(Jenny)ML-Supervised Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cd78ec4",
   "metadata": {},
   "source": [
    "## Jenny Mobile Distributor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "627aef1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24cf0954",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Mobile phone dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4daf82ec",
   "metadata": {},
   "source": [
    "### Checking the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c07ee75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Battery_Capacity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>iPhone 13 Pro</td>\n",
       "      <td>128 GB</td>\n",
       "      <td>6 GB</td>\n",
       "      <td>6.1</td>\n",
       "      <td>12 + 12 + 12</td>\n",
       "      <td>3095</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>Galaxy S21 Ultra</td>\n",
       "      <td>256 GB</td>\n",
       "      <td>12 GB</td>\n",
       "      <td>6.8</td>\n",
       "      <td>108 + 10 + 10 + 12</td>\n",
       "      <td>5000</td>\n",
       "      <td>1199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OnePlus</td>\n",
       "      <td>9 Pro</td>\n",
       "      <td>128 GB</td>\n",
       "      <td>8 GB</td>\n",
       "      <td>6.7</td>\n",
       "      <td>48 + 50 + 8 + 2</td>\n",
       "      <td>4500</td>\n",
       "      <td>899</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Redmi Note 10 Pro</td>\n",
       "      <td>128 GB</td>\n",
       "      <td>6 GB</td>\n",
       "      <td>6.67</td>\n",
       "      <td>64 + 8 + 5 + 2</td>\n",
       "      <td>5020</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Google</td>\n",
       "      <td>Pixel 6</td>\n",
       "      <td>128 GB</td>\n",
       "      <td>8 GB</td>\n",
       "      <td>6.4</td>\n",
       "      <td>50 + 12.2</td>\n",
       "      <td>4614</td>\n",
       "      <td>799</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Brand              Model Storage    RAM Screen_Size              Camera  \\\n",
       "0    Apple      iPhone 13 Pro  128 GB   6 GB         6.1        12 + 12 + 12   \n",
       "1  Samsung   Galaxy S21 Ultra  256 GB  12 GB         6.8  108 + 10 + 10 + 12   \n",
       "2  OnePlus              9 Pro  128 GB   8 GB         6.7     48 + 50 + 8 + 2   \n",
       "3   Xiaomi  Redmi Note 10 Pro  128 GB   6 GB        6.67      64 + 8 + 5 + 2   \n",
       "4   Google            Pixel 6  128 GB   8 GB         6.4           50 + 12.2   \n",
       "\n",
       "   Battery_Capacity Price  Label  \n",
       "0              3095   999      1  \n",
       "1              5000  1199      1  \n",
       "2              4500   899      1  \n",
       "3              5020   279      0  \n",
       "4              4614   799      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5efc99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Brand             407 non-null    object\n",
      " 1   Model             407 non-null    object\n",
      " 2   Storage           407 non-null    object\n",
      " 3   RAM               407 non-null    object\n",
      " 4   Screen_Size       407 non-null    object\n",
      " 5   Camera            407 non-null    object\n",
      " 6   Battery_Capacity  407 non-null    int64 \n",
      " 7   Price             407 non-null    object\n",
      " 8   Label             407 non-null    int64 \n",
      "dtypes: int64(2), object(7)\n",
      "memory usage: 28.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59d01009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand               0\n",
       "Model               0\n",
       "Storage             0\n",
       "RAM                 0\n",
       "Screen_Size         0\n",
       "Camera              0\n",
       "Battery_Capacity    0\n",
       "Price               0\n",
       "Label               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1136b2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(407, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea919add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>239</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>41</td>\n",
       "      <td>143</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>Poco X3 Pro</td>\n",
       "      <td>128GB</td>\n",
       "      <td>4GB</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13MP + 2MP + 2MP</td>\n",
       "      <td>$149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>79</td>\n",
       "      <td>6</td>\n",
       "      <td>125</td>\n",
       "      <td>68</td>\n",
       "      <td>108</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Brand        Model Storage  RAM Screen_Size            Camera  Price\n",
       "count       407          407     407  407         407               407    407\n",
       "unique       16          239      13   20          41               143     89\n",
       "top     Samsung  Poco X3 Pro   128GB  4GB         6.5  13MP + 2MP + 2MP  $149 \n",
       "freq         79            6     125   68         108                29     18"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[\"object\", \"bool\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b26cd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Battery_Capacity</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4676.476658</td>\n",
       "      <td>0.692875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>797.193713</td>\n",
       "      <td>0.461870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1821.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Battery_Capacity       Label\n",
       "count        407.000000  407.000000\n",
       "mean        4676.476658    0.692875\n",
       "std          797.193713    0.461870\n",
       "min         1821.000000    0.000000\n",
       "25%         4300.000000    0.000000\n",
       "50%         5000.000000    1.000000\n",
       "75%         5000.000000    1.000000\n",
       "max         7000.000000    1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab6f879",
   "metadata": {},
   "source": [
    "## Cleaning Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5a2e6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Brand', 'Model', 'Storage', 'RAM', 'Screen_Size', 'Camera',\n",
       "       'Battery_Capacity', 'Price', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18fc658b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['999', '1199', '899', '279', '799', '249', '699', '329', '449',\n",
       "       '199', '299', '379', '179', '729', '599', '139', '189', '399',\n",
       "       '259', '159', '229', '499', '129', '529', '369', '1099', '169',\n",
       "       '99', '459', '239', '1299', '429', '659', '269', '359', '$799 ',\n",
       "       '$399 ', '$699 ', '$329 ', '$999 ', '$549 ', '$1,299 ', '$899 ',\n",
       "       '$449 ', '$319 ', '$269 ', '$349 ', '$279 ', '$249 ', '$299 ',\n",
       "       '$969 ', '$1,199 ', '$149 ', '$139 ', '$99 ', '$199 ', '$169 ',\n",
       "       '$499 ', '$179 ', '$219 ', '$229 ', '$239 ', '$109 ', '$189 ',\n",
       "       '$389 ', '$309 ', '$369 ', '$129 ', '$849 ', '$469 ', '$209 ',\n",
       "       '$119 ', '$339 ', '$429 ', '$159 ', '$379 ', '$289 ', '130', '749',\n",
       "       '149', '969', '649', '349', '419', '1399', '1999', '119', '319',\n",
       "       '1049'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Price.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2248a750",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Price\"]= df[\"Price\"].str.replace(\"$\",\"\")\n",
    "df[\"Price\"]= df[\"Price\"].str.replace(\",\",\"\")\n",
    "df[\"Price\"]= df[\"Price\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a353fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Series name: Price\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "407 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.Price.info();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1553f14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['128 GB', '256 GB', '64 GB', '32 GB', '128GB', '256GB', '64GB',\n",
       "       '32GB', '256', '64', '128', '512', '32'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Storage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9364674f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Storage\"]= df[\"Storage\"].str.replace(\"GB\",\"\")\n",
    "df[\"Storage\"]= df[\"Storage\"].str.replace(\" \",\"\")\n",
    "df[\"Storage\"]= df[\"Storage\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27385578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([128., 256.,  64.,  32., 512.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Storage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8300f7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Series name: Storage\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "407 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.Storage.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "311f211b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6 GB', '12 GB', '8 GB', '4 GB', '3 GB', '2 GB', '4GB', '8GB',\n",
       "       '6GB', '12GB', '3GB', '2GB', '5GB', '12', '3', '6', '8', '4', '16',\n",
       "       '2'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RAM.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5eef49d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"RAM\"]= df[\"RAM\"].str.replace(\"GB\",\"\")\n",
    "df[\"RAM\"]= df[\"RAM\"].str.replace(\" \",\"\")\n",
