Mobile_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mobile Price Classsification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(r'train.csv')\n",
    "test=pd.read_csv(r'test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows',None)\n",
    "pd.set_option('display.max_columns',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  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  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</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>1</td>\n",
       "      <td>1043</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>193</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>226</td>\n",
       "      <td>1412</td>\n",
       "      <td>3476</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>841</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>0.8</td>\n",
       "      <td>191</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>746</td>\n",
       "      <td>857</td>\n",
       "      <td>3895</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1807</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1270</td>\n",
       "      <td>1366</td>\n",
       "      <td>2396</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1546</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>295</td>\n",
       "      <td>1752</td>\n",
       "      <td>3893</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1434</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0.5</td>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>749</td>\n",
       "      <td>810</td>\n",
       "      <td>1773</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
       "0   1           1043     1          1.8         1  14       0           5   \n",
       "1   2            841     1          0.5         1   4       1          61   \n",
       "2   3           1807     1          2.8         0   1       0          27   \n",
       "3   4           1546     0          0.5         1  18       1          25   \n",
       "4   5           1434     0          1.4         0  11       1          49   \n",
       "\n",
       "   m_dep  mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "0    0.1        193        3  16        226      1412  3476    12     7   \n",
       "1    0.8        191        5  12        746       857  3895     6     0   \n",
       "2    0.9        186        3   4       1270      1366  2396    17    10   \n",
       "3    0.5         96        8  20        295      1752  3893    10     0   \n",
       "4    0.5        108        6  18        749       810  1773    15     8   \n",
       "\n",
       "   talk_time  three_g  touch_screen  wifi  \n",
       "0          2        0             1     0  \n",
       "1          7        1             0     0  \n",
       "2         10        0             1     1  \n",
       "3          7        1             1     0  \n",
       "4          7        1             0     1  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <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",
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       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
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       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1043</td>\n",
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       "      <td>14</td>\n",
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       "      <td>226</td>\n",
       "      <td>1412</td>\n",
       "      <td>3476</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>841</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>0.8</td>\n",
       "      <td>191</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>746</td>\n",
       "      <td>857</td>\n",
       "      <td>3895</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1270</td>\n",
       "      <td>1366</td>\n",
       "      <td>2396</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1546</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>295</td>\n",
       "      <td>1752</td>\n",
       "      <td>3893</td>\n",
       "      <td>10</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1434</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0.5</td>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>749</td>\n",
       "      <td>810</td>\n",
       "      <td>1773</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0           1043     1          1.8         1  14       0           5    0.1   \n",
       "1            841     1          0.5         1   4       1          61    0.8   \n",
       "2           1807     1          2.8         0   1       0          27    0.9   \n",
       "3           1546     0          0.5         1  18       1          25    0.5   \n",
       "4           1434     0          1.4         0  11       1          49    0.5   \n",
       "\n",
       "   mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        193        3  16        226      1412  3476    12     7          2   \n",
       "1        191        5  12        746       857  3895     6     0          7   \n",
       "2        186        3   4       1270      1366  2396    17    10         10   \n",
       "3         96        8  20        295      1752  3893    10     0          7   \n",
       "4        108        6  18        749       810  1773    15     8          7   \n",
       "\n",
       "   three_g  touch_screen  wifi  \n",
       "0        0             1     0  \n",
       "1        1             0     0  \n",
       "2        0             1     1  \n",
       "3        1             1     0  \n",
       "4        1             0     1  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25277992ba8>"
      ]
     },
     "execution_count": 131,
     "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": [
    "sns.countplot(train['price_range'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2000, 21), (1000, 20))"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0\n",
       "blue             0\n",
       "clock_speed      0\n",
       "dual_sim         0\n",
       "fc               0\n",
       "four_g           0\n",
       "int_memory       0\n",
       "m_dep            0\n",
       "mobile_wt        0\n",
       "n_cores          0\n",
       "pc               0\n",
       "px_height        0\n",
       "px_width         0\n",
       "ram              0\n",
       "sc_h             0\n",
       "sc_w             0\n",
       "talk_time        0\n",
       "three_g          0\n",
       "touch_screen     0\n",
       "wifi             0\n",
       "price_range      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.2 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  1000 non-null   int64  \n",
      " 1   blue           1000 non-null   int64  \n",
      " 2   clock_speed    1000 non-null   float64\n",
      " 3   dual_sim       1000 non-null   int64  \n",
      " 4   fc             1000 non-null   int64  \n",
      " 5   four_g         1000 non-null   int64  \n",
      " 6   int_memory     1000 non-null   int64  \n",
      " 7   m_dep          1000 non-null   float64\n",
      " 8   mobile_wt      1000 non-null   int64  \n",
      " 9   n_cores        1000 non-null   int64  \n",
      " 10  pc             1000 non-null   int64  \n",
      " 11  px_height      1000 non-null   int64  \n",
      " 12  px_width       1000 non-null   int64  \n",
      " 13  ram            1000 non-null   int64  \n",
      " 14  sc_h           1000 non-null   int64  \n",
      " 15  sc_w           1000 non-null   int64  \n",
      " 16  talk_time      1000 non-null   int64  \n",
      " 17  three_g        1000 non-null   int64  \n",
      " 18  touch_screen   1000 non-null   int64  \n",
      " 19  wifi           1000 non-null   int64  \n",
      "dtypes: float64(2), int64(18)\n",
      "memory usage: 156.4 KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>9.916500</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>6.064315</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000   \n",
       "\n",
       "                pc    px_height     px_width          ram         sc_h  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean      9.916500   645.108000  1251.515500  2124.213000    12.306500   \n",
