mobprice-sagemaker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/ozerozdal/Library/Application Support/sagemaker/config.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sagemaker\n",
    "from sklearn.model_selection import train_test_split\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_boto3 = boto3.client(\"sagemaker\")\n",
    "sess = sagemaker.Session()\n",
    "region = sess.boto_session.region_name\n",
    "bucket = os.environ[\"BUCKET_NAME\"] # get the name of the S3 bucket name from .env file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/mobile_price_range_Data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "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>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>...</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>...</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n",
       "\n",
       "         px_height     px_width          ram         sc_h         sc_w  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n",
       "std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n",
       "min       0.000000   500.000000   256.000000     5.000000     0.000000   \n",
       "25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n",
       "50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n",
       "75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n",
       "max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n",
       "\n",
       "         talk_time      three_g  touch_screen         wifi  price_range  \n",
       "count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n",
       "mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n",
       "std       5.463955     0.426273      0.500116     0.500076     1.118314  \n",
       "min       2.000000     0.000000      0.000000     0.000000     0.000000  \n",
       "25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n",
       "50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n",
       "75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n",
       "max      20.000000     1.000000      1.000000     1.000000     3.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range\n",
       "1    0.25\n",
       "2    0.25\n",
       "3    0.25\n",
       "0    0.25\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 (low cost), 1(medium cost), 2 (high cost) and 3 (very high cost)\n",
    "df['price_range'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['battery_power',\n",
       " 'blue',\n",
       " 'clock_speed',\n",
       " 'dual_sim',\n",
       " 'fc',\n",
       " 'four_g',\n",
       " 'int_memory',\n",
       " 'm_dep',\n",
       " 'mobile_wt',\n",
       " 'n_cores',\n",
       " 'pc',\n",
       " 'px_height',\n",
       " 'px_width',\n",
       " 'ram',\n",
       " 'sc_h',\n",
       " 'sc_w',\n",
       " 'talk_time',\n",
       " 'three_g',\n",
       " 'touch_screen',\n",
       " 'wifi',\n",
       " 'price_range']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    0.0\n",
       "blue             0.0\n",
       "clock_speed      0.0\n",
       "dual_sim         0.0\n",
       "fc               0.0\n",
       "four_g           0.0\n",
       "int_memory       0.0\n",
       "m_dep            0.0\n",
       "mobile_wt        0.0\n",
       "n_cores          0.0\n",
       "pc               0.0\n",
       "px_height        0.0\n",
       "px_width         0.0\n",
       "ram              0.0\n",
       "sc_h             0.0\n",
       "sc_w             0.0\n",
       "talk_time        0.0\n",
       "three_g          0.0\n",
       "touch_screen     0.0\n",
       "wifi             0.0\n",
       "price_range      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the Percentage of Values are missing\n",
    "df.isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'price_range'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.columns.to_list()\n",
    "label = features.pop(-1)\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[features]\n",
    "y = df[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range\n",
       "1    500\n",
       "2    500\n",
       "3    500\n",
       "0    500\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1700, 20)\n",
      "(300, 20)\n",
      "(1700,)\n",
      "(300,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = pd.DataFrame(X_train)\n",
    "trainX[label] = y_train\n",
    "\n",
    "testX = pd.DataFrame(X_test)\n",
    "testX[label] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1700, 21)\n",
      "(300, 21)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape)\n",
    "print(testX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>581</th>\n",
       "      <td>1512</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.1</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1079</td>\n",
       "      <td>1897</td>\n",
       "      <td>3607</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1040</td>\n",
