Interim_Project_Report-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RIO-125: Rank features of a smartphone - Build a python application to classify and rank dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIFE CYCLE OF A DATA SCIENCE PROJECT\n",
    "\n",
    "#### 1 Data Gathering \n",
    "\n",
    "#### 2 Data Analysis\n",
    "\n",
    "#### 3 Feature Engineering \n",
    "\n",
    "#### 4 Feature Selection\n",
    "\n",
    "#### 5 Model Building \n",
    "\n",
    "#### 6 Hyper-Parametric Tuning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3- Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Missing Values\n",
    "\n",
    "#### 2. Temporal Variables\n",
    "\n",
    "#### 3. Categorical Variables : remove rare labels\n",
    "\n",
    "#### 4. Transform the values of the variables to same range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORTING SOME LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CREATING THE DATA - FRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\hp\\Desktop\\PROJECT\\train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AS WE HAVE CHECKED WHILE DATA ANALYSIS THAT DATA SET HAS NO MISSING VALUE SO NO FEATURE ENGG REQUIRED FOR  MISSING VALUES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THE DATASET DOESN'T HAVE ANY TEMPORAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price_range.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SPLITTING FEATURES INTO CONTINUOUS AND CATEGORICAL FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_num_features = [column for column in df.columns if len(df[column].unique())>=15]\n",
    "categorical_num_features = [column for column in df.columns if column not in continuous_num_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blue  has  [0, 1]  labels\n",
      "dual_sim  has  [0, 1]  labels\n",
      "four_g  has  [0, 1]  labels\n",
      "m_dep  has  [0.6, 0.7, 0.9, 0.8, 0.1, 0.5, 1.0, 0.3, 0.4, 0.2]  labels\n",
      "n_cores  has  [2, 3, 5, 6, 1, 8, 4, 7]  labels\n",
      "three_g  has  [0, 1]  labels\n",
      "touch_screen  has  [0, 1]  labels\n",
      "wifi  has  [1, 0]  labels\n",
      "price_range  has  [1, 2, 3, 0]  labels\n"
     ]
    }
   ],
   "source": [
    "for column in categorical_num_features:\n",
    "    print(column , \" has \", list(df[column].unique()),\" labels\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WE CAN SEE THAT FEATURE IS NOT HAVING LARGER NO OF LABELS . AT MAX IT HAS 10 LABELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    500\n",
       "2    500\n",
       "1    500\n",
       "0    500\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IT'S A BALANCED DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AS WE HAVE SEEN THAT IS DATASET DOESN'T MAKE BELL SHAPED DISTRIBUTION IN DATA ANALYSIS SO WE WILL APPLY NORMALIZATION ON THE DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COPYING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LET'S NORMALIZE THE FEATURES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data1.drop('price_range',axis =1)\n",
    "y = data1.price_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2   2         20       756  2549     9     7         19   \n",
       "1        136        3   6        905      1988  2631    17     3          7   \n",
       "2        145        5   6       1263      1716  2603    11     2          9   \n",
       "3        131        6   9       1216      1786  2769    16     8         11   \n",
       "4        141        2  14       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  \n",
       "0        0             0     1  \n",
       "1        1             1     0  \n",
       "2        1             1     0  \n",
       "3        1             0     0  \n",
       "4        1             1     0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HERE x IS INPUT INDEPENDENT VARIABLES AND y IS OUTPUT DEPENDENT VARIABLE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.227789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.170895</td>\n",
       "      <td>0.612774</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.347361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.461735</td>\n",
       "      <td>0.993324</td>\n",
       "      <td>0.634687</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.629032</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.644388</td>\n",
       "      <td>0.811749</td>\n",
       "      <td>0.627205</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.076152</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.620408</td>\n",
       "      <td>0.858478</td>\n",
       "      <td>0.671566</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.881764</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.616327</td>\n",
       "      <td>0.475300</td>\n",
       "      <td>0.308658</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.907148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.512245</td>\n",
       "      <td>0.770360</td>\n",
       "      <td>0.216729</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.881764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.194388</td>\n",
       "      <td>0.345794</td>\n",
       "      <td>0.792090</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.970608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.891667</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.261224</td>\n",
       "      <td>0.433244</td>\n",
       "      <td>0.118653</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.630595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.196939</td>\n",
       "      <td>0.224299</td>\n",
       "      <td>0.225281</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.005344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.580102</td>\n",
       "      <td>0.483311</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim        fc  four_g  int_memory  \\\n",
       "0       0.227789   0.0         0.68       0.0  0.052632     0.0    0.080645   \n",
       "1       0.347361   1.0         0.00       1.0  0.000000     1.0    0.822581   \n",
       "2       0.041416   1.0         0.00       1.0  0.105263     1.0    0.629032   \n",
       "3       0.076152   1.0         0.80       0.0  0.000000     0.0    0.129032   \n",
       "4       0.881764   1.0         0.28       0.0  0.684211     1.0    0.677419   \n",
       "5       0.907148   0.0         0.00       1.0  0.157895     0.0    0.322581   \n",
       "6       0.881764   0.0         0.48       0.0  0.210526     1.0    0.129032   \n",
       "7       0.970608   0.0         0.00       1.0  0.000000     0.0    0.354839   \n",
       "8       0.630595   1.0         0.00       0.0  0.000000     0.0    0.822581   \n",
       "9       0.005344   1.0         0.04       1.0  0.105263     1.0    0.112903   \n",
       "\n",
       "      m_dep  mobile_wt   n_cores    pc  px_height  px_width       ram  \\\n",
       "0  0.555556   0.900000  0.142857  0.10   0.010204  0.170895  0.612774   \n",
       "1  0.666667   0.466667  0.285714  0.30   0.461735  0.993324  0.634687   \n",
       "2  0.888889   0.541667  0.571429  0.30   0.644388  0.811749  0.627205   \n",
       "3  0.777778   0.425000  0.714286  0.45   0.620408  0.858478  0.671566   \n",
       "4  0.555556   0.508333  0.142857  0.70   0.616327  0.475300  0.308658   \n",
       "5  0.666667   0.700000  0.000000  0.35   0.512245  0.770360  0.216729   \n",
       "6  0.777778   0.491667  1.000000  0.50   0.194388  0.345794  0.792090   \n",
       "7  0.777778   0.891667  0.428571  0.00   0.261224  0.433244  0.118653   \n",
       "8  0.666667   0.783333  0.857143  0.70   0.196939  0.224299  0.225281   \n",
       "9  0.000000   0.108333  0.571429  0.75   0.580102  0.483311  0.068680   \n",
       "\n",
       "       sc_h      sc_w  talk_time  three_g  touch_screen  wifi  \n",
       "0  0.285714  0.388889   0.944444      0.0           0.0   1.0  \n",
       "1  0.857143  0.166667   0.277778      1.0           1.0   0.0  \n",
       "2  0.428571  0.111111   0.388889      1.0           1.0   0.0  \n",
       "3  0.785714  0.444444   0.500000      1.0           0.0   0.0  \n",
       "4  0.214286  0.111111   0.722222      1.0           1.0   0.0  \n",
       "5  0.857143  0.055556   0.444444      1.0           0.0   0.0  \n",
       "6  0.571429  0.444444   0.888889      1.0           0.0   1.0  \n",
       "7  0.785714  0.166667   0.166667      1.0           1.0   1.0  \n",
       "8  0.857143  0.055556   1.000000      1.0           0.0   0.0  \n",
       "9  1.000000  0.555556   0.555556      1.0           0.0   0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaled_norm = MinMaxScaler()\n",
    "scaled_norm = pd.DataFrame(scaled_norm.fit_transform(x),columns = data1.columns[:-1])\n",
    "scaled_norm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SO I SCALED THE FEATURES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOW FEATURE'S VALUES IS BETWEEN 0 AND 1 (INCLUDING 0 AND 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADDING OUTPUT FEATURE TO THE scaled_norm data frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_norm['price_range'] = data1.price_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>0.227789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.170895</td>\n",
       "      <td>0.612774</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.347361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.461735</td>\n",
       "      <td>0.993324</td>\n",
       "      <td>0.634687</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.629032</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.644388</td>\n",