       "std       6.064315   443.780811   432.199447  1084.732044     4.213245   \n",
       "min       0.000000     0.000000   500.000000   256.000000     5.000000   \n",
       "25%       5.000000   282.750000   874.750000  1207.500000     9.000000   \n",
       "50%      10.000000   564.000000  1247.000000  2146.500000    12.000000   \n",
       "75%      15.000000   947.250000  1633.000000  3064.500000    16.000000   \n",
       "max      20.000000  1960.000000  1998.000000  3998.000000    19.000000   \n",
       "\n",
       "              sc_w    talk_time      three_g  touch_screen         wifi  \\\n",
       "count  2000.000000  2000.000000  2000.000000   2000.000000  2000.000000   \n",
       "mean      5.767000    11.011000     0.761500      0.503000     0.507000   \n",
       "std       4.356398     5.463955     0.426273      0.500116     0.500076   \n",
       "min       0.000000     2.000000     0.000000      0.000000     0.000000   \n",
       "25%       2.000000     6.000000     1.000000      0.000000     0.000000   \n",
       "50%       5.000000    11.000000     1.000000      1.000000     1.000000   \n",
       "75%       9.000000    16.000000     1.000000      1.000000     1.000000   \n",
       "max      18.000000    20.000000     1.000000      1.000000     1.000000   \n",
       "\n",
       "       price_range  \n",
       "count  2000.000000  \n",
       "mean      1.500000  \n",
       "std       1.118314  \n",
       "min       0.000000  \n",
       "25%       0.750000  \n",
       "50%       1.500000  \n",
       "75%       2.250000  \n",
       "max       3.000000  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train.plot(x='price_range',y='ram',kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B0rPXeDVr5eTPWieU64fmJ48VwQdVPJKZ3l0iMs6tJSWgsS48rM3Kyd/EmvC+2FZO/rqPh4fkWjn56zAmA1p5FmLOaY4BeE5EVgA40heq6sczbxVRYvPeI1E5+Wsw1kuycvLX0xvuxrXyLMQUjx8mb8NWd2/4FM/Kyd+2/eF1xayc/G0/cCwqJ38NNSOxvXNgV25Djd9ilYMuHqp6r4hUApihqi+6tShH7LYqPRdNGYuWrQOXvbhoytgcWkMAcMKYSW7l5G9izWgAA5dfL+Q+Bj3aSkT+C4A1AH6UfHyxiLgsjZ6XbcZe5VZO/iaPC//xWzn5m2psjmbl5K++OnwN0MqzEDNU9y9R2E/8AACo6hoAsxzalJtR5eFfh5WTv86j4Qt+Vk7+3jynPionf9Y5n+e5YMyzYq+q9l9QaFidpz6/I7z0upWTvxZjkpOVkz/rGqzjtVk6jdbdxrp8Rp6FmAvm60XkvwEoE5EmAB8H8HOfZuWDT1Slh2cepcc6EecJen4Od/dG5VmIebg/BmA+gG4A9wPoBPBJj0blhUthlJ5JY8N9tlZO/lpeDe/bYeXk78o5E6LyLMSceUxW1U8D+LRXY/JWbrx0snLyVzUy/Cdq5eSv92S4f8rKyZ9K+DnKyrMQ853vEZGXReQBEfl9EXnt6b5ARJaJyJ5kv/L+n/sTEVERqU8+FhG5Q0RaRWSdiFySOnaJiGxK3pZEtDnKaGMFSisnf+2HeqJy8tdojKqycvJ33NiW2cqzMOjioapXAbgIwFcB1AH4oYic7mLAPQCu6x+KSCOAtwPYmoqvB9CUvC0FcGdy7HgAtwG4HIXRXreJSN1g2x3D2jCQGwnmp74m3GVo5eRvWm24SFg5+Vu3I7w5mpVnIWaex5UA/hiFbqt3AngYwC3FvkZVnwAQKjB/B+DPcOporcUA7tOClQBqRWQKgGsBrFDV/araAWAFAgUpC00Tw7tuWTn5q6sKz5C1cvK3fsfAyWjFcvLXZVwYt/IsxHQc/yeAFgB/BeARVT2jfgMReTeA7aq6VuSU7qBpALalPm5LMisPfe+lKJy1YMaMGdFt4wXz0pPH5CcqLo8uEipu+vhKvLhn4Hpv08f7nQ3GFI8JAN4E4CoAHxeRkwCeUtX/NdhvICJVKJy5LAp9OpBpkXxgqHoXgLsAoLm5ObqzafyY8KtZKyd/h7vDT0hWTv4OHAu/mrVy8jdlXLhIWHkWYq55HADwCoDNAHYCmI1CIYkxG4VZ6WtFZAuA6QCeFZHJKJxRNKaOnQ5gR5E8c9zipvQ0VIcLt5WTv7fMDc8kt3Ly13xe+DKwlWch5prHywD+FsB4AF8DMFdVfyPmh6nqc6o6UVVnqupMFArDJaq6C8ByADclo64WAuhU1Z0AHgWwSETqkgvli5Isc/uOhHvirJz8lZcZw6eNnPyNN7oMrZz8/WBd+PW0lWchptuqSVWjBnKLyP0ArgZQLyJtAG5T1buNwx8B8A4ArQC6AHwYAFR1v4h8DsDq5LjbVdVlynevsbWplZM/diWWnu3GQqFWTv5+EVh5uliehZjiMVVEvorCdQ8F8CSAT6hqm/UFqnpjsW+YnH303VYYo7dUdRmAZRFtPSOb9oTXgbFy8lczOvwnauXkr9tYxMrKyd8o40zcyrMQ852/jkLX0lQURjv9IMmGjdEVxh7mRk7+XjYKt5WTv+6e8IVxKyd/Y40RoVaehZji0aCqX1fV3uTtHgANTu3KRR7rw1BxXKyy9Dz+0t6onPzNmxreHM3KsxBTPPaKyAdFpCx5+yCAfV4NywOXmi49eawWSsVNGBN+NWvl5G/mhDFReRZiisfNAH4LwK7k7X1JNmzUVYX/+K2c/HUacwesnPxVlIe7ca2c/G3t6IrKsxCzh/lWAO92a0kJeGZrh5lfM2/yWW4NAYC1JiXXqszPQWMvFSsnfyONC+NWnoWYeR7ni8gPRKQ9WSn3+yJyvlvLcpDHblxUXPXo8FmflZO/emOYtJWTvzyGT8eUpW8BeBDAFBRGXP0bCptCDRs1xsgEKyd/cyeHF6W0cvK3vfNYVE7+2g6Ei4SVZyGmeIiq/mtqtNU3MMz2MH+zMarKysnf7IbqqJz8jRkZvrZh5eTvA5dOj8qzEFM8HheRW0VkpoicJyJ/hsKeHuOTPTeGPE4SLD37u8JLw1g5+btsVvjf3crJ36Uzw797K89CzDTdDyTvf69ffjMKZyBD/vrH1n0DlzQulpO/LXvDhdvKyV8eK7hScQ+s3mbmzbN8ek5iRlvNKvZ5EXm7qq749ZuUH87zKD2v7gsPNbRy8pfHbGYq7lXjBa6VZyHLcVx/k+H3ysV+Y/VcKyd/FWXhMblWTv72Hw5fGLdy8