{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8e5428ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "603e5705",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"train.csv\")\n",
    "test_df = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "270e8edd",
   "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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "177b770a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n",
       "       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n",
       "       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n",
       "       'touch_screen', 'wifi', 'price_range'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3e651bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 21)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f0c19f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n",
       "       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n",
       "       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n",
       "       'touch_screen', 'wifi', 'price_range'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "aecfa7bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.3 KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5b0d519a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>...</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>...</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n",
       "\n",
       "         px_height     px_width          ram         sc_h         sc_w  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n",
       "std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n",
       "min       0.000000   500.000000   256.000000     5.000000     0.000000   \n",
       "25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n",
       "50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n",
       "75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n",
       "max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n",
       "\n",
       "         talk_time      three_g  touch_screen         wifi  price_range  \n",
       "count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n",
       "mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n",
       "std       5.463955     0.426273      0.500116     0.500076     1.118314  \n",
       "min       2.000000     0.000000      0.000000     0.000000     0.000000  \n",
       "25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n",
       "50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n",
       "75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n",
       "max      20.000000     1.000000      1.000000     1.000000     3.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "84212064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2738e779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0\n",
       "blue             0\n",
       "clock_speed      0\n",
       "dual_sim         0\n",
       "fc               0\n",
       "four_g           0\n",
       "int_memory       0\n",
       "m_dep            0\n",
       "mobile_wt        0\n",
       "n_cores          0\n",
       "pc               0\n",
       "px_height        0\n",
       "px_width         0\n",
       "ram              0\n",
       "sc_h             0\n",
       "sc_w             0\n",
       "talk_time        0\n",
       "three_g          0\n",
       "touch_screen     0\n",
       "wifi             0\n",
       "price_range      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9fc91b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       2\n",
       "3       2\n",
       "4       1\n",
       "       ..\n",
       "1995    0\n",
       "1996    2\n",
       "1997    3\n",
       "1998    0\n",
       "1999    3\n",