       "      <td>0.811749</td>\n",
       "      <td>0.627205</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.076152</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620408</td>\n",
       "      <td>0.858478</td>\n",
       "      <td>0.671566</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.881764</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616327</td>\n",
       "      <td>0.475300</td>\n",
       "      <td>0.308658</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.907148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512245</td>\n",
       "      <td>0.770360</td>\n",
       "      <td>0.216729</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.881764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194388</td>\n",
       "      <td>0.345794</td>\n",
       "      <td>0.792090</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.970608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.891667</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261224</td>\n",
       "      <td>0.433244</td>\n",
       "      <td>0.118653</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.630595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196939</td>\n",
       "      <td>0.224299</td>\n",
       "      <td>0.225281</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.005344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.580102</td>\n",
       "      <td>0.483311</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim        fc  four_g  int_memory  \\\n",
       "0       0.227789   0.0         0.68       0.0  0.052632     0.0    0.080645   \n",
       "1       0.347361   1.0         0.00       1.0  0.000000     1.0    0.822581   \n",
       "2       0.041416   1.0         0.00       1.0  0.105263     1.0    0.629032   \n",
       "3       0.076152   1.0         0.80       0.0  0.000000     0.0    0.129032   \n",
       "4       0.881764   1.0         0.28       0.0  0.684211     1.0    0.677419   \n",
       "5       0.907148   0.0         0.00       1.0  0.157895     0.0    0.322581   \n",
       "6       0.881764   0.0         0.48       0.0  0.210526     1.0    0.129032   \n",
       "7       0.970608   0.0         0.00       1.0  0.000000     0.0    0.354839   \n",
       "8       0.630595   1.0         0.00       0.0  0.000000     0.0    0.822581   \n",
       "9       0.005344   1.0         0.04       1.0  0.105263     1.0    0.112903   \n",
       "\n",
       "      m_dep  mobile_wt   n_cores  ...  px_height  px_width       ram  \\\n",
       "0  0.555556   0.900000  0.142857  ...   0.010204  0.170895  0.612774   \n",
       "1  0.666667   0.466667  0.285714  ...   0.461735  0.993324  0.634687   \n",
       "2  0.888889   0.541667  0.571429  ...   0.644388  0.811749  0.627205   \n",
       "3  0.777778   0.425000  0.714286  ...   0.620408  0.858478  0.671566   \n",
       "4  0.555556   0.508333  0.142857  ...   0.616327  0.475300  0.308658   \n",
       "5  0.666667   0.700000  0.000000  ...   0.512245  0.770360  0.216729   \n",
       "6  0.777778   0.491667  1.000000  ...   0.194388  0.345794  0.792090   \n",
       "7  0.777778   0.891667  0.428571  ...   0.261224  0.433244  0.118653   \n",
       "8  0.666667   0.783333  0.857143  ...   0.196939  0.224299  0.225281   \n",
       "9  0.000000   0.108333  0.571429  ...   0.580102  0.483311  0.068680   \n",
       "\n",
       "       sc_h      sc_w  talk_time  three_g  touch_screen  wifi  price_range  \n",
       "0  0.285714  0.388889   0.944444      0.0           0.0   1.0            1  \n",
       "1  0.857143  0.166667   0.277778      1.0           1.0   0.0            2  \n",
       "2  0.428571  0.111111   0.388889      1.0           1.0   0.0            2  \n",
       "3  0.785714  0.444444   0.500000      1.0           0.0   0.0            2  \n",
       "4  0.214286  0.111111   0.722222      1.0           1.0   0.0            1  \n",
       "5  0.857143  0.055556   0.444444      1.0           0.0   0.0            1  \n",
       "6  0.571429  0.444444   0.888889      1.0           0.0   1.0            3  \n",
       "7  0.785714  0.166667   0.166667      1.0           1.0   1.0            0  \n",
       "8  0.857143  0.055556   1.000000      1.0           0.0   0.0            0  \n",
       "9  1.000000  0.555556   0.555556      1.0           0.0   0.0            0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_norm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LET'S SPLIT DATASET INTO TRAIN AND TEST DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "##splitting into 80:20\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(scaled_norm.drop('price_range',axis=1),scaled_norm['price_range'],test_size = 0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step -4 Feature Selection\n",
    "\n",
    "1-> REMOVE THE LOW VARIANCE FEATURES\n",
    "\n",
    "2-> CHECK THE PEARSON RELATIONSHIP BETWWEN THE INDEPENDENT FEATURES\n",
    "\n",
    "3-> TO SELECT ON BASIS OF MUTUAL GAIN HAVING CLASSIFIER O/P\n",
    "\n",
    "4-> TO SELECT ON BASIS OF MUTUAL GAIN HAVING REGRESSOR O/P\n",
    "\n",
    "5-> TO SELECT ON BASIS OF FIESCHER TEST\n",
    "\n",
    "6-> FEATURE IMPORTANCE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 1 REMOVE LOW VARIANCE FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "var_thresh = VarianceThreshold()\n",
    "var_thresh.fit(x_train,y_train)\n",
    "x_train.columns[var_thresh.get_support()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS SHOWS NO ONE HAS LOW VARIANCE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 2 CHECK RELATION BETWEEN INDEPENDENT VARIABLES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RELATIONSHIP BETWWEN INDEPENDENT FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>battery_power</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010458</td>\n",
       "      <td>-0.010598</td>\n",
       "      <td>-0.043354</td>\n",
       "      <td>0.032715</td>\n",
       "      <td>0.020508</td>\n",
       "      <td>-0.009002</td>\n",
       "      <td>0.034707</td>\n",
       "      <td>-0.003179</td>\n",
       "      <td>0.019731</td>\n",
       "      <td>0.020406</td>\n",
       "      <td>0.017076</td>\n",
       "      <td>-0.006008</td>\n",
       "      <td>-0.009150</td>\n",
       "      <td>-0.027405</td>\n",
       "      <td>-0.027801</td>\n",
       "      <td>0.062304</td>\n",
       "      <td>0.017268</td>\n",
       "      <td>-0.001153</td>\n",
       "      <td>-0.004483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>0.010458</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>0.031842</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>0.033523</td>\n",
       "      <td>-0.002841</td>\n",
       "      <td>-0.013677</td>\n",
       "      <td>0.034514</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>-0.012154</td>\n",
       "      <td>-0.042549</td>\n",
       "      <td>0.035320</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>0.013211</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>-0.030005</td>\n",
       "      <td>0.023285</td>\n",
       "      <td>-0.014138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock_speed</th>\n",
       "      <td>-0.010598</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002722</td>\n",
       "      <td>0.002617</td>\n",
       "      <td>-0.052252</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>-0.003874</td>\n",
       "      <td>-0.014589</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>-0.012487</td>\n",
       "      <td>-0.027630</td>\n",
       "      <td>-0.011592</td>\n",
       "      <td>0.004078</td>\n",
       "      <td>-0.037809</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>-0.003051</td>\n",
       "      <td>-0.047452</td>\n",
       "      <td>0.015020</td>\n",
       "      <td>-0.042348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dual_sim</th>\n",
       "      <td>-0.043354</td>\n",
       "      <td>0.031842</td>\n",
       "      <td>-0.002722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042541</td>\n",
       "      <td>-0.000386</td>\n",
       "      <td>-0.031895</td>\n",
       "      <td>-0.014072</td>\n",
       "      <td>0.005609</td>\n",
       "      <td>-0.025906</td>\n",
       "      <td>-0.030417</td>\n",
       "      <td>-0.011655</td>\n",
       "      <td>0.016823</td>\n",
       "      <td>0.037883</td>\n",
       "      <td>-0.010797</td>\n",
       "      <td>-0.009144</td>\n",
       "      <td>-0.043238</td>\n",
       "      <td>-0.018555</td>\n",
       "      <td>-0.022282</td>\n",
       "      <td>0.045864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>0.032715</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.002617</td>\n",
       "      <td>-0.042541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012684</td>\n",
       "      <td>-0.026783</td>\n",
       "      <td>-0.020219</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>-0.004624</td>\n",
       "      <td>0.635178</td>\n",
       "      <td>-0.023735</td>\n",
       "      <td>-0.005145</td>\n",
       "      <td>0.009466</td>\n",
       "      <td>-0.020976</td>\n",
       "      <td>-0.005686</td>\n",
       "      <td>0.012942</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>-0.018058</td>\n",
       "      <td>0.039885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four_g</th>\n",
       "      <td>0.020508</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>-0.052252</td>\n",
       "      <td>-0.000386</td>\n",
       "      <td>-0.012684</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>-0.008002</td>\n",
       "      <td>-0.020647</td>\n",