tfdG97fxsqzkGXxGPL/zZXGGlZWTv6qR4WHf1o5+Vu+dmdUTv7edtGkqDwLWRaPIT/yqtYYp27l5G92Q3h5BSsnf8eOh2f3Wzn5e+Ps8EZcVp4F7qiTcuJE+BTPyskfl8IoPROMF1NWTv7W7zgYlWchy+KxJcPvlYsjx8NFwsrJ3xXnh4caWjn5m1JbFZWTv3pjW2Yrz0LM8iQtInJLshXsAKr6m9k1Kx8Hj4ZPu62c/G3eGx4tYuXkb6cxa9nKyd/ksaOj8izEnHncgMKyJKtF5AERuVZEil4kF5FlyTpY61PZF0XkBRFZJyL/V0RqU5/7lIi0isiLInJtKr8uyVpF5NaINkeZUGWcjhs5+fv5K+FV/62c/B0yVjS2cvK3anN4fxsrz8Kgi4eqtqrqpwFcgMI6V8sAbBWRzxaZYX4PgOv6ZSsAvEZVXwfgJQCfAgARmYdCgZqffM0/9u0dAuAfAFwPYB6AG5NjM3fM2KvcysnfpJpRUTn5O3A0PHTdyml4irrmISKvA/C3AL4I4Lso7OlxEMBPQ8er6hMA9vfLfqyqfS9RVgLoW3xlMYAHVLVbVTcDaAWwIHlrVdVXVLUHwAPJsZlrrA2f4lk5+ePaVqXncHf4GqCVk79r54e3jLDyLAx6hrmIPAPgAIC7Adyqqt3Jp1aJyJvO8OffDODbye1pKBSTPm1JBgDb+uWXn+HPK2rN1s6onPx1G9P7rZz8vWn2BLS2D7zm9KbZXEA0L3XGSDcrz8KgzjxEZASA76rqNar6rVThAHBmF8tF5NMAegF8sy8KHKZF8tD3XJpc2G9pb2+PbRJ2HjQuBBo5+Rtv/PFbOfmbPj48qsrKyd+PN+yKyrMwqOKhqicx8NrFGRORJQDeBeC3VbWvELQBaEwdNh3AjiJ5qJ13qWqzqjY3NDREt6vHeDVr5eSPe5iXnhd3hecOWDn527Aj3Dti5VmIueaxQkT+REQa+5ZhP5Ol2EXkOgB/DuDdqppe3W45gBtEZJSIzALQBOBpAKsBNInILBEZicJF9eWxP3cw+Cq39Dy+cU9UTv5OnAwvJmHl5G/+1HFReRZilmS/OXl/SyoruhS7iNwP4GoA9SLSBuA2FEZXjUKhGAHASlX9H6q6QUQeBPA8Ct1Zt6jqieT7/AGARwGUAVimqhsi2j1oE8eOwqsdAxd3mziWI3vy0n64Oyonf2UjwiP0rZz8nTBGhFp5FjJbkt34mhsD8d1Fjv88gM8H8kcAPBL782ONKg//Oqyc/E2vrcSmwMXZ6bXcOyIvlRXh/wcrJ38PPxe+tvHwc7vwwTdGP3UPSswM8yoR+Z8iclfycZOIvMulVTmZZgzJtXLyx50ES0/bgfBeKlZO/qqNLYCtPAsx1zy+DqAHwBuTj9sA/O/MW5Sj9sPhJyQrJ38V5cZ+HkZO/iorwk8bVk7+Jhtn4laehZhHe7aqfgHAcQBQ1aMYBnt4pE0yrm1YOfmbYqzNY+Xkb4SEnzasnPxVGi+mrDwLMY92j4hUIpljISKzAQyrq5aTjCckKyd/R4whuVZO/iYaS8NYOflbubkjKs9CTPH4SwA/AtAoIt8E8BgKQ26HjSM9xhOVkZO/9duNfQqMnPw1TaqJyslf43hjaSUjz0LMwog/BvCbAD4E4H4Azar6uFO7cjF/ytionPxNqwvPWrZyonPRJTPCU+6sPAsxo60eU9V9qvpDVX1YVfeKyGNuLcvBurbwbEwrJ3/vfN2UqJz8rWs7EJWTvw5j9KGVZ+G0A7NFZDSAKhQm+tXhVxfJx6Kwv8ewsWn34aic/PGaR+nZdXDgRNpiOfmrGhl+KrfyLAzmzOP3ADwD4MLkfd/b91HYZ2PYqK8xtnI0cvL3s5fDmz5ZOflrmmhc8zBy8nfh5PDv3sqzcNrioapfSWaX/6mqnq+qs5K31wP4Z7eWEQE4ZGwwZOXkr9dY8sLKyd8Luw5F5VmIGW31oUD2VEbtKAmdR49H5eSvvCw8Q9bKyd+eQ+ER+lZO/to6jFn/Rp6FwVzzmIzCpkyVIvIGnHrNY1gNeVHjhZOVk7+aUeEiYeXkr6LMmPVv5OSvxzjrs/IsDOZqyrUonHVMB/DlVH4IwF84tCk3a9rCE2qsnPxVjw7/iVo5+eM2tKWnfkx4gqaVZ+G0/4Gqei+Ae0Xkvar6XbeWlABrzyfuBZWf6tEVUTn5G10e7u22cvK370i4y9DKsxCzJPt3ReSdAOYDGJ3Kb/doWB7Or6/C2u0DLzCdXz+seueGlP3GopRWTv5e31iLHwSWAH99Y20OrSEAqDFeTFl5FmImCX4NwAcAfAyF6x7vB3CeU7tyMaU2XCSsnPzl8YqKaKg5ccLY3dHIsxBznvlGVb0JQIeqfhbAFTh1b/Eh78rZE6Jy8ldl7Edg5eQvj2GhVNzzO8OrYFh5FmKKx9HkfZeITEVhafaiW1SJyDIR2SMi61PZeBFZISKbkvd1SS4icoeItIrIOhG5JPU1S5LjN4nIkog2R+k1irSVk7+ZE8ZE5eSvxhisYOXkb1xVuHvKyrMQUzweFpFaAF9AYYb5FgAPnOZr7gFwXb/sVgCPqWoTCivz3prk1wNoSt6WArgTKBQbFPY+vxzAAgC39RWcrNVXGyMWjJz8dR4z5t4YOfm7wFg918rJX21VeBUMK89CTPH4EoCbAfwOCpMDv4DAfuNpqvoEgP394sUA7k1u3wvgPan8Pi1YCaBWRKagMFR4haruV9UOACswsCBlYrKx6ZOVk79xxqtZKyd/h411xayc/M0wVpm28izEFI97URhpdQeArwK4CMB9Z/AzJ6nqTgBI3k9M8mkAtqWOa0syK8/cN1ZtjcrJ3/bO8IVxKyd/L+4K76Vi5eQvj9UxYl6+zU3Ws+rzuIiszbAtoempWiQf+A1ElqLQ5YUZM2ZEN2D9jvCS0lZO/morw322Vk7+uozN0ayc/PUYo6qsPAsxZx6/EJGFfR+IyOUAfnYGP3N30h2F5P2eJG/DqaO3pgPYUSQfQFXvUtVmVW1uaGiIbliDMRvTysnfFKPL0MrJ36Gj4SJh5eTvwknVUXkWTls8ROQ5EVmHwgXrn4vIFhHZjMJ1j6vO4GcuB9A3YmoJCku79+U3JaOuFgLoTLq1HgWwSETqkgvli5Isc2Ujwr8OKyd/a4ztZq2c/E0fH+5Ht3Ly94Kx55CVZ2Ew3VbvOtNvLiL3A7gahY2k2lAYNfXXAB4UkY8A2IrCZEMAeATAOwC0AugC8GEAUNX9IvI5AKuT425X1f4X4TPRYOzbYeXk7+JpY7F6y8C1xS6exq2B8zJ5bHhfbCsnfydOhtdQsvIsDGZtq1fP9Jur6o3Gp64JHKsAbjG+zzIAy860HYM1pbYyKid/KuGzPisnf09uajfzT7x97lluDQHA6IrwpFkrzwL/A1PqjDHRVk7+jh8Pr9Rq5eRv98HwSDcrJ38HusKjqqw8CyweKS+3h/sHrZz87T5sPFEZOfmbMzF8EdbKyV+7sRGXlWeBxSNlz8FjUTn5y2NvZipurHEmbuXkr8kYVWXlWWDxSGk0Vs+1cvI3ZlT4spyVk7/z6sIXxq2c/F05pz4qzwKLR8r+rvApnpWTvxeMWctWTv627A+fiVs5+eswrm1YeRZYPFJe2hO+tmHl5O/kyfAMWSsnf9bmaNw0LT/WJo6emzuyeKRs7+iKyskf98suPbONC+NWTv4e27g7Ks8Ci0dKuTGT3MrJH/fLLj2HjhnLkxg5+dt+INxlaOVfVRVSAAARVElEQVRZ4H9gyvkN4VdOVk7+OHGz9Py/TXuicvJXXx0e6WblWWDxSLl23qSonPx19YS7p6yc/G0zLoxbOfm7bFZ4q2wrzwKLR8pzO8IjeKyc/OXxioqKmzspvAWwlZO/uca8JyvPAotHyst7DkXl5K/W2IPZysnf9k6jf93IyV+VsYaVlWeBxSPlcHd4BUorJ3/cDKr01FYa+2UbOfn74XM7o/IssHikVI4M/zqsnPw92bovKid/oyuMEXBGTv42Gft2WHkW+GinTDdG8Fg5+Tto7MFs5eRv76GeqJz8NY4PP0dZeRZYPFL2GCu1Wjn5Kze6bK2c/NVUhn/5Vk7+akaHu3GtPAssHim1xi/aysnfLmOPCCsnfyOMjbisnPw9b4wItfIs8