    "df[\"RAM\"]= df[\"RAM\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d149e0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6., 12.,  8.,  4.,  3.,  2.,  5., 16.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RAM.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98857b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Series name: RAM\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "407 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.RAM.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17d027a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Brand', 'Model', 'Storage', 'RAM', 'Screen_Size', 'Camera',\n",
       "       'Battery_Capacity', 'Price', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09f8cc08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6.1', '6.8', '6.7', '6.67', '6.4', '6.55', '6.78', '6.43', '6.5',\n",
       "       '6.62', '5.4', '6.2', '6.51', '6.6', '4.7', '6.58', '6.52', '6.44',\n",
       "       '6.53', '6.56', '6.8 + 3.9', '4.5', '6.39', '5.9', '5.5', '6.81',\n",
       "       '5.99', '6.82', '6.3', '6.22', '6', '6.35', '6.9', '6.76', '6.49',\n",
       "       '6.72', '5.7', '6.47', '7.6 (unfolded)', '6.15', '6.57'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Screen_Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3b9774a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"Screen_Size\"]==\"6.8 + 3.9\",\"Screen_Size\"] = \"6.5\"\n",
    "df.loc[df[\"Screen_Size\"]==\"7.6 (unfolded)\",\"Screen_Size\"] = \"6.5\"\n",
    "df[\"Screen_Size\"]= df[\"Screen_Size\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7f73607e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.1 , 6.8 , 6.7 , 6.67, 6.4 , 6.55, 6.78, 6.43, 6.5 , 6.62, 5.4 ,\n",
       "       6.2 , 6.51, 6.6 , 4.7 , 6.58, 6.52, 6.44, 6.53, 6.56, 4.5 , 6.39,\n",
       "       5.9 , 5.5 , 6.81, 5.99, 6.82, 6.3 , 6.22, 6.  , 6.35, 6.9 , 6.76,\n",
       "       6.49, 6.72, 5.7 , 6.47, 6.15, 6.57])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Screen_Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "393683c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Series name: Screen_Size\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "407 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.Screen_Size.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08d369d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['12 + 12 + 12', '108 + 10 + 10 + 12', '48 + 50 + 8 + 2',\n",
       "       '64 + 8 + 5 + 2', '50 + 12.2', '12 + 12', '48 + 8 + 2 + 2',\n",
       "       '50 + 13 + 16 + 2', '50 + 48 + 12 + 8', '64 + 8 + 2',\n",
       "       '64 + 12 + 5 + 5', '50 + 50 + 13 + 3', '64 + 8 + 5',\n",
       "       '64 + 12 + 12', '50 + 2 + 2', '48 + 2 + 2', '50 + 8 + 2 + 2',\n",
       "       '48 + 5', '48 + 50 + 2', '108 + 8 + 5 + 2', '13 + 2 + 2', '13 + 2',\n",
       "       '12', '48 + 5 + 2', '50 + 8 + 2', '64 + 8 + 2 + 2', '48 + 8 + 5',\n",
       "       '64 + 8', '48 + 5 + 2 + 2', '50 + 2', '64 + 12 + 8 + 5', '64 + 2',\n",
       "       '48 + 13 + 13', '12MP + 12MP', '64MP + 12MP + 12MP',\n",
       "       '50MP + 8MP + 2MP', '50MP + 12MP', '48MP + 8MP + 2MP + 2MP',\n",
       "       '64MP + 8MP + 2MP', '50MP + 48MP + 12MP + 8MP',\n",
       "       '50MP + 16MP + 13MP + 2MP', '108MP + 16MP + 8MP', '48MP + 13MP',\n",
       "       '12MP + 12MP + 12MP', '64MP + 13MP + 12MP', '64MP + 12MP + 8MP',\n",
       "       '13MP + 5MP', '64MP + 12MP + 8MP + 5MP', '108MP + 8MP + 5MP + 2MP',\n",
       "       '48MP + 50MP + 2MP', '50MP + 2MP + 2MP', '108MP + 8MP + 2MP',\n",
       "       '48MP + 5MP + 2MP', '64MP + 5MP + 2MP + 2MP', '64MP + 12MP',\n",
       "       '48MP + 50MP + 8MP', '50MP + 48MP + 48MP', '13MP + 2MP + 2MP',\n",
       "       '13MP + 2MP', '8MP + 2MP', '12MP + 12MP + 8MP', '48MP + 2MP + 2MP',\n",
       "       '64MP + 13MP + 5MP', '48MP + 8MP + 5MP', '12MP + 13MP',\n",
       "       '50MP + 13MP + 16MP + 2MP', '50MP + 8MP + 2MP + 2MP', '50MP + 2MP',\n",
       "       '48MP + 5MP', '64MP + 8MP + 2MP + 2MP', '64MP + 8MP + 5MP',\n",
       "       '64MP + 8MP + 5MP + 5MP', '64MP + 12MP + 5MP + 5MP',\n",
       "       '48MP + 8MP + 2MP', '48MP + 2MP', '108MP + 12MP + 10MP + 10MP',\n",
       "       '64MP + 2MP', '64MP + 8MP', '64MP + 8MP + 5MP + 2MP', '50MP + 5MP',\n",
       "       '48MP + 8MP + 5MP + 5MP', '108MP + 8MP + 2MP + 2MP',\n",
       "       '48MP + 5MP + 2MP + 2MP', '13MP', '48MP + 8MP + 5MP + 2MP',\n",
       "       '13MP + 5MP + 2MP', '8MP', '13MP + 5MP + 2MP + 2MP',\n",
       "       '13MP + 8MP + 5MP + 2MP', '13MP + 8MP + 2MP + 2MP',\n",
       "       '12MP + 2MP + 2MP', '13MP + 8MP + 2MP', '25MP + 8MP + 5MP',\n",
       "       '108+12+12', '48+8+2+2', '12.2+16', '50+20+12+3D', '64+8+2+2',\n",
       "       '64+12+5+5', '64+12+2+2', '12+12+12', '108+13+5', '48+2+2',\n",
       "       '48+16+5+2', '13+8+5+2', '13+2+2', '48+5+2', '12+7',\n",
       "       '108+10+10+12', '108+8+5+2', '50+16+13+2', '50+12+40+3D',\n",
       "       '48+50+8+2', '48+8+2', '48+5+2+2', '12+12', '50+8+2+2',\n",
       "       '50+8+16+2', '48+12+5', '48+48+8+5', '48+8+5', '40+40+8+3D',\n",
       "       '48+12+13', '13+5+2', '48+8+5+2', '40+20+8+ToF', '12+12+8',\n",
       "       '48+16+8', '16+8+5', '16+8+8+2', '12+64+12', '64+8+5',\n",
       "       '50+40+12+3D', '108+48+12+3D', '48+50+2', '64+2+2', '48+13+12+2',\n",
       "       '13+2', '48+8+5+5', '64+12+12', '108+48+12+0.3', '48+16+2',\n",
       "       '48+13+8+2'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Camera.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e035962d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Camera\"]= df[\"Camera\"].str.replace(\"MP\",\"\")\n",
    "df[\"Camera\"]= df[\"Camera\"].str.replace(\"D\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15687411",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Camera\"]= df[\"Camera\"].str.split(\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2539816",
   "metadata": {},
   "outputs": [],
   "source": [
    "listx=[]\n",
    "for i in df.index:\n",
    "    listx.append(df[\"Camera\"][i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c8c6cd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Camera\"]= listx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7137dc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Camera\"]= df[\"Camera\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "040c9efe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Series name: Camera\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "407 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.Camera.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e48d5de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 12. , 108. ,  48. ,  64. ,  50. ,  13. ,   8. ,  25. ,  12.2,\n",
       "        40. ,  16. ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Camera.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "344b71d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 407 entries, 0 to 406\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Brand             407 non-null    object \n",
      " 1   Model             407 non-null    object \n",
      " 2   Storage           407 non-null    float64\n",
      " 3   RAM               407 non-null    float64\n",
      " 4   Screen_Size       407 non-null    float64\n",
      " 5   Camera            407 non-null    float64\n",
      " 6   Battery_Capacity  407 non-null    int64  \n",
      " 7   Price             407 non-null    float64\n",
      " 8   Label             407 non-null    int64  \n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 28.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b6f2a47",
   "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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Battery_Capacity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>iPhone 13 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3095</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>Galaxy S21 Ultra</td>\n",
       "      <td>256.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>108.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OnePlus</td>\n",
       "      <td>9 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Redmi Note 10 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.67</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5020</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Google</td>\n",
       "      <td>Pixel 6</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4614</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Brand              Model  Storage   RAM  Screen_Size  Camera  \\\n",
       "0    Apple      iPhone 13 Pro    128.0   6.0         6.10    12.0   \n",
       "1  Samsung   Galaxy S21 Ultra    256.0  12.0         6.80   108.0   \n",
       "2  OnePlus              9 Pro    128.0   8.0         6.70    48.0   \n",
       "3   Xiaomi  Redmi Note 10 Pro    128.0   6.0         6.67    64.0   \n",
       "4   Google            Pixel 6    128.0   8.0         6.40    50.0   \n",
       "\n",
       "   Battery_Capacity   Price  Label  \n",
       "0              3095   999.0      1  \n",
       "1              5000  1199.0      1  \n",
       "2              4500   899.0      1  \n",
       "3              5020   279.0      0  \n",