       "      <td>1071</td>\n",
       "      <td>907</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>1176</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.5</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>674</td>\n",
       "      <td>1455</td>\n",
       "      <td>1534</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>1550</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.1</td>\n",
       "      <td>126</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1541</td>\n",
       "      <td>1619</td>\n",
       "      <td>1571</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>1042</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.6</td>\n",
       "      <td>139</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>1018</td>\n",
       "      <td>2826</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "581            1512     1          0.5         0   8       1          18   \n",
       "76             1114     0          2.8         0   4       1           9   \n",
       "1916           1176     1          2.1         0   2       1          62   \n",
       "1414           1550     1          2.7         0   2       0          32   \n",
       "780            1042     0          2.2         0  15       1          11   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "581     0.1         88        3  ...       1079      1897  3607    12    10   \n",
       "76      0.4        197        3  ...       1040      1071   907    10     7   \n",
       "1916    0.5        168        2  ...        674      1455  1534     6     2   \n",
       "1414    0.1        126        7  ...       1541      1619  1571    12     3   \n",
       "780     0.6        139        5  ...         68      1018  2826    18     0   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  price_range  \n",
       "581           6        1             1     1            3  \n",
       "76           17        1             1     0            0  \n",
       "1916         17        1             0     0            1  \n",
       "1414         14        0             0     0            2  \n",
       "780           2        1             0     0            2  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>1860</th>\n",
       "      <td>1646</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.6</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>211</td>\n",
       "      <td>1608</td>\n",
       "      <td>686</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>1182</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>138</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>275</td>\n",
       "      <td>986</td>\n",
       "      <td>2563</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>1972</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.4</td>\n",
       "      <td>196</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>293</td>\n",
       "      <td>952</td>\n",
       "      <td>1316</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>989</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.2</td>\n",
       "      <td>166</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>256</td>\n",
       "      <td>1394</td>\n",
       "      <td>3892</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.5</td>\n",
       "      <td>130</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1021</td>\n",
       "      <td>1958</td>\n",
       "      <td>1906</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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  \\\n",
       "1860           1646     0          2.5         0   3       1          25   \n",
       "353            1182     0          0.5         0   7       1           8   \n",
       "1333           1972     0          2.9         0   9       0          14   \n",
       "905             989     1          2.0         0   4       0          17   \n",
       "1289            615     1          0.5         1   7       0          58   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "1860    0.6        200        2   5        211      1608   686     8     6   \n",
       "353     0.5        138        8  16        275       986  2563    19    17   \n",
       "1333    0.4        196        7  18        293       952  1316     8     1   \n",
       "905     0.2        166        3  19        256      1394  3892    18     7   \n",
       "1289    0.5        130        5   8       1021      1958  1906    14     5   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  \n",
       "1860         11        1             1     0  \n",
       "353          19        1             0     0  \n",
       "1333          8        1             1     0  \n",
       "905          19        1             1     0  \n",
       "1289          5        1             0     0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "581     3\n",
