Machine learning with car engine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39522c68",
   "metadata": {},
   "source": [
    "# Car Engine health prediction - Machine Learning 🏎️"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102ec4e6",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0d7176a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as st\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21551110",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700</td>\n",
       "      <td>2.493592</td>\n",
       "      <td>11.790927</td>\n",
       "      <td>3.178981</td>\n",
       "      <td>84.144163</td>\n",
       "      <td>81.632187</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>876</td>\n",
       "      <td>2.941606</td>\n",
       "      <td>16.193866</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>77.640934</td>\n",
       "      <td>82.445724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>520</td>\n",
       "      <td>2.961746</td>\n",
       "      <td>6.553147</td>\n",
       "      <td>1.064347</td>\n",
       "      <td>77.752266</td>\n",
       "      <td>79.645777</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>473</td>\n",
       "      <td>3.707835</td>\n",
       "      <td>19.510172</td>\n",
       "      <td>3.727455</td>\n",
       "      <td>74.129907</td>\n",
       "      <td>71.774629</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>619</td>\n",
       "      <td>5.672919</td>\n",
       "      <td>15.738871</td>\n",
       "      <td>2.052251</td>\n",
       "      <td>78.396989</td>\n",
       "      <td>87.000225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1221</td>\n",
       "      <td>3.989226</td>\n",
       "      <td>6.679231</td>\n",
       "      <td>2.214250</td>\n",
       "      <td>76.401152</td>\n",
       "      <td>75.669818</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>716</td>\n",
       "      <td>3.568896</td>\n",
       "      <td>5.312266</td>\n",
       "      <td>2.461067</td>\n",
       "      <td>83.646589</td>\n",
       "      <td>79.792411</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>729</td>\n",
       "      <td>3.845166</td>\n",
       "      <td>10.191126</td>\n",
       "      <td>2.362998</td>\n",
       "      <td>77.921202</td>\n",
       "      <td>71.671761</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>845</td>\n",
       "      <td>4.877239</td>\n",
       "      <td>3.638269</td>\n",
       "      <td>3.525604</td>\n",
       "      <td>76.301626</td>\n",
       "      <td>70.496024</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>824</td>\n",
       "      <td>3.741228</td>\n",
       "      <td>7.626214</td>\n",
       "      <td>1.301032</td>\n",
       "      <td>77.066520</td>\n",
       "      <td>85.143297</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0         700          2.493592      11.790927          3.178981   \n",
       "1         876          2.941606      16.193866          2.464504   \n",
       "2         520          2.961746       6.553147          1.064347   \n",
       "3         473          3.707835      19.510172          3.727455   \n",
       "4         619          5.672919      15.738871          2.052251   \n",
       "5        1221          3.989226       6.679231          2.214250   \n",
       "6         716          3.568896       5.312266          2.461067   \n",
       "7         729          3.845166      10.191126          2.362998   \n",
       "8         845          4.877239       3.638269          3.525604   \n",
       "9         824          3.741228       7.626214          1.301032   \n",
       "\n",
       "   lub oil temp  Coolant temp  Engine Condition  \n",
       "0     84.144163     81.632187                 1  \n",
       "1     77.640934     82.445724                 0  \n",
       "2     77.752266     79.645777                 1  \n",
       "3     74.129907     71.774629                 1  \n",
       "4     78.396989     87.000225                 0  \n",
       "5     76.401152     75.669818                 0  \n",
       "6     83.646589     79.792411                 1  \n",
       "7     77.921202     71.671761                 1  \n",
       "8     76.301626     70.496024                 0  \n",
       "9     77.066520     85.143297                 0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"engine_data.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9deede22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19535 entries, 0 to 19534\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Engine rpm        19535 non-null  int64  \n",
      " 1   Lub oil pressure  19535 non-null  float64\n",