       "Name: price_range, Length: 2000, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"price_range\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b1576292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train_df[\"price_range\"],kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1f2c165b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(train_df,x=\"battery_power\",y=\"price_range\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "59609286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NhAkT3KsVAACAm9x+iJ/6fqnly5fLL7/8ot83bdpU+vbtK/7+bnUGeQ0P8QMAoPLxyhOKKyvCDQAAlY9XnlCcmpqqp6NHj+penKLmzp3rbrEAAACXxa1wM3nyZJkyZYr+wswGDRroh/YBAABU2nCTmJgoSUlJ8tBDD3m+RgAAAJfBrdG/586dc3qAHwAAQKUON0OHDpX58+d7vjYAAAC+uCx19uxZ/fULK1askBtuuEECAwOdlr/xxhuXWy8AAADvhZuffvpJ2rZtq19v377daRmDiwEAQKULN6tWrfJ8TQAAADzA3o8TBgAAKCe3H+K3efNm+fzzzyU9PV3fPVXUl19+6W6xAAAA3u+5WbBggb4VfOfOnZKcnCznz5+XHTt2yMqVK/WjkQEAACpVuHnllVdk+vTp8t///leCgoJk5syZsmvXLrn//vvlmmuu8XwtAQAAKjLc7Nu3T+688079WoWb3NxcfZfUU089pW8RBwAAqFThplatWpKTk6NfX3311Y7bwU+ePCmnT5/2bA0BAAAqekBxz549JSUlRVq3bi333XefjBo1So+3UfN69+7tTpEAAAC+67l5++235YEHHtCvx48fL2PGjJEjR47In//8Z3n//fddLmft2rVy9913S8OGDfVlrUWLFpW5/urVq/V6xafMzEx3fg0AAGCgcvfcXLhwQZYsWSKxsbH6vb+/vzz33HNu/XA1VqdNmzYyZMgQuffee13+3O7duyU8PNzxvm7dum79fAAAYJ5yh5uAgAB5/PHH9W3gl+uOO+7QU3mpMHPFFVdc9s8HAADmceuyVKdOneSHH34QX1Hfa9WgQQPp27evrF+/3mf1AAAAhgwofuKJJ/Q4m4yMDGnfvr3UqFHDabn6pvCKoAJNYmKidOjQQfLy8uS9996TXr16yaZNm6Rdu3Ylfkatp6ZC2dnZFVI3AABgD36WZVnl/ZAaZ3NRQX5+oopS/+bn55e/In5++mnHcXFx5frczTffrB8c+PHHH5e4fNKkSTJ58uSL5mdlZTmN2wEAAPalOifUtyC4cv52q+dm//79YhfqEtm6detKXT527Fjdy1S0caKiorxUOwAA4G1uhZvo6GiX1lNPMVaXjtTlpIqixv6UVX5wcLCeAABA1eD2t4K7+hybM2fOlLr81KlTsnfvXqceIRVWateurS81qV6X3377TT766CO9fMaMGdK4cWNp2bKlnD17Vgcn9fDA5cuXV+SvAQAAKpEKDTeXsnnzZrnlllsc7wsvHw0aNEiSkpLk8OHDkp6e7lh+7tw5+cc//qEDT/Xq1fXA5RUrVjiVAQAAqja3BhS7KiwsTH788Ue59tprpTIOSAIAAJXv/O3Wc24AAADsinADAACMQrgBAABGqdBwM27cOH3nEwAAgO3DjXoicPfu3aVhw4Zy8OBBx63aixcvdqyjbuXmCy4BAIDtw83s2bP1bdv9+vWTkydPOr5uQQUZFXAAAAAqVbh566235N1335Xx48dLtWrVHPPVF1qmpaV5sn4AAAAVH27Uk4RvvPHGi+arrznIzc11p0gAAADfhRv1FQjqaxKKW7ZsmTRv3twT9QIAAPDe1y+o8TbDhw/X3++kHnD8/fffy6effioJCQn6+54AAAAqVbgZOnSohIaGyoQJE+T06dPy17/+Vd81NXPmTHnggQc8X0sAAABvfbeUCjfq273r1q0rlQHfLQUAQOVTnvN3gLsDii9cuCBNmjTR386tJmXPnj0SGBgojRo1cq/mAAAAvhhQPHjwYNmwYcNF8zdt2qSXAQAAVKpws23bNv104uK6dOlS4l1UAAAAtg43fn5+kpOTc9F8dR2s8GnFAAAAlSbc9OzZU9/2XTTIqNdqXo8ePTxZPwAAgHJxa0Dxa6+9pgNO06ZN5aabbtLzvv32Wz2SeeXKle4UCQAA4LuemxYtWshPP/0k999/vxw9elRfooqPj5ddu3ZJq1atPFMzAAAAXzznprLhOTcAAFQ+FfKcG9VTo3pl/P399euy3HDDDa7XFgAAwINcDjdt27aVzMxM/SRi9VrdMVVSp4+azx1TAADA9uFGPZX4qquucrwGAACo1OEmOjpa/3v+/HmZPHmyPP/889K4ceOKrBsAAEDF3y2lvjvqiy++KP9PAgAAsOut4HFxcbJo0SLP1wYAAMAXD/FT3wY+ZcoUWb9+vbRv315q1KjhtPzJJ5+83HoBAAB47zk3ZY21UXdL/frrr2JXPOcGAIDKp0Kec1NU0bulCrORCjUAAACVcsyN8v777+uH+oWEhOhJvX7vvfc8WzsAAIBycqvn5oUXXpA33nhDRo4cKV27dtXzNm7cKE899ZSkp6fr8TgAAACVZsyNepjfm2++KQMHDnSa/+mnn+rAc+zYMbErxtwAAFD5lOf87dZlKfUgvw4dOlw0X905deHCBXeKBAAA8Ai3ws1DDz0ks2fPvmj+O++8Iw8++KAn6gUAAOC9MTeFA4qXL18uXbp00e83bdqkx9vEx8fLmDFjHOupsTkAAAC2Djfbt2+Xdu3a6df79u3T/9apU0dPalkhbg8HAACVItysWrXK8zUBAADw5XNuAAAA7IhwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwik/Dzdq1a+Xuu++Whg0b6qcZL1q06JKfWb16tX46cnBwsMTExEhSUpJX6goAAAz/bilPyM3NlTZt2siQIUPk3nvvveT6+/fvlzvvvFMef/xx+eSTTyQ1NVWGDh0qDRo0kNjYWPGlQydOS87ZC5J95rxEhAZKzZAAiaxVnXJoH7Yf9guOHRwTOWdUpXBzxx136MlViYmJ0rhxY5k2bZp+37x5c1m3bp1Mnz7dp+Hm4PFcGZecJuv3HnfM6xFzpbx8T2uJvrIG5dA+bD/sFxw7OCZyzvCiSjXmZuPGjdKnTx+neSrUqPm+7LEpHmyUdXuPy/jkNL2ccmgfth/2C44dHBM5Z1SRnpvyyszMlHr16jnNU++zs7PlzJkzEhoaetFn8vLy9FRIretJqsu9eLApGnDUcsqhfdh+2C84dnBM5JzhPZWq58YdCQkJEhER4ZiioqI8Wr4aY1OWnLNlL6cc2oftp+rtF3asE+XQPnbZN6pcuKlfv74cOXLEaZ56Hx4eXmKvjTJ27FjJyspyTBkZGR6tU3hoYJnLw0LKXk45tA/bT9XbL+xYJ8qhfeyyb1S5cNO1a1d9h1RRKSkpen5p1C3jKvwUnTwpLCRADx4uiZqvllMO7cP2w37BsYNjIueMKhJuTp06JT/88IOeCm/1Vq/T09MdvS7x8fGO9dUt4L/++qs888wzsmvXLvnXv/4ln3/+uTz11FM++x3U7d7qrqjiAafwzgBXbwenHNqH7afq7Bd2rBPl0D522Tc8wc+yLEt8RD2Q75Zbbrlo/qBBg/TD+QYPHiwHDhzQ6xX9jAozP//8s0RGRsrzzz+v13OVGlCsxt6oS1Se7MUpfM6Nuq6out/CLvM5N5RD+7D9mL9f2LFOlEP7hNlg+7nc87dPw40vVFS4AQAA9jh/V6oxNwAAAJdCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAotgg3s2bNkkaNGklISIh07txZvv/++1LXTUpKEj8/P6dJfQ4AAMAW4eazzz6TMWPGyMSJE2Xr1q3Spk0biY2NlaNHj5b6mfDwcDl8+LBjOnjwoFfrDAAA7Mvn4eaNN96QRx99VB5++GFp0aKFJCYmSvXq1WXu3Lmlfkb11tSvX98x1atXz6t1BgAA9uXTcHPu3DnZsmWL9OnT538V8vfX7zdu3Fjq506dOiXR0dESFRUl/fv3lx07dnipxgAAwO58Gm6OHTsm+fn5F/W8qPeZmZklfqZp06a6V2fx4sUyb948KSgokG7dusmhQ4dKXD8vL0+ys7OdJgAAYC6fX5Yqr65du0p8fLy0bdtWbr75Zvnyyy/lqquukjlz5pS4fkJCgkRERDgm1dsDAADM5dNwU6dOHalWrZocOXLEab56r8bSuCIwMFBuvPFG2bt3b4nLx44dK1lZWY4pIyPDI3UHAAD25NNwExQUJO3bt5fU1FTHPHWZSb1XPTSuUJe10tLSpEGDBiUuDw4O1ndXFZ0AAIC5AnxdAXUb+KBBg6RDhw7SqVMnmTFjhuTm5uq7pxR1Cerqq6/Wl5eUKVOmSJcuXSQmJkZOnjwpU6dO1beCDx061Me/CQAAsAOfh5sBAwbIH3/8IS+88IIeRKzG0ixbtswxyDg9PV3fQVXoxIkT+tZxtW6tWrV0z8+GDRv0beQAAAB+lmVZVakZ1N1SamCxGn/DJSoAAMw7f1e6u6UAAADKQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKLYIN7NmzZJGjRpJSEiIdO7cWb7//vsy11+4cKE0a9ZMr9+6dWv5+uuvvVZXAABgbwG+rsBnn30mY8aMkcTERB1sZsyYIbGxsbJ7926pW7fuRetv2LBBBg4cKAkJCXLXXXfJ/PnzJS4uTrZu3SqtWrUSXzl04rTknL0g2WfOS0RooNQMCZDIWtUph/Zh+2G/4NjBMZFzhpf5WZZliQ+pQNOxY0d5++239fuCggKJioqSkSNHynPPPXfR+gMGDJDc3FxZsmSJY16XLl2kbdu2OiBdSnZ2tkREREhWVpaEh4d75Hc4eDxXxiWnyfq9xx3zesRcKS/f01qir6xBObQP2w/7BccOjomcMy5Tec7fPr0sde7cOdmyZYv06dPnfxXy99fvN27cWOJn1Pyi6yuqp6e09b3RY1M82Cjr9h6X8clpejnl0D5sP+wXHDs4JnLOqCKXpY4dOyb5+flSr149p/nq/a5du0r8TGZmZonrq/klycvL01PR5OdJqsu9eLApGnDUcsqhfdh+2C84dnBM5JxRxQYUVyQ1Nkd1YxVO6pKXJ6kxNmXJOVv2csqhfdh+qt5+Ycc6UQ7tY5d9o9KHmzp16ki1atXkyJEjTvPV+/r165f4GTW/POuPHTtWX58rnDIyMjz4G4iEhwaWuTwspOzllEP7sP1Uvf3CjnWiHNrHLvtGpQ83QUFB0r59e0lNTXXMUwOK1fuuXbuW+Bk1v+j6SkpKSqnrBwcH64FHRSdPCgsJ0IOHS6Lmq+WUQ/uw/bBfcOzgmMg5owpdllK3gb/77rvy4Ycfys6dO2XYsGH6bqiHH35YL4+Pj9e9L4VGjRoly5Ytk2nTpulxOZMmTZLNmzfLiBEjfFJ/dbu3uiuqeMApvDPA1dvBKYf2YfupOvuFHetEObSPXfYNI24FV9Rt4FOnTtWDgtUt3W+++aa+RVzp1auXfsBfUlKS00P8JkyYIAcOHJAmTZrI66+/Lv369XPpZ1XEreBFn3Ojriuq7rewy3zODeXQPmw/5u8XdqwT5dA+YTbYfi73/G2LcONNFRVuAABAxak0z7kBAADwNMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADCKa19ZbZDCb5tQj3EGAACVQ+F525Vvjapy4SYnJ0f/GxUV5euqAAAAN87j6jumylLlvjizoKBAfv/9dwkLCxM/Pz+Pp0oVmjIyMvhSzgpEO3sH7ew9tDXtbJLsCjoXqriigk3Dhg3F37/sUTVVrudGNUhkZGSF/gz1x+Qbxyse7ewdtLP30Na0s0nCK+BceKkem0IMKAYAAEYh3AAAAKMQbjwoODhYJk6cqP9FxaGdvYN29h7amnY2SbANzoVVbkAxAAAwGz03AADAKIQbAABgFMINAAAwCuGmnGbNmiWNGjWSkJAQ6dy5s3z//felrpuUlKQfFFh0Up9D2dauXSt33323flCTarNFixZdsslWr14t7dq10wPYYmJidNvDs+2s2rj49qymzMxMmroMCQkJ0rFjR/3g0Lp160pcXJzs3r37km22cOFCadasmT5mtG7dWr7++mva2cPtzDG6/GbPni033HCD4xk2Xbt2laVLl9puWybclMNnn30mY8aM0aPAt27dKm3atJHY2Fg5evRoqZ9Rf/zDhw87poMHD3ri72a03Nxc3bYqSLpi//79cuedd8ott9wiP/zwg4wePVqGDh0q33zzTYXXtSq1cyF1wii6TasTCUq3Zs0aGT58uHz33XeSkpIi58+fl9tuu023f2k2bNggAwcOlEceeUS2bdumT9Rq2r59O03twXZWOEaXj3oI7quvvipbtmyRzZs3y6233ir9+/eXHTt22GtbVndLwTWdOnWyhg8f7nifn59vNWzY0EpISChx/Q8++MCKiIigeS+D2kSTk5PLXOeZZ56xWrZs6TRvwIABVmxsLG3vwXZetWqVXu/EiRO062U4evSobsc1a9aUus79999v3XnnnU7zOnfubP3973+n7T3YzhyjPaNWrVrWe++9Z6ttmZ4bF507d04n1T59+jh9lYN6v3HjxlI/d+rUKYmOjtbfs1FWuoX7VPsX/bsoqketrL8L3Ne2bVtp0KCB9O3bV9avX09TllNWVpb+t3bt2qWuwzbtnXZWOEa7Lz8/XxYsWKB7x9TlKTtty4QbFx07dkz/IevVq+c0X70vbcxB06ZNZe7cubJ48WKZN2+e/tLObt26yaFDhy7/LwcH1f4l/V3Ul7edOXOGlvIQFWgSExPliy++0JMK7L169dKXaOEadQxQl027d+8urVq1Kvc2zfgmz7Yzx2j3pKWlSc2aNfUYx8cff1ySk5OlRYsWttqWq9wXZ3qTSrJF06wKNs2bN5c5c+bIiy++6NO6AeWlTgRqKro979u3T6ZPny4ff/wxDeoCNSZEjTVYt24d7WWDduYY7R51HFDjG1Xv2L///W8ZNGiQHvNUWsDxBXpuXFSnTh2pVq2aHDlyxGm+el+/fn2XyggMDJQbb7xR9u7dW/6/FEql2r+kv4saKBgaGkrLVaBOnTqxPbtoxIgRsmTJElm1apUelOnONu3qsaYqK087F8cx2jVBQUH6rtT27dvru9TUjQkzZ8601bZMuCnHH1P9IVNTU526PtX70q41Fqcua6nuPNW9D89R7V/076KouyVc/bvAfer/3tiey6bGa6sTruq6X7lypTRu3PiS7co27Z12Lo5jtHvUuTAvL89e23KFDlc2zIIFC6zg4GArKSnJ+vnnn63HHnvMuuKKK6zMzEy9/KGHHrKee+45x/qTJ0+2vvnmG2vfvn3Wli1brAceeMAKCQmxduzY4cPfwv5ycnKsbdu26Ultom+88YZ+ffDgQb1ctbFq60K//vqrVb16devpp5+2du7cac2aNcuqVq2atWzZMh/+Fua18/Tp061FixZZe/bssdLS0qxRo0ZZ/v7+1ooVK3z4W9jfsGHD9F2Tq1evtg4fPuyYTp8+7Vin+LFj/fr1VkBAgPXPf/5Tb9MTJ060AgMDdbvDc+3MMbr8VPupO9D2799v/fTTT/q9n5+ftXz5cltty4Sbcnrrrbesa665xgoKCtK3hn/33XeOZTfffLM1aNAgx/vRo0c71q1Xr57Vr18/a+vWrZ776xmq8Jbj4lNh26p/VVsX/0zbtm11W1977bX6Fk94tp1fe+0167rrrtMBvXbt2lavXr2slStX0syXUFIbq6noNlr82KF8/vnn1vXXX6+3afWog6+++oq29nA7c4wuvyFDhljR0dF6u7zqqqus3r17O4KNnbZlvhUcAAAYhTE3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAPOLAgQPi5+env2/KjuXZhfqdFi1a5OtqAEYj3AAAAKMQbgAAgFEINwDKpaCgQF5//XWJiYmR4OBgueaaa+Tll18ucd01a9ZIp06d9HoNGjSQ5557Ti5cuOBWWfn5+TJkyBBp1qyZpKenl1lH9T2KkyZN0uWpchs2bChPPvmkY3mjRo3kxRdflIEDB0qNGjXk6quvllmzZjmVcfLkSRk6dKhcddVVEh4eLrfeeqv8+OOPTussXrxY2rVrJyEhIXLttdfK5MmTnX6/PXv2SM+ePfXyFi1aSEpKyiVaF4AnBHikFABVxtixY+Xdd9+V6dOnS48ePeTw4cOya9eui9b77bffpF+/fjJ48GD56KOP9DqPPvqoPtGr4FGesvLy8nQQUeNwvv32Wx04yvLFF1/oMhcsWCAtW7aUzMzMi4LJ1KlTZdy4cTqQfPPNNzJq1Ci5/vrrpW/fvnr5fffdJ6GhobJ06VKJiIiQOXPmSO/eveWXX36R2rVr63rEx8fLm2++KTfddJPs27dPHnvsMf3ZiRMn6uB27733Sr169WTTpk2SlZUlo0ePvqy2B+CiCv/ecQDGyM7OtoKDg6133333omX79++31CFl27Zt+v24ceOspk2bWgUFBY51Zs2aZdWsWdPKz88vs6yi5X377bdW7969rR49elgnT550qZ7Tpk2zrr/+euvcuXMlLo+OjrZuv/12p3kDBgyw7rjjDv1a/czw8HDr7NmzTutcd9111pw5c/RrVadXXnnFafnHH39sNWjQQL/+5ptvrICAAOu3335zLF+6dKn+nZKTk136PQC4h8tSAFy2c+dO3YuiejBcWbdr16767qBC3bt3l1OnTsmhQ4dcLkv12OTm5sry5ct1D4orVK/LmTNn9KUi1VuUnJzsdLlIUXUr/l7VSVG9PKqeV155pdSsWdMx7d+/X/fQFK4zZcoUp+XqZ6nep9OnT+uyoqKi9CWx0n4mgIrBZSkALlOXabxdlrq0NW/ePNm4caMe9+IKFSp2794tK1as0ONcnnjiCX0ZSo0BCgwMvOTnVbBRY4RWr1590bIrrrjCsY66pKUuPRWnLr0B8B16bgC4rEmTJjqUpKamXnLd5s2b60CiBvcWWr9+vYSFhUlkZKTLZQ0bNkxeffVV+dOf/qTDiatU2XfffbceE6NCiqpLWlqaY/l3333ntL56r+qsqEHCapxOQECAHuxcdKpTp45jHRWgii9Xk7+/vy4rIyND9+SU9jMBVAx6bgC4TPVIPPvss/LMM89IUFCQvsz0xx9/yI4dOy66vKR6S2bMmCEjR46UESNG6CCgBtqOGTNGn/zLKuuRRx5xKkuVoe6Wuuuuu/QAXzX4uCxJSUl6/c6dO0v16tV1z48KO9HR0U5BS92pFRcXp3t3Fi5cKF999ZVe1qdPH30JSS1T66iBxr///rtefs8990iHDh3khRde0PVRd2T95S9/0b+TulS1fft2eemll3QZ6nODBg3SvUbZ2dkyfvx4tjbAG9wcqwOgilKDgV966SU9KDcwMNC65ppr9MDa4gOKldWrV1sdO3a0goKCrPr161vPPvusdf78+UuWpZRUnhooHBYWZq1fv77MOqoBu507d9aDgmvUqGF16dLFWrFihWO5+nmTJ0+27rvvPqt69eq6bjNnznQqQw14HjlypNWwYUNdt6ioKOvBBx+00tPTHessW7bM6tatmxUaGqp/VqdOnax33nnHsXz37t16ILT6/dUAZ7U+A4qBiuen/uOVFAUANqGec6Nuy+bWbMBMjLkBAABGIdwAqHQ++eQTp1uwi07qoX0AqjYuSwGodHJycuTIkSMlLlO3ehcdOAyg6iHcAAAAo3BZCgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAQk/w/78kA6VE51XgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(train_df,x=\"clock_speed\",y=\"price_range\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b87f6cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(train_df,x=\"int_memory\",y=\"price_range\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "963d4896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(train_df,x=\"int_memory\",y=\"n_cores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "4ad03008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(train_df,x=\"battery_power\",y=\"n_cores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "77a793ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "sns.heatmap(train_df.corr(),annot=True,fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d6fe1faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n",
       "       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n",
       "       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n",
       "       'touch_screen', 'wifi', 'price_range'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>136</td>\n",
       "      <td>6</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>131</td>\n",
       "      <td>9</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>141</td>\n",
       "      <td>14</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  dual_sim  fc  int_memory  mobile_wt  pc  px_height  \\\n",
       "0            842     0         0   1           7        188   2         20   \n",
       "1           1021     1         1   0          53        136   6        905   \n",
       "2            563     1         1   2          41        145   6       1263   \n",
       "3            615     1         0   0          10        131   9       1216   \n",
       "4           1821     1         0  13          44        141  14       1208   \n",
       "\n",
       "   px_width   ram  sc_h  sc_w  talk_time  three_g  touch_screen  wifi  \\\n",
       "0       756  2549     9     7         19        0             0     1   \n",
       "1      1988  2631    17     3          7        1             1     0   \n",
       "2      1716  2603    11     2          9        1             1     0   \n",
       "3      1786  2769    16     8         11        1             0     0   \n",
       "4      1212  1411     8     2         15        1             1     0   \n",
       "\n",
       "   price_range  \n",
       "0            1  \n",
       "1            2  \n",
       "2            2  \n",
       "3            2  \n",
       "4            1  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop([\"four_g\",\"m_dep\",\"n_cores\",\"clock_speed\"],axis=1,inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3d25c330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0.031898\n",
       "blue             0.020016\n",
       "dual_sim        -0.038035\n",
       "fc               1.019811\n",
       "int_memory       0.057889\n",
       "mobile_wt        0.006558\n",
       "pc               0.017306\n",
       "px_height        0.666271\n",
       "px_width         0.014787\n",
       "ram              0.006628\n",
       "sc_h            -0.098884\n",
       "sc_w             0.633787\n",
       "talk_time        0.009512\n",
       "three_g         -1.228142\n",
       "touch_screen    -0.012009\n",
       "wifi            -0.028024\n",