       "      <td>-0.039688</td>\n",
       "      <td>-0.009383</td>\n",
       "      <td>-0.014683</td>\n",
       "      <td>0.016999</td>\n",
       "      <td>-0.006799</td>\n",
       "      <td>0.013480</td>\n",
       "      <td>0.025315</td>\n",
       "      <td>-0.031618</td>\n",
       "      <td>0.578418</td>\n",
       "      <td>0.032839</td>\n",
       "      <td>-0.009349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_memory</th>\n",
       "      <td>-0.009002</td>\n",
       "      <td>0.033523</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>-0.031895</td>\n",
       "      <td>-0.026783</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>-0.034186</td>\n",
       "      <td>-0.034209</td>\n",
       "      <td>-0.016490</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>-0.018514</td>\n",
       "      <td>0.044134</td>\n",
       "      <td>0.029094</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>0.010259</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>-0.049438</td>\n",
       "      <td>0.010829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_dep</th>\n",
       "      <td>0.034707</td>\n",
       "      <td>-0.002841</td>\n",
       "      <td>-0.003874</td>\n",
       "      <td>-0.014072</td>\n",
       "      <td>-0.020219</td>\n",
       "      <td>-0.008002</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028306</td>\n",
       "      <td>0.004624</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.034968</td>\n",
       "      <td>0.022862</td>\n",
       "      <td>-0.002356</td>\n",
       "      <td>-0.014470</td>\n",
       "      <td>-0.014818</td>\n",
       "      <td>0.029997</td>\n",
       "      <td>-0.007955</td>\n",
       "      <td>-0.000659</td>\n",
       "      <td>-0.019703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobile_wt</th>\n",
       "      <td>-0.003179</td>\n",
       "      <td>-0.013677</td>\n",
       "      <td>-0.014589</td>\n",
       "      <td>0.005609</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>-0.020647</td>\n",
       "      <td>-0.034186</td>\n",
       "      <td>0.028306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011986</td>\n",
       "      <td>0.032316</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>0.016343</td>\n",
       "      <td>-0.002199</td>\n",
       "      <td>-0.022960</td>\n",
       "      <td>-0.016157</td>\n",
       "      <td>-0.010605</td>\n",
       "      <td>0.007794</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cores</th>\n",
       "      <td>0.019731</td>\n",
       "      <td>0.034514</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>-0.025906</td>\n",
       "      <td>-0.004624</td>\n",
       "      <td>-0.039688</td>\n",
       "      <td>-0.034209</td>\n",
       "      <td>0.004624</td>\n",
       "      <td>-0.011986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010303</td>\n",
       "      <td>-0.019491</td>\n",
       "      <td>0.026453</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>-0.010207</td>\n",
       "      <td>0.034910</td>\n",
       "      <td>0.005088</td>\n",
       "      <td>-0.016280</td>\n",
       "      <td>0.025346</td>\n",
       "      <td>-0.014116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc</th>\n",
       "      <td>0.020406</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>-0.012487</td>\n",
       "      <td>-0.030417</td>\n",
       "      <td>0.635178</td>\n",
       "      <td>-0.009383</td>\n",
       "      <td>-0.016490</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.032316</td>\n",
       "      <td>0.010303</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032194</td>\n",
       "      <td>-0.002565</td>\n",
       "      <td>0.023036</td>\n",
       "      <td>-0.000256</td>\n",
       "      <td>-0.016062</td>\n",
       "      <td>0.024480</td>\n",
       "      <td>-0.013140</td>\n",
       "      <td>-0.014012</td>\n",
       "      <td>0.026924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_height</th>\n",
       "      <td>0.017076</td>\n",
       "      <td>-0.012154</td>\n",
       "      <td>-0.027630</td>\n",
       "      <td>-0.011655</td>\n",
       "      <td>-0.023735</td>\n",
       "      <td>-0.014683</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>0.034968</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>-0.019491</td>\n",
       "      <td>-0.032194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.504994</td>\n",
       "      <td>-0.022382</td>\n",
       "      <td>0.052621</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>-0.002808</td>\n",
       "      <td>-0.021269</td>\n",
       "      <td>0.038065</td>\n",
       "      <td>0.044800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_width</th>\n",
       "      <td>-0.006008</td>\n",
       "      <td>-0.042549</td>\n",
       "      <td>-0.011592</td>\n",
       "      <td>0.016823</td>\n",
       "      <td>-0.005145</td>\n",
       "      <td>0.016999</td>\n",
       "      <td>-0.018514</td>\n",
       "      <td>0.022862</td>\n",
       "      <td>0.016343</td>\n",
       "      <td>0.026453</td>\n",
       "      <td>-0.002565</td>\n",
       "      <td>0.504994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001098</td>\n",
       "      <td>0.016640</td>\n",
       "      <td>0.040157</td>\n",
       "      <td>-0.009860</td>\n",
       "      <td>0.002008</td>\n",
       "      <td>0.022821</td>\n",
       "      <td>0.032956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>-0.009150</td>\n",
       "      <td>0.035320</td>\n",
       "      <td>0.004078</td>\n",
       "      <td>0.037883</td>\n",
       "      <td>0.009466</td>\n",
       "      <td>-0.006799</td>\n",
       "      <td>0.044134</td>\n",
       "      <td>-0.002356</td>\n",
       "      <td>-0.002199</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.023036</td>\n",
       "      <td>-0.022382</td>\n",
       "      <td>-0.001098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010407</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>-0.004645</td>\n",
       "      <td>-0.003943</td>\n",
       "      <td>-0.056593</td>\n",
       "      <td>0.002119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_h</th>\n",
       "      <td>-0.027405</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>-0.037809</td>\n",
       "      <td>-0.010797</td>\n",
       "      <td>-0.020976</td>\n",
       "      <td>0.013480</td>\n",
       "      <td>0.029094</td>\n",
       "      <td>-0.014470</td>\n",
       "      <td>-0.022960</td>\n",
       "      <td>-0.010207</td>\n",
       "      <td>-0.000256</td>\n",
       "      <td>0.052621</td>\n",
       "      <td>0.016640</td>\n",
       "      <td>0.010407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.518655</td>\n",
       "      <td>-0.005700</td>\n",
       "      <td>-0.000901</td>\n",
       "      <td>-0.001660</td>\n",
       "      <td>0.022965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_w</th>\n",
       "      <td>-0.027801</td>\n",
       "      <td>0.013211</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>-0.009144</td>\n",
       "      <td>-0.005686</td>\n",
       "      <td>0.025315</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>-0.014818</td>\n",
       "      <td>-0.016157</td>\n",
       "      <td>0.034910</td>\n",
       "      <td>-0.016062</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>0.040157</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.518655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011688</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.016929</td>\n",
       "      <td>0.033906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk_time</th>\n",
       "      <td>0.062304</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>-0.003051</td>\n",
       "      <td>-0.043238</td>\n",
       "      <td>0.012942</td>\n",
       "      <td>-0.031618</td>\n",
       "      <td>0.010259</td>\n",
       "      <td>0.029997</td>\n",
       "      <td>-0.010605</td>\n",
       "      <td>0.005088</td>\n",
       "      <td>0.024480</td>\n",
       "      <td>-0.002808</td>\n",
       "      <td>-0.009860</td>\n",
       "      <td>-0.004645</td>\n",
       "      <td>-0.005700</td>\n",
       "      <td>-0.011688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046551</td>\n",
       "      <td>0.030462</td>\n",
       "      <td>-0.006071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_g</th>\n",
       "      <td>0.017268</td>\n",
       "      <td>-0.030005</td>\n",
       "      <td>-0.047452</td>\n",
       "      <td>-0.018555</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>0.578418</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>-0.007955</td>\n",
       "      <td>0.007794</td>\n",
       "      <td>-0.016280</td>\n",
       "      <td>-0.013140</td>\n",
       "      <td>-0.021269</td>\n",
       "      <td>0.002008</td>\n",
       "      <td>-0.003943</td>\n",
       "      <td>-0.000901</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>-0.046551</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025805</td>\n",
       "      <td>0.009745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch_screen</th>\n",
       "      <td>-0.001153</td>\n",
       "      <td>0.023285</td>\n",
       "      <td>0.015020</td>\n",
       "      <td>-0.022282</td>\n",
       "      <td>-0.018058</td>\n",
       "      <td>0.032839</td>\n",
       "      <td>-0.049438</td>\n",
       "      <td>-0.000659</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>0.025346</td>\n",
       "      <td>-0.014012</td>\n",