      " 2   Fuel pressure     19535 non-null  float64\n",
      " 3   Coolant pressure  19535 non-null  float64\n",
      " 4   lub oil temp      19535 non-null  float64\n",
      " 5   Coolant temp      19535 non-null  float64\n",
      " 6   Engine Condition  19535 non-null  int64  \n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbcf6e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "      <td>19535.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>791.239</td>\n",
       "      <td>3.304</td>\n",
       "      <td>6.656</td>\n",
       "      <td>2.335</td>\n",
       "      <td>77.643</td>\n",
       "      <td>78.427</td>\n",
       "      <td>0.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>267.611</td>\n",
       "      <td>1.022</td>\n",
       "      <td>2.761</td>\n",
       "      <td>1.036</td>\n",
       "      <td>3.111</td>\n",
       "      <td>6.207</td>\n",
       "      <td>0.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>71.322</td>\n",
       "      <td>61.673</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>593.000</td>\n",
       "      <td>2.519</td>\n",
       "      <td>4.917</td>\n",
       "      <td>1.600</td>\n",
       "      <td>75.726</td>\n",
       "      <td>73.895</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>746.000</td>\n",
       "      <td>3.162</td>\n",
       "      <td>6.202</td>\n",
       "      <td>2.167</td>\n",
       "      <td>76.817</td>\n",
       "      <td>78.347</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>934.000</td>\n",
       "      <td>4.055</td>\n",
       "      <td>7.745</td>\n",
       "      <td>2.849</td>\n",
       "      <td>78.072</td>\n",
       "      <td>82.915</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2239.000</td>\n",
       "      <td>7.266</td>\n",
       "      <td>21.138</td>\n",
       "      <td>7.479</td>\n",
       "      <td>89.581</td>\n",
       "      <td>195.528</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "count   19535.000         19535.000      19535.000         19535.000   \n",
       "mean      791.239             3.304          6.656             2.335   \n",
       "std       267.611             1.022          2.761             1.036   \n",
       "min        61.000             0.003          0.003             0.002   \n",
       "25%       593.000             2.519          4.917             1.600   \n",
       "50%       746.000             3.162          6.202             2.167   \n",
       "75%       934.000             4.055          7.745             2.849   \n",
       "max      2239.000             7.266         21.138             7.479   \n",
       "\n",
       "       lub oil temp  Coolant temp  Engine Condition  \n",
       "count     19535.000     19535.000         19535.000  \n",
       "mean         77.643        78.427             0.631  \n",
       "std           3.111         6.207             0.483  \n",
       "min          71.322        61.673             0.000  \n",
       "25%          75.726        73.895             0.000  \n",
       "50%          76.817        78.347             1.000  \n",
       "75%          78.072        82.915             1.000  \n",
       "max          89.581       195.528             1.000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "add839cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine Condition\n",
       "1    12317\n",
       "0     7218\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Engine Condition'].value_counts()\n",
    "# \"0\" Engine in a normal or healthy condition\n",
    "# \"1\" Engine in a faulty or problematic condition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1baa5973",
   "metadata": {},
   "source": [
    "### Check for outlier (Increase the accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b30caef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.boxplot(data=df, palette='binary')\n",
    "plt.title(\"Boxplot for Outliers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cad5e066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12, 6))\n",
    "scatter = plt.scatter(\n",
    "    df[\"Engine rpm\"],\n",
    "    df[\"Coolant temp\"],\n",
    "    c = df[\"Engine rpm\"],\n",
    "    cmap = 'turbo',\n",
    "    alpha = 0.7\n",
    ")\n",
    "plt.colorbar(scatter, label = 'Engine rpm')\n",
    "plt.title(\"Engine rpm vs Coolant temp\")\n",
    "plt.xlabel(\"Engine rpm\")\n",
    "plt.ylabel(\"Coolant temp\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d496fc",
   "metadata": {},
   "source": [
    "#### Using IQR technique (Inter Quantile Range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c23da81b",
   "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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700</td>\n",