       "price_range      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "18142a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\"battery_power\",\"blue\",\"int_memory\",\"mobile_wt\",\"pc\",\"px_width\",\"ram\",\"talk_time\"]\n",
    "skew_cols = [\"dual_sim\",\"fc\",\"px_height\",\"sc_h\",\"three_g\",\"touch_screen\",\"wifi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "20f941d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,FunctionTransformer\n",
    "\n",
    "skew_pip = Pipeline(steps=[\n",
    "    (\"skew\",FunctionTransformer(np.log1p)),\n",
    "    (\"scaler\",StandardScaler())\n",
    "])\n",
    "num_pip = Pipeline(steps=[\n",
    "    (\"scaler\",StandardScaler())\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "df11a39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[\n",
    "    (\"skew_pip_col\",skew_pip,skew_cols),\n",
    "    (\"num_pip_col\",num_pip,num_cols)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "55ab1846",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = train_df.drop(\"price_range\",axis=1)\n",
    "y = train_df[\"price_range\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c702beb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Training 0.9164053741971551\n",
      "Accuracy of Testing 0.9155899274904516\n",
      "MAE 0.27122678586385246\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "\n",
    "lr_pipeline = Pipeline(steps=[\n",
    "    (\"preprocessor\",preprocessor),\n",
    "    (\"regressor\",LinearRegression())\n",
    "])\n",
    "\n",
    "lr_pipeline.fit(X_train,y_train)\n",
    "lr_preds = lr_pipeline.predict(X_test)\n",
    "lr_preds_train = lr_pipeline.predict(X_train)\n",
    "\n",
    "lr_r2_testing = r2_score(y_test,lr_preds)\n",
    "lr_r2_training = r2_score(y_train,lr_preds_train)\n",
    "lr_mae = mean_absolute_error(y_test,lr_preds)\n",
    "\n",
    "print(f\"Accuracy of Training {lr_r2_training}\")\n",
    "print(f\"Accuracy of Testing {lr_r2_testing}\")\n",
    "print(f'MAE {lr_mae}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5c62ecb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Training 0.9911062650858907\n",
      "Accuracy of Testing 0.9388473830309898\n",
      "MAE : 0.17321666666666666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_pipeline = Pipeline(steps=[\n",
    "    (\"preprocessor\",preprocessor),\n",
    "    (\"random forest\",RandomForestRegressor(random_state=42))\n",
    "])\n",
    "\n",
    "rf_pipeline.fit(X_train,y_train)\n",
    "rf_preds = rf_pipeline.predict(X_test)\n",
    "rf_preds_train = rf_pipeline.predict(X_train)\n",
    "\n",
    "rf_r2_testing = r2_score(y_test,rf_preds)\n",
    "rf_r2_training = r2_score(y_train,rf_preds_train)\n",
    "rf_mae = mean_absolute_error(y_test,rf_preds)\n",
    "\n",
    "print(f\"Accuracy of Training {rf_r2_training}\")\n",
    "print(f\"Accuracy of Testing {rf_r2_testing}\")\n",
    "print(f\"MAE : {rf_mae}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "64a4c26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;skew_pip_col&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;skew&#x27;,\n",
       "                                                                                                FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),\n",
       "                                                                                               (&#x27;scaler&#x27;,\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;dual_sim&#x27;,\n",
       "                                                                                &#x27;fc&#x27;,\n",
       "                                                                                &#x27;px_height&#x27;,\n",
       "                                                                                &#x27;sc_h&#x27;,\n",
       "                                                                                &#x27;three_g&#x27;,\n",
       "                                                                                &#x27;touch_screen&#x27;,\n",
       "                                                                                &#x27;wifi&#x27;]),\n",
       "                                                                              (&#x27;num_pip_col&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;battery...\n",