       "      <td>0.038065</td>\n",
       "      <td>0.022821</td>\n",
       "      <td>-0.056593</td>\n",
       "      <td>-0.001660</td>\n",
       "      <td>0.016929</td>\n",
       "      <td>0.030462</td>\n",
       "      <td>0.025805</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wifi</th>\n",
       "      <td>-0.004483</td>\n",
       "      <td>-0.014138</td>\n",
       "      <td>-0.042348</td>\n",
       "      <td>0.045864</td>\n",
       "      <td>0.039885</td>\n",
       "      <td>-0.009349</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>-0.019703</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>-0.014116</td>\n",
       "      <td>0.026924</td>\n",
       "      <td>0.044800</td>\n",
       "      <td>0.032956</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.022965</td>\n",
       "      <td>0.033906</td>\n",
       "      <td>-0.006071</td>\n",
       "      <td>0.009745</td>\n",
       "      <td>0.006596</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               battery_power      blue  clock_speed  dual_sim        fc  \\\n",
       "battery_power       1.000000  0.010458    -0.010598 -0.043354  0.032715   \n",
       "blue                0.010458  1.000000     0.015063  0.031842  0.030418   \n",
       "clock_speed        -0.010598  0.015063     1.000000 -0.002722  0.002617   \n",
       "dual_sim           -0.043354  0.031842    -0.002722  1.000000 -0.042541   \n",
       "fc                  0.032715  0.030418     0.002617 -0.042541  1.000000   \n",
       "four_g              0.020508  0.014594    -0.052252 -0.000386 -0.012684   \n",
       "int_memory         -0.009002  0.033523     0.005075 -0.031895 -0.026783   \n",
       "m_dep               0.034707 -0.002841    -0.003874 -0.014072 -0.020219   \n",
       "mobile_wt          -0.003179 -0.013677    -0.014589  0.005609  0.024096   \n",
       "n_cores             0.019731  0.034514     0.005265 -0.025906 -0.004624   \n",
       "pc                  0.020406  0.006574    -0.012487 -0.030417  0.635178   \n",
       "px_height           0.017076 -0.012154    -0.027630 -0.011655 -0.023735   \n",
       "px_width           -0.006008 -0.042549    -0.011592  0.016823 -0.005145   \n",
       "ram                -0.009150  0.035320     0.004078  0.037883  0.009466   \n",
       "sc_h               -0.027405 -0.001262    -0.037809 -0.010797 -0.020976   \n",
       "sc_w               -0.027801  0.013211    -0.001768 -0.009144 -0.005686   \n",
       "talk_time           0.062304  0.004029    -0.003051 -0.043238  0.012942   \n",
       "three_g             0.017268 -0.030005    -0.047452 -0.018555 -0.000363   \n",
       "touch_screen       -0.001153  0.023285     0.015020 -0.022282 -0.018058   \n",
       "wifi               -0.004483 -0.014138    -0.042348  0.045864  0.039885   \n",
       "\n",
       "                 four_g  int_memory     m_dep  mobile_wt   n_cores        pc  \\\n",
       "battery_power  0.020508   -0.009002  0.034707  -0.003179  0.019731  0.020406   \n",
       "blue           0.014594    0.033523 -0.002841  -0.013677  0.034514  0.006574   \n",
       "clock_speed   -0.052252    0.005075 -0.003874  -0.014589  0.005265 -0.012487   \n",
       "dual_sim      -0.000386   -0.031895 -0.014072   0.005609 -0.025906 -0.030417   \n",
       "fc            -0.012684   -0.026783 -0.020219   0.024096 -0.004624  0.635178   \n",
       "four_g         1.000000    0.002975 -0.008002  -0.020647 -0.039688 -0.009383   \n",
       "int_memory     0.002975    1.000000 -0.004534  -0.034186 -0.034209 -0.016490   \n",
       "m_dep         -0.008002   -0.004534  1.000000   0.028306  0.004624  0.025757   \n",
       "mobile_wt     -0.020647   -0.034186  0.028306   1.000000 -0.011986  0.032316   \n",
       "n_cores       -0.039688   -0.034209  0.004624  -0.011986  1.000000  0.010303   \n",
       "pc            -0.009383   -0.016490  0.025757   0.032316  0.010303  1.000000   \n",
       "px_height     -0.014683   -0.000976  0.034968   0.004612 -0.019491 -0.032194   \n",
       "px_width       0.016999   -0.018514  0.022862   0.016343  0.026453 -0.002565   \n",
       "ram           -0.006799    0.044134 -0.002356  -0.002199  0.012695  0.023036   \n",
       "sc_h           0.013480    0.029094 -0.014470  -0.022960 -0.010207 -0.000256   \n",
       "sc_w           0.025315    0.004877 -0.014818  -0.016157  0.034910 -0.016062   \n",
       "talk_time     -0.031618    0.010259  0.029997  -0.010605  0.005088  0.024480   \n",
       "three_g        0.578418   -0.003715 -0.007955   0.007794 -0.016280 -0.013140   \n",
       "touch_screen   0.032839   -0.049438 -0.000659  -0.008882  0.025346 -0.014012   \n",
       "wifi          -0.009349    0.010829 -0.019703   0.001727 -0.014116  0.026924   \n",
       "\n",
       "               px_height  px_width       ram      sc_h      sc_w  talk_time  \\\n",
       "battery_power   0.017076 -0.006008 -0.009150 -0.027405 -0.027801   0.062304   \n",
       "blue           -0.012154 -0.042549  0.035320 -0.001262  0.013211   0.004029   \n",
       "clock_speed    -0.027630 -0.011592  0.004078 -0.037809 -0.001768  -0.003051   \n",
       "dual_sim       -0.011655  0.016823  0.037883 -0.010797 -0.009144  -0.043238   \n",
       "fc             -0.023735 -0.005145  0.009466 -0.020976 -0.005686   0.012942   \n",
       "four_g         -0.014683  0.016999 -0.006799  0.013480  0.025315  -0.031618   \n",
       "int_memory     -0.000976 -0.018514  0.044134  0.029094  0.004877   0.010259   \n",
       "m_dep           0.034968  0.022862 -0.002356 -0.014470 -0.014818   0.029997   \n",
       "mobile_wt       0.004612  0.016343 -0.002199 -0.022960 -0.016157  -0.010605   \n",
       "n_cores        -0.019491  0.026453  0.012695 -0.010207  0.034910   0.005088   \n",
       "pc             -0.032194 -0.002565  0.023036 -0.000256 -0.016062   0.024480   \n",
       "px_height       1.000000  0.504994 -0.022382  0.052621  0.039619  -0.002808   \n",
       "px_width        0.504994  1.000000 -0.001098  0.016640  0.040157  -0.009860   \n",
       "ram            -0.022382 -0.001098  1.000000  0.010407  0.028986  -0.004645   \n",
       "sc_h            0.052621  0.016640  0.010407  1.000000  0.518655  -0.005700   \n",
       "sc_w            0.039619  0.040157  0.028986  0.518655  1.000000  -0.011688   \n",
       "talk_time      -0.002808 -0.009860 -0.004645 -0.005700 -0.011688   1.000000   \n",
       "three_g        -0.021269  0.002008 -0.003943 -0.000901  0.024122  -0.046551   \n",
       "touch_screen    0.038065  0.022821 -0.056593 -0.001660  0.016929   0.030462   \n",
       "wifi            0.044800  0.032956  0.002119  0.022965  0.033906  -0.006071   \n",
       "\n",
       "                three_g  touch_screen      wifi  \n",
       "battery_power  0.017268     -0.001153 -0.004483  \n",
       "blue          -0.030005      0.023285 -0.014138  \n",
       "clock_speed   -0.047452      0.015020 -0.042348  \n",
       "dual_sim      -0.018555     -0.022282  0.045864  \n",
       "fc            -0.000363     -0.018058  0.039885  \n",
       "four_g         0.578418      0.032839 -0.009349  \n",
       "int_memory    -0.003715     -0.049438  0.010829  \n",
       "m_dep         -0.007955     -0.000659 -0.019703  \n",
       "mobile_wt      0.007794     -0.008882  0.001727  \n",
       "n_cores       -0.016280      0.025346 -0.014116  \n",
       "pc            -0.013140     -0.014012  0.026924  \n",
       "px_height     -0.021269      0.038065  0.044800  \n",
       "px_width       0.002008      0.022821  0.032956  \n",
       "ram           -0.003943     -0.056593  0.002119  \n",
       "sc_h          -0.000901     -0.001660  0.022965  \n",
       "sc_w           0.024122      0.016929  0.033906  \n",
       "talk_time     -0.046551      0.030462 -0.006071  \n",
       "three_g        1.000000      0.025805  0.009745  \n",
       "touch_screen   0.025805      1.000000  0.006596  \n",
       "wifi           0.009745      0.006596  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,12))\n",
    "sns.heatmap(x_train.corr(),annot =True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FROM PLOT WE CAN SEE THAT MAX TWO FEATURE ARE CORRELATED WITH FRACTION 0.64 WHICH PROVES THAT THEY ARE NOT MUCH RELATED TO EACH OTHER "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LET'S GO FOR ANOTHER SELECTION WAY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 3 SELECTION ON BASIS OF MUTUAL GAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.84510383e-02 2.08563583e-02 0.00000000e+00 0.00000000e+00\n",
      " 2.84735694e-02 0.00000000e+00 1.55032937e-02 0.00000000e+00\n",
      " 0.00000000e+00 3.00092465e-04 0.00000000e+00 4.08665902e-02\n",
      " 2.20613400e-02 8.29219689e-01 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.17890538e-02 2.93201133e-02]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mutual_info = mutual_info_classif(x_train,y_train)\n",
    "print(mutual_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ram              0.829220\n",
       "battery_power    0.048451\n",
       "px_height        0.040867\n",
       "wifi             0.029320\n",
       "fc               0.028474\n",
       "px_width         0.022061\n",
       "blue             0.020856\n",
       "int_memory       0.015503\n",
       "touch_screen     0.011789\n",
       "n_cores          0.000300\n",
       "sc_w             0.000000\n",
       "sc_h             0.000000\n",
       "talk_time        0.000000\n",
       "three_g          0.000000\n",
       "pc               0.000000\n",
       "mobile_wt        0.000000\n",
       "m_dep            0.000000\n",
       "four_g           0.000000\n",
       "dual_sim         0.000000\n",