       "76      0\n",
       "1916    1\n",
       "1414    2\n",
       "780     2\n",
       "       ..\n",
       "1130    3\n",
       "1294    0\n",
       "860     2\n",
       "1459    3\n",
       "1126    1\n",
       "Name: price_range, Length: 1700, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1860    0\n",
       "353     2\n",
       "1333    1\n",
       "905     3\n",
       "1289    1\n",
       "       ..\n",
       "316     1\n",
       "45      1\n",
       "1719    0\n",
       "1831    1\n",
       "427     3\n",
       "Name: price_range, Length: 300, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX.to_csv(\"outputs/train-V-1.csv\", index=False)\n",
    "testX.to_csv(\"outputs/test-V-1.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://mobprice-aws-sagemaker/sagemaker/mobile_price_classification/sklearncontainer/train-V-1.csv\n",
      "s3://mobprice-aws-sagemaker/sagemaker/mobile_price_classification/sklearncontainer/test-V-1.csv\n"
     ]
    }
   ],
   "source": [
    "# send data to S3 bucket\n",
    "# SageMaker will take training data from S3\n",
    "sk_prefix = \"sagemaker/mobile_price_classification/sklearncontainer\"\n",
    "\n",
    "train_path = sess.upload_data(\n",
    "    path='outputs/train-V-1.csv', bucket =bucket, key_prefix=sk_prefix\n",
    ")\n",
    "\n",
    "test_path = sess.upload_data(\n",
    "    path='outputs/test-V-1.csv', bucket =bucket, key_prefix=sk_prefix\n",
    ")\n",
    "\n",
    "print(train_path)\n",
    "print(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting script.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile script.py\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_score\n",
    "import sklearn\n",
    "import joblib\n",
    "import boto3\n",
    "import pathlib\n",
    "from io import StringIO\n",
    "import argparse\n",
    "import joblib\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def model_fn(model_dir):\n",
    "    clf = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n",
    "    return clf\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    print(\"[INFO] Extracting arguments\")\n",
    "    parser = argparse.ArgumentParser()\n",
    "\n",
    "    # hyperparameters sent by the client are passed as command-line arguments\n",
    "    parser.add_argument(\"--n_estimators\", type=int, default=100)\n",
    "    parser.add_argument(\"--random_state\", type=int, default=42)\n",
    "\n",
    "    # Data, model, and output directories\n",
    "    parser.add_argument(\"--model-dir\", type=str, default=os.environ.get(\"SM_MODEL_DIR\"))\n",
    "    parser.add_argument(\"--train\", type=str, default=os.environ.get(\"SM_CHANNEL_TRAIN\"))\n",
    "    parser.add_argument(\"--test\", type=str, default=os.environ.get(\"SM_CHANNEL_TEST\"))\n",
    "    parser.add_argument(\"--train-file\", type=str, default=\"train-V-1.csv\")\n",
    "    parser.add_argument(\"--test-file\", type=str, default=\"test-V-1.csv\")\n",
    "\n",
    "    args, *_ = parser.parse_known_args()\n",
    "\n",
    "    print(\"SKLearn Version: \", sklearn.__version__)\n",
    "    print(\"Joblib Version: \", joblib.__version__)\n",
    "\n",
    "    print(\"[INFO] Reading Data\")\n",
    "    print()\n",
    "    train_df = pd.read_csv(os.path.join(args.train, args.train_file))\n",
    "    test_df = pd.read_csv(os.path.join(args.test, args.test_file))\n",
    "\n",
    "    features = train_df.columns.to_list()\n",
    "    label = features.pop(-1)\n",
    "\n",
    "    print(\"Building training and testing datasets\")\n",
    "    print()\n",
    "    X_train = train_df[features]\n",
    "    X_test = test_df[features]\n",
    "    y_train = train_df[label]\n",
    "    y_test = test_df[label]\n",
    "\n",
    "    print(\"Column order: \")\n",
    "    print(features)\n",
    "    print()\n",
    "\n",
    "    print(\"Label column is: \", label)\n",
    "    print()\n",
    "\n",
    "    print(\"Data Shape: \")\n",
    "    print()\n",
    "    print(\"---- SHAPE OF TRAINING DATA (85%) ----\")\n",
    "    print(X_train.shape)\n",
    "    print(y_train.shape)\n",
    "    print()\n",
    "    print(\"---- SHAPE OF TESTING DATA (15%) ----\")\n",
    "    print(X_test.shape)\n",
    "    print(y_test.shape)\n",
    "    print()\n",
    "\n",
    "    print(\"Training Random Forest Model......\")\n",
    "    print()\n",
    "    model = RandomForestClassifier(n_estimators=args.n_estimators, random_state=args.random_state)\n",
    "    model.fit(X_train, y_train)\n",
    "    print()\n",
    "\n",
    "    model_path = os.path.join(args.model_dir, \"model.joblib\")\n",
    "    joblib.dump(model, model_path)\n",
    "    print(\"Model persisted at \" + model_path)\n",
    "    print()\n",
    "\n",
    "    y_pred_test = model.predict(X_test)\n",
    "    test_acc = accuracy_score(y_test, y_pred_test)\n",
    "    test_rep = classification_report(y_test, y_pred_test)\n",