       "                                                                                &#x27;int_memory&#x27;,\n",
       "                                                                                &#x27;mobile_wt&#x27;,\n",
       "                                                                                &#x27;pc&#x27;,\n",
       "                                                                                &#x27;px_width&#x27;,\n",
       "                                                                                &#x27;ram&#x27;,\n",
       "                                                                                &#x27;talk_time&#x27;])])),\n",
       "                                             (&#x27;random forest&#x27;,\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   n_iter=15, n_jobs=-1,\n",
       "                   param_distributions={&#x27;random forest__max_depth&#x27;: [5, 10, 15],\n",
       "                                        &#x27;random forest__min_samples_leaf&#x27;: [1,\n",
       "                                                                            2,\n",
       "                                                                            4],\n",
       "                                        &#x27;random forest__min_samples_split&#x27;: [2,\n",
       "                                                                             5,\n",
       "                                                                             10],\n",
       "                                        &#x27;random forest__n_estimators&#x27;: [100,\n",
       "                                                                        200,\n",
       "                                                                        300]},\n",
       "                   random_state=42, scoring=&#x27;r2&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator&nbsp;</td>\n",
       "            <td class=\"value\">Pipeline(step...m_state=42))])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_distributions',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_distributions&nbsp;</td>\n",
       "            <td class=\"value\">{&#x27;random forest__max_depth&#x27;: [5, 10, ...], &#x27;random forest__min_samples_leaf&#x27;: [1, 2, ...], &#x27;random forest__min_samples_split&#x27;: [2, 5, ...], &#x27;random forest__n_estimators&#x27;: [100, 200, ...]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter&nbsp;</td>\n",
       "            <td class=\"value\">15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;r2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">transformers&nbsp;</td>\n",
       "            <td class=\"value\">[(&#x27;skew_pip_col&#x27;, ...), (&#x27;num_pip_col&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">remainder&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sparse_threshold&nbsp;</td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">transformer_weights&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose_feature_names_out&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">force_int_remainder_cols&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>skew_pip_col</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__skew_pip_col__\"><pre>[&#x27;dual_sim&#x27;, &#x27;fc&#x27;, &#x27;px_height&#x27;, &#x27;sc_h&#x27;, &#x27;three_g&#x27;, &#x27;touch_screen&#x27;, &#x27;wifi&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log1p</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__skew_pip_col__skew__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('func',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">func&nbsp;</td>\n",
