 non-null    int64  \n",
+      " 5   Number of Apps Installed    700 non-null    int64  \n",
+      " 6   Data Usage (MB/day)         700 non-null    int64  \n",
+      " 7   Age                         700 non-null    int64  \n",
+      " 8   Gender                      700 non-null    object \n",
+      " 9   User Behavior Class         700 non-null    int64  \n",
+      "dtypes: float64(1), int64(6), object(3)\n",
+      "memory usage: 54.8+ KB\n",
+      "None\n",
+      "\n",
+      "\n",
+      "       App Usage Time (min/day)  Screen On Time (hours/day)  \\\n",
+      "count                700.000000                  700.000000   \n",
+      "mean                 271.128571                    5.272714   \n",
+      "std                  177.199484                    3.068584   \n",
+      "min                   30.000000                    1.000000   \n",
+      "25%                  113.250000                    2.500000   \n",
+      "50%                  227.500000                    4.900000   \n",
+      "75%                  434.250000                    7.400000   \n",
+      "max                  598.000000                   12.000000   \n",
+      "\n",
+      "       Battery Drain (mAh/day)  Number of Apps Installed  Data Usage (MB/day)  \\\n",
+      "count               700.000000                700.000000           700.000000   \n",
+      "mean               1525.158571                 50.681429           929.742857   \n",
+      "std                 819.136414                 26.943324           640.451729   \n",
+      "min                 302.000000                 10.000000           102.000000   \n",
+      "25%                 722.250000                 26.000000           373.000000   \n",
+      "50%                1502.500000                 49.000000           823.500000   \n",
+      "75%                2229.500000                 74.000000          1341.000000   \n",
+      "max                2993.000000                 99.000000          2497.000000   \n",
+      "\n",
+      "              Age  User Behavior Class  \n",
+      "count  700.000000           700.000000  \n",
+      "mean    38.482857             2.990000  \n",
+      "std     12.012916             1.401476  \n",
+      "min     18.000000             1.000000  \n",
+      "25%     28.000000             2.000000  \n",
+      "50%     38.000000             3.000000  \n",
+      "75%     49.000000             4.000000  \n",
+      "max     59.000000             5.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.info()) # in our dataset no null value\n",
+    "print()\n",
+    "print()\n",
+    "print(df.describe()) # summary statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>P_Model</th>\n",
+       "      <th>OS</th>\n",
+       "      <th>App_Time(hours/day)</th>\n",
+       "      <th>Screen_time(hours/day)</th>\n",
+       "      <th>Battery_Drain(mAh/day)</th>\n",
+       "      <th>Installed_app</th>\n",
+       "      <th>Data_Usage(GB/day)</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>User Behavior Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>393</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>1872</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1122</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OnePlus 9</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>268</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>42</td>\n",
+       "      <td>944</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Xiaomi Mi 11</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>154</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>761</td>\n",
+       "      <td>32</td>\n",
+       "      <td>322</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>239</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1676</td>\n",
+       "      <td>56</td>\n",
+       "      <td>871</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>iPhone 12</td>\n",
+       "      <td>iOS</td>\n",
+       "      <td>187</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>1367</td>\n",
+       "      <td>58</td>\n",
+       "      <td>988</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          P_Model       OS  App_Time(hours/day)  Screen_time(hours/day)  \\\n",
+       "0  Google Pixel 5  Android                  393                     6.4   \n",
+       "1       OnePlus 9  Android                  268                     4.7   \n",
+       "2    Xiaomi Mi 11  Android                  154                     4.0   \n",
+       "3  Google Pixel 5  Android                  239                     4.8   \n",
+       "4       iPhone 12      iOS                  187                     4.3   \n",
+       "\n",
+       "   Battery_Drain(mAh/day)  Installed_app  Data_Usage(GB/day)  Age  Gender  \\\n",
+       "0                    1872             67                1122   40    Male   \n",
+       "1                    1331             42                 944   47  Female   \n",
+       "2                     761             32                 322   42    Male   \n",
+       "3                    1676             56                 871   20    Male   \n",
+       "4                    1367             58                 988   31  Female   \n",
+       "\n",
+       "   User Behavior Class  \n",
+       "0                    4  \n",
+       "1                    3  \n",
+       "2                    2  \n",
+       "3                    3  \n",
+       "4                    3  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''Rename column name in dataframe'''\n",
+    "df.rename(columns={'Device Model':'P_Model','Operating System':'OS','App Usage Time (min/day)':'App_Time(hours/day)',\n",
+    "                   'Screen On Time (hours/day)':'Screen_time(hours/day)','Battery Drain (mAh/day)':'Battery_Drain(mAh/day)',\n",
+    "                   'Number of Apps Installed':'Installed_app','Data Usage (MB/day)':'Data_Usage(GB/day)'},inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# App time convert minit into the hours \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mApp_Time(hours/day)\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[43mdf\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mApp_Time(hours/day)\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m60\u001b[39m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# convert data use MB into GB\u001b[39;00m\n\u001b[1;32m      5\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData_Usage(GB/day)\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData_Usage(GB/day)\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1024\u001b[39m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# App time convert minit into the hours \n",
+    "df['App_Time(hours/day)']=df['App_Time(hours/day)']/60\n",
+    "\n",
+    "# convert data use MB into GB\n",