NFO2bzvSFRO/o73hgcrWDn5a6gZFZWTv2O94XlPVp6F3IqHiPyhiGwQkfUicr+IjBaRWSKySkQ2ici3RWRkcuyo5OPW5PMzPdrUbexOZ+Xkb0J1+AnJysnfVOMaoJWTvzKRqDwLuRQPEZkG4OMAmlX1NQDKANwA4G8A/J2qNgHoAPCR5Es+AqBDVecA+LvkuMw11IT3I7By8ldnzOewcvLXY5z1WTn5Gz8mPEzayrOQZ7dVOYBKESkHUAVgJ4C3AvhO8vl7Abwnub04+RjJ568Ryb6kXjR1bFRO/o4Yq+daOfnjCLjSU2Y8k1t5FnIpHqq6HcCXAGxFoWh0AngGwAFV7Vuasw3AtOT2NADbkq/tTY7PfNGWY8fDq4JaOfnrOBIe/mnl5G/Djs6onPxNNHpHrDwLeXVb1aFwNjELwFQAYwBcHzi0b8ef0FnGgN2ARGSpiLSISEt7e3t0uzq6wkXCysnfyYEPc9Gc/J1U4zExcvKXx+rTeXVbvQ3AZlVtV9XjAL4H4I0AapNuLACYDmBHcrsNQCMAJJ8fB2B//2+qqneparOqNjc0NEQ36p2vmRyVk7//evG0qJz8Ta0N7xho5eSvo8s4QzfyLORVPLYCWCgiVcm1i2sAPA/gcQDvS45ZAuD7ye3lycdIPv9T1exf5mztOBqVk7/aMeFRVVZO/poawqvnWjn527I3vAyJlWchr2seq1C48P0sgOeSdtwF4M8B/JGItKJwTePu5EvuBjAhyf8IwK0e7fr+2u1ROfl7fme4H93Kyd9PXwxv+mTl5G+PMWnWyrNQfvpDfKjqbQBu6xe/AmBB4NhjAN7v3qYT4aGGVk7+xowM/4laOfnjrP/Sc2VTA1q2DnxBdWVTfPf9YHGGecr1r5kalZO/abXh0SJWTv4mjw13GVo5+btyTn1UngUWj5SmSeG9yq2c/G3YGd6Iy8rJ340LZkTl5G+9MUzayrPA4pHy0NodUTn5O78+fBHWyslfeVl49VwrJ3/11eEzcSvPAotHylFjMqCVk79KY4MhKyd/P2sNz6GycvKXx/8J/wNTjhwzlsIwcvJ331NbonLy17Y/PHTdysnfI+vD281aeRZYPFJ6jFFVVk7+2ow5NlZO/qYbu9NZOfnrOW48dxl5Flg8UsqMpRatnPxZ6186rItJg3R+Q3gAiZWTv9kTw797K88Ci0dKw1hjSXYjJ38jjWVBrZz8cUn20lNbFV563cqzwP/AlNdOGReVk796Y3c6Kyd/500Ij3SzcvJXbpyIW3kWWDxSdhw8FpWTvwUzx0fl5I8j4EpPHkvG8NFO2bI3vFe5lZO/MuOCk5WTv8eNJyQrJ39lI8JP5VaeBRaPlK6e8HwOKyd/x3rCw6StnPy9ui/8YsrKyd9rjN1OrTwLLB4pdVXhxfasnPwdPR4uElZO/toPhfeIsHLyl8c0AxaPlJHl4SJh5eTv8LHwvthWTv5+44LwSq1WTv6qjFWmrTwLLB4peWwiT8VVj66IyslfY114MqCVk7/LZ4UHkFh5Fvi0mDKqPLywm5WTv7ox4XHqVk7+/uXJzVE5+esyunGtPAssHinzpobnc1g5+eMkwdJz0OgytHI6G6zRh36jEnP7DxSRWhH5joi8ICIbReQKERkvIitEZFPyvi45VkTkDhFpFZF1InKJR5vGVYb7B62c/O09HL4Ia+Xkb7wxa9nKyd/UceFVMKw8C3m+fPsKgB+p6oUAXg9gIwp7kz+mqk0AHsOv9iq/HkBT8rYUwJ0eDWK3Vek53hseJm3l5G/Hga6onPyt3rI/Ks9CLsVDRMYCuArA3QCgqj2qegDAYgD3JofdC+A9ye3FAO7TgpUAakVkStbt2t8VfjVr5eRv7bbwTmhWTv7KjE2frJz8bdhxMCrPQl5nHucDaAfwdRH5hYj8i4iMATBJVXcCQPJ+YnL8NADbUl/flmSZ2nEgvMy3lZO/4yfD49StnPxdOCm8hpWVk7+G6nCXoZVnIa/iUQ7gEgB3quobABzBr7qoQkJXfXTAQSJLRaRFRFra2+N3NXtxV7hKWzn5u2ByeIaslZO/3cZkQCsnfz0nwqOqrDwLeRWPNgBtqroq+fg7KBST3X3dUcn7PanjG1NfPx3AgI3FVfUuVW1W1eaGhvgJS0eMJS+snPzVVobnc1g5+as3LoxbOfnr6gmfiVt5FnIpHqq6C8A2EZmbRNcAeB7AcgBLkmwJgO8nt5cDuCkZdbUQQGdf91aWphgjE6yc/B03llewcvK353B3VE7+qkaGrzdZeRbyHIP6MQDfFJGRAF4B8GEUitmDIvIRAFsBvD859hEA7wDQCqArOTZzI0eEf9FWTv7EGKdu5eRvxIjw797Kyd/0uqqoPAu5FQ9VXQOgOfCpawLHKoBbvNtUY8znsHLyt67tQFRO/hbMrMPWjoEn/gtm1uXQGgKAacbSMFaeBU7TTTnSHZ47YOXk78TJAeMiiubkb3tnuHvKysnf1HHhImHlWWDxSNlzKLxjoJWTv5n14dNuKyd/MycYj4mRk78jPSdQ3u/ZvHyE72AfFo+Uw8fCv2grJ38jJPwnauXk772XTI/Kyd+YkWXo7TeGpPdkIffC/8AUawAPB/bkZ1T/l1Onycnfk617o3Lyt6PTmOBs5Fngf2DKyPLwaBErJ3+zJ1ZH5eTvF9vCgxWsnM6Gc2hV3VJUW2VMSDNy8ldhLL1u5eTv4mnh2f1WTv6O94a71q08C/wPTBkhxvh1Iyd/r+49EpWTv427Dkfl5O+pV8Kr51p5Flg8UsqMGmHl5K/l1Y6onPxt2Rcu3FZO/iaPHRWVZ4HFI6XGWC/JyskfuxJLz8wJ4dVzrZz85bGAKItHym9ffl5UTv5uecucqJz8Lb3q/Kic/M2fOjY4z2P+VBaPs+KaeZMxt9+eBHMnjcE18ybn1CLiY1J6mmdNwJvnTDgle/OcCWieNcH4CvI2oXoUvvxbF2NUuaCqogyjygVf/q2LMaHar9tKCstGDT/Nzc3a0tJyRl/72PO78OPnd2PRvEl8kioRfExKT8vmfXhi015c1VTPwlEi9h3uRlvHUUyvqzzjwiEiz6hqaN3BU49j8SAioj6DLR7stiIiomgsHkREFI3Fg4iIorF4EBFRNBYPIiKKNmxHW4lIO4BXf41vUQ9guKwxPVzuy3C5H8DwuS/D5X4Aw+e+/Lr34zxVbTjdQcO2ePy6RKRlMMPVhoLhcl+Gy/0Ahs99GS73Axg+9+Vs3Q92WxERUTQWDyIiisbiYbsr7wZkaLjcl+FyP4Dhc1+Gy/0Ahs99OSv3g9c8iIgoGs88iIgo2jlfPETkOhF5UURaReTWwOdHici3k8+vEpGZZ7+VpzeI+/EhEWkXkTXJ23/Po52nIyLLRGSPiKw3Pi8ickdyP9eJyCVnu42DNYj7crWIdKYek8+c7TYOhog0isjjIrJRRDaIyCcCxwyJx2WQ96XkHxcRGS0iT4vI2uR+fDZwjO9zl6qes28AygC8DOB8ACMBrAUwr98xvw/ga8ntGwB8O+92n+H9+BCAv8+7rYO4L1cBuATAeuPz7wDw7wAEwEIAq/Ju869xX64G8HDe7RzE/ZgC4JLkdg2AlwJ/X0PicRnkfSn5xyX5PVcntysArAKwsN8xrs9d5/qZxwIArar6iqr2AHgAwOJ+xywGcG9y+zsArhGRUtvVfDD3Y0hQ1ScA7C9yyGIA92nBSgC1IjLl7LQuziDuy5CgqjtV9dnk9iEAGwFM63fYkHhcBnlfSl7yez6cfFiRvPW/gO363HWuF49pALalPm7DwD+kXx6jqr0AOgGU2s43g7kfAPDepEvhOyLSeHaalrnB3teh4oqk6+HfRWR+3o05naTr4w0ovNJNG3KPS5H7AgyBx0VEykRkDYA9AFaoqvmYeDx3nevFI1SF+1fvwRyTt8G08QcAZqrq6wD8BL96RTLUDIXHY7CeRWEpiNcD+CqAh3JuT1EiUg3guwA+qaoH+3868CUl+7ic5r4MicdFVU+o6sUApgNYICKv6XeI62NyrhePNgDpV+DTAeywjhGRcgDjUHpdEae9H6q6T1W7kw//GcClZ6ltWRvMYzYkqOrBvq4HVX0EQIWI1OfcrCARqUDhyfabqvq9wCFD5nE53X0ZSo8LAKjqAQD/AeC6fp