Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adf3410a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed93b32",
   "metadata": {},
   "source": [
    "\n",
    "# Extracting and storing train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "721081bc",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2/4/2015 17:51</td>\n",
       "      <td>23.18</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.25</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2/4/2015 17:51</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
       "      <td>714.00</td>\n",
       "      <td>0.004783</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2/4/2015 17:53</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
       "      <td>713.50</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2/4/2015 17:54</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>708.25</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2/4/2015 17:55</td>\n",
       "      <td>23.10</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>704.50</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Id            Date  Temperature  Humidity  Light     CO2  HumidityRatio  \\\n",
       "0   1  2/4/2015 17:51        23.18   27.2720  426.0  721.25       0.004793   \n",
       "1   2  2/4/2015 17:51        23.15   27.2675  429.5  714.00       0.004783   \n",
       "2   3  2/4/2015 17:53        23.15   27.2450  426.0  713.50       0.004779   \n",
       "3   4  2/4/2015 17:54        23.15   27.2000  426.0  708.25       0.004772   \n",
       "4   5  2/4/2015 17:55        23.10   27.2000  426.0  704.50       0.004757   \n",
       "\n",
       "   Occupancy  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/naive bayes/datatraining.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7eb032cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8143, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f0259e6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "      <td>8143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4072.000000</td>\n",
       "      <td>20.619084</td>\n",
       "      <td>25.731507</td>\n",
       "      <td>119.519375</td>\n",
       "      <td>606.546243</td>\n",
       "      <td>0.003863</td>\n",
       "      <td>0.212330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2350.825954</td>\n",
       "      <td>1.016916</td>\n",
       "      <td>5.531211</td>\n",
       "      <td>194.755805</td>\n",
       "      <td>314.320877</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.408982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.745000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>412.750000</td>\n",
       "      <td>0.002674</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2036.500000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>439.000000</td>\n",
       "      <td>0.003078</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4072.000000</td>\n",
       "      <td>20.390000</td>\n",
       "      <td>26.222500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>453.500000</td>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6107.500000</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>30.533333</td>\n",
       "      <td>256.375000</td>\n",
       "      <td>638.833333</td>\n",
       "      <td>0.004352</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8143.000000</td>\n",
       "      <td>23.180000</td>\n",
       "      <td>39.117500</td>\n",
       "      <td>1546.333333</td>\n",
       "      <td>2028.500000</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id  Temperature     Humidity        Light          CO2  \\\n",
       "count  8143.000000  8143.000000  8143.000000  8143.000000  8143.000000   \n",
       "mean   4072.000000    20.619084    25.731507   119.519375   606.546243   \n",
       "std    2350.825954     1.016916     5.531211   194.755805   314.320877   \n",
       "min       1.000000    19.000000    16.745000     0.000000   412.750000   \n",
       "25%    2036.500000    19.700000    20.200000     0.000000   439.000000   \n",
       "50%    4072.000000    20.390000    26.222500     0.000000   453.500000   \n",
       "75%    6107.500000    21.390000    30.533333   256.375000   638.833333   \n",
       "max    8143.000000    23.180000    39.117500  1546.333333  2028.500000   \n",
       "\n",
       "       HumidityRatio    Occupancy  \n",
       "count    8143.000000  8143.000000  \n",
       "mean        0.003863     0.212330  \n",
       "std         0.000852     0.408982  \n",
       "min         0.002674     0.000000  \n",
       "25%         0.003078     0.000000  \n",
       "50%         0.003801     0.000000  \n",
       "75%         0.004352     0.000000  \n",
       "max         0.006476     1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b89cd08",
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   "outputs": [
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2/4/2015 17:51</td>\n",
       "      <td>23.18</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.25</td>\n",
       "      <td>0.004793</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2/4/2015 17:51</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
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       "      <td>0.004783</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2/4/2015 17:53</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
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       "   Id            Date  Temperature  Humidity  Light     CO2  HumidityRatio\n",
       "0   1  2/4/2015 17:51        23.18   27.2720  426.0  721.25       0.004793\n",
       "1   2  2/4/2015 17:51        23.15   27.2675  429.5  714.00       0.004783\n",
       "2   3  2/4/2015 17:53        23.15   27.2450  426.0  713.50       0.004779"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train.drop('Occupancy', axis=1)\n",
    "train_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4970f381",
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   "outputs": [
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       "      <th>Id</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23.18</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.25</td>\n",
       "      <td>0.004793</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
       "      <td>714.00</td>\n",
       "      <td>0.004783</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
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      "text/plain": [
       "   Id  Temperature  Humidity  Light     CO2  HumidityRatio\n",
       "0   1        23.18   27.2720  426.0  721.25       0.004793\n",
       "1   2        23.15   27.2675  429.5  714.00       0.004783\n",
       "2   3        23.15   27.2450  426.0  713.50       0.004779"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf1 = train_df.drop('Date', axis=1)\n",
    "traindf1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27541a71",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.18</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.25</td>\n",
       "      <td>0.004793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
       "      <td>714.00</td>\n",
       "      <td>0.004783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
       "      <td>713.50</td>\n",
       "      <td>0.004779</td>\n",
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      ],
      "text/plain": [
       "   Temperature  Humidity  Light     CO2  HumidityRatio\n",
       "0        23.18   27.2720  426.0  721.25       0.004793\n",
       "1        23.15   27.2675  429.5  714.00       0.004783\n",
       "2        23.15   27.2450  426.0  713.50       0.004779"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1 = traindf1.drop('Id', axis=1)\n",