       "      <td>2.493592</td>\n",
       "      <td>11.790927</td>\n",
       "      <td>3.178981</td>\n",
       "      <td>84.144163</td>\n",
       "      <td>81.632187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>876</td>\n",
       "      <td>2.941606</td>\n",
       "      <td>16.193866</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>77.640934</td>\n",
       "      <td>82.445724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>520</td>\n",
       "      <td>2.961746</td>\n",
       "      <td>6.553147</td>\n",
       "      <td>1.064347</td>\n",
       "      <td>77.752266</td>\n",
       "      <td>79.645777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>473</td>\n",
       "      <td>3.707835</td>\n",
       "      <td>19.510172</td>\n",
       "      <td>3.727455</td>\n",
       "      <td>74.129907</td>\n",
       "      <td>71.774629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>619</td>\n",
       "      <td>5.672919</td>\n",
       "      <td>15.738871</td>\n",
       "      <td>2.052251</td>\n",
       "      <td>78.396989</td>\n",
       "      <td>87.000225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19530</th>\n",
       "      <td>902</td>\n",
       "      <td>4.117296</td>\n",
       "      <td>4.981360</td>\n",
       "      <td>4.346564</td>\n",
       "      <td>75.951627</td>\n",
       "      <td>87.925087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19531</th>\n",
       "      <td>694</td>\n",
       "      <td>4.817720</td>\n",
       "      <td>10.866701</td>\n",
       "      <td>6.186689</td>\n",
       "      <td>75.281430</td>\n",
       "      <td>74.928459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19532</th>\n",
       "      <td>684</td>\n",
       "      <td>2.673344</td>\n",
       "      <td>4.927376</td>\n",
       "      <td>1.903572</td>\n",
       "      <td>76.844940</td>\n",
       "      <td>86.337345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19533</th>\n",
       "      <td>696</td>\n",
       "      <td>3.094163</td>\n",
       "      <td>8.291816</td>\n",
       "      <td>1.221729</td>\n",
       "      <td>77.179693</td>\n",
       "      <td>73.624396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19534</th>\n",
       "      <td>504</td>\n",
       "      <td>3.775246</td>\n",
       "      <td>3.962480</td>\n",
       "      <td>2.038647</td>\n",
       "      <td>75.564313</td>\n",
       "      <td>80.421421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19535 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0             700          2.493592      11.790927          3.178981   \n",
       "1             876          2.941606      16.193866          2.464504   \n",
       "2             520          2.961746       6.553147          1.064347   \n",
       "3             473          3.707835      19.510172          3.727455   \n",
       "4             619          5.672919      15.738871          2.052251   \n",
       "...           ...               ...            ...               ...   \n",
       "19530         902          4.117296       4.981360          4.346564   \n",
       "19531         694          4.817720      10.866701          6.186689   \n",
       "19532         684          2.673344       4.927376          1.903572   \n",
       "19533         696          3.094163       8.291816          1.221729   \n",
       "19534         504          3.775246       3.962480          2.038647   \n",
       "\n",
       "       lub oil temp  Coolant temp  \n",
       "0         84.144163     81.632187  \n",
       "1         77.640934     82.445724  \n",
       "2         77.752266     79.645777  \n",
       "3         74.129907     71.774629  \n",
       "4         78.396989     87.000225  \n",
       "...             ...           ...  \n",
       "19530     75.951627     87.925087  \n",
       "19531     75.281430     74.928459  \n",
       "19532     76.844940     86.337345  \n",
       "19533     77.179693     73.624396  \n",
       "19534     75.564313     80.421421  \n",
       "\n",
       "[19535 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = df.drop(columns=[\"Engine Condition\"])\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7123ce39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine rpm      593.000000\n",
      "Coolant temp     73.895421\n",
      "Name: 0.25, dtype: float64\n",
      "-------------------------------\n",
      "Engine rpm      934.000000\n",
      "Coolant temp     82.915411\n",
      "Name: 0.75, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Q1 = df_temp[['Engine rpm', 'Coolant temp']].quantile(0.25)\n",