       "            <td class=\"value\">&lt;ufunc &#x27;log1p&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inverse_func',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validate&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('accept_sparse',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('check_inverse',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kw_args',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kw_args&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__skew_pip_col__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_mean&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_std&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num_pip_col</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__num_pip_col__\"><pre>[&#x27;battery_power&#x27;, &#x27;blue&#x27;, &#x27;int_memory&#x27;, &#x27;mobile_wt&#x27;, &#x27;pc&#x27;, &#x27;px_width&#x27;, &#x27;ram&#x27;, &#x27;talk_time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___preprocessor__num_pip_col__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_mean&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_std&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___random forest__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
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       "    \n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
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       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
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       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
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       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
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       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">oob_score&nbsp;</td>\n",
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       "    \n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
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       "    \n",
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       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_samples&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('preprocessor',\n",
       "                                              ColumnTransformer(transformers=[('skew_pip_col',\n",
       "                                                                               Pipeline(steps=[('skew',\n",
       "                                                                                                FunctionTransformer(func=<ufunc 'log1p'>)),\n",
       "                                                                                               ('scaler',\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               ['dual_sim',\n",
       "                                                                                'fc',\n",
       "                                                                                'px_height',\n",
       "                                                                                'sc_h',\n",
       "                                                                                'three_g',\n",
       "                                                                                'touch_screen',\n",
       "                                                                                'wifi']),\n",
       "                                                                              ('num_pip_col',\n",
       "                                                                               Pipeline(steps=[('scaler',\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               ['battery...\n",
       "                                                                                'int_memory',\n",
       "                                                                                'mobile_wt',\n",
       "                                                                                'pc',\n",
       "                                                                                'px_width',\n",
       "                                                                                'ram',\n",
       "                                                                                'talk_time'])])),\n",
       "                                             ('random forest',\n",
       "                                              RandomForestRegressor(random_state=42))]),\n",
       "                   n_iter=15, n_jobs=-1,\n",
       "                   param_distributions={'random forest__max_depth': [5, 10, 15],\n",
       "                                        'random forest__min_samples_leaf': [1,\n",
       "                                                                            2,\n",
       "                                                                            4],\n",