       "clock_speed      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info = pd.Series(mutual_info , index = x_train.columns)\n",
    "mutual_info.sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS SHOWS THAT FEATURE WHICH IS HAVING 0 VALUE IMPLIES THAT THAT FEATURE IS OF 0 IMPOTANCE FOR THE O/P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS IS THE RANK OF THE FEATURE FROM HIGHER TO LOWER WICH ARE IMPORTANT WHILE PREDICTING THE OUTPUT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ram FEATURE IS VERY IMPORTANT WHILE PREDICTING THE O/P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutual_info.sort_values(ascending =False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOW AM GOING TO SELECT THE FEATURES WHO HAS MORE IMPORTANCE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = {column : round(mutual_info[column],5) for column in mutual_info.keys() if mutual_info[column]>0 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'battery_power': 0.04845,\n",
       " 'blue': 0.02086,\n",
       " 'fc': 0.02847,\n",
       " 'int_memory': 0.0155,\n",
       " 'n_cores': 0.0003,\n",
       " 'px_height': 0.04087,\n",
       " 'px_width': 0.02206,\n",
       " 'ram': 0.82922,\n",
       " 'touch_screen': 0.01179,\n",
       " 'wifi': 0.02932}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIST OF ELEMENTS HAVING LOW IMPORTANCE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove = [column for column in mutual_info.keys() if column not in select]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clock_speed',\n",
       " 'dual_sim',\n",
       " 'four_g',\n",
       " 'm_dep',\n",
       " 'mobile_wt',\n",
       " 'pc',\n",
       " 'sc_h',\n",
       " 'sc_w',\n",
       " 'talk_time',\n",
       " 'three_g']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "NOW REMOVING THE ELEMENTS NON IMPORTANT FROM THE SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.drop(remove , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.488310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.184184</td>\n",
       "      <td>0.206275</td>\n",
       "      <td>0.267504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.894456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158673</td>\n",
       "      <td>0.697597</td>\n",
       "      <td>0.219669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>0.795591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.968878</td>\n",
       "      <td>0.937250</td>\n",
       "      <td>0.941475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019898</td>\n",
       "      <td>0.038051</td>\n",
       "      <td>0.108765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>0.317969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.532258</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.766327</td>\n",
       "      <td>0.909212</td>\n",
       "      <td>0.924105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue        fc  int_memory   n_cores  px_height  \\\n",
       "582        0.488310   0.0  0.052632    0.354839  0.571429   0.184184   \n",
       "159        0.894456   0.0  0.631579    0.516129  0.000000   0.158673   \n",
       "1827       0.795591   0.0  0.210526    0.000000  0.000000   0.968878   \n",
       "318        0.004676   0.0  0.368421    0.645161  0.000000   0.019898   \n",
       "708        0.317969   1.0  0.105263    0.532258  0.142857   0.766327   \n",
       "\n",
       "      px_width       ram  touch_screen  wifi  \n",
       "582   0.206275  0.267504           0.0   0.0  \n",
       "159   0.697597  0.219669           0.0   0.0  \n",
       "1827  0.937250  0.941475           1.0   1.0  \n",
       "318   0.038051  0.108765           0.0   0.0  \n",
       "708   0.909212  0.924105           1.0   1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = x_test.drop(remove , axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.636607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.845270</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>0.394790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.238776</td>\n",
       "      <td>0.047397</td>\n",
       "      <td>0.128541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>0.683367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.078571</td>\n",
       "      <td>0.033378</td>\n",
       "      <td>0.647247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>0.872411</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>0.590120</td>\n",
       "      <td>0.440941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>0.390782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.661290</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.433912</td>\n",
       "      <td>0.809460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue        fc  int_memory   n_cores  px_height  \\\n",
       "405        0.636607   1.0  0.052632    0.516129  0.428571   0.127551   \n",
       "1190       0.394790   1.0  0.526316    0.145161  0.285714   0.238776   \n",
       "1132       0.683367   1.0  0.000000    0.129032  0.428571   0.078571   \n",
       "731        0.872411   1.0  0.105263    0.758065  0.000000   0.171939   \n",
       "1754       0.390782   1.0  0.000000    0.661290  0.714286   0.028571   \n",
       "\n",
       "      px_width       ram  touch_screen  wifi  \n",
       "405   0.355808  0.845270           1.0   0.0  \n",
       "1190  0.047397  0.128541           1.0   0.0  \n",
       "1132  0.033378  0.647247           0.0   1.0  \n",
       "731   0.590120  0.440941           1.0   1.0  \n",
       "1754  0.433912  0.809460           1.0   0.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOW WE HAVE IDENTIFIED THE FEATURES WHICH ARE IMPORTANT AND REMOVED IT TOO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOW WE NEED TO BUILD MODELS-MODEL BUILDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 4 OF FEATURE SELECTION IE; MUTUAL_INFO IS FOR REGRESSION TYPE O/P- NO NEED TO APPLY HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 5 OF FEATURE SELECTION FIESHER TEST. LET'S DO IT NOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(scaled_norm.drop('price_range',axis=1),scaled_norm['price_range'],test_size = 0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "f_p_val = chi2(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.28216560e+01, 1.40127358e+00, 2.02153844e-01, 6.24301869e-01,\n",
       "        4.65038748e-01, 1.24890071e+00, 1.28452957e+00, 4.31708942e-01,\n",
       "        1.80101591e+00, 2.69945111e+00, 2.79315718e-01, 6.66283473e+00,\n",
       "        7.92207674e+00, 2.24552217e+02, 7.01452074e-01, 8.27028597e-01,\n",
       "        5.22862902e-01, 2.31936485e-02, 3.33175408e+00, 9.94585406e-02]),\n",
       " array([5.03859579e-03, 7.05236224e-01, 9.77240847e-01, 8.90847171e-01,\n",
       "        9.26502303e-01, 7.41301351e-01, 7.32809921e-01, 9.33617017e-01,\n",
       "        6.14713886e-01, 4.40320581e-01, 9.63870506e-01, 8.34572305e-02,\n",
       "        4.76498970e-02, 2.08269442e-48, 8.72862353e-01, 8.42991822e-01,\n",
       "        9.13840575e-01, 9.99067062e-01, 3.43247465e-01, 9.91902322e-01]))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HERE , f_p_val[0] IS GIVING F - SCORE\n",
    "HERE ,f_p_val[1] IS GIVING P - VALUE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 THE MORE VALUE OF THE F-SCORE THE MORE IMPORTANT IS THE FEATURE \n",
    "#### 2 THE LESSER THE VALUE OF P THE MORE IMPORTANT IS THE FEATURE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WE CAN GO WITH THE EITHER ONE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ram              224.552217\n",
      "battery_power     12.821656\n",
      "px_width           7.922077\n",
      "px_height          6.662835\n",
      "touch_screen       3.331754\n",
      "n_cores            2.699451\n",
      "mobile_wt          1.801016\n",
      "blue               1.401274\n",
      "int_memory         1.284530\n",
      "four_g             1.248901\n",
      "sc_w               0.827029\n",
      "sc_h               0.701452\n",
      "dual_sim           0.624302\n",
      "talk_time          0.522863\n",
      "fc                 0.465039\n",
      "m_dep              0.431709\n",
      "pc                 0.279316\n",
      "clock_speed        0.202154\n",
      "wifi               0.099459\n",
      "three_g            0.023194\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "f_score = pd.Series(f_p_val[0],index = x_train.columns)\n",
    "print(f_score.sort_values(ascending =False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SO HERE ram IS THE MOST IMPORTANT FEATURE CONTRIBUTIN TO THE O/P AND three_g IS THE LEAST IMPORTANT FEATURE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_score.sort_values(ascending =False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BY THIS WAY WE WILL ANOTHER TYPE OF LIST OF FEATURES TO BE REOMVED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = {column : round(f_score[column],5) for column in f_score.keys() if f_score[column]>1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'battery_power': 12.82166,\n",
       " 'blue': 1.40127,\n",
       " 'four_g': 1.2489,\n",
       " 'int_memory': 1.28453,\n",
       " 'mobile_wt': 1.80102,\n",
       " 'n_cores': 2.69945,\n",
       " 'px_height': 6.66283,\n",
       " 'px_width': 7.92208,\n",
       " 'ram': 224.55222,\n",
       " 'touch_screen': 3.33175}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove = [column for column in f_score.keys() if column not in select]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clock_speed',\n",
       " 'dual_sim',\n",
       " 'fc',\n",