       "4              4614   799.0      1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0047057",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "60ba0ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TUrwWLVpg69atSElJwcCBA1GhQgXUrFkTa9eu1Wzz7NkzXLp0Kdtz1cHBAUKITM9XCwuLbPm09TwwBtynXgC7du1CampqjgdPZdS8eXM0b94cqampOHPmDH7++WeMGTMGZcqUKdTSK0D9pM4qLCwMwP9+wdJHg+kHvKQr6vpnFxcXhIaGZrv86dOnAICSJUsW6fb1fT/pqlWrlmmVQ1Y2NjbZDlQDcn88sx6wl76vM+PBaUWxdetWxMbGYvPmzZlGyRcuXMi2ba1atbBu3ToIIXDp0iWsWLEC06ZNg62tLcaPH4+SJUtCpVIhKCgoxyP00y9Lf26lP9cyCgsLy9ca8KxWrVqFli1b4tdff810eX4ORMtJXhm15euvv8ayZcs0fyjllCG3529Rn7tmZmZvfa4C6p9Z1t99IPc/ht/2fM06W1YYq1atgqenJ9avX5/pvnLK2K1bN3Tr1g2JiYk4ceIEZs6cib59+8LDwwONGzdGyZIlYWtrm+n4kozSH18XFxekpKQgIiIiU7Fr83lg6DhSz6eQkBB89tlncHR0xMcff5yvrzE3N4efn59mJHXu3DkA+R+5ZvTq1Sts374902Vr1qyBmZkZWrRoAQCaF9hLly5l2i7r1xU0Q+vWrXHt2jVN/nSBgYFQqVRo1apVvr+PvO7n0KFDmhLPeD92dnaZlvTog4eHB27dupXpRSgiIgLHjx/P19dXqVIFXl5eWLZsWY4vZAWV/sKYsYSFEFi8ePFbv8bHxwc//PADSpQoofkZdu7cGUIIPHnyBPXr18/2UatWLQDqXRQ2NjZYvXp1pts9fvx4nrtT3pYp6x8Sly5dKvTulapVq6JcuXJYu3Ztpt1jDx8+zPfPKj9cXFzwxRdf4M8//8SpU6cyXde4cWPY2tpi1apVmS5//PixZrdSusL8/ueHh4dHtt/9Q4cO4fXr1/n6+nbt2sHc3DzbH1uFpVKpYGVllanQw8LCcjz6PZ21tTX8/f0xa9YsAOrdTYD6+Xr37l24uLjk+HxNf+1Lfy3K+nxds2aNVr4nY8CReg6uXLmi2Wfz/PlzBAUFYfny5TA3N8eWLVveerTpokWLcOjQIXTq1Alubm5ISEjQ/HXZpk0bAOr94+7u7ti2bRtat24NZ2dnlCxZ8q2jHhcXF4wcORIhISGoUqUKdu/ejcWLF2PkyJFwc3MDoJ5iatOmDWbOnAknJye4u7vj4MGD2Lx5c7bbS3/RnjVrFjp06ABzc3PUrl0bVlZW2bYdO3YsAgMD0alTJ0ybNg3u7u7YtWsXFi5ciJEjR6JKlSr5fmzfZvLkydi5cydatWqFSZMmwdnZGatXr8auXbswe/bstx45rwsDBgzAb7/9hv79+2P48OGIiIjA7NmzUbx48XzfxoIFC9ClSxc0atQIY8eOhZubG0JCQrB3795sLzx5adu2LaysrNCnTx+MGzcOCQkJ+PXXXxEVFZVpu507d2LhwoXo3r07KlWqBCEENm/ejOjoaLRt2xYA0LRpU4wYMQIfffQRzpw5gxYtWqBYsWIIDQ3FP//8g1q1amHkyJFwcnLCZ599hm+++QbDhg3DBx98gEePHmHKlCmFntLs3Lkzpk+fjsmTJ8Pf3x83b97EtGnT4OnpiZSUlALfnpmZGaZPn45hw4bhvffew/DhwxEdHV2kjLkZM2YMFixYkG1JX4kSJTBx4kRMmDABAwcORJ8+fRAREYGpU6fCxsYGkydP1mxbkN+9ghgwYAAmTpyISZMmwd/fH9euXcMvv/yS798bDw8PTJgwAdOnT0d8fLxmeeW1a9fw4sWLAp8wp3Pnzti8eTNGjRqlWTUxffp0lCtXLtMujEmTJuHx48do3bo1KlSogOjoaMyfPx+Wlpbw9/cHoH7cN23ahBYtWmDs2LGoXbs20tLSEBISgn379uHTTz+Fn58f2rVrhxYtWmDcuHGIjY1F/fr1cezYMaxcubJA2Y2atEP0DFD6UaHpH1ZWVqJ06dLC399ffPvtt+L58+fZvibr0e/BwcHivffeE+7u7sLa2lq4uLgIf39/sX379kxfd+DAAVGnTh1hbW0tAGiOTs3t6PcaNWqII0eOiPr16wtra2tRrlw5MWHCBJGcnJzpdkNDQ8X7778vnJ2dhaOjo+jfv7/maOiMR78nJiaKYcOGiVKlSgmVSpXpPrMeLSuEEA8fPhR9+/YVLi4uwtLSUlStWlV8//33IjU1VbNN1iOGM0I+jzy9fPmy6NKli3B0dBRWVlbCx8cnU+6Mt1fUo9/ftsoh3R9//CGqVasmbGxsRPXq1cX69etzPfo9p+9bCPVzokOHDsLR0VFYW1sLLy8vMXbsWM31WY+kzpoz43Nhx44dwsfHR9jY2AhXV1fx+eefa474TV/1cOPGDdGnTx/h5eUlbG1thaOjo2jYsKFYsWJFtmzLli0Tfn5+olixYsLW1lZ4eXmJgQMHijNnzmi2SUtLEzNnzhQVK1YUVlZWonbt2mLHjh25rg7IS2Jiovjss8+Eq6ursLGxEXXr1hVbt24t0OOa0/NpyZIlonLlysLKykpUqVJFLFu2LNtt5tfb7vv333/XvEZk/ZktWbJE1K5dW1hZWQlHR0fRrVs3cfXq1Wzff26/ezlJP/o9L4mJiWLcuHGiYsWKwtbWVvj7+4sLFy7kevR7bs//wMBA0aBBA2FjYyPs7e1FnTp1Mv0Opr8e5ZQz62P93XffCQ8PD2FtbS2qVasmFi9enO01c+fOnaJDhw7C1dVV85rbsWNHERQUlOm2Xr9+Lb7++mtRtWpVzeNbq1YtMXbsWM2KFSGEiI6OFkOGDBElSpQQdnZ2om3btuLGjRsmc/S7Soh8LuIlIiIig8Z96kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkEBayAxCRdiSkJCAiLgIR8RGZ/o1OiMarpFd4lfhK/e+b/8clx0FAwExlBjOVGVRQ/e//KlWOl9tZ2sHF1gUudi7Z/i1pVxIuti5wsHaQ/VAQmSyWOpGRSExJxP3o+7gfdR/3ou7hfvT//r0fdR8xiTGyIwIArMyt4GzrDBdbF5QqVgqeJTxR2bkyKrtURmXnyvB29kYxq2KyYxIpkkoIIWSHICK1lLQUXAu/hothF3E36m6m8g59FQoBZfy6lrMvl6nk00vf29kbdpZ2suMRGS2WOpEkqWmpuBZ+DWeensHZ0LM4G3oWF8MuIj4lXnY0aVRQoZJTJfhV8IOfq/rDt6wvrC2sZUcjMgosdSI9SE1LxfUX19UF/lRd4BfCLph0geeXlbkVfMr4wM/VDw1dG8Kvgh8qO1eGSqWSHY3I4LDUiXRACIGLzy5i39192Hd3H4IfByMuOU52LMVwsnFCA9cG8HP1QzO3Zmjh3gI2FjayYxFJx1In0pLQV6HqEr+3DwfuHcDz2OeyI5kMO0s7tPRoiQ7eHdDBuwO8nL1kRyKSgqVOVEjxyfH4++Hf2H93P/bd24crz6/IjkRvVHaujI6VO6KDdwf4e/hzFE8mg6VOVABhr8Ow8epGbLu5Df+E/IPE1ETZkSgPtha2aOXZiqN4MgksdaI8RCdEY9O1TVh7ZS2OPDiCVJEqOxIVQfVS1dG3Zl/0r90f7iXcZcch0iqWOlEOYpNisf3mdqy9shZ77+5FUmqS7EikZSqo0MytGfrX7o9eNXqhhE0J2ZGIioylTvRGUmoS9tzeg7VX1mLHrR08Wt2EWJtbo1OVThhQewA6Vu4IK3Mr2ZGICoWlTnk6evQovv/+e5w9exahoaHYsmULunfvLjuW1gQ/CsaSc0uw+cZmRCdEy45DkjnbOuOD6h9gQO0BaOrWVHYcogLhu7RRnmJjY+Hj44NffvlFdhStSUxJxB8X/kD93+ujybImWHZhGQudAACR8ZH47exvaLa8GSrNr4QZR2cgIi5CdiyifOFInQpEpVIZ9Uj9Ucwj/HrmVyw5twThceGy45CRsLWwRf/a/TGm0RhUL1VddhyiXLHUqUCMtdT/fvA3fj71M7be2Mqj16lI2nm1wxi/MWjv3Z6nqiWDw7deJcWKS47Dqkur8MupX3D5+WXZcUgh0k/9W61kNfyf3/9hoM9AvrMcGQyO1KlAjGGkHh4bjjnH52DxucWISoiSHYcUztnWGSPqjsC/G/4brsVdZcchE8cD5UgxIuIiMP7AeHjO98Ts47NZ6KQXkfGR+O7Yd/CY74F+m/vhevh12ZHIhLHUyehFxUfh60Nfw3O+J2Ydm4XY5FjZkcgEpaSlYM3lNaj5a03039wftyNuy45EJojT75Sn169f486dOwCAOnXqYN68eWjVqhWcnZ3h5uYmLVdMQgx+OPEDfjzxI2ISY6TlIMqJucocA30GYmKLifB08pQdh0wES53ydOTIEbRq1Srb5YMGDcKKFSv0nudV4iv8eOJH/HDiB06xk8GzNLPE0DpDMbnlZJS1Lys7DikcS52MRmxSLH46+RPmBs9FRDxPBkLGxc7SDmP8xuCLZl+guHVx2XFIoVjqZPCEEFhxYQXGHxyP57HPZcchKhIXWxd81fwrjGowCtYW1rLjkMKw1MmgnQs9h9G7R+PE4xOyoxBplbujO+YFzEOPaj1kRyEFYamTQYqMj8SEgxOw+NxipIk02XGIdKZT5U74peMv8CjhITsKKQBLnQxKmkjDknNLMOHgBO43J5Nha2GLr1t8jc+bfA5Lc0vZcciIsdTJYJx6cgqjd4/GmadnZEchkqJayWpY2GkhWnq0lB2FjBRLnaR7EfcC4w+Mx7LzyyDApyPRgNoDMKfdHJQuVlp2FDIyLHWSRgiB38/+ji8Pfsn15kRZONk4YWbrmRhRbwTfDY7yjaVOUoTEhGDItiE4eP+g7ChEBs3P1Q+LOi+Cb1lf2VHICLDUSe+WnluK/+77L14mvpQdhcgoWJpZYmrLqfii2RcwU/EtOyh3LHXSm9BXoRi2Yxh2394tOwqRUfJ398fK91aiomNF2VHIQLHUSS/+vPYnPt75MSLjI2VHITJqJWxK4LfOv6FXjV6yo5ABYqmTTr1Oeo1P9nyCFRdWyI5CpCiDfAbhl46/wN7KXnYUMiAsddKZk49Pot/mfrgbdVd2FCJF8nLywuoeq+FXwU92FDIQPOKCtC5NpGH639PRbHkzFjqRDt2Nuotmy5th+t/TkZqWKjsOGQCO1EmrohOi0XdTX+y5s0d2FCKT0sytGVa9twruJdxlRyGJWOqkNVefX0X39d1xJ/KO7ChEJsnR2hFreq5Bx8odZUchSTj9Tlrx57U/0WhpIxY6kUQxiTHosrYLZh+bLTsKScKROhVJmkjD14e+xsx/ZsqOQkQZ9K/dH4u7LIaNhY3sKKRHLHUqtKj4KPTd3Bd/3flLdhQiykFD14bY0nsLyjuUlx2F9ISlToVy5fkVdF/XnUe3Exm48g7lsaX3FjR0bSg7CukB96lTgW28uhGNljRioRMZgaevnsJ/hT9WXVolOwrpAUud8k0IgQkHJ6DXn70QmxwrOw4R5VNCSgIGbBmAcfvHIU2kyY5DOsTpd8qXlLQUDN0+FIEXA2VHIaIi6ODdAWt7roWjjaPsKKQDLHXKU3xyPHr92Qs7b+2UHYWItKBGqRrYP2A/yjmUkx2FtIylTm8VnRCNLmu74J+Qf2RHISItquRUCQcGHICnk6fsKKRFLHXKVeirULRf3R6Xnl2SHYWIdMDVwRX7B+xHtVLVZEchLWGpU47uRt5F25VtcT/6vuwoRKRDJe1KYm//vahbrq7sKKQFPPqdsrkQdgFNlzVloROZgBdxL9Dqj1YIehgkOwppAUudMjn68CharmiJZ7HPZEchIj15mfgSAasCsOc2313R2LHUSWPbjW0IWBWAmMQY2VGISM/iU+LRbV03bLy6UXYUKgKWOgEANl3bhJ4beiIhJUF2FCKSJDktGX029cGy88tkR6FCYqkT9tzegz6b+iBVpMqOQkSSpYpUDNs+DPNPzJcdhQqBR7+buCMPjqDj6o6IT4mXHYWIDMziLosxrO4w2TGoAFjqJuzE4xNou7ItXie9lh2FiAyQmcoM63quwwc1PpAdhfKJpW6iLoZdRMs/WiI6IVp2FCIyYFbmVtjRZwfaebWTHYXygfvUTdCNFzfQblU7FjoR5SkpNQk91vdA8KNg2VEoH1jqJuZ+1H20CWyD57HPZUchIiMRmxyLTms64fKzy7KjUB5Y6ibkycsnaB3YGk9ePZEdhYiMTFRCFAJWBeBe1D3ZUegtWOomIjw2HG1WtuGpX4mo0EJfh6LtyrYIfRUqOwrlgqVuAuKS49BhdQfceHFDdhQiMnL3ou6h3ap2iIqPkh2FcsBSVzghBAZvHYyzoWdlRyEihbjy/Ao6rumI2KRY2VEoC5a6wk39eyo2XuO5nIlIu048PoFBWweBq6INC0tdwTZe3Yhpf0+THYOIFGrT9U2YETRDdgzKgCefUaizT8+i+fLmPP0rEemUCips/XArulbtKjsKgaWuSKGvQtFgcQMuXSMivXCwcsDJYSdRrVQ12VFMHqffFSYhJQHd13dnoROR3rxKeoVu67rxLJUGgKWuMEO2DcGpJ6dkxyAiE3M78jb6buqLNJEmO4pJY6kryIyjM7D2ylrZMYjIRO25swdfHvhSdgyTxn3qCrH1xlb0WN8DAvxxEpFca3qsQZ9afWTHMEksdQV4EP0Avot8EZMYIzsKERFsLWxxbMgx1ClXR3YUk8PpdyOXkpaCvpv6stCJyGDEp8Sj+/rueBH3QnYUk8NSN3JTj0xF8GO+zzERGZaQmBCM2DFCdgyTw1I3YkcfHsW3/3wrOwYRUY623NiCZeeXyY5hUrhP3UhFxUfBZ5EPHr18JDsKEVGu7K3sceHjC/By9pIdxSRwpG6khu8YzkInIoP3Ouk1BmwZgNS0VNlRTAJL3QgtPrsYm65vkh2DiChfgh8H841f9ITT70bmevh11F9cH3HJcbKjEBHlm4WZBY4NOYaGrg1lR1E0jtSNSGJKIvps6sNCJyKjk5KWgv6b+yM2KVZ2FEVjqRuRLw58gYvPLsqOQURUKLcjb2Ps3rGyYygap9+NRNDDIPiv8OdpYInI6G3/cDu6VO0iO4YisdSNQFJqEnwX+eL6i+uyoxARFVkpu1K4PPIyytiXkR1FcTj9bgS+P/Y9C52IFCM8Lhz/99f/yY6hSBypG7i7kXdR89eaSEhJkB2FiEir9vTbg/be7WXHUBSO1A3cyF0jWehEpEijdo1CfHK87BiKwlI3YGsvr8X+e/tlxyAi0on70fcx/eh02TEUhdPvBio6IRrv/PIOnsU+kx3FuL0EsB/AHQDJAFwAdANQ/s31r99cfxdAAgB3AB3fbJfuLwAXAFgBaAugVobrrgC4BKCvrr4BImWzNLPE+Y/Po0bpGrKjKAJH6gZq/IHxLPSiigewFIA5gH4ARgMIAGDz5noBYB2AKAB9APwLQAkAgQCS3mxzE8BlAAOgLvRtANLP/RMP4BDUfwQQUaEkpyVj5K6RsmMoBkvdAAU/CsbvZ3+XHcP4/QPAEUB3ABUAOAGoBMD5zfURAB4D6AzAFUBJAJ2gLvTLb7YJB+Dx5vpaAKyh/iMAUI/wG0D9hwARFVpQSBDWXF4jO4YisNQNTEpaCj7e+TFPMqMNN6GeZt8AYDaARQDOZrg+/U2jLDJcZgb1yD7kzedlATyFelT+FOopfGcADwGEAvDTUXYiEzNu/zieQlYLWOoG5ofgH3D5+eW8N6S8RQE4DXUJDwBQH8AeqPePA+qRuSOAA1CXdgqAIKj3s79+s403gNoAfgewFcB7ACwB7ALQ5c3t/wz1NP9z3X47REr25NUTfHP0G9kxjB4PlDMgkfGRqDS/EmISY2RHUYZpUI/Uh2W4bDfUI+70y55CvZ/8GQAV1NPzqjfX9c/ldg8DSATgC2AlgFEAbgE4BeBjraUnMjlW5la4MvIKKrtUlh3FaHGkbkBmBs1koWuTA4BSWS4rBSDjQ1wewEgA4wF8BvWIPh7q/e85CYd6f3srAA+gPlq+GIAaUE/H85QCRIWWlJqEMXvHyI5h1FjqBuLxy8f45fQvsmMoS0WoD4bLKALqKfesbKAu5wioR+9Vc9hGANgBoB3UB8wJAGlvrkvNsA0RFdru27vx94O/ZccwWix1AzHlyBSeOU7bGkN9dPtRqMv6EtQHyjXMsM1VAPcBRAK4AfVytneg3pee1Vmoi/+dN59XfPO1jwCcgHoWwFbb3wSR6Zl4eKLsCEaL+9QNwM0XN1FjYQ2kitS8N6aCuQngINSl7gR10dfLcP0JAMehPjDOAYAPgBbIfEQ83ly/GMBQAMUzXH4EwEmoy7471EvniKjI/ur3FwK8A2THMDosdQPw/ob3sen6JtkxiIgMRoPyDXBq+CnZMYwOp98lO/P0DAudiCiL009PY9uNbbJjGB2WumTjD4yXHYGIyCBNOjIJnEwuGJa6RAfuHcDB+wdlxyAiMkiXnl3CxmsbZccwKix1SYQQ+PLgl7JjEBEZtMlHJiM1jQcR5xdLXZJN1zfhzNMzsmMQERm0Gy9uYPXl1bJjGA0e/S5Jnd/q4ELYBdkxiIgMXiWnSrj575uwMMu61pSy4khdgn1397HQiYjy6V7UPSw7v0x2DKPAUpdg9rHZsiMQERmV2cdmI02k5b2hiWOp69m50HM84p2IqIDuRt3Frlu7ZMcweCx1PZt1bJbsCERERmn+yfmyIxg8lroe3Y+6j03XePY4IqLCOHj/IK4+vyo7hkFjqevRz6d+5pu2EBEVwU8nf5IdwaBxSZuevE56jQrzKiAmMUZ2FCIio2VnaYdHYx/B2dZZdhSDxJG6nqy4sIKFTkRURHHJcVhybonsGAaLpa4HQgj8fOpn2TGIiBRhwekFPHVsLljqerDnzh7cirglOwYRkSKExIRgy40tsmMYJJa6Hiw4vUB2BCIiReEBczljqetY2Osw7L2zV3YMIiJFCQoJwvnQ87JjGByWuo6tvrSay9iIiHTgl1O/yI5gcFjqOhZ4KVB2BCIiRfrz+p9ISEmQHcOgsNR16GLYRVx6dkl2DCIiRXqZ+BI7bu6QHcOgsNR1KPAiR+lERLq05soa2REMCktdR1LTUvlkIyLSsd23dyM6IVp2DIPBUteRfXf3Iex1mOwYRESKlpSahD+v/Sk7hsFgqesID5AjItKP1ZdXy45gMFjqOvAy8SW23dgmOwYRkUk4+vAonrx8IjuGQWCp68DGqxsRnxIvOwYRkUlIE2lYe2Wt7BgGgaWuA5x6JyLSrzWXeWAywFLXutBXoQh6GCQ7BhGRSTkfdh7Xw6/LjiEdS13Ldt/eDQEhOwYRkcnhaJ2lrnW7bu+SHYGIyCRtuLZBdgTpWOpalJSahAP3DsiOQURkkm5F3ML9qPuyY0jFUteioIdBeJX0SnYMIiKT9dedv2RHkIqlrkWceicikmvv3b2yI0jFUtciljoRkVyH7h9Ccmqy7BjSsNS15E7kHdyKuCU7BhGRSXuV9ArHHx2XHUMalrqW7LrFUToRkSEw5f3qLHUt4dQ7EZFhMOX96ix1LYhNisXRh0dlxyAiIgAXwi7geexz