    "\n",
    "    print()\n",
    "    print(\"---- METRICS RESULTS FOR TESTING DATA ----\")\n",
    "    print()\n",
    "    print(\"Total Rows are: \", X_test.shape[0])\n",
    "    print('[TESTING] Model Accuracy is: ', test_acc)\n",
    "    print('[TESTING] Testing Report: ')\n",
    "    print(test_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.sklearn.estimator import SKLearn\n",
    "\n",
    "FRAMEWORK_VERSION = \"0.23-1\"\n",
    "\n",
    "sklearn_estimator = SKLearn(\n",
    "    entry_point=\"script.py\",\n",
    "    role=os.environ[\"ROLE\"],\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.m5.large\",\n",
    "    framework_version=FRAMEWORK_VERSION,\n",
    "    base_job_name=\"RF-custom-sklearn\",\n",
    "    hyperparameters={\n",
    "        \"n_estimators\": 100,\n",
    "        \"random_state\": 42,\n",
    "    },\n",
    "    use_spot_instances=True,\n",
    "    max_wait=7200,\n",
    "    max_run=3600\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: RF-custom-sklearn-2024-05-11-17-55-53-016\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-05-11 17:55:54 Starting - Starting the training job...\n",
      "2024-05-11 17:56:08 Starting - Preparing the instances for training...\n",
      "2024-05-11 17:56:37 Downloading - Downloading input data...\n",
      "2024-05-11 17:57:02 Downloading - Downloading the training image...\n",
      "2024-05-11 17:57:53 Training - Training image download completed. Training in progress.\n",
      "2024-05-11 17:57:53 Uploading - Uploading generated training model2024-05-11 17:57:47,989 sagemaker-containers INFO     Imported framework sagemaker_sklearn_container.training\n",
      "2024-05-11 17:57:47,992 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\n",
      "2024-05-11 17:57:48,036 sagemaker_sklearn_container.training INFO     Invoking user training script.\n",
      "2024-05-11 17:57:48,184 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\n",
      "2024-05-11 17:57:48,196 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\n",
      "2024-05-11 17:57:48,207 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\n",
      "2024-05-11 17:57:48,216 sagemaker-training-toolkit INFO     Invoking user script\n",
      "Training Env:\n",
      "{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"test\": \"/opt/ml/input/data/test\",\n",
      "        \"train\": \"/opt/ml/input/data/train\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_sklearn_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"n_estimators\": 100,\n",
      "        \"random_state\": 42\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"test\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"RF-custom-sklearn-2024-05-11-17-55-53-016\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-east-1-905418178928/RF-custom-sklearn-2024-05-11-17-55-53-016/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"script\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 2,\n",
      "    \"num_gpus\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.m5.large\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.m5.large\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"script.py\"\n",
      "}\n",
      "Environment variables:\n",
      "SM_HOSTS=[\"algo-1\"]\n",
      "SM_NETWORK_INTERFACE_NAME=eth0\n",
      "SM_HPS={\"n_estimators\":100,\"random_state\":42}\n",
      "SM_USER_ENTRY_POINT=script.py\n",
      "SM_FRAMEWORK_PARAMS={}\n",
      "SM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.large\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.large\"}],\"network_interface_name\":\"eth0\"}\n",
      "SM_INPUT_DATA_CONFIG={\"test\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\n",
      "SM_OUTPUT_DATA_DIR=/opt/ml/output/data\n",
      "SM_CHANNELS=[\"test\",\"train\"]\n",
      "SM_CURRENT_HOST=algo-1\n",
      "SM_MODULE_NAME=script\n",
      "SM_LOG_LEVEL=20\n",
      "SM_FRAMEWORK_MODULE=sagemaker_sklearn_container.training:main\n",
      "SM_INPUT_DIR=/opt/ml/input\n",
      "SM_INPUT_CONFIG_DIR=/opt/ml/input/config\n",
      "SM_OUTPUT_DIR=/opt/ml/output\n",
      "SM_NUM_CPUS=2\n",
      "SM_NUM_GPUS=0\n",
      "SM_MODEL_DIR=/opt/ml/model\n",
      "SM_MODULE_DIR=s3://sagemaker-us-east-1-905418178928/RF-custom-sklearn-2024-05-11-17-55-53-016/source/sourcedir.tar.gz\n",
      "SM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"test\":\"/opt/ml/input/data/test\",\"train\":\"/opt/ml/input/data/train\"},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_sklearn_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"n_estimators\":100,\"random_state\":42},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"test\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"RF-custom-sklearn-2024-05-11-17-55-53-016\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-905418178928/RF-custom-sklearn-2024-05-11-17-55-53-016/source/sourcedir.tar.gz\",\"module_name\":\"script\",\"network_interface_name\":\"eth0\",\"num_cpus\":2,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.large\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.large\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"script.py\"}\n",