       " 'm_dep',\n",
       " 'pc',\n",
       " 'sc_h',\n",
       " 'sc_w',\n",
       " 'talk_time',\n",
       " 'three_g',\n",
       " 'wifi']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DROPPING FEATURES WHICH ARE NOT IMPORTANT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.drop(remove,axis=1)\n",
    "x_test = x_test.drop(remove , axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>touch_screen</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.488310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.741667</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.184184</td>\n",
       "      <td>0.206275</td>\n",
       "      <td>0.267504</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.894456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158673</td>\n",
       "      <td>0.697597</td>\n",
       "      <td>0.219669</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>0.795591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.968878</td>\n",
       "      <td>0.937250</td>\n",
       "      <td>0.941475</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019898</td>\n",
       "      <td>0.038051</td>\n",
       "      <td>0.108765</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>0.317969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532258</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.766327</td>\n",
       "      <td>0.909212</td>\n",
       "      <td>0.924105</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  four_g  int_memory  mobile_wt   n_cores  px_height  \\\n",
       "582        0.488310   0.0     1.0    0.354839   0.741667  0.571429   0.184184   \n",
       "159        0.894456   0.0     0.0    0.516129   0.516667  0.000000   0.158673   \n",
       "1827       0.795591   0.0     1.0    0.000000   0.216667  0.000000   0.968878   \n",
       "318        0.004676   0.0     1.0    0.645161   0.116667  0.000000   0.019898   \n",
       "708        0.317969   1.0     0.0    0.532258   0.708333  0.142857   0.766327   \n",
       "\n",
       "      px_width       ram  touch_screen  \n",
       "582   0.206275  0.267504           0.0  \n",
       "159   0.697597  0.219669           0.0  \n",
       "1827  0.937250  0.941475           1.0  \n",
       "318   0.038051  0.108765           0.0  \n",
       "708   0.909212  0.924105           1.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>touch_screen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.636607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.845270</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>0.394790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.238776</td>\n",
       "      <td>0.047397</td>\n",
       "      <td>0.128541</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>0.683367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.078571</td>\n",
       "      <td>0.033378</td>\n",
       "      <td>0.647247</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>0.872411</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>0.590120</td>\n",
       "      <td>0.440941</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>0.390782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.661290</td>\n",
       "      <td>0.258333</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.433912</td>\n",
       "      <td>0.809460</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  four_g  int_memory  mobile_wt   n_cores  px_height  \\\n",
       "405        0.636607   1.0     0.0    0.516129   0.025000  0.428571   0.127551   \n",
       "1190       0.394790   1.0     0.0    0.145161   0.725000  0.285714   0.238776   \n",
       "1132       0.683367   1.0     0.0    0.129032   0.783333  0.428571   0.078571   \n",
       "731        0.872411   1.0     0.0    0.758065   0.375000  0.000000   0.171939   \n",
       "1754       0.390782   1.0     1.0    0.661290   0.258333  0.714286   0.028571   \n",
       "\n",
       "      px_width       ram  touch_screen  \n",
       "405   0.355808  0.845270           1.0  \n",
       "1190  0.047397  0.128541           1.0  \n",
       "1132  0.033378  0.647247           0.0  \n",
       "731   0.590120  0.440941           1.0  \n",
       "1754  0.433912  0.809460           1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOW WE JUST NEED TO BUILD THE MODELS ON THIS BASIS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 6 FEATURE IMPORTANCE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2   2         20       756  2549     9     7         19   \n",
       "1        136        3   6        905      1988  2631    17     3          7   \n",
       "2        145        5   6       1263      1716  2603    11     2          9   \n",
       "3        131        6   9       1216      1786  2769    16     8         11   \n",
       "4        141        2  14       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  \n",
       "0        0             0     1  \n",
       "1        1             1     0  \n",
       "2        1             1     0  \n",
       "3        1             0     0  \n",
       "4        1             1     0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    1\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extratc=ExtraTreesClassifier()\n",
    "extratc.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06071522, 0.01957195, 0.03257787, 0.02042774, 0.03254662,\n",
       "       0.01676288, 0.0341203 , 0.03405401, 0.0358907 , 0.03264112,\n",
       "       0.03313124, 0.04739315, 0.04852741, 0.39562478, 0.03375832,\n",
       "       0.03440139, 0.03388514, 0.014899  , 0.01843156, 0.02063959])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extratc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_f=pd.Series(extratc.feature_importances_,index=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "extra_f.sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIST OF FEATURES TO BE REMOVED FROM THE DATA SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wifi', 'dual_sim', 'blue', 'touch_screen', 'four_g', 'three_g']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove=list(extra_f.sort_values(ascending=False).index[14:])\n",
    "remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REMOVING FROM THE TRAIN AND TEST SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.drop(remove,axis=1)\n",
    "x_test = x_test.drop(remove , axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SO BASICALLY WHAT I HAVE DONE HERE I NORMALIZED MY DATA SET (FEATURE SCALING) AND THEN APPLIED THE FEATURE SELECTION WAYS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I WILL TRY TO TRANSFORM MY CONTINUOUS AND CATEGORICAL NUMERICAL FEATURE TO GIVE A NORMAL DISTRIBUTION AND THEN , I WILL PERFORM STANDARDIZATION or NORMALIZATION AND THEN FEATURE SELECTION  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HERE I WILL IMPUTE THE CONTINUOUS VARIABLES EXPONENTIALLY AND TRANFORMING THE CATEGORICAL VAIORABLES BY COUNT/ FREQUENCY COUNT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 =df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "## FUNCTION TO PLOT\n",
    "\n",
    "import pylab\n",
    "import scipy.stats as stat\n",
    "\n",
    "def plot_QQ(dataset,feature):\n",
    "    plt.subplot(1,2,1)\n",
    "    dataset[feature].hist(bins =20)\n",
    "    plt.subplot(1,2,2)\n",
    "    stat.probplot(dataset[feature],plot = pylab)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### applying the exponential transformation on continuous variables\n",
    "\n",
    "for feature in continuous_num_features:\n",
    "    data2[feature] = df[feature]**(1/1.2)\n",
    "    plot_QQ(data2,feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I CAN SEE THAT MODET OF THE POINTS ARE FALLING ON THE LINE WHICH IMPLIES IT A MORE LIKELY A NORMAL DISTRIBUTED "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "## CATEGORICAL FEATURES\n",
    "\n",
    "def freq_count(dataset,feature):\n",
    "    if feature == 'price_range':\n",
    "        pass\n",
    "    else:\n",
    "        count = dataset[feature].value_counts().to_dict()\n",
    "        \n",
    "        dataset[feature] = dataset[feature].map(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in categorical_num_features:\n",
    "    freq_count(data2,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>274.006002</td>\n",
       "      <td>1010</td>\n",
       "      <td>1.929089</td>\n",
       "      <td>981</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>957</td>\n",
       "      <td>5.061140</td>\n",
       "      <td>186</td>\n",
       "      <td>78.547351</td>\n",
       "      <td>247</td>\n",
       "      <td>...</td>\n",
       "      <td>12.139245</td>\n",
       "      <td>250.477179</td>\n",
       "      <td>689.670309</td>\n",
       "      <td>6.240251</td>\n",
       "      <td>5.061140</td>\n",
       "      <td>11.631293</td>\n",
       "      <td>477</td>\n",
       "      <td>994</td>\n",
       "      <td>1014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321.752146</td>\n",
       "      <td>990</td>\n",
       "      <td>0.561231</td>\n",
       "      <td>1019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1043</td>\n",
       "      <td>27.346173</td>\n",
       "      <td>200</td>\n",
       "      <td>59.972077</td>\n",
       "      <td>246</td>\n",
       "      <td>...</td>\n",
       "      <td>290.987153</td>\n",
       "      <td>560.635142</td>\n",
       "      <td>708.109966</td>\n",
       "      <td>10.601666</td>\n",
       "      <td>2.498050</td>\n",
       "      <td>5.061140</td>\n",
       "      <td>1523</td>\n",
       "      <td>1006</td>\n",