    "print(Q1)\n",
    "print(\"-------------------------------\")\n",
    "Q3 = df_temp[['Engine rpm', 'Coolant temp']].quantile(0.75)\n",
    "print(Q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d58708a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine rpm      341.00000\n",
      "Coolant temp      9.01999\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76744cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine rpm      81.500000\n",
      "Coolant temp    60.365435\n",
      "dtype: float64\n",
      "-------------------------------\n",
      "Engine rpm      1445.500000\n",
      "Coolant temp      96.445397\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "lower_limit = Q1 - 1.5*IQR\n",
    "print(lower_limit)\n",
    "print(\"-------------------------------\")\n",
    "upper_limit = Q3 + 1.5*IQR\n",
    "print(upper_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "273cbc40",
   "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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1674</td>\n",
       "      <td>2.501620</td>\n",
       "      <td>3.624157</td>\n",
       "      <td>2.043075</td>\n",
       "      <td>76.772327</td>\n",
       "      <td>74.641940</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1776</td>\n",
       "      <td>3.648060</td>\n",
       "      <td>5.873874</td>\n",
       "      <td>1.318790</td>\n",
       "      <td>76.481421</td>\n",
       "      <td>71.233786</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1481</td>\n",
       "      <td>3.972600</td>\n",
       "      <td>5.701680</td>\n",
       "      <td>1.556111</td>\n",
       "      <td>75.997690</td>\n",
       "      <td>93.791609</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1661</td>\n",
       "      <td>3.518121</td>\n",
       "      <td>11.802902</td>\n",
       "      <td>2.951991</td>\n",
       "      <td>76.725921</td>\n",
       "      <td>72.872725</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1620</td>\n",
       "      <td>1.536989</td>\n",
       "      <td>6.398304</td>\n",
       "      <td>2.423895</td>\n",
       "      <td>75.163334</td>\n",
       "      <td>77.340058</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19241</th>\n",
       "      <td>2136</td>\n",
       "      <td>3.311171</td>\n",
       "      <td>7.282090</td>\n",
       "      <td>1.678657</td>\n",
       "      <td>75.783308</td>\n",
       "      <td>83.707670</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19357</th>\n",
       "      <td>1556</td>\n",
       "      <td>4.786442</td>\n",
       "      <td>7.678463</td>\n",
       "      <td>4.029723</td>\n",
       "      <td>78.774495</td>\n",
       "      <td>71.273450</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19409</th>\n",
       "      <td>1586</td>\n",
       "      <td>5.044422</td>\n",
       "      <td>8.794704</td>\n",
       "      <td>2.523254</td>\n",
       "      <td>77.276628</td>\n",
       "      <td>70.349750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>1807</td>\n",
       "      <td>3.727537</td>\n",
       "      <td>14.140429</td>\n",
       "      <td>4.446769</td>\n",
       "      <td>77.008190</td>\n",
       "      <td>90.369222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19466</th>\n",
       "      <td>1579</td>\n",
       "      <td>2.745121</td>\n",
       "      <td>4.776110</td>\n",
       "      <td>1.730032</td>\n",
       "      <td>75.771064</td>\n",
       "      <td>89.964517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>466 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "55           1674          2.501620       3.624157          2.043075   \n",
       "88           1776          3.648060       5.873874          1.318790   \n",
       "102          1481          3.972600       5.701680          1.556111   \n",
       "108          1661          3.518121      11.802902          2.951991   \n",
       "143          1620          1.536989       6.398304          2.423895   \n",
       "...           ...               ...            ...               ...   \n",
       "19241        2136          3.311171       7.282090          1.678657   \n",
       "19357        1556          4.786442       7.678463          4.029723   \n",
       "19409        1586          5.044422       8.794704          2.523254   \n",
       "19447        1807          3.727537      14.140429          4.446769   \n",
       "19466        1579          2.745121       4.776110          1.730032   \n",
       "\n",
       "       lub oil temp  Coolant temp  Engine Condition  \n",
       "55        76.772327     74.641940                 0  \n",
       "88        76.481421     71.233786                 0  \n",