       "                                        'random forest__min_samples_split': [2,\n",
       "                                                                             5,\n",
       "                                                                             10],\n",
       "                                        'random forest__n_estimators': [100,\n",
       "                                                                        200,\n",
       "                                                                        300]},\n",
       "                   random_state=42, scoring='r2')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'random forest__n_estimators'       : [100,200,300],\n",
    "    'random forest__max_depth'         : [5,10,15],\n",
    "    'random forest__min_samples_split' : [2,5,10],\n",
    "    'random forest__min_samples_leaf'  : [1,2,4]\n",
    "}\n",
    "\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=rf_pipeline,\n",
    "    param_distributions=param_grid,\n",
    "    n_jobs=-1,\n",
    "    cv=5,\n",
    "    random_state=42,\n",
    "    n_iter=15,\n",
    "    scoring=\"r2\"\n",
    ")\n",
    "\n",
    "random_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4951f456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score for random forest after hyperparameter tuning 0.9399933065866165\n",
      "MAE Score for random forest after hyperparameter tuning 0.17231081489498157\n"
     ]
    }
   ],
   "source": [
    "rf_best_pipeline = random_search.best_estimator_\n",
    "rf_best_preds = random_search.predict(X_test)\n",
    "\n",
    "print(f\"R2 Score for random forest after hyperparameter tuning {r2_score(y_test, rf_best_preds)}\")\n",
    "print(f\"MAE Score for random forest after hyperparameter tuning {mean_absolute_error(y_test, rf_best_preds)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "f9374eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Model        R2  Training R2       MAE      RMSE\n",
      "0        Linear Regression  0.915590     0.916405  0.271227  0.327398\n",
      "1  Random Forest (default)  0.938847     0.991106  0.173217  0.278667\n",
      "2    Random Forest (tuned)  0.939993     0.984786  0.172311  0.276044\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Calculate RMSE for Linear Regression\n",
    "lr_rmse = np.sqrt(mean_squared_error(y_test, lr_preds))\n",
    "\n",
    "# Calculate RMSE for default Random Forest\n",
    "rf_rmse = np.sqrt(mean_squared_error(y_test, rf_preds))\n",
    "\n",
    "# Calculate RMSE for tuned Random Forest\n",
    "rf_tuned_preds          = random_search.best_estimator_.predict(X_test)\n",
    "rf_tuned_rmse           = np.sqrt(mean_squared_error(y_test, rf_tuned_preds))\n",
    "rf_tuned_training_preds = random_search.best_estimator_.predict(X_train)\n",
    "r2_tuned_training       = r2_score(y_train, rf_tuned_training_preds)\n",
    "rf_tuned_mae            = mean_absolute_error(y_test, rf_tuned_preds)\n",
    "rf_tuned_r2             = r2_score(y_test, rf_tuned_preds)\n",
    "\n",
    "\n",
    "# Create a DataFrame to consolidate results\n",
    "results = pd.DataFrame({\n",
    "    'Model': ['Linear Regression', 'Random Forest (default)', 'Random Forest (tuned)'],\n",
    "    'R2': [lr_r2_testing, rf_r2_testing, rf_tuned_r2],\n",
    "    'Training R2': [lr_r2_training, rf_r2_training, r2_tuned_training],\n",
    "    'MAE': [lr_mae, rf_mae, rf_tuned_mae],\n",
    "    'RMSE': [lr_rmse, rf_rmse, rf_tuned_rmse]\n",
    "})\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f5ca3a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Model.pkl']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "dump(rf_pipeline,\"Model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c29f93f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['battery_power',\n",
       " 'blue',\n",
       " 'dual_sim',\n",
       " 'fc',\n",
       " 'int_memory',\n",
       " 'mobile_wt',\n",
       " 'pc',\n",
       " 'px_height',\n",
       " 'px_width',\n",
       " 'ram',\n",
       " 'sc_h',\n",
       " 'sc_w',\n",
       " 'talk_time',\n",
       " 'three_g',\n",
       " 'touch_screen',\n",
       " 'wifi']"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns.tolist()"
   ]
  }
 ],
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