2TGkYKlrwYF7B5CYmig7BhERARAQ2HvHNEfrLHUt2H9vv+wIRESUgalOwbPUtSD4cbDsCERElMG+u/sghOm9DwdLvYjikuNw6dkl2TGIiCiD8LhwXHx2UXYMvWOpF9GZp2eQkpYiOwYREWVx6skp2RH0jqVeRCcen5AdgYiIcnD6yWnZEfSOpV5E3J9ORGSYTj9lqVMBnXx8UnYEIiLKwdXwq4hPjpcdQ69Y6kXwMPohQl+Hyo5BREQ5SElLwfmw87Jj6BVLvQg49U5EZNhMbb86S70IeJAcEZFhM7X96iz1ImCpExEZNpY65UtiSqLJ7ashIjI2tyNuIyYhRnYMvWGpF9LFZxeRlJokOwYREb2FgMCZp2dkx9Ablnoh3XhxQ3YEIiLKB1OagmepF9LNFzdlRyAionzgSJ3ydCvyluwIRESUDzcjTGcQxlIvpFsRLHUiImNwL+qe7Ah6w1IvBCEEbkfclh2DiIjyIS45DqGvTOPsnyz1Qnj08hHiU0zrfMJERMbsTuQd2RH0gqVeCJx6JyIyLnej7sqOoBcs9ULgke9ERMblbiRLnXLBkToRkXHhSJ1yxeVsRETGhaVOueL0OxGRceH0O+UoKTUJD2Meyo5BREQFEBEfYRJv7MJSL6DQV6FIE2myYxARUQGZwhQ8S72AXsS9kB2BiIgKwRTWqrPUCyg8Llx2BCIiKoTHLx/LjqBzLPUCCo9lqRMRGaPI+EjZEXSOpV5AnH4nIjJOUfFRsiPoHEu9gDj9TkRknCITlD9St8jvhtu3b8/3jXbt2rVQYYwBp9+JiIyTKYzU813q3bt3z9d2KpUKqamphc1j8DhSJyIyTqawTz3fpZ6WxrXZAPepExEZK1Mo9SLvU09ISNBGDqPBkToRkXGKSlD+9HuhSj01NRXTp0+Hq6sr7O3tce/ePQDAxIkTsXTpUq0GNDTcp05EZJyiE6IhhJAdQ6cKVeozZszAihUrMHv2bFhZWWkur1WrFpYsWaK1cIYmJS0F0QnRsmMQEVEhpIk0xCQq+/zvhSr1wMBA/P777+jXrx/Mzc01l9euXRs3btzQWjhDE5MQAwFl/5VHRKRkSt+vXqhSf/LkCby9vbNdnpaWhuTk5CKHMlTJacr93oiITAFLPQc1atRAUFBQtss3btyIOnXqFDmUoUpNU+5SPSIiU6D0Xaj5XtKW0eTJkzFgwAA8efIEaWlp2Lx5M27evInAwEDs3LlT2xkNRqpgqRMRGbOk1CTZEXSqUCP1Ll26YP369di9ezdUKhUmTZqE69evY8eOHWjbtq22MxqMlLQU2RGIiKgIlD7jWqiROgAEBAQgICBAm1kMntKfDERESqf0GddClzoAnDlzBtevX4dKpUK1atVQr149beUySEp/MhARKZ3SB2eFKvXHjx+jT58+OHbsGEqUKAEAiI6ORpMmTbB27VpUrFhRmxkNhtKfDERESqf0wVmh9qkPGTIEycnJuH79OiIjIxEZGYnr169DCIGhQ4dqO6PB4D51IiLjpvTBWaFG6kFBQTh+/DiqVq2quaxq1ar4+eef0bRpU62FMzRK/wuP6M6ZpvDaeUx2DCLdqQKgluwQulOokbqbm1uOJ5lJSUmBq6trkUMZKqX/hUemrZywR6VD52XHINKtDGdBVaJClfrs2bPxySef4MyZM5qT4585cwb/+c9/MGfOHK0GNCScficlm/HCF6q4ONkxiHTLrMhvTmrQ8j397uTkBJVKpfk8NjYWfn5+sLBQ30RKSgosLCwwZMgQdO/eXetBDQGn30nJepx6KTsCke4pfKSe71L/8ccfdRjDOFibW8uOQKQTrVLd4Hj6kuwYRLrHkbraoEGDdJnDKBS3Li47ApFOTH3gCSBEdgwi3bMo0ulZDF6Rv7v4+PhsB80VL67M8mOpkxKpBND48B3ZMYj0w8FBdgKdKtQ8RGxsLP7973+jdOnSsLe3h5OTU6YPpWKpkxL9N94XFo+fyI5BpB+OjrIT6FShSn3cuHE4dOgQFi5cCGtrayxZsgRTp05F+fLlERgYqO2MBqOYVTFYmCl76oZMz6irdrIjEOnPm7OgKpVKpK9JKwA3NzcEBgaiZcuWKF68OM6dOwdvb2+sXLkSa9euxe7du3WR1SA4z3JGVEKU7BhEWlFO2OPJ92lcykamIypK0cVeqJF6ZGQkPD09Aaj3n0dGRgIAmjVrhqNHj2ovnQHiFDwpCdemk0lRqQCFHvOVrlClXqlSJTx48AAAUL16dWzYsAEAsGPHDjgqfH8FS52UhGvTyaQ4OCh+SVuhvruPPvoIFy9eBAB8+eWXmn3rY8eOxbhx47Qa0NCw1EkpuDadTI6Cp93TFeqor7Fjx2r+36pVK9y4cQNnzpxBqVKlsHz5cq2FM0QsdVIKrk0nk6PwmWSgkCP1rNzc3NCjRw8UL14cf/zxhzZu0mCx1EkJuDadTJIJjNSVvXNBBxytlf+XHikf16aTSWKpU1YlbErIjkBUZFybTiaJpU5ZuRZX7vvFk2koJ+zhefiC7BhE+ueq/NfvAh0o16NHj7deHx0dXZQsRqFi8YqyIxAViXpt+j+yYxDpn7u77AQ6V6BSz2sNuqOjIwYOHFikQIauoiNLnYwb16aTyWKpZ6b05Wr5wZE6GTOuTSeTZgKlzn3qBVS6WGlYmVvJjkFUKOq16UQmiqVOWalUKlQoXkF2DKICMxcqrk0n0+XiAhQrJjuFzrHUC8GjhIfsCEQFxrXpZNJMYJQOsNQLxcvJS3YEogIbecVWdgQieVjqlBuWOhkbV+EAjyMXZMcgksfDQ3YCvWCpF0Ilp0qyIxAVyIwXPnzfdDJtHKlTbrycOVIn49L9ZIzsCERysdQpN5x+J2PybqobHM9clh2DSK7q1WUn0AuWeiE42jiivEN52TGI8oVr08nk2dkB3t6yU+gFS72Q6pStIzsCUZ7MhQqNDt+WHYNIrlq1ADPTqDvT+C51oG65urIjEOVJvTb9qewYRHL5+MhOoDcs9ULiSJ2MAdemE4GlTnnjSJ0MHdemE73BUqe8uJdwh4uti+wYRLni2nQiACqVep+6iWCpF0GdcpyCJ8PFtelEUJ9Jrnhx2Sn0hqVeBNyvToaKa9OJ3jChqXeApV4k3K9Ohopr04neYKlTfrHUyRBxbTpRBix1yq/KzpXhYOUgOwZRJlybTpRBkyayE+gVS70IVCoVfMqa1l+BZPi4Np3ojXfeAcqUkZ1Cr1jqRdSwfEPZEYg0uDadKAN/f9kJ9I6lXkTver4rOwKRBtemE2XAUqeC8vfwh6WZpewYRAC4Np0oE5Y6FZS9lT38KvjJjkGENinuXJtOlM7bGyhvem+RzVLXgjaebWRHIMKUBx6yIxAZDhMcpQMsda1oU4mlTnKZCxX8jnBtOpEGS50Ky6+CH9erk1Sfcm06UWYsdSosCzML+HuY5hOIDMO/uDad6H88PAA3N9kppGCpawn3q5MsrsIBHofPy45BZDhatZKdQBqWupZwvzrJ8u0LH6ji42XHIDIcXbrITiANS11LapSugXL25WTHIBPU/QTXphNp2NoCAQGyU0jDUtei1pVay45AJqZNijuKn+XadCKNNm0AOzvZKaRhqWtRgJfp/nVIcnBtOlEW3brJTiCVSgghZIdQipiEGJSZUwaJqYmyo5AJMBcqJCwtx6VsROnMzICwMKBUKdlJpOFIXYscbRwR4M3ROukH16YTZdGkiUkXOsBS17oPa3woOwKZCK5NJ8rCxKfeAZa61nWp2gW2FnyxJd3i2nSiHHTvLjuBdCx1LbO3skenKp1kxyCF49p0oiyqV1e/M5uJY6nrQO8avWVHIIXj2nSiLDhKB8BS14lOlTvB3spedgxSKK5NJ8pBr16yExgElroO2FraoksV0z1NIekW16YTZeHrC/j4yE5hEFjqOsIpeNIFvm86UQ4++kh2AoPBUteR9t7t4WjtKDsGKQzXphNlYWUF9OsnO4XBYKnriLWFNbq9wzWTpF1cm06URZcugIuL7BQGg6WuQ/1q8a9H0h6uTSfKweDBshMYFJa6DrWt1Bbezlw3Sdrx7Qtfrk0nyqhsWaBDB9kpDApLXYdUKhVG1h8pOwYpRPcTUbIjEBmWAQMAc3PZKQwKS13HPvL9iKeNpSJrl+qB4mevyI5BZFh41Hs2LHUdc7J1Qt9afWXHICM36b677AhEhqVhQ6BaNdkpDA5LXQ9GNxgtOwIZMXOhgt/hW7JjEBmWoUNlJzBILHU9qFOuDhpXaCw7Bhmpz+PqwOJJqOwYRIbDxUW9P52yYanryagGo2RHICP18RVr2RGIDMvIkYAtj1XKiUoIIWSHMAWJKYmo+ENFhMeFy45CRqSiKI6Hs5O5lI0onbU18PAhUKaM7CQGiSN1PbG2sMawusNkxyAjMyO8NgudKKN+/Vjob8FS16N/1f8XzFVcU0n51+1ktOwIRIblv/+VncCgsdT1yM3RDZ2rdJYdg4wE16YTZdG+PVCjhuwUBo2lrmefN/lcdgQyElybTpTFp5/KTmDwWOp61tStKdpUaiM7Bhk4rk0nysLHB2jD1868sNQlmOI/RXYEMnBcm06UBfel5wtLXQKO1ikvXJtOlEH58kCfPrJTGAWWuiQcrVNuKoricD9yQXYMIsMxfjxgaSk7hVFgqUvC0TrlhmvTiTKoWBEYMUJ2CqPBUpeIo3XKCdemE2Xw1Vfqs8hRvrDUJeJonbLi2nSiDDw9gSFDZKcwKix1yThap4y4Nr3ofgVQG0DxNx+NAezJcP1mAAEASgJQAbiQw238F4AzADcA67JctwFAF60mplxNnMh96QXEUpeMo3VKx7Xp2lEBwHcAzrz5eBdANwBX31wfC6Dpm21ysgPAGgD7AMwC8BGAiDfXRQP4CsACHeSmLKpVAwYOlJ3C6LDUDQBH6wRwbbq2dAHQEUCVNx8zANgDOPHm+gEAJgHI7U/p6wBaAqgPoA/Uo/17b64bB2AU1CN40rFvvwXM+V4ZBcVSNwBN3ZrynPDEtek6kAr19Hks1NPw+eED9Qg/CsBZAPEAvAH8A+AcgP/TfkzKqnFjoHt32SmMEkvdQPwQ8AOszK1kxyBJuDZduy5DPTq3BvAvAFsAVM/n1wYA6A+gAYDBAP4AUAzASAC/Qb3PvirUU/hXc74JKqpZs2QnMFosdQPh7eyN/zbiaRBN1bdcm65VVaE+AO4E1GU8CMC1Anz9FAB3oP7j4D0A30I9XW8J4BuoR+3DAHCPrw507gw0by47hdFSCSGE7BCkFpsUi6q/VMWTV09kRyE9e7m9BhzOcdynK20AeEE90k73AIAngPMAfN/ytTeg3k9/HsAyqAt9A9RT+vYAYqDe705aYG0NXLkCeHvLTmK0OFI3IMWsimF229myY5CetU/xZKHrmACQWMivGwFgLtQFngog+c116f+mFTkdaYwbx0IvIpa6gelbqy+auTWTHYP0aOJ9HkutTRMABEE9Er8M9RK0IwD6vbk+Euqp+fTp+JtvPg/L4bYWAygNoOubz5sCOAT1tP4PUO+nL6HN8KbM0xP48kvZKYweS90A/dzhZ5ip+KMxBeq16Tdlx1CUZ1AvW6sKoDWAkwD+AtD2zfXbAdQB0OnN5x+++XxRDrfzLYCfMlzWEMCnb752A4Dl2o9vuubPB2xtZacwetynbqBG7hyJRWezvsyQ0nwZWxfffn9OdgwiuTp3BnbskJ1CETgcNFDfvPsNnG2dZccgHfv4Mtemk4mztQV++inv7ShfWOoGysXOBdNbTZcdg3SooigOtyPnZccgkmv8ePX+dNIKlroB+7jex/Ap4yM7BunIt+G1oUpIkB2DSB5vb+CLL2SnUBSWugEzNzPH4i6LYWFmITsK6UC3E1GyIxDJ9fPPfK90LWOpG7gGrg3wZTMu81Aark0nk9e7N9C+vewUisNSNwITW0xE3XJ1ZccgLeLadDJp5csDCxfKTqFILHUjYGluicDugbA25zSVEnBtOpm8pUsBZ67u0QWWupGoUboGj4ZXiHFxdWD+NKfzlxGZgH/9i9PuOsSTzxiRNJEG/xX++CfkH9lRqAgenGgM97+CZccg0j9vb+DCBaBYMdlJFIsjdSNipjLDim4rUMySvxDGimvTyWSZmwOBgSx0HWOpGxkvZy/MaTdHdgwqJK5NJ5M1bhzQuLHsFIrH6Xcj1X5Ve+y9u1d2DCogvm86mSRfX+DUKcDSUnYSxeNI3Ugt7boUJWxKyI5BBcC16WSSrK2BlStZ6HrCUjdSrsVdsagT38XNmHBtOpmk774DataUncJksNSNWO+avTG6wWjZMSgfuDadTFKvXsCYMbJTmBSWupGbFzAPfq5+smNQHr6Iq8u16WRaqldXn2SG9IqlbuSszK2w4YMNcLF1kR2F3mLEZSvZEYj0x8EB2LwZsLeXncTksNQVwM3RDat7rIaZij9OQ+TGtelkalasAKpWlZ3CJLEFFCLAOwCT/SfLjkE5+Dbch2vTyXR8/jnQo4fsFCaL69QVRAiBnht6YsuNLbKjUAYvt9WAw3kuZSMT0KoVsH+/+uxxJAVH6gqiUqkQ+F4gapSqITsKvdEhxZOFTqahQgVg3ToWumQsdYWxt7LH1g+3wsnGSXYUAtemk4mwsgI2bgRKl5adxOSx1BXI29kba3uu5YFzklkIFRoeuiE7BpHuLVoENGokOwWBpa5YAd4BmNturuwYJm1cXB2Yhz6THYNIt6ZOBT76SHYKeoOlrmBjGo3B500+lx3DZI24bC07ApFuDRsGTJokOwVlwKPfFU4IgcHbBiPwYqDsKCbFTRTHg1lJXMpGytWxI7BtG2BhITsJZcCRusKpVCos7boUHSt3lB3FpHBtOilavXrAhg0sdAPEUjcBFmYW2PjBRp4jXo+6BkfKjkCkG56ewK5dQLFispNQDljqJsLO0g67+u7COyXfkR1F8bg2nRTLxQX46y+gTBnZSSgXLHUT4mLngr3998LVwVV2FEXj2nRSJFtbYMcOoEoV2UnoLVjqJsbN0Q17++/lyWl0hGvTSZEsLIA1a4DGjWUnoTyw1E1QjdI1sL3Pdtha2MqOojhcm06KY2GhPv1r9+6yk1A+sNRNVDO3Zlj3/jpYmlnKjqIoXJtOimJhAaxdC/TsKTsJ5RNL3YR1rdoVf/b6E9bmLCJt4Pumk6KkT7m//77sJFQALHUT17VqV2z7cBun4rWAa9NJMSwsgNWrgQ8+kJ2ECoilTgjwDsDufrthb2UvO4pR49p0UgRzc3Wh9+olOwkVAkudAAAtPVpiX/99cLR2lB3FKHFtOimCubl6yp2FbrRY6qTRuGJjHBx4EM62zrKjGJ1JXJtOxo4jdEVgqVMm9crXw5FBR1C6WGnZUYyGpTBDg0M3ZccgKjwrK/VR7r17y05CRcRSp2xqlamFvwf/jfIO5WVHMQpfxNWBeWiY7BhEhePoCOzdy4PiFIKlTjl6p+Q7ODr4KNwd3WVHMXjDL3GtPxmpChWAf/4BWraUnYS0hKVOufJy9sLRj46iRqkasqMYLDdRHBW5Np2MUc2aQHCw+l9SDJY6vZWboxuODz2ODt4dZEcxSN8994EqMVF2DKKC8fcHgoLUI3VSFJY65am4dXHs6LMD//H7j+woBqdLcITsCEQF06uXeh96iRKyk5AOsNQpX8zNzPFj+x/xa6dfYWFmITuOQeiUUgn2F67JjkGUf2PHqt+cxZqnhlYqljoVyL/q/wt7+u1BCZsSsqNI9/W9irIjEOWPmRkwdy4wbx6gUslOQzqkEkII2SHI+Nx8cROd13bGncg7sqNIYSnMEP97aS5lI8Pn7Kw+S1xAgOwkpAccqVOhVC1ZFSeHnUQrj1ayo0jBtelkFOrUAc6cYaGbEJY6FZqzrTP29t+LYXWGyY6id1ybTgZv4EDg2DHA01N2EtIjljoViaW5JRZ3XYwfA36ElbmV7Dh6wbXpZNAsLYEFC4A//gBs+ZbKpoalTlrxn0b/wfEhx+Ht7C07is5xbToZrPLlgSNHgFGjZCchSVjqpDX1ytfDuRHn0L92f9lRdIpr08kgNW8OnDsHNGkiOwlJxFInrXKwdsDK91bij+5/wN7KXnYcrePadDJIY8YAhw4BZcrITkKSsdRJJwb6DMTZEWdRp2wd2VG0imvTyaCULQvs2QP88ANgwZNCEUuddKiKSxWcGHZCMaeXtRRmaHCY75tOBqJrV+DSJaB9e9lJyICw1EmnrMyt8GP7H7Gzz06UtCspO06RcG06GQQ7O2DRImDbNqBUKdlpyMCw1EkvOlXphIv/uoh3Pd+VHaXQuDadpGvSBLh4Efj4Y9lJyECx1ElvyjuUx4EBB/Bb59+M7tzxXJtOUllbA7Nnq98u1Vv5y0ap8FjqpFcqlQoj6o3A9dHX0atGL9lx8o1r00ma+vXVS9U+/1z9xixEb8FnCElR1r4s1r+/Hjv77ISbo5vsOHni2nTSOwcH9buqBQcD1avLTkNGgqVOUnWq0gnXRl3D2EZjYa4ylx0nR1ybTnrXuzdw44b6/c+5VI0KgKVO0hWzKoZ5AfNwcthJg1zXPpFr00lfqlQB9u8H1q1Tn/KVqIBY6mQw6pWvh9PDT2NO2zkoZllMdhwA6rXp9Q/dkB2DlM7WFpgxA7h8GWjTRnYaMmIsdTIo5mbm+LTJp7gy6gq6Ve0mOw7Gx9aBedgz2TFIybp2Ba5dAyZMAKxM450OSXdY6mSQPEp4YOuHW/HPR/+gSUV5b1Ax/DLXppOOeHoC27erTyLj4SE7DSkES50MWlO3pjg25Bi29t6KaiWr6fW+PdIcUYFr00nbypQBfv5ZfSBcly56u9uFCxfC09MTNjY2qFevHoKCgvR236Q/LHUyCt3e6YbLIy9jcZfFcHVw1ct9zgyvzbXppD0lSqj3m9+9C/z733qdal+/fj3GjBmDr776CufPn0fz5s3RoUMHhISE6C0D6YdKCCFkhyAqiPjkeMw/OR/f/fMdYhJjdHY/r7ZUh/1FLmWjIrKzAz75BPjiC8DJSUoEPz8/1K1bF7/++qvmsmrVqqF79+6YOXOmlEykGxypk9GxtbTF+Gbjce8/9/DfRv+Ftbm11u+jS4oXC52KxtISGDUKuHMH+O47aYWelJSEs2fPol27dpkub9euHY4fPy4lE+kOS52MlrOtM+YGzMWtT25hWJ1hWi33r+5V0NptkYkxMwP691fvM1+wAChXTmqcFy9eIDU1FWXKlMl0eZkyZRAWxncdVBqWOhk9N0c3LO66GA/GPMCXzb6Ek03RRkRcm06FYmMDjBihXp62ciVQqZLsRJmoVKpMnwshsl1Gxo+lTopR1r4svm39LULGhuDHgB/hUcKjULfDtelUICVLApMmASEhwG+/AVWryk6UScmSJWFubp5tVP78+fNso3cyfix1Uhx7K3v8p9F/cOeTO1jbcy3qlatXoK/n2nTKl8qVgYUL1WU+dSpQqpTsRDmysrJCvXr1sH///kyX79+/H02ayDsHBOkGj34nk3D4/mHMCZ6DPbf3QCD3p7xHmiPuzUrgUjbKXdOmwGefqc8EZyRvhbp+/XoMGDAAixYtQuPGjfH7779j8eLFuHr1Ktzd3WXHIy3i2/+QSWjl2QqtPFvh6vOrmBs8F+uurEN8Sny27dRr03lSDsrC2hro3h0YMwZo1Eh2mgLr3bs3IiIiMG3aNISGhqJmzZrYvXs3C12BOFInkxSTEIPVl1dj6fmlOBd6TnM516ZTJr6+wJAhQL9+gLOz7DREeWKpk8k7H3oeS88vRdS5Y1g98YLsOCSbs7O6xIcMUZc6kRFhqROlS0gAduwAAgOBv/4CUlJkJyJ9MTMD2rVTF3nXrurpdiIjxFInykl4OLBunXq98enTstOQrvj6Ah98AAwcCFTgCYfI+LHUifLy8OH/3iLz7785gjdm5ubqo9ffe0994Bvf8pQUhqVOVBDR0cDu3eqS37MHePlSdiLKi40N0LatusS7dlWfLIZIoVjqRIWVlAQcOaIewW/fDjx+LDsRpXNyAjp2VI/I27cHihWTnYhIL1jqRNpy4QJw+LB6iv6ff4CICNmJTEeJEkCLFkDLloC/v3pfuZGcGIZIm1jqRLogBHD1KnD06P8+QkNlp1IOJyegeXN1ibdsCfj4sMSJwFIn0p87d/5X8MeOAXfvqsuf3k6lAry91aPvJk3UJV67NkucKAcsdSJZXr8GLl8GLl3638fly0BMjOxk8tjZAbVqqUfevr7qj1q1AHt72cmIjAJLncjQPHyYueivXVNf9uqV7GTaY2cHeHqq33O8Rg11efv4AFWqcAROVAQsdSJjERWlfpvPkBB1yaf/P/3zsDAgLU12SrXixQE3N6BiRfWHm5v6o1Il9Ue5crITEikSS51IKZKT1QfjRUer18/HxGT+yHrZ69f/26evUqk/Mv4/4+dmZoCDg/oo87w+nJzUI3Ei0juWOhERkUJw5xUREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArBUiciIlIIljoREZFCsNSJiIgUgqVORESkECx1IiIihWCpExERKQRLnYiISCFY6kRERArx/0v92pg3EWkfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Label\"].value_counts().plot(kind=\"pie\", autopct=\"%1.0f%%\", figsize=(6,6), colors=[\"green\",\"red\"], title= \"Distibution