      "SM_USER_ARGS=[\"--n_estimators\",\"100\",\"--random_state\",\"42\"]\n",
      "SM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\n",
      "SM_CHANNEL_TEST=/opt/ml/input/data/test\n",
      "SM_CHANNEL_TRAIN=/opt/ml/input/data/train\n",
      "SM_HP_N_ESTIMATORS=100\n",
      "SM_HP_RANDOM_STATE=42\n",
      "PYTHONPATH=/opt/ml/code:/miniconda3/bin:/miniconda3/lib/python37.zip:/miniconda3/lib/python3.7:/miniconda3/lib/python3.7/lib-dynload:/miniconda3/lib/python3.7/site-packages\n",
      "Invoking script with the following command:\n",
      "/miniconda3/bin/python script.py --n_estimators 100 --random_state 42\n",
      "[INFO] Extracting arguments\n",
      "SKLearn Version:  0.23.2\n",
      "Joblib Version:  1.2.0\n",
      "[INFO] Reading Data\n",
      "Building training and testing datasets\n",
      "Column order: \n",
      "['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g', 'touch_screen', 'wifi']\n",
      "Label column is:  price_range\n",
      "Data Shape: \n",
      "---- SHAPE OF TRAINING DATA (85%) ----\n",
      "(1700, 20)\n",
      "(1700,)\n",
      "---- SHAPE OF TESTING DATA (15%) ----\n",
      "(300, 20)\n",
      "(300,)\n",
      "Training Random Forest Model......\n",
      "Model persisted at /opt/ml/model/model.joblib\n",
      "---- METRICS RESULTS FOR TESTING DATA ----\n",
      "Total Rows are:  300\n",
      "[TESTING] Model Accuracy is:  0.88\n",
      "[TESTING] Testing Report: \n",
      "              precision    recall  f1-score   support\n",
      "           0       0.91      0.94      0.93        80\n",
      "           1       0.87      0.83      0.85        72\n",
      "           2       0.81      0.84      0.83        68\n",
      "           3       0.91      0.90      0.91        80\n",
      "    accuracy                           0.88       300\n",
      "   macro avg       0.88      0.88      0.88       300\n",
      "weighted avg       0.88      0.88      0.88       300\n",
      "2024-05-11 17:57:49,686 sagemaker-containers INFO     Reporting training SUCCESS\n",
      "\n",
      "2024-05-11 17:58:09 Completed - Training job completed\n",
      "Training seconds: 92\n",
      "Billable seconds: 43\n",
      "Managed Spot Training savings: 53.3%\n"
     ]
    }
   ],
   "source": [
    "# launch training job with asynchronous call\n",
    "sklearn_estimator.fit({\"train\": train_path, \"test\": test_path}, wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2024-05-11 17:58:09 Starting - Preparing the instances for training\n",
      "2024-05-11 17:58:09 Downloading - Downloading the training image\n",
      "2024-05-11 17:58:09 Training - Training image download completed. Training in progress.\n",
      "2024-05-11 17:58:09 Uploading - Uploading generated training model\n",
      "2024-05-11 17:58:09 Completed - Training job completed\n",
      "Model artifact persisted at s3://sagemaker-us-east-1-905418178928/RF-custom-sklearn-2024-05-11-17-55-53-016/output/model.tar.gz\n"
     ]
    }
   ],
   "source": [
    "sklearn_estimator.latest_training_job.wait(logs=\"None\")\n",
    "artifacts = sm_boto3.describe_training_job(\n",
    "    TrainingJobName=sklearn_estimator.latest_training_job.name\n",
    ")[\"ModelArtifacts\"][\"S3ModelArtifacts\"]\n",
    "\n",
    "print(\"Model artifact persisted at \" + artifacts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-905418178928/RF-custom-sklearn-2024-05-11-17-55-53-016/output/model.tar.gz'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "artifacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.sklearn import SKLearnModel\n",
    "from time import gmtime, strftime\n",
    "\n",
    "model_name = \"Custom-sklearn-model-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "model = SKLearnModel(\n",
    "    name = model_name,\n",
    "    model_data = artifacts,\n",
    "    role=os.environ[\"ROLE\"],\n",
    "    entry_point=\"script.py\",\n",
    "    framework_version=FRAMEWORK_VERSION,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sagemaker.sklearn.model.SKLearnModel at 0x7fa03fb89ca0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Endpoint Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EndpointName=Custom-sklearn-model-2024-05-11-17-59-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: Custom-sklearn-model-2024-05-11-17-58-55\n",
      "INFO:sagemaker:Creating endpoint-config with name Custom-sklearn-model-2024-05-11-17-58-5-2024-05-11-17-59-07-644\n",
      "INFO:sagemaker:Creating endpoint with name Custom-sklearn-model-2024-05-11-17-58-5-2024-05-11-17-59-07-644\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----!"