9yfe4614vHagBNIjJLREaicFFpeb9jlgNYktx+H4CfanIFqoSc9n70639+Nwp9vUPRcgA3JaN7FgLoVNWdeTfqTIjI5L4+aBFZgML/4758WzVQ0sa7AWxU1S8bhw2Jx2Uw92UoPC4i0iAitcntSgBvA/BCv8Ncn7vKs/pGQ5Gq9orIHwB4FIURS8tUdYOI3A6gRVWXo/CH9q8i0opC1b4hvxaHDfJ+fFxE3g2gF4X78aHcGlyEiNyPwmiXehFpA3AbChcDoapfA/AICiN7WgF0AfhwPi09vUHcl/cB+KiI9AI4CuCGEnxhAgBvAvA7AJ5L+tgB4C8AzACG3OMymPsyFB6XKQDuFZEyFIrbg6r68Nl87uIMcyIiinaud1sREdEZYPEgIqJoLB5ERBSNxYOIiKKxeBARUTQWDyIiisbiQTRIInK7iLwt73YQlQLO8yAaBBEpU9UTQ+17E3nhmQed80Rkpoi8ICL3plYdrhKRLSLyGRF5EsD7ReQeEXlf8jWXicjPk5VXnxaRmmSV0y+KyOrk+/xekZ95dbIp0bcAPJdkD4nIM8nmPktTxx4Wkc8nP2uliExK8tnJx6uTs6LDqa/501Q7BmwURPTrYvEgKpgL4K5k1eGDKGykAwDHVPVKVX2g78Bk/bBvA/hEsvLq21BYxuIjKKzpdBmAywD8rojMKvIzFwD4tKrOSz6+WVUvBdCMwnIyfctnjwGwMvlZTwD43ST/CoCvJD/vl4sQisgiAE3J978YwKUiclX8r4TIxuJBVLBNVX+W3P4GgCuT298OHDsXwE5VXQ38chXWXgCLUFgccA0Ke0RMQOFJ3PK0qm5OffxxEVkLYCUKq6H2fW0PgIeT288AmJncvgLAvyW3v5X6PouSt1+gsLz4hadpB1G0c3phRKKU/hf/+j4+EjhWAsf35R9T1UcH+TN/+b1F5GoUzmCuUNUuEfkPAKOTTx9PLcx3Aqf/vxUAf6Wq/zTIdhBF45kHUcEMEbkiuX0jgCeLHPsCgKkichkAJNc7ylFY1fijyX4REJELRGTMIH/+OAAdSeG4EIV9wE9nJYD3JrfTK6Y+CuDmZMMjiMg0EZk4yHYQDQqLB1HBRgBLRGQdgPEA7rQOTPaJ/wCArybdTCtQOEv4FwDPA3hWRNYD+CcM/uz+RwDKk5//ORQKw+l8EsAficjTKCzR3Zm078codGM9JSLPobB/dc0g20E0KByqS+c8Kexl/bCq9t/Gs6SJSBWAo6qqInIDgBtVdXHe7aJzA695EA1dlwL4+2TXuwMAbs65PXQO4ZkHkSMReS2Af+0Xd6vq5Xm0hygrLB5ERBSNF8yJiCgaiwcREUVj8SAiomgsHkREFI3Fg4iIov1/3SeBonISMvQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(x='price_range',y='battery_power',kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train.plot(x='price_range',y='fc',kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train.plot(x='price_range',y='n_cores',kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(train.corr(),annot=True,cmap=plt.cm.Accent_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252784c9b70>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind='box',figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('price_range',axis=1)\n",
    "y = train['price_range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.1,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "test = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.62737257, -0.98675438, -1.01271559, ..., -1.78222729,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [-0.75199354,  1.01342342,  0.58093235, ..., -1.78222729,\n",
       "         0.99115027, -0.99888951],\n",
       "       [-0.20630271,  1.01342342,  0.70352065, ...,  0.56109566,\n",
       "        -1.00892875,  1.00111173],\n",
       "       ...,\n",
       "       [ 0.69636086,  1.01342342, -0.03200917, ...,  0.56109566,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [ 0.83733099, -0.98675438, -1.2578922 , ...,  0.56109566,\n",
       "        -1.00892875,  1.00111173],\n",
       "       [ 0.4144206 , -0.98675438, -0.39977408, ...,  0.56109566,\n",
       "         0.99115027,  1.00111173]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28481903, -0.98675438, -1.2578922 , ...,  0.56109566,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [-1.44092821, -0.98675438, -1.2578922 , ...,  0.56109566,\n",
       "         0.99115027,  1.00111173],\n",
       "       [-1.49322358, -0.98675438, -0.15459747, ...,  0.56109566,\n",
       "        -1.00892875,  1.00111173],\n",
       "       ...,\n",
       "       [-0.55418061,  1.01342342,  0.33575574, ...,  0.56109566,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [ 0.09610095, -0.98675438, -0.89012729, ...,  0.56109566,\n",
       "         0.99115027,  1.00111173],\n",
       "       [-1.60690917, -0.98675438,  1.07128556, ...,  0.56109566,\n",
       "         0.99115027, -0.99888951]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4541373 ,  1.01342342,  0.33575574, ..., -1.78222729,\n",
       "         0.99115027, -0.99888951],\n",
       "       [-0.91342707,  1.01342342, -1.2578922 , ...,  0.56109566,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [ 1.2829785 ,  1.01342342,  1.56163877, ..., -1.78222729,\n",
       "         0.99115027,  1.00111173],\n",
       "       ...,\n",
       "       [-0.13127022, -0.98675438, -0.15459747, ...,  0.56109566,\n",