    "train_df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "781f4cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Occupancy, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target = train['Occupancy']\n",
    "train_target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e89bab",
   "metadata": {},
   "source": [
    "# Extracting and storing test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b425a465",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/naive bayes/datatest2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "370a965e",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/11/2015 14:48</td>\n",
       "      <td>21.7600</td>\n",
       "      <td>31.133333</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1029.666667</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/11/2015 14:49</td>\n",
       "      <td>21.7900</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.005009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/11/2015 14:50</td>\n",
       "      <td>21.7675</td>\n",
       "      <td>31.122500</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>1003.750000</td>\n",
       "      <td>0.005022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/11/2015 14:51</td>\n",
       "      <td>21.7675</td>\n",
       "      <td>31.122500</td>\n",
       "      <td>439.000000</td>\n",
       "      <td>1009.500000</td>\n",
       "      <td>0.005022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/11/2015 14:51</td>\n",
       "      <td>21.7900</td>\n",
       "      <td>31.133333</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1005.666667</td>\n",
       "      <td>0.005030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  Temperature   Humidity       Light          CO2  \\\n",
       "0  2/11/2015 14:48      21.7600  31.133333  437.333333  1029.666667   \n",
       "1  2/11/2015 14:49      21.7900  31.000000  437.333333  1000.000000   \n",
       "2  2/11/2015 14:50      21.7675  31.122500  434.000000  1003.750000   \n",
       "3  2/11/2015 14:51      21.7675  31.122500  439.000000  1009.500000   \n",
       "4  2/11/2015 14:51      21.7900  31.133333  437.333333  1005.666667   \n",
       "\n",
       "   HumidityRatio  Occupancy  \n",
       "0       0.005021          1  \n",
       "1       0.005009          1  \n",
       "2       0.005022          1  \n",
       "3       0.005022          1  \n",
       "4       0.005030          1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e994fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data= test.drop('Occupancy', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13c62e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df1= test_data.drop('date', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4545b2df",
   "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>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.7600</td>\n",
       "      <td>31.133333</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1029.666667</td>\n",
       "      <td>0.005021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.7900</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.005009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.7675</td>\n",
       "      <td>31.122500</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>1003.750000</td>\n",
       "      <td>0.005022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.7675</td>\n",
       "      <td>31.122500</td>\n",
       "      <td>439.000000</td>\n",
       "      <td>1009.500000</td>\n",
       "      <td>0.005022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.7900</td>\n",
       "      <td>31.133333</td>\n",
       "      <td>437.333333</td>\n",
       "      <td>1005.666667</td>\n",
       "      <td>0.005030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature   Humidity       Light          CO2  HumidityRatio\n",
       "0      21.7600  31.133333  437.333333  1029.666667       0.005021\n",
       "1      21.7900  31.000000  437.333333  1000.000000       0.005009\n",
       "2      21.7675  31.122500  434.000000  1003.750000       0.005022\n",
       "3      21.7675  31.122500  439.000000  1009.500000       0.005022\n",
       "4      21.7900  31.133333  437.333333  1005.666667       0.005030"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fce0c30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9752, 7)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6811c852",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_target = test['Occupancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0ec2b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77e00306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.51847007, 0.27852622, 1.57376283, 0.36494808, 1.09175673],\n",
       "       [2.48896731, 0.27771261, 1.59173515, 0.34188106, 1.080555  ],\n",
       "       [2.48896731, 0.27364453, 1.57376283, 0.34029023, 1.07588869],\n",
       "       ...,\n",
       "       [0.47294534, 1.87375407, 1.60970748, 0.61073113, 2.033527  ],\n",
       "       [0.47294534, 1.90358662, 1.60970748, 0.68019732, 2.06381051],\n",
       "       [0.47294534, 1.89273842, 1.68159679, 0.68231843, 2.05279885]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1 = sc.fit_transform(train_df1)\n",
    "train_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bc1b7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.42897500e-01,  3.14074332e-01,  1.50936314e+00,\n",
       "         9.30527184e-01,  8.14063358e-01],\n",
       "       [ 7.72290797e-01,  2.80341614e-01,  1.50936314e+00,\n",
       "         8.30666611e-01,  7.90652817e-01],\n",
       "       [ 7.50245824e-01,  3.11333549e-01,  1.49335371e+00,\n",
       "         8.43289436e-01,  8.15114290e-01],\n",
       "       ...,\n",
       "       [-1.09508097e-01, -5.43158393e-01,  1.44292400e+00,\n",
       "         2.58608082e+00, -6.62878117e-01],\n",
       "       [-1.09508097e-01, -4.72952171e-01,  1.42011056e+00,\n",
       "         2.95803340e+00, -5.82519504e-01],\n",
       "       [-1.73267669e-03, -4.53345028e-01,  1.37328297e+00,\n",
       "         3.73896552e+00, -5.04835305e-01]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1 = sc.fit_transform(test_df1)\n",
    "test_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f20c845",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "train_target = le.fit_transform(train_target)\n",
    "test_target = le.fit_transform(test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7fb185f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8c75708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_df1, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e80fb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "77339a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2409"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1682 +727)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c58a9e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2665 - (1682 +727)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1095e32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7635,   68],\n",
       "       [  15, 2034]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_target, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb73627e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9788775635515167"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(train_df1, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c2df64f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008511074651353569"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred, test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db542036",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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