     ]
    }
   ],
   "source": [
    "## Endpoint deployment\n",
    "endpoint_name = \"Custom-sklearn-model-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "print(\"EndpointName={}\".format(endpoint_name))\n",
    "\n",
    "predictor = model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.m4.xlarge\",\n",
    "    endpoint_nam=endpoint_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sagemaker.sklearn.model.SKLearnPredictor at 0x7fa03f1f1e20>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Custom-sklearn-model-2024-05-11-17-59-05'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1646.0,\n",
       "  0.0,\n",
       "  2.5,\n",
       "  0.0,\n",
       "  3.0,\n",
       "  1.0,\n",
       "  25.0,\n",
       "  0.6,\n",
       "  200.0,\n",
       "  2.0,\n",
       "  5.0,\n",
       "  211.0,\n",
       "  1608.0,\n",
       "  686.0,\n",
       "  8.0,\n",
       "  6.0,\n",
       "  11.0,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  0.0],\n",
       " [1182.0,\n",
       "  0.0,\n",
       "  0.5,\n",
       "  0.0,\n",
       "  7.0,\n",
       "  1.0,\n",
       "  8.0,\n",
       "  0.5,\n",
       "  138.0,\n",
       "  8.0,\n",
       "  16.0,\n",
       "  275.0,\n",
       "  986.0,\n",
       "  2563.0,\n",
       "  19.0,\n",
       "  17.0,\n",
       "  19.0,\n",
       "  1.0,\n",
       "  0.0,\n",
       "  0.0]]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First two rows of X_test and convert them into the list\n",
    "X_test[features][0:2].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting predictions for the first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 1 3 1 2 2 0 3 1]\n"
     ]
    }
   ],
   "source": [
    "# Make predictions using the deployed endpoint\n",
    "response = predictor.predict(X_test[features][0:10].values.tolist())\n",
    "\n",
    "# Process the response\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning - Remove all endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting endpoint: Custom-sklearn-model-2024-05-11-17-58-5-2024-05-11-17-59-07-644\n",
      "All endpoints deleted successfully.\n"
     ]
    }
   ],
   "source": [
    "def remove_all_endpoints():\n",
    "    # Create a SageMaker client\n",
    "    sagemaker = boto3.client('sagemaker')\n",
    "\n",
    "    # Get a list of all endpoints\n",
    "    endpoints_response = sagemaker.list_endpoints()\n",
    "\n",
    "    # Check if there are any endpoints to delete\n",
    "    if 'Endpoints' in endpoints_response:\n",
    "        endpoints = endpoints_response['Endpoints']\n",
    "        for endpoint in endpoints:\n",
    "            endpoint_name = endpoint['EndpointName']\n",
    "            print(f\"Deleting endpoint: {endpoint_name}\")\n",
    "            sagemaker.delete_endpoint(EndpointName=endpoint_name)\n",
    "        print(\"All endpoints deleted successfully.\")\n",
    "    else:\n",
    "        print(\"No endpoints found.\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    remove_all_endpoints()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def random_forest(X_train, y_train, X_test, y_test):\n",
    "    \"\"\"\n",
    "    Random Forest Classifier\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"\\n\")\n",
    "    print(\"============================= Random Forest Classifier ==================================\")\n",
    "    \n",
    "    # Define the parameter grid\n",
    "    param_grid = {'n_estimators': [100, 200, 300], \n",
    "              'max_depth': [None, 10, 20, 30],\n",
    "              'min_samples_split': [2, 5, 10],\n",
    "              'min_samples_leaf': [1, 2, 4]}\n",
    "\n",
    "    # Initialize Random Forest classifier\n",
    "    dt = RandomForestClassifier(random_state=42)\n",
    "    \n",
    "    # Initialize GridSearchCV with the classifier and parameter grid\n",
    "    grid_search = GridSearchCV(estimator=dt, param_grid=param_grid, scoring='accuracy', cv=5, n_jobs=-1)\n",
    "    \n",
    "    # Perform grid search on training data\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    \n",
    "    # Get the scores from the grid search\n",
    "    scores = grid_search.cv_results_['mean_test_score']\n",
    "    \n",
    "    # Get the best parameters and best accuracy\n",