+    "df['Data_Usage(GB/day)']=df['Data_Usage(GB/day)']/1024\n",
+    "\n",
+    "df.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visulization between cetegorical column\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#visulization-1\n",
+    "# Visuliztion of Adroid user Ios  user\n",
+    "df.OS.hist(color = 'green')\n",
+    "plt.show()\n",
+    "# By this visulization  we can see that most of the users are using android"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'By this visulization we can say that iPhone 12 and Xiaomi Mi 11 are equal and this is the heigest  model \\n   used by users Lowest mode use by user is One Plus 9 '"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visulization-2\n",
+    "df.P_Model.hist(color = 'pink') \n",
+    "plt.show()\n",
+    "'''By this visulization we can say that iPhone 12 and Xiaomi Mi 11 are equal and this is the heigest  model \n",
+    "   used by users Lowest mode use by user is One Plus 9 '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  visulization-3\n",
+    "df['User Behavior Class'].hist(color = 'blue')\n",
+    "plt.show()\n",
+    "# Heigest  user behavior class is 2 and lowest user behavior is 1 & 5 both are equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visulization-4\n",
+    "df['Gender'].value_counts().plot(kind='bar')\n",
+    "plt.xlabel('Gender')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Age Distribution')\n",
+    "plt.show()\n",
+    "# Male mobile users more than female user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\nBy this visulization we can say that most of the male user are using samsuang user  and  most female user are using xiaomi Mi 11'"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visulization-5\n",
+    "crosstab = pd.crosstab(df['P_Model'], df['Gender'])\n",
+    "# Plot the cross-tab as a grouped bar chart\n",
+    "crosstab.plot(kind='bar',stacked=False)\n",
+    "plt.title('P_Model vs Gender')\n",
+    "plt.xlabel('P_Model')\n",
+    "plt.ylabel('Count')\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "'''\n",
+    "By this visulization we can say that most of the male user are using samsuang user  and  most female user are using xiaomi Mi 11'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'By this visulization we see that in famle and male are same for ios user '"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visulization-6\n",
+    "crosstab = pd.crosstab(df['OS'], df['Gender'])\n",
+    "# Plot the cross-tab as a grouped bar chart\n",
+    "crosstab.plot(kind='bar',stacked=False)\n",
+    "plt.title('OS vs Gender')\n",
+    "plt.xlabel('OS')\n",
+    "plt.ylabel('Gender')\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "'''By this visulization we see that in famle and male are same for ios user '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>P_Model</th>\n",
+       "      <th>OS</th>\n",
+       "      <th>App_Time(hours/day)</th>\n",
+       "      <th>Screen_time(hours/day)</th>\n",
+       "      <th>Battery_Drain(mAh/day)</th>\n",
+       "      <th>Installed_app</th>\n",
+       "      <th>Data_Usage(GB/day)</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>User Behavior Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.109167</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>1872</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1.095703</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OnePlus 9</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.074444</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Xiaomi Mi 11</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.042778</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>761</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.314453</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.066389</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1676</td>\n",
+       "      <td>56</td>\n",
+       "      <td>0.850586</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>iPhone 12</td>\n",
+       "      <td>iOS</td>\n",
+       "      <td>0.051944</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>1367</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.964844</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          P_Model       OS  App_Time(hours/day)  Screen_time(hours/day)  \\\n",
+       "0  Google Pixel 5  Android             0.109167                     6.4   \n",
+       "1       OnePlus 9  Android             0.074444                     4.7   \n",
+       "2    Xiaomi Mi 11  Android             0.042778                     4.0   \n",
+       "3  Google Pixel 5  Android             0.066389                     4.8   \n",
+       "4       iPhone 12      iOS             0.051944                     4.3   \n",
+       "\n",
+       "   Battery_Drain(mAh/day)  Installed_app  Data_Usage(GB/day)  Age  Gender  \\\n",
+       "0                    1872             67            1.095703   40    Male   \n",
+       "1                    1331             42            0.921875   47  Female   \n",
+       "2                     761             32            0.314453   42    Male   \n",
+       "3                    1676             56            0.850586   20    Male   \n",
+       "4                    1367             58            0.964844   31  Female   \n",
+       "\n",
+       "   User Behavior Class  \n",
+       "0                    4  \n",
+       "1                    3  \n",
+       "2                    2  \n",
+       "3                    3  \n",
+       "4                    3  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visulization between cetegorical  and numerical column\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'By this visulization we say that older people use ios more than android'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visulization-1\n",
+    "# Create a box plot\n",
+    "sns.boxplot(x='OS', y='Age', data=df)\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "'''By this visulization we say that older people use ios more than android'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Series.corr() missing 1 required positional argument: 'other'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[49], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Correlation matrix\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m correlation_matrix \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      4\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(correlation_matrix, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: Series.corr() missing 1 required positional argument: 'other'"
+     ]
+    }
+   ],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env-global",
+   "language": "python",
+   "name": "env-global"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}