       "        -1.00892875, -0.99888951],\n",
       "       [ 0.65998148,  1.01342342, -1.2578922 , ..., -1.78222729,\n",
       "         0.99115027, -0.99888951],\n",
       "       [ 0.06199528,  1.01342342, -1.2578922 , ...,  0.56109566,\n",
       "        -1.00892875,  1.00111173]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(X_train , Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 1, 0, 1, 2, 1, 1, 1, 0, 1, 2, 1, 1, 0, 1, 1, 1, 0, 3, 1,\n",
       "       2, 3, 2, 2, 2, 1, 0, 0, 2, 3, 0, 0, 3, 0, 0, 0, 1, 1, 1, 2, 3, 2,\n",
       "       2, 1, 1, 3, 3, 1, 0, 0, 2, 3, 3, 2, 0, 3, 2, 3, 2, 2, 3, 1, 3, 2,\n",
       "       0, 1, 0, 2, 1, 2, 3, 2, 1, 3, 3, 2, 0, 2, 0, 0, 2, 1, 2, 2, 2, 1,\n",
       "       0, 0, 3, 3, 0, 2, 0, 3, 2, 0, 2, 3, 0, 1, 2, 3, 0, 2, 0, 0, 2, 0,\n",
       "       1, 0, 3, 2, 2, 2, 1, 3, 2, 0, 3, 3, 2, 3, 1, 3, 3, 2, 1, 1, 0, 0,\n",
       "       1, 1, 0, 2, 3, 0, 2, 3, 1, 3, 0, 1, 0, 0, 1, 3, 3, 0, 2, 1, 3, 2,\n",
       "       3, 3, 2, 0, 3, 1, 2, 2, 2, 2, 1, 2, 1, 1, 3, 3, 1, 2, 0, 3, 2, 3,\n",
       "       1, 2, 3, 1, 2, 1, 0, 1, 3, 3, 1, 2, 1, 3, 1, 0, 2, 2, 0, 3, 0, 0,\n",
       "       3, 0], dtype=int64)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = dtc.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83\n",
      "[[43  4  0  0]\n",
      " [ 7 37  7  0]\n",
      " [ 0  5 47  3]\n",
      " [ 0  0  8 39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "dtc_acc = accuracy_score(pred,Y_test)\n",
    "print(dtc_acc)\n",
    "print(confusion_matrix(pred,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "knn=SVC()\n",
    "knn.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 3, 1,\n",
       "       2, 3, 2, 2, 2, 2, 0, 0, 2, 3, 0, 0, 3, 0, 0, 0, 1, 1, 1, 1, 3, 2,\n",
       "       3, 0, 2, 3, 3, 1, 0, 1, 2, 3, 2, 2, 0, 3, 2, 3, 2, 2, 3, 1, 3, 1,\n",
       "       0, 1, 0, 2, 1, 2, 3, 2, 1, 3, 3, 2, 1, 2, 0, 0, 2, 2, 2, 2, 2, 1,\n",
       "       0, 0, 3, 2, 0, 2, 0, 3, 2, 0, 2, 3, 0, 1, 3, 3, 0, 3, 0, 0, 2, 0,\n",
       "       1, 0, 3, 2, 1, 1, 1, 3, 1, 0, 3, 2, 2, 3, 1, 2, 3, 2, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 3, 0, 2, 3, 1, 3, 0, 0, 0, 1, 1, 3, 2, 0, 2, 0, 2, 2,\n",
       "       3, 2, 2, 0, 3, 2, 2, 2, 1, 2, 1, 2, 1, 0, 3, 3, 1, 2, 0, 3, 1, 3,\n",
       "       2, 2, 3, 2, 1, 1, 0, 1, 2, 2, 2, 2, 0, 3, 1, 0, 2, 2, 0, 2, 0, 0,\n",
       "       3, 0], dtype=int64)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1 = knn.predict(X_test)\n",
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.88\n",
      "[[46  3  0  0]\n",
      " [ 4 40  8  0]\n",
      " [ 0  3 52  4]\n",
      " [ 0  0  2 38]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "svc_acc = accuracy_score(pred1,Y_test)\n",
    "print(svc_acc)\n",
    "print(confusion_matrix(pred1,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression  # its a classification\n",
    "lr=LogisticRegression()\n",
    "lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 3, 1,\n",
       "       2, 3, 2, 2, 2, 2, 0, 0, 2, 3, 0, 0, 3, 0, 0, 0, 1, 1, 1, 2, 3, 2,\n",
       "       3, 0, 1, 3, 3, 1, 0, 0, 3, 3, 3, 3, 1, 3, 2, 3, 2, 2, 3, 1, 3, 1,\n",
       "       0, 0, 0, 2, 1, 2, 3, 2, 1, 3, 3, 2, 0, 2, 0, 0, 2, 1, 2, 2, 2, 1,\n",
       "       0, 0, 3, 2, 0, 2, 0, 3, 2, 0, 2, 3, 0, 1, 3, 3, 0, 3, 0, 0, 2, 0,\n",
       "       1, 0, 3, 2, 2, 1, 1, 3, 1, 0, 3, 2, 2, 3, 1, 2, 3, 2, 1, 1, 1, 0,\n",
       "       0, 1, 0, 2, 3, 0, 2, 3, 1, 3, 0, 0, 0, 1, 1, 2, 2, 0, 3, 1, 2, 2,\n",
       "       3, 2, 2, 0, 3, 2, 2, 2, 2, 2, 1, 2, 1, 1, 3, 3, 1, 2, 0, 3, 1, 3,\n",
       "       2, 2, 3, 2, 2, 1, 0, 1, 3, 2, 1, 2, 0, 3, 1, 0, 2, 2, 0, 2, 0, 0,\n",
       "       3, 0], dtype=int64)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2 = lr.predict(X_test)\n",
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.955\n",
      "[[49  1  0  0]\n",
      " [ 1 45  3  0]\n",
      " [ 0  0 56  1]\n",
      " [ 0  0  3 41]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "lr_acc = accuracy_score(pred2,Y_test)\n",
    "print(lr_acc)\n",
    "print(confusion_matrix(pred2,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.bar(x=['dtc','svc','lr'],height=[dtc_acc,svc_acc,lr_acc])\n",