       "      <td>986</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195.925214</td>\n",
       "      <td>990</td>\n",
       "      <td>0.561231</td>\n",
       "      <td>1019</td>\n",
       "      <td>1.781797</td>\n",
       "      <td>1043</td>\n",
       "      <td>22.079363</td>\n",
       "      <td>195</td>\n",
       "      <td>63.261575</td>\n",
       "      <td>246</td>\n",
       "      <td>...</td>\n",
       "      <td>384.151729</td>\n",
       "      <td>495.942206</td>\n",
       "      <td>701.824417</td>\n",
       "      <td>7.376108</td>\n",
       "      <td>1.781797</td>\n",
       "      <td>6.240251</td>\n",
       "      <td>1523</td>\n",
       "      <td>1006</td>\n",
       "      <td>986</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.893210</td>\n",
       "      <td>990</td>\n",
       "      <td>2.145936</td>\n",
       "      <td>981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>957</td>\n",
       "      <td>6.812921</td>\n",
       "      <td>208</td>\n",
       "      <td>58.128986</td>\n",
       "      <td>230</td>\n",
       "      <td>...</td>\n",
       "      <td>372.201380</td>\n",
       "      <td>512.744738</td>\n",
       "      <td>738.928578</td>\n",
       "      <td>10.079368</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>7.376108</td>\n",
       "      <td>1523</td>\n",
       "      <td>994</td>\n",
       "      <td>986</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521.104658</td>\n",
       "      <td>990</td>\n",
       "      <td>1.164084</td>\n",
       "      <td>981</td>\n",
       "      <td>8.477858</td>\n",
       "      <td>1043</td>\n",
       "      <td>23.417682</td>\n",
       "      <td>186</td>\n",
       "      <td>61.803907</td>\n",
       "      <td>247</td>\n",
       "      <td>...</td>\n",
       "      <td>370.159681</td>\n",
       "      <td>371.180811</td>\n",
       "      <td>421.313955</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>1.781797</td>\n",
       "      <td>9.551598</td>\n",
       "      <td>1523</td>\n",
       "      <td>1006</td>\n",
       "      <td>986</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  \\\n",
       "0     274.006002  1010     1.929089       981  1.000000     957    5.061140   \n",
       "1     321.752146   990     0.561231      1019  0.000000    1043   27.346173   \n",
       "2     195.925214   990     0.561231      1019  1.781797    1043   22.079363   \n",
       "3     210.893210   990     2.145936       981  0.000000     957    6.812921   \n",
       "4     521.104658   990     1.164084       981  8.477858    1043   23.417682   \n",
       "\n",
       "   m_dep  mobile_wt  n_cores  ...   px_height    px_width         ram  \\\n",
       "0    186  78.547351      247  ...   12.139245  250.477179  689.670309   \n",
       "1    200  59.972077      246  ...  290.987153  560.635142  708.109966   \n",
       "2    195  63.261575      246  ...  384.151729  495.942206  701.824417   \n",
       "3    208  58.128986      230  ...  372.201380  512.744738  738.928578   \n",
       "4    186  61.803907      247  ...  370.159681  371.180811  421.313955   \n",
       "\n",
       "        sc_h      sc_w  talk_time  three_g  touch_screen  wifi  price_range  \n",
       "0   6.240251  5.061140  11.631293      477           994  1014            1  \n",
       "1  10.601666  2.498050   5.061140     1523          1006   986            2  \n",
       "2   7.376108  1.781797   6.240251     1523          1006   986            2  \n",
       "3  10.079368  5.656854   7.376108     1523           994   986            2  \n",
       "4   5.656854  1.781797   9.551598     1523          1006   986            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I WILL DO STANDARDIATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data2.drop('price_range',axis =1)\n",
    "y = data2['price_range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>-0.892800</td>\n",
       "      <td>0.990050</td>\n",
       "      <td>0.844417</td>\n",
       "      <td>-1.019184</td>\n",
       "      <td>-0.748565</td>\n",
       "      <td>-1.043966</td>\n",
       "      <td>-1.434700</td>\n",
       "      <td>-0.502556</td>\n",
       "      <td>1.331597</td>\n",
       "      <td>-0.291494</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.587624</td>\n",
       "      <td>-1.153108</td>\n",
       "      <td>0.425282</td>\n",
       "      <td>-0.765764</td>\n",
       "      <td>0.347508</td>\n",
       "      <td>1.412550</td>\n",
       "      <td>-1.786861</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>0.986097</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.468513</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>-1.293774</td>\n",
       "      <td>0.981177</td>\n",
       "      <td>-1.103236</td>\n",
       "      <td>0.957886</td>\n",
       "      <td>1.132465</td>\n",
       "      <td>-0.235746</td>\n",
       "      <td>-0.098429</td>\n",
       "      <td>-0.372487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629749</td>\n",
       "      <td>1.653063</td>\n",
       "      <td>0.496971</td>\n",
       "      <td>1.101376</td>\n",
       "      <td>-0.587597</td>\n",
       "      <td>-0.701156</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>0.994018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.586652</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>-1.293774</td>\n",
       "      <td>0.981177</td>\n",
       "      <td>-0.471285</td>\n",
       "      <td>0.957886</td>\n",
       "      <td>0.525745</td>\n",
       "      <td>-0.331035</td>\n",
       "      <td>0.154814</td>\n",
       "      <td>-0.372487</td>\n",
       "      <td>...</td>\n",
       "      <td>1.370585</td>\n",
       "      <td>1.067750</td>\n",
       "      <td>0.472534</td>\n",
       "      <td>-0.279499</td>\n",
       "      <td>-0.848910</td>\n",
       "      <td>-0.321820</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>0.994018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.453641</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>1.183385</td>\n",
       "      <td>-1.019184</td>\n",
       "      <td>-1.103236</td>\n",
       "      <td>-1.043966</td>\n",
       "      <td>-1.232901</td>\n",
       "      <td>-0.083283</td>\n",
       "      <td>-0.240321</td>\n",
       "      <td>-1.668374</td>\n",
       "      <td>...</td>\n",
       "      <td>1.275557</td>\n",
       "      <td>1.219772</td>\n",
       "      <td>0.616787</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.564845</td>\n",
       "      <td>0.043600</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.302998</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>-0.351414</td>\n",
       "      <td>-1.019184</td>\n",
       "      <td>1.903610</td>\n",
       "      <td>0.957886</td>\n",
       "      <td>0.679915</td>\n",
       "      <td>-0.502556</td>\n",
       "      <td>0.042595</td>\n",
       "      <td>-0.291494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.259322</td>\n",
       "      <td>-0.061035</td>\n",
       "      <td>-0.618024</td>\n",
       "      <td>-1.015519</td>\n",
       "      <td>-0.848910</td>\n",
       "      <td>0.743485</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>0.994018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.383386</td>\n",
       "      <td>0.990050</td>\n",
       "      <td>-1.293774</td>\n",
       "      <td>0.981177</td>\n",
       "      <td>-0.217251</td>\n",
       "      <td>-1.043966</td>\n",
       "      <td>-0.503727</td>\n",
       "      <td>-0.235746</td>\n",
       "      <td>0.681089</td>\n",
       "      <td>-0.696459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.838839</td>\n",
       "      <td>0.932238</td>\n",
       "      <td>-0.958305</td>\n",
       "      <td>1.101376</td>\n",
       "      <td>-1.134137</td>\n",
       "      <td>-0.137585</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.302998</td>\n",
       "      <td>0.990050</td>\n",
       "      <td>0.261391</td>\n",
       "      <td>-1.019184</td>\n",
       "      <td>0.022773</td>\n",
       "      <td>0.957886</td>\n",
       "      <td>-1.232901</td>\n",
       "      <td>-0.083283</td>\n",
       "      <td>-0.013713</td>\n",
       "      <td>0.437443</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.558922</td>\n",
       "      <td>-0.515373</td>\n",
       "      <td>1.001722</td>\n",
       "      <td>0.192165</td>\n",
       "      <td>0.564845</td>\n",
       "      <td>1.247695</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>0.986097</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.583173</td>\n",
       "      <td>0.990050</td>\n",
       "      <td>-1.293774</td>\n",
       "      <td>0.981177</td>\n",
       "      <td>-1.103236</td>\n",
       "      <td>-1.043966</td>\n",
       "      <td>-0.389870</td>\n",
       "      <td>-0.083283</td>\n",
       "      <td>1.304781</td>\n",
       "      <td>1.895317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.244705</td>\n",
       "      <td>-0.207148</td>\n",
       "      <td>-1.342690</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>-0.587597</td>\n",
       "      <td>-1.099282</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>0.994018</td>\n",
       "      <td>0.986097</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.491255</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>-1.293774</td>\n",
       "      <td>-1.019184</td>\n",
       "      <td>-1.103236</td>\n",
       "      <td>-1.043966</td>\n",
       "      <td>1.132465</td>\n",
       "      <td>-0.235746</td>\n",
       "      <td>0.953940</td>\n",