    "    best_params = grid_search.best_params_\n",
    "    best_score = grid_search.best_score_\n",
    "    \n",
    "    # Initialize RandomForestClassifier with the best parameters\n",
    "    best_classifier = RandomForestClassifier(**best_params, random_state=42)\n",
    "    \n",
    "    # Fit the classifier on the training data\n",
    "    best_classifier.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict the target variable for the test data\n",
    "    y_pred = best_classifier.predict(X_test)\n",
    "    \n",
    "    # Calculate evaluation metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    conf_matrix = multilabel_confusion_matrix(y_test, y_pred)\n",
    "    clf_report = classification_report(y_test, y_pred)\n",
    "    \n",
    "    # Output the best parameters and evaluation metrics\n",
    "    print(\"Best Parameters:\", best_params)\n",
    "    print(\"F1 Macro Score: {:.2f} %\".format(best_score*100))\n",
    "    print(\"Accuracy: {:.2f} %\".format(accuracy*100))\n",
    "    \n",
    "    # Calculate the standard deviation of scores\n",
    "    std_dev = np.std(scores)*100\n",
    "    \n",
    "    # Output the standard deviation\n",
    "    print(\"Standard Deviation of Cross Validation Scores: {:.2f} %\".format(std_dev))\n",
    "    \n",
    "    # Confusion Matrix\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "    \n",
    "    # Classification Report\n",
    "    print(\"Classification Report:\\n\", clf_report)\n",
    "\n",
    "    # Return the best parameters and best estimator\n",
    "    return grid_search.best_params_, grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "============================= Random Forest Classifier ==================================\n",
      "Best Parameters: {'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "F1 Macro Score: 88.29 %\n",
      "Accuracy: 89.00 %\n",
      "Standard Deviation of Cross Validation Scores: 0.37 %\n",
      "Confusion Matrix:\n",
      " [[[216   4]\n",
      "  [  4  76]]\n",
      "\n",
      " [[220   8]\n",
      "  [  9  63]]\n",
      "\n",
      " [[217  15]\n",
      "  [ 10  58]]\n",
      "\n",
      " [[214   6]\n",
      "  [ 10  70]]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95        80\n",
      "           1       0.89      0.88      0.88        72\n",
      "           2       0.79      0.85      0.82        68\n",
      "           3       0.92      0.88      0.90        80\n",
      "\n",
      "    accuracy                           0.89       300\n",
      "   macro avg       0.89      0.89      0.89       300\n",
      "weighted avg       0.89      0.89      0.89       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_best_params, rf_best_estimator = random_forest(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# save the Random Forest model to disk\n",
    "filename = 'outputs/RandomForest.sav'\n",
    "pickle.dump(rf_best_estimator, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1646.0,\n",
       "  0.0,\n",
       "  2.5,\n",
       "  0.0,\n",
       "  3.0,\n",
       "  1.0,\n",
       "  25.0,\n",
       "  0.6,\n",
       "  200.0,\n",
       "  2.0,\n",
       "  5.0,\n",
       "  211.0,\n",
       "  1608.0,\n",
       "  686.0,\n",
       "  8.0,\n",
       "  6.0,\n",
       "  11.0,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  0.0]]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0:1].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ozerozdal/anaconda3/envs/mob_price/lib/python3.8/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 2])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting predictions for the first two lines\n",
    "rf_best_estimator.predict(X_test[0:2].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ozerozdal/anaconda3/envs/mob_price/lib/python3.8/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.75      , 0.14888889, 0.08166667, 0.01944444],\n",
       "       [0.08033333, 0.25522222, 0.531     , 0.13344444]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction probabilities for the first two lines\n",
    "rf_best_estimator.predict_proba(X_test[0:2].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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