of 'Purchased' and 'Not Purchased' \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0322bf70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Brand.value_counts().plot(kind=\"bar\", figsize=(10,5), title= \"Distribution by Brands\", color= \"blue\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a73d6cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.scatterplot(data=df, x=\"Brand\", y=\"Price\", hue=\"Storage\", palette=\"bright\")\n",
    "plt.title(\"Price by Brand and Storage\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70d0da16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Prices of Brand with Boxplot to visualize Outliers')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for outliers\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.boxplot(x=\"Brand\", y=\"Price\", data=df, palette=\"deep\")\n",
    "plt.title(\"Prices of Brand with Boxplot to visualize Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ceaf2c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "corr_matrix= df.corr()\n",
    "sns.heatmap(corr_matrix, cmap= \"magma\", annot=True, cbar=False)\n",
    "plt.title(\"Correlation of Features\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cd23034f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1750x1750 with 56 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairplt= sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62996a61",
   "metadata": {},
   "source": [
    "## Machine Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb19026",
   "metadata": {},
   "source": [
    "### Scaling the Dataset for Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "69451e06",
   "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>Model</th>\n",
       "      <th>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Battery_Capacity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_LG</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iPhone 13 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3095</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Galaxy S21 Ultra</td>\n",
       "      <td>256.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>108.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Redmi Note 10 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.67</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5020</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel 6</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4614</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Galaxy Note20 5G</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4300</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Mi 10 Lite 5G</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.57</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4160</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>iPhone 12 Pro Max</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3687</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Reno3</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4025</td>\n",
       "      <td>429.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Galaxy S10 Lite</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>407 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Storage   RAM  Screen_Size  Camera  Battery_Capacity  \\\n",
       "0        iPhone 13 Pro    128.0   6.0         6.10    12.0              3095   \n",
       "1     Galaxy S21 Ultra    256.0  12.0         6.80   108.0              5000   \n",
       "2                9 Pro    128.0   8.0         6.70    48.0              4500   \n",
       "3    Redmi Note 10 Pro    128.0   6.0         6.67    64.0              5020   \n",
       "4              Pixel 6    128.0   8.0         6.40    50.0              4614   \n",
       "..                 ...      ...   ...          ...     ...               ...   \n",
       "402   Galaxy Note20 5G    128.0   8.0         6.70    12.0              4300   \n",
       "403      Mi 10 Lite 5G    128.0   6.0         6.57    48.0              4160   \n",
       "404  iPhone 12 Pro Max    128.0   6.0         6.70    12.0              3687   \n",
       "405              Reno3    128.0   8.0         6.40    48.0              4025   \n",
       "406    Galaxy S10 Lite    128.0   6.0         6.70    48.0              4500   \n",
       "\n",
       "      Price  Label  Brand_Apple  Brand_Asus  ...  Brand_LG  Brand_Motorola  \\\n",
       "0     999.0      1            1           0  ...         0               0   \n",
       "1    1199.0      1            0           0  ...         0               0   \n",
       "2     899.0      1            0           0  ...         0               0   \n",
       "3     279.0      0            0           0  ...         0               0   \n",
       "4     799.0      1            0           0  ...         0               0   \n",
       "..      ...    ...          ...         ...  ...       ...             ...   \n",
       "402  1049.0      1            0           0  ...         0               0   \n",
       "403   349.0      0            0           0  ...         0               0   \n",
       "404  1099.0      1            1           0  ...         0               0   \n",
       "405   429.0      0            0           0  ...         0               0   \n",
       "406   649.0      1            0           0  ...         0               0   \n",
       "\n",
       "     Brand_Nokia  Brand_OnePlus  Brand_Oppo  Brand_Realme  Brand_Samsung  \\\n",
       "0              0              0           0             0              0   \n",
       "1              0              0           0             0              1   \n",
       "2              0              1           0             0              0   \n",
       "3              0              0           0             0              0   \n",
       "4              0              0           0             0              0   \n",
       "..           ...            ...         ...           ...            ...   \n",
       "402            0              0           0             0              1   \n",
       "403            0              0           0             0              0   \n",
       "404            0              0           0             0              0   \n",
       "405            0              0           1             0              0   \n",
       "406            0              0           0             0              1   \n",
       "\n",
       "     Brand_Sony  Brand_Vivo  Brand_Xiaomi  \n",
       "0             0           0             0  \n",
       "1             0           0             0  \n",
       "2             0           0             0  \n",
       "3             0           0             1  \n",
       "4             0           0             0  \n",
       "..          ...         ...           ...  \n",
       "402           0           0             0  \n",
       "403           0           0             1  \n",
       "404           0           0             0  \n",
       "405           0           0             0  \n",
       "406           0           0             0  \n",
       "\n",
       "[407 rows x 24 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.get_dummies(df,columns=[\"Brand\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab2bdd29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Model', 'Storage', 'RAM', 'Screen_Size', 'Camera', 'Battery_Capacity',\n",
       "       'Price', 'Label', 'Brand_Apple', 'Brand_Asus', 'Brand_Blackberry',\n",
       "       'Brand_CAT', 'Brand_Google', 'Brand_Huawei', 'Brand_LG',\n",
       "       'Brand_Motorola', 'Brand_Nokia', 'Brand_OnePlus', 'Brand_Oppo',\n",
       "       'Brand_Realme', 'Brand_Samsung', 'Brand_Sony', 'Brand_Vivo',\n",
       "       'Brand_Xiaomi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "88dd81b1",
   "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>Model</th>\n",
       "      <th>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Battery_Capacity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_LG</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iPhone 13 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3095</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Galaxy S21 Ultra</td>\n",