       "      <td>0.680422</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.546629</td>\n",
       "      <td>-0.954959</td>\n",
       "      <td>-0.925953</td>\n",
       "      <td>1.101376</td>\n",
       "      <td>-1.134137</td>\n",
       "      <td>1.575965</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.726968</td>\n",
       "      <td>-1.010051</td>\n",
       "      <td>-1.149824</td>\n",
       "      <td>0.981177</td>\n",
       "      <td>-0.471285</td>\n",
       "      <td>0.957886</td>\n",
       "      <td>-1.298871</td>\n",
       "      <td>2.051197</td>\n",
       "      <td>-1.351755</td>\n",
       "      <td>-0.372487</td>\n",
       "      <td>...</td>\n",
       "      <td>1.114430</td>\n",
       "      <td>-0.033349</td>\n",
       "      <td>-1.551088</td>\n",
       "      <td>1.542164</td>\n",
       "      <td>0.986618</td>\n",
       "      <td>0.222056</td>\n",
       "      <td>0.559641</td>\n",
       "      <td>-1.006018</td>\n",
       "      <td>-1.014099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power      blue  clock_speed  dual_sim        fc    four_g  \\\n",
       "0      -0.892800  0.990050     0.844417 -1.019184 -0.748565 -1.043966   \n",
       "1      -0.468513 -1.010051    -1.293774  0.981177 -1.103236  0.957886   \n",
       "2      -1.586652 -1.010051    -1.293774  0.981177 -0.471285  0.957886   \n",
       "3      -1.453641 -1.010051     1.183385 -1.019184 -1.103236 -1.043966   \n",
       "4       1.302998 -1.010051    -0.351414 -1.019184  1.903610  0.957886   \n",
       "5       1.383386  0.990050    -1.293774  0.981177 -0.217251 -1.043966   \n",
       "6       1.302998  0.990050     0.261391 -1.019184  0.022773  0.957886   \n",
       "7       1.583173  0.990050    -1.293774  0.981177 -1.103236 -1.043966   \n",
       "8       0.491255 -1.010051    -1.293774 -1.019184 -1.103236 -1.043966   \n",
       "9      -1.726968 -1.010051    -1.149824  0.981177 -0.471285  0.957886   \n",
       "\n",
       "   int_memory     m_dep  mobile_wt   n_cores  ...  px_height  px_width  \\\n",
       "0   -1.434700 -0.502556   1.331597 -0.291494  ...  -1.587624 -1.153108   \n",
       "1    1.132465 -0.235746  -0.098429 -0.372487  ...   0.629749  1.653063   \n",
       "2    0.525745 -0.331035   0.154814 -0.372487  ...   1.370585  1.067750   \n",
       "3   -1.232901 -0.083283  -0.240321 -1.668374  ...   1.275557  1.219772   \n",
       "4    0.679915 -0.502556   0.042595 -0.291494  ...   1.259322 -0.061035   \n",
       "5   -0.503727 -0.235746   0.681089 -0.696459  ...   0.838839  0.932238   \n",
       "6   -1.232901 -0.083283  -0.013713  0.437443  ...  -0.558922 -0.515373   \n",
       "7   -0.389870 -0.083283   1.304781  1.895317  ...  -0.244705 -0.207148   \n",
       "8    1.132465 -0.235746   0.953940  0.680422  ...  -0.546629 -0.954959   \n",
       "9   -1.298871  2.051197  -1.351755 -0.372487  ...   1.114430 -0.033349   \n",
       "\n",
       "        ram      sc_h      sc_w  talk_time   three_g  touch_screen      wifi  \\\n",
       "0  0.425282 -0.765764  0.347508   1.412550 -1.786861     -1.006018  0.986097   \n",
       "1  0.496971  1.101376 -0.587597  -0.701156  0.559641      0.994018 -1.014099   \n",
       "2  0.472534 -0.279499 -0.848910  -0.321820  0.559641      0.994018 -1.014099   \n",
       "3  0.616787  0.877778  0.564845   0.043600  0.559641     -1.006018 -1.014099   \n",
       "4 -0.618024 -1.015519 -0.848910   0.743485  0.559641      0.994018 -1.014099   \n",
       "5 -0.958305  1.101376 -1.134137  -0.137585  0.559641     -1.006018 -1.014099   \n",
       "6  1.001722  0.192165  0.564845   1.247695  0.559641     -1.006018  0.986097   \n",
       "7 -1.342690  0.877778 -0.587597  -1.099282  0.559641      0.994018  0.986097   \n",
       "8 -0.925953  1.101376 -1.134137   1.575965  0.559641     -1.006018 -1.014099   \n",
       "9 -1.551088  1.542164  0.986618   0.222056  0.559641     -1.006018 -1.014099   \n",
       "\n",
       "   price_range  \n",
       "0            1  \n",
       "1            2  \n",
       "2            2  \n",
       "3            2  \n",
       "4            1  \n",
       "5            1  \n",
       "6            3  \n",
       "7            0  \n",
       "8            0  \n",
       "9            0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaled_std = StandardScaler()\n",
    "scaled_std = pd.DataFrame(scaled_std.fit_transform(x),columns  = data2.columns[:-1])\n",
    "scaled_std['price_range'] = data2['price_range']\n",
    "scaled_std.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = scaled_std.drop('price_range',axis =1)\n",
    "y = scaled_std['price_range']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FEATURE SELECTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size =0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### APPLY WAY -3 OF FEATURE SEELCTION BY MUTUAL GAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.53051130e-02, 3.11584355e-02, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.31161186e-02, 6.54259215e-03, 9.61514134e-03, 1.62603847e-02,\n",
       "       1.54676635e-02, 0.00000000e+00, 0.00000000e+00, 3.60132996e-02,\n",
       "       2.12614748e-02, 8.25480584e-01, 0.00000000e+00, 4.23239323e-02,\n",
       "       2.05741831e-02, 0.00000000e+00, 5.18828412e-04, 1.25708497e-02])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info = mutual_info_classif(x_train,y_train)\n",
    "mutual_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ram              0.825481\n",
       "battery_power    0.045305\n",
       "sc_w             0.042324\n",
       "px_height        0.036013\n",
       "blue             0.031158\n",
       "fc               0.023116\n",
       "px_width         0.021261\n",
       "talk_time        0.020574\n",
       "m_dep            0.016260\n",
       "mobile_wt        0.015468\n",
       "wifi             0.012571\n",
       "int_memory       0.009615\n",
       "four_g           0.006543\n",
       "touch_screen     0.000519\n",
       "pc               0.000000\n",
       "sc_h             0.000000\n",
       "dual_sim         0.000000\n",
       "clock_speed      0.000000\n",
       "three_g          0.000000\n",
       "n_cores          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info = pd.Series(mutual_info,index = x_train.columns)\n",
    "mutual_info.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS IS THE RANK OF THE FEATURES HIGHER TO LOWER ON BASIS OF MUTUAL GAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ram IS THE MOST IMPORTANT WHILE n_cores IS OF LESS IMPORTANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutual_info.sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SELECTING THE IMPORTANT FEATUERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = {column : round(mutual_info[column],5) for column in mutual_info.keys() if mutual_info[column]>0 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'battery_power': 0.04531,\n",
       " 'blue': 0.03116,\n",
       " 'fc': 0.02312,\n",
       " 'four_g': 0.00654,\n",
       " 'int_memory': 0.00962,\n",
       " 'm_dep': 0.01626,\n",
       " 'mobile_wt': 0.01547,\n",
       " 'px_height': 0.03601,\n",
       " 'px_width': 0.02126,\n",
       " 'ram': 0.82548,\n",
       " 'sc_w': 0.04232,\n",
       " 'talk_time': 0.02057,\n",
       " 'touch_screen': 0.00052,\n",
       " 'wifi': 0.01257}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIST OF UN IMPORTANT FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove = [column for column in mutual_info.keys() if mutual_info[column]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clock_speed', 'dual_sim', 'n_cores', 'pc', 'sc_h', 'three_g']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DROPPING THE VARIABLES FROM TRAIN AND TEST SET NOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.drop(remove,axis=1)\n",
    "x_test = x_test.drop(remove,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 5 OF FEATURE SELECTION FIESHER TEST. WE CANNOT APPLY THIS WAY AS THIS WAY TAKES ONLY POSITIVE INPUT AND STANDARIZATION GIVES POSITIVE AND NEGATIVE  VALUES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAY - 6 FEATURE IMPORTANCE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extratc=ExtraTreesClassifier()\n",
    "extratc.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06024855, 0.01993029, 0.03400949, 0.0195522 , 0.03309041,\n",
       "       0.01838243, 0.03598098, 0.03216256, 0.03846913, 0.03211993,\n",
       "       0.03418176, 0.04682197, 0.05056462, 0.38811185, 0.0338522 ,\n",
       "       0.03486314, 0.03500464, 0.01510688, 0.01697331, 0.02057366])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extratc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_f=pd.Series(extratc.feature_importances_,index=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "extra_f.sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIST OF FEATURES TO BE REMOVED FROM THE DATA SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wifi', 'blue', 'dual_sim', 'four_g', 'touch_screen', 'three_g']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove=list(extra_f.sort_values(ascending=False).index[14:])\n",
    "remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REMOVING FROM THE TRAIN AND TEST SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.drop(remove,axis=1)\n",
    "x_test = x_test.drop(remove , axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOW WE HAVE FOUND THE VARIABLES WHICH ARE IMPORTANT FOR PREDICTING OUTPUT... \n",
    "#### NEXT WE NEED TO BUILD MODELS AND PERFORM HYPER PARAMETRIC TUNING WHERE EVER NEEDED"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}