       "102       75.997690     93.791609                 0  \n",
       "108       76.725921     72.872725                 0  \n",
       "143       75.163334     77.340058                 0  \n",
       "...             ...           ...               ...  \n",
       "19241     75.783308     83.707670                 1  \n",
       "19357     78.774495     71.273450                 0  \n",
       "19409     77.276628     70.349750                 1  \n",
       "19447     77.008190     90.369222                 1  \n",
       "19466     75.771064     89.964517                 0  \n",
       "\n",
       "[466 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_mask = (df[['Engine rpm', 'Coolant temp']] < lower_limit) | (df[['Engine rpm', 'Coolant temp']] > upper_limit)\n",
    "outliers = df[outlier_mask.any(axis=1)] \n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d471831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loss due to outlier : 2.385461991297671\n"
     ]
    }
   ],
   "source": [
    "percentage = (466/19535)*100\n",
    "print(\"Data loss due to outlier :\", percentage)  # That's Okay "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66963ba5",
   "metadata": {},
   "source": [
    "#### Removing Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60bd8724",
   "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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700</td>\n",
       "      <td>2.493592</td>\n",
       "      <td>11.790927</td>\n",
       "      <td>3.178981</td>\n",
       "      <td>84.144163</td>\n",
       "      <td>81.632187</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>876</td>\n",
       "      <td>2.941606</td>\n",
       "      <td>16.193866</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>77.640934</td>\n",
       "      <td>82.445724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>520</td>\n",
       "      <td>2.961746</td>\n",
       "      <td>6.553147</td>\n",
       "      <td>1.064347</td>\n",
       "      <td>77.752266</td>\n",
       "      <td>79.645777</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>473</td>\n",
       "      <td>3.707835</td>\n",
       "      <td>19.510172</td>\n",
       "      <td>3.727455</td>\n",
       "      <td>74.129907</td>\n",
       "      <td>71.774629</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>619</td>\n",
       "      <td>5.672919</td>\n",
       "      <td>15.738871</td>\n",
       "      <td>2.052251</td>\n",
       "      <td>78.396989</td>\n",
       "      <td>87.000225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19530</th>\n",
       "      <td>902</td>\n",
       "      <td>4.117296</td>\n",
       "      <td>4.981360</td>\n",
       "      <td>4.346564</td>\n",
       "      <td>75.951627</td>\n",
       "      <td>87.925087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19531</th>\n",
       "      <td>694</td>\n",
       "      <td>4.817720</td>\n",
       "      <td>10.866701</td>\n",
       "      <td>6.186689</td>\n",
       "      <td>75.281430</td>\n",
       "      <td>74.928459</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19532</th>\n",
       "      <td>684</td>\n",
       "      <td>2.673344</td>\n",
       "      <td>4.927376</td>\n",
       "      <td>1.903572</td>\n",
       "      <td>76.844940</td>\n",
       "      <td>86.337345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19533</th>\n",
       "      <td>696</td>\n",
       "      <td>3.094163</td>\n",
       "      <td>8.291816</td>\n",
       "      <td>1.221729</td>\n",
       "      <td>77.179693</td>\n",
       "      <td>73.624396</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19534</th>\n",
       "      <td>504</td>\n",
       "      <td>3.775246</td>\n",
       "      <td>3.962480</td>\n",
       "      <td>2.038647</td>\n",
       "      <td>75.564313</td>\n",
       "      <td>80.421421</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19069 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0             700          2.493592      11.790927          3.178981   \n",
       "1             876          2.941606      16.193866          2.464504   \n",
       "2             520          2.961746       6.553147          1.064347   \n",
       "3             473          3.707835      19.510172          3.727455   \n",
       "4             619          5.672919      15.738871          2.052251   \n",
       "...           ...               ...            ...               ...   \n",
       "19530         902          4.117296       4.981360          4.346564   \n",
       "19531         694          4.817720      10.866701          6.186689   \n",