       "      <td>256.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>108.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Redmi Note 10 Pro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.67</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5020</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel 6</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4614</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Galaxy Note20 5G</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4300</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Mi 10 Lite 5G</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.57</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4160</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>iPhone 12 Pro Max</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3687</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Reno3</td>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4025</td>\n",
       "      <td>429.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Galaxy S10 Lite</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>407 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Storage   RAM  Screen_Size  Camera  Battery_Capacity  \\\n",
       "0        iPhone 13 Pro    128.0   6.0         6.10    12.0              3095   \n",
       "1     Galaxy S21 Ultra    256.0  12.0         6.80   108.0              5000   \n",
       "2                9 Pro    128.0   8.0         6.70    48.0              4500   \n",
       "3    Redmi Note 10 Pro    128.0   6.0         6.67    64.0              5020   \n",
       "4              Pixel 6    128.0   8.0         6.40    50.0              4614   \n",
       "..                 ...      ...   ...          ...     ...               ...   \n",
       "402   Galaxy Note20 5G    128.0   8.0         6.70    12.0              4300   \n",
       "403      Mi 10 Lite 5G    128.0   6.0         6.57    48.0              4160   \n",
       "404  iPhone 12 Pro Max    128.0   6.0         6.70    12.0              3687   \n",
       "405              Reno3    128.0   8.0         6.40    48.0              4025   \n",
       "406    Galaxy S10 Lite    128.0   6.0         6.70    48.0              4500   \n",
       "\n",
       "      Price  Label  Brand_Asus  Brand_Blackberry  ...  Brand_LG  \\\n",
       "0     999.0      1           0                 0  ...         0   \n",
       "1    1199.0      1           0                 0  ...         0   \n",
       "2     899.0      1           0                 0  ...         0   \n",
       "3     279.0      0           0                 0  ...         0   \n",
       "4     799.0      1           0                 0  ...         0   \n",
       "..      ...    ...         ...               ...  ...       ...   \n",
       "402  1049.0      1           0                 0  ...         0   \n",
       "403   349.0      0           0                 0  ...         0   \n",
       "404  1099.0      1           0                 0  ...         0   \n",
       "405   429.0      0           0                 0  ...         0   \n",
       "406   649.0      1           0                 0  ...         0   \n",
       "\n",
       "     Brand_Motorola  Brand_Nokia  Brand_OnePlus  Brand_Oppo  Brand_Realme  \\\n",
       "0                 0            0              0           0             0   \n",
       "1                 0            0              0           0             0   \n",
       "2                 0            0              1           0             0   \n",
       "3                 0            0              0           0             0   \n",
       "4                 0            0              0           0             0   \n",
       "..              ...          ...            ...         ...           ...   \n",
       "402               0            0              0           0             0   \n",
       "403               0            0              0           0             0   \n",
       "404               0            0              0           0             0   \n",
       "405               0            0              0           1             0   \n",
       "406               0            0              0           0             0   \n",
       "\n",
       "     Brand_Samsung  Brand_Sony  Brand_Vivo  Brand_Xiaomi  \n",
       "0                0           0           0             0  \n",
       "1                1           0           0             0  \n",
       "2                0           0           0             0  \n",
       "3                0           0           0             1  \n",
       "4                0           0           0             0  \n",
       "..             ...         ...         ...           ...  \n",
       "402              1           0           0             0  \n",
       "403              0           0           0             1  \n",
       "404              0           0           0             0  \n",
       "405              0           0           0             0  \n",
       "406              1           0           0             0  \n",
       "\n",
       "[407 rows x 23 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.get_dummies(df,columns=[\"Brand\"],drop_first=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b03fae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= data.drop([\"Price\", \"Label\", \"Model\"],axis=1)\n",
    "y= data[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "62925f0e",
   "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>Storage</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Screen_Size</th>\n",
       "      <th>Camera</th>\n",
       "      <th>Battery_Capacity</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>Brand_CAT</th>\n",
       "      <th>Brand_Google</th>\n",
       "      <th>Brand_Huawei</th>\n",
       "      <th>Brand_LG</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.10</td>\n",
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       "      <td>256.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>108.0</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.67</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.57</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3687</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>128.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4025</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>128.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>407 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Storage   RAM  Screen_Size  Camera  Battery_Capacity  Brand_Asus  \\\n",
       "0      128.0   6.0         6.10    12.0              3095           0   \n",
       "1      256.0  12.0         6.80   108.0              5000           0   \n",
       "2      128.0   8.0         6.70    48.0              4500           0   \n",
       "3      128.0   6.0         6.67    64.0              5020           0   \n",
       "4      128.0   8.0         6.40    50.0              4614           0   \n",
       "..       ...   ...          ...     ...               ...         ...   \n",
       "402    128.0   8.0         6.70    12.0              4300           0   \n",
       "403    128.0   6.0         6.57    48.0              4160           0   \n",
       "404    128.0   6.0         6.70    12.0              3687           0   \n",
       "405    128.0   8.0         6.40    48.0              4025           0   \n",
       "406    128.0   6.0         6.70    48.0              4500           0   \n",
       "\n",
       "     Brand_Blackberry  Brand_CAT  Brand_Google  Brand_Huawei  Brand_LG  \\\n",
       "0                   0          0             0             0         0   \n",
       "1                   0          0             0             0         0   \n",
       "2                   0          0             0             0         0   \n",
       "3                   0          0             0             0         0   \n",
       "4                   0          0             1             0         0   \n",
       "..                ...        ...           ...           ...       ...   \n",
       "402                 0          0             0             0         0   \n",
       "403                 0          0             0             0         0   \n",
       "404                 0          0             0             0         0   \n",
       "405                 0          0             0             0         0   \n",
       "406                 0          0             0             0         0   \n",
       "\n",
       "     Brand_Motorola  Brand_Nokia  Brand_OnePlus  Brand_Oppo  Brand_Realme  \\\n",
       "0                 0            0              0           0             0   \n",
       "1                 0            0              0           0             0   \n",
       "2                 0            0              1           0             0   \n",
       "3                 0            0              0           0             0   \n",
       "4                 0            0              0           0             0   \n",
       "..              ...          ...            ...         ...           ...   \n",
       "402               0            0              0           0             0   \n",
       "403               0            0              0           0             0   \n",
       "404               0            0              0           0             0   \n",
       "405               0            0              0           1             0   \n",