    "plt.xlabel(\"Algorithms\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 2, 3, 1, 3, 3, 1, 3, 0, 3, 3, 0, 0, 2, 0, 2, 1, 3, 2, 1, 3,\n",
       "       1, 1, 3, 0, 2, 0, 3, 0, 2, 0, 3, 0, 1, 1, 3, 1, 2, 1, 1, 2, 0, 0,\n",
       "       0, 1, 0, 3, 1, 2, 1, 0, 3, 0, 3, 0, 3, 1, 1, 3, 3, 3, 0, 1, 1, 1,\n",
       "       2, 3, 1, 2, 1, 2, 2, 3, 3, 0, 2, 0, 1, 3, 0, 3, 3, 0, 3, 0, 3, 1,\n",
       "       3, 0, 1, 2, 2, 1, 2, 2, 0, 2, 1, 2, 1, 0, 0, 3, 0, 2, 1, 1, 2, 3,\n",
       "       3, 3, 1, 3, 3, 3, 3, 2, 3, 0, 0, 3, 2, 1, 2, 0, 3, 2, 2, 2, 0, 2,\n",
       "       2, 1, 3, 1, 1, 0, 3, 2, 1, 2, 1, 3, 2, 3, 3, 3, 2, 3, 2, 3, 1, 0,\n",
       "       3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 0, 3, 0, 0, 0, 2, 1, 0, 1,\n",
       "       0, 0, 1, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 0, 0, 1, 0, 3, 1, 0, 2, 2,\n",
       "       3, 3, 1, 2, 3, 2, 3, 2, 2, 1, 0, 0, 1, 3, 0, 2, 3, 3, 0, 2, 0, 3,\n",
       "       2, 3, 3, 1, 0, 1, 0, 3, 0, 1, 0, 2, 2, 1, 3, 1, 3, 0, 3, 1, 2, 0,\n",
       "       0, 2, 1, 3, 3, 3, 1, 1, 3, 0, 0, 2, 3, 3, 1, 3, 1, 1, 3, 2, 1, 2,\n",
       "       3, 3, 3, 1, 0, 0, 2, 3, 1, 1, 3, 2, 1, 3, 0, 0, 3, 0, 0, 3, 2, 3,\n",
       "       3, 2, 1, 3, 3, 2, 3, 1, 2, 1, 2, 0, 2, 3, 1, 0, 0, 3, 0, 3, 0, 1,\n",
       "       2, 0, 2, 3, 1, 3, 2, 2, 1, 2, 0, 0, 0, 1, 3, 2, 0, 0, 0, 3, 2, 0,\n",
       "       2, 3, 1, 2, 2, 2, 3, 1, 3, 3, 2, 2, 2, 3, 3, 0, 3, 0, 3, 1, 3, 1,\n",
       "       2, 3, 0, 1, 0, 3, 1, 3, 2, 3, 0, 0, 0, 0, 2, 0, 0, 2, 2, 1, 2, 2,\n",
       "       2, 0, 1, 0, 0, 3, 2, 0, 3, 1, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 2, 0,\n",
       "       1, 1, 0, 3, 2, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 2, 2, 3, 2, 3, 0, 3,\n",
       "       0, 3, 0, 1, 1, 0, 2, 0, 3, 2, 3, 3, 1, 3, 1, 3, 1, 2, 2, 0, 1, 2,\n",
       "       1, 1, 0, 0, 0, 1, 2, 1, 0, 3, 2, 0, 2, 2, 0, 0, 3, 1, 2, 0, 2, 3,\n",
       "       3, 0, 3, 0, 2, 3, 2, 3, 0, 2, 0, 2, 3, 0, 1, 1, 0, 0, 1, 1, 1, 3,\n",
       "       3, 3, 2, 3, 1, 2, 2, 3, 3, 3, 2, 0, 2, 1, 2, 2, 1, 0, 2, 2, 0, 0,\n",
       "       0, 3, 1, 0, 2, 2, 2, 0, 3, 1, 2, 2, 1, 3, 0, 2, 3, 0, 1, 1, 3, 3,\n",
       "       2, 1, 1, 3, 2, 0, 3, 0, 2, 0, 3, 3, 1, 3, 2, 2, 3, 0, 1, 2, 3, 1,\n",
       "       3, 2, 3, 1, 1, 0, 0, 3, 1, 0, 3, 2, 3, 3, 0, 3, 3, 3, 2, 3, 3, 1,\n",
       "       2, 0, 2, 2, 3, 1, 0, 1, 1, 2, 2, 2, 0, 0, 2, 2, 3, 2, 0, 2, 1, 3,\n",
       "       3, 0, 1, 3, 0, 2, 1, 1, 0, 0, 2, 1, 0, 1, 1, 2, 2, 0, 2, 2, 1, 0,\n",
       "       3, 0, 0, 3, 2, 0, 0, 0, 0, 0, 3, 0, 3, 1, 3, 2, 1, 3, 3, 0, 1, 0,\n",
       "       3, 2, 2, 2, 0, 3, 0, 2, 0, 2, 0, 0, 1, 1, 1, 2, 1, 3, 1, 3, 2, 2,\n",
       "       1, 3, 2, 0, 2, 2, 0, 3, 3, 0, 2, 1, 1, 2, 0, 3, 2, 0, 3, 2, 3, 0,\n",
       "       0, 3, 0, 2, 2, 3, 2, 2, 2, 2, 1, 2, 3, 0, 1, 0, 1, 2, 1, 0, 0, 1,\n",
       "       0, 0, 3, 0, 1, 2, 0, 1, 0, 1, 3, 0, 3, 2, 3, 0, 0, 1, 2, 2, 1, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 3, 3, 0, 3, 1, 1, 3, 0, 1, 0, 2, 2, 0, 3, 1,\n",
       "       0, 3, 0, 1, 0, 3, 3, 3, 2, 3, 0, 3, 2, 0, 0, 0, 3, 3, 2, 0, 2, 1,\n",
       "       3, 0, 0, 2, 2, 0, 3, 1, 2, 1, 1, 1, 3, 1, 1, 1, 2, 1, 0, 2, 2, 0,\n",
       "       2, 0, 0, 0, 0, 2, 3, 3, 3, 0, 1, 2, 1, 1, 0, 0, 2, 1, 0, 2, 0, 3,\n",
       "       2, 2, 1, 2, 0, 2, 1, 3, 0, 0, 3, 2, 3, 0, 0, 2, 3, 3, 1, 3, 2, 1,\n",
       "       0, 0, 3, 3, 1, 3, 0, 0, 0, 2, 2, 1, 2, 0, 3, 2, 1, 2, 3, 3, 0, 1,\n",
       "       1, 2, 1, 2, 2, 0, 1, 3, 1, 1, 3, 0, 2, 3, 2, 1, 1, 1, 3, 3, 0, 2,\n",
       "       3, 0, 2, 3, 2, 2, 2, 3, 2, 0, 1, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1,\n",
       "       0, 1, 3, 1, 0, 1, 2, 3, 1, 0, 0, 3, 2, 2, 3, 0, 3, 2, 2, 1, 3, 0,\n",
       "       1, 3, 1, 1, 1, 2, 3, 2, 0, 3, 0, 2, 3, 0, 3, 1, 3, 3, 1, 0, 2, 3,\n",
       "       1, 0, 2, 1, 2, 1, 2, 0, 2, 2, 0, 2, 3, 2, 3, 0, 2, 1, 1, 2, 2, 3,\n",
       "       3, 0, 2, 1, 2, 1, 3, 1, 1, 3, 0, 1, 0, 0, 3, 3, 2, 0, 0, 0, 0, 3,\n",
       "       2, 3, 3, 0, 0, 2, 1, 0, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}