       "406               0            0              0           0             0   \n",
       "\n",
       "     Brand_Samsung  Brand_Sony  Brand_Vivo  Brand_Xiaomi  \n",
       "0                0           0           0             0  \n",
       "1                1           0           0             0  \n",
       "2                0           0           0             0  \n",
       "3                0           0           0             1  \n",
       "4                0           0           0             0  \n",
       "..             ...         ...         ...           ...  \n",
       "402              1           0           0             0  \n",
       "403              0           0           0             1  \n",
       "404              0           0           0             0  \n",
       "405              0           0           0             0  \n",
       "406              1           0           0             0  \n",
       "\n",
       "[407 rows x 20 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "22f0af28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       999.0\n",
       "1      1199.0\n",
       "2       899.0\n",
       "3       279.0\n",
       "4       799.0\n",
       "        ...  \n",
       "402    1049.0\n",
       "403     349.0\n",
       "404    1099.0\n",
       "405     429.0\n",
       "406     649.0\n",
       "Name: Price, Length: 407, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "23b153b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling dataset\n",
    "# importing the scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler= StandardScaler()\n",
    "scaled_x= scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3a642e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.07634194,  0.06676114, -1.16844872, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308],\n",
       "       [ 2.04911449,  2.53692344,  1.05527232, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308],\n",
       "       [ 0.07634194,  0.89014858,  0.73759789, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308],\n",
       "       ...,\n",
       "       [ 0.07634194,  0.06676114,  0.73759789, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308],\n",
       "       [ 0.07634194,  0.89014858, -0.21542542, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308],\n",
       "       [ 0.07634194,  0.06676114,  0.73759789, ..., -0.04962917,\n",
       "        -0.30673445, -0.44391308]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ed136d",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5ee2625c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(scaled_x, y, test_size=0.1, random_state=343)\n",
    "#import regression models from sckitlearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5ebfd58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#instantiating and fitting model on trainign data set\n",
    "price_model= LinearRegression()\n",
    "price_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "92157c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred= price_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ca2fbea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE SCORE: 8069.384524387137\n",
      "R2 SCORE: 0.9195627309824653\n",
      "RMSE SCORE: 89.82975300192658\n"
     ]
    }
   ],
   "source": [
    "mse= mean_squared_error(y_test,pred)\n",
    "print(f\"MSE SCORE: {mse}\")\n",
    "\n",
    "r2= r2_score(y_test,pred)\n",
    "print(f\"R2 SCORE: {r2}\")\n",
    "\n",
    "rmse= np.sqrt(mse)\n",
    "print(f\"RMSE SCORE: {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "914cb926",
   "metadata": {
    "scrolled": false
   },
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>loss/residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>219.0</td>\n",
       "      <td>320.519772</td>\n",
       "      <td>-101.519772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>699.0</td>\n",
       "      <td>586.334053</td>\n",
       "      <td>112.665947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>249.0</td>\n",
       "      <td>373.146784</td>\n",
       "      <td>-124.146784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>279.0</td>\n",
       "      <td>336.632122</td>\n",
       "      <td>-57.632122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>799.0</td>\n",
       "      <td>890.739650</td>\n",
       "      <td>-91.739650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>129.0</td>\n",
       "      <td>43.922561</td>\n",
       "      <td>85.077439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>699.0</td>\n",
       "      <td>704.867474</td>\n",
       "      <td>-5.867474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>329.0</td>\n",
       "      <td>315.949129</td>\n",
       "      <td>13.050871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>179.0</td>\n",
       "      <td>342.810153</td>\n",
       "      <td>-163.810153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>999.0</td>\n",
       "      <td>936.247455</td>\n",
       "      <td>62.752545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>129.0</td>\n",
       "      <td>88.861558</td>\n",
       "      <td>40.138442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>119.0</td>\n",
       "      <td>155.036721</td>\n",
       "      <td>-36.036721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>289.0</td>\n",
       "      <td>389.516206</td>\n",
       "      <td>-100.516206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>159.0</td>\n",
       "      <td>173.467284</td>\n",
       "      <td>-14.467284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>239.0</td>\n",
       "      <td>215.194594</td>\n",
       "      <td>23.805406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>399.0</td>\n",
       "      <td>360.369643</td>\n",
       "      <td>38.630357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>169.0</td>\n",
       "      <td>120.671673</td>\n",
       "      <td>48.328327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>249.0</td>\n",
       "      <td>363.967516</td>\n",
       "      <td>-114.967516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1099.0</td>\n",
       "      <td>1052.764032</td>\n",
       "      <td>46.235968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>449.0</td>\n",
       "      <td>483.226580</td>\n",
       "      <td>-34.226580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>349.0</td>\n",
       "      <td>407.243340</td>\n",
       "      <td>-58.243340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>799.0</td>\n",
       "      <td>928.087086</td>\n",
       "      <td>-129.087086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>159.0</td>\n",
       "      <td>173.467284</td>\n",
       "      <td>-14.467284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>149.0</td>\n",
       "      <td>214.847325</td>\n",
       "      <td>-65.847325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>349.0</td>\n",
       "      <td>295.731964</td>\n",
       "      <td>53.268036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1399.0</td>\n",
       "      <td>1655.797611</td>\n",
       "      <td>-256.797611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>179.0</td>\n",
       "      <td>109.212287</td>\n",
       "      <td>69.787713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>149.0</td>\n",
       "      <td>9.584979</td>\n",
       "      <td>139.415021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1199.0</td>\n",
       "      <td>991.037270</td>\n",
       "      <td>207.962730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>349.0</td>\n",
       "      <td>433.573608</td>\n",
       "      <td>-84.573608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>149.0</td>\n",
       "      <td>195.023422</td>\n",
       "      <td>-46.023422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>149.0</td>\n",
       "      <td>112.664771</td>\n",
       "      <td>36.335229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>449.0</td>\n",
       "      <td>485.188426</td>\n",
       "      <td>-36.188426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>199.0</td>\n",
       "      <td>228.969921</td>\n",
       "      <td>-29.969921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>249.0</td>\n",
       "      <td>178.230977</td>\n",
       "      <td>70.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>699.0</td>\n",
       "      <td>644.633987</td>\n",
       "      <td>54.366013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>259.0</td>\n",
       "      <td>345.315882</td>\n",
       "      <td>-86.315882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>279.0</td>\n",
       "      <td>342.810153</td>\n",
       "      <td>-63.810153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>249.0</td>\n",
       "      <td>294.963685</td>\n",
       "      <td>-45.963685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>449.0</td>\n",
       "      <td>471.428920</td>\n",
       "      <td>-22.428920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>239.0</td>\n",
       "      <td>350.789229</td>\n",
       "      <td>-111.789229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual    Predicted  loss/residual\n",
       "200   219.0   320.519772    -101.519772\n",
       "45    699.0   586.334053     112.665947\n",
       "340   249.0   373.146784    -124.146784\n",
       "3     279.0   336.632122     -57.632122\n",
       "115   799.0   890.739650     -91.739650\n",
       "285   129.0    43.922561      85.077439\n",
       "80    699.0   704.867474      -5.867474\n",
       "400   329.0   315.949129      13.050871\n",
       "356   179.0   342.810153    -163.810153\n",
       "351   999.0   936.247455      62.752545\n",
       "166   129.0    88.861558      40.138442\n",
       "199   119.0   155.036721     -36.036721\n",
       "232   289.0   389.516206    -100.516206\n",
       "265   159.0   173.467284     -14.467284\n",
       "55    239.0   215.194594      23.805406\n",
       "352   399.0   360.369643      38.630357\n",
       "226   169.0   120.671673      48.328327\n",
       "174   249.0   363.967516    -114.967516\n",
       "338  1099.0  1052.764032      46.235968\n",
       "167   449.0   483.226580     -34.226580\n",
       "403   349.0   407.243340     -58.243340\n",
       "335   799.0   928.087086    -129.087086\n",
       "306   159.0   173.467284     -14.467284\n",
       "144   149.0   214.847325     -65.847325\n",
       "161   349.0   295.731964      53.268036\n",
       "367  1399.0  1655.797611    -256.797611\n",
       "156   179.0   109.212287      69.787713\n",
       "105   149.0     9.584979     139.415021\n",
       "309  1199.0   991.037270     207.962730\n",
       "324   349.0   433.573608     -84.573608\n",
       "275   149.0   195.023422     -46.023422\n",
       "198   149.0   112.664771      36.335229\n",
       "379   449.0   485.188426     -36.188426\n",
       "394   199.0   228.969921     -29.969921\n",
       "118   249.0   178.230977      70.769023\n",
       "99    699.0   644.633987      54.366013\n",
       "33    259.0   345.315882     -86.315882\n",
       "339   279.0   342.810153     -63.810153\n",
       "155   249.0   294.963685     -45.963685\n",
       "11    449.0   471.428920     -22.428920\n",
       "185   239.0   350.789229    -111.789229"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result= pd.DataFrame({\"Actual\":y_test,\"Predicted\":pred})\n",
    "df_result[\"loss/residual\"]= df_result[\"Actual\"] - df_result[\"Predicted\"]\n",
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f38357c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             feature        coef\n",
      "1                RAM  160.738822\n",
      "0            Storage   95.745783\n",
      "2        Screen_Size   70.167376\n",
      "17        Brand_Sony   17.155358\n",
      "6   Brand_Blackberry   -0.122880\n",
      "7          Brand_CAT   -3.197366\n",
      "5         Brand_Asus   -4.441436\n",
      "8       Brand_Google  -28.759049\n",
      "10          Brand_LG  -30.586232\n",
      "9       Brand_Huawei  -49.305347\n",
      "4   Battery_Capacity  -52.204715\n",
      "3             Camera  -56.229614\n",
      "13     Brand_OnePlus  -81.797073\n",
      "11    Brand_Motorola  -89.897272\n",
      "12       Brand_Nokia -104.923687\n",
      "16     Brand_Samsung -126.241375\n",
      "15      Brand_Realme -151.357345\n",
      "18        Brand_Vivo -154.322372\n",
      "19      Brand_Xiaomi -169.762770\n",
      "14        Brand_Oppo -182.193594\n"
     ]
    }
   ],
   "source": [
    "feature_coefs = price_model.coef_\n",
    "feature_names = x.columns\n",
    "\n",
    "# Create a data frame of feature importance coefficients\n",
    "feature_importance = pd.DataFrame({'feature': feature_names, 'coef': feature_coefs})\n",
    "\n",
    "# Sort feature importance coefficients in descending order\n",
    "feature_importance.sort_values(by='coef', ascending=False, inplace=True)\n",
    "print(feature_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3768842e",
   "metadata": {},
   "source": [
    "### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a13f2269",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3df7b114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decitree  = DecisionTreeRegressor()\n",
    "decitree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f981c711",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = decitree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4dd56402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE SCORE: 3177.670901084011\n",
      "R2 SCORE: 0.9683243290306457\n",
      "RMSE SCORE: 56.37083378028048\n"
     ]
    }
   ],
   "source": [
    "mse= mean_squared_error(y_test,pred)\n",
    "print(f\"MSE SCORE: {mse}\")\n",
    "\n",
    "r2= r2_score(y_test,pred)\n",
    "print(f\"R2 SCORE: {r2}\")\n",
    "\n",
    "rmse= np.sqrt(mse)\n",
    "print(f\"RMSE SCORE: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b91795",
   "metadata": {},
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f5775483",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d883518d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_pred = RandomForestRegressor()\n",
    "forest_pred.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "843a263b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = forest_pred.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d61dd1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE SCORE: 1654.9274847960742\n",
      "R2 SCORE: 0.9835033456521067\n",
      "RMSE SCORE: 40.68079995275504\n"
     ]
    }
   ],
   "source": [
    "mse= mean_squared_error(y_test,pred)\n",
    "print(f\"MSE SCORE: {mse}\")\n",
    "\n",
    "r2= r2_score(y_test,pred)\n",
    "print(f\"R2 SCORE: {r2}\")\n",
    "\n",
    "rmse= np.sqrt(mse)\n",
    "print(f\"RMSE SCORE: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c0bf07",
   "metadata": {},
   "source": [
    "## Classification (Yes / No)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c616f3",
   "metadata": {},
   "source": [
    "### Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ec0e4311",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split#spliting the dataset\n",
    "from sklearn.tree import DecisionTreeClassifier#Decision tree algorithm\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score#metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2799c776",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop([\"Price\",'Label','Model'],axis=1)\n",
    "y = data[\"Label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e3dea398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.67\n",
      "Precision: 0.81\n",
      "recall: 0.72\n",
      "f1: 0.76\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "# Training the Decision Tree Classifier\n",
    "classf = DecisionTreeClassifier()\n",
    "classf.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions on the test set\n",
    "pred = classf.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "accuracy = accuracy_score(y_test, pred)\n",
    "precision = precision_score(y_test, pred)\n",
    "recall = recall_score(y_test, pred)\n",
    "f1 = f1_score(y_test, pred)\n",
    "\n",
    "print('Accuracy: %.2f' % accuracy)\n",
    "print('Precision: %.2f' % precision)\n",
    "print('recall: %.2f' % recall)\n",
    "print('f1: %.2f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "77e9ce0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Model accuracy: 0.6707317073170732\n",
      "classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.55      0.47        22\n",
      "           1       0.81      0.72      0.76        60\n",
      "\n",
      "    accuracy                           0.67        82\n",
      "   macro avg       0.61      0.63      0.62        82\n",
      "weighted avg       0.70      0.67      0.68        82\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#model evaluation\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "print(\"Random Forest Model accuracy:\", accuracy_score(y_test,pred))\n",
    "print(\"classification report: \\n\", classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0ab1da99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12 10]\n",
      " [17 43]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c58de8",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "af836a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logcla = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "978606d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.68\n",
      "Precision: 0.72\n",
      "recall: 0.93\n",
      "f1: 0.81\n"
     ]
    }
   ],
   "source": [
    "logcla.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions on the test set\n",
    "y_pred = logcla.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print('Accuracy: %.2f' % accuracy)\n",
    "print('Precision: %.2f' % precision)\n",
    "print('recall: %.2f' % recall)\n",
    "print('f1: %.2f' % f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b41625",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "617ab834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "06d6cb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ranfoclass = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5088653a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.70\n",
      "Precision: 0.80\n",
      "recall: 0.78\n",
      "f1: 0.79\n"
     ]
    }
   ],
   "source": [
    "ranfoclass.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions on the test set\n",
    "y_pred = ranfoclass.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print('Accuracy: %.2f' % accuracy)\n",
    "print('Precision: %.2f' % precision)\n",
    "print('recall: %.2f' % recall)\n",
    "print('f1: %.2f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "263ac563",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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