       "19532         684          2.673344       4.927376          1.903572   \n",
       "19533         696          3.094163       8.291816          1.221729   \n",
       "19534         504          3.775246       3.962480          2.038647   \n",
       "\n",
       "       lub oil temp  Coolant temp  Engine Condition  \n",
       "0         84.144163     81.632187                 1  \n",
       "1         77.640934     82.445724                 0  \n",
       "2         77.752266     79.645777                 1  \n",
       "3         74.129907     71.774629                 1  \n",
       "4         78.396989     87.000225                 0  \n",
       "...             ...           ...               ...  \n",
       "19530     75.951627     87.925087                 1  \n",
       "19531     75.281430     74.928459                 1  \n",
       "19532     76.844940     86.337345                 1  \n",
       "19533     77.179693     73.624396                 1  \n",
       "19534     75.564313     80.421421                 1  \n",
       "\n",
       "[19069 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df[~outlier_mask.any(axis=1)]\n",
    "df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2e3022b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = df_cleaned.corr()\n",
    "\n",
    "plt.figure(figsize =(12, 8))\n",
    "sns.heatmap(correlation, annot = True, cmap = 'coolwarm', fmt = '.2f', linewidths = 0.5)\n",
    "plt.title(\"Correlation Heatmap for engine health\")\n",
    "plt.show() \n",
    "# All the features have weak correlations with the target variable Engine Condition. \n",
    "#This suggests that none of these features alone strongly predict the engine condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ca6b320",
   "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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700</td>\n",
       "      <td>2.494</td>\n",
       "      <td>11.791</td>\n",
       "      <td>3.179</td>\n",
       "      <td>84.144</td>\n",
       "      <td>81.632</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>876</td>\n",
       "      <td>2.942</td>\n",
       "      <td>16.194</td>\n",
       "      <td>2.465</td>\n",
       "      <td>77.641</td>\n",
       "      <td>82.446</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>520</td>\n",
       "      <td>2.962</td>\n",
       "      <td>6.553</td>\n",
       "      <td>1.064</td>\n",
       "      <td>77.752</td>\n",
       "      <td>79.646</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>473</td>\n",
       "      <td>3.708</td>\n",
       "      <td>19.510</td>\n",
       "      <td>3.727</td>\n",
       "      <td>74.130</td>\n",
       "      <td>71.775</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>619</td>\n",
       "      <td>5.673</td>\n",
       "      <td>15.739</td>\n",
       "      <td>2.052</td>\n",
       "      <td>78.397</td>\n",
       "      <td>87.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19530</th>\n",
       "      <td>902</td>\n",
       "      <td>4.117</td>\n",
       "      <td>4.981</td>\n",
       "      <td>4.347</td>\n",
       "      <td>75.952</td>\n",
       "      <td>87.925</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19531</th>\n",
       "      <td>694</td>\n",
       "      <td>4.818</td>\n",
       "      <td>10.867</td>\n",
       "      <td>6.187</td>\n",
       "      <td>75.281</td>\n",
       "      <td>74.928</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19532</th>\n",
       "      <td>684</td>\n",
       "      <td>2.673</td>\n",
       "      <td>4.927</td>\n",
       "      <td>1.904</td>\n",
       "      <td>76.845</td>\n",
       "      <td>86.337</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19533</th>\n",
       "      <td>696</td>\n",
       "      <td>3.094</td>\n",
       "      <td>8.292</td>\n",
       "      <td>1.222</td>\n",
       "      <td>77.180</td>\n",
       "      <td>73.624</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19534</th>\n",
       "      <td>504</td>\n",
       "      <td>3.775</td>\n",
       "      <td>3.962</td>\n",
       "      <td>2.039</td>\n",
       "      <td>75.564</td>\n",
       "      <td>80.421</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19069 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0             700             2.494         11.791             3.179   \n",
       "1             876             2.942         16.194             2.465   \n",
       "2             520             2.962          6.553             1.064   \n",
       "3             473             3.708         19.510             3.727   \n",
       "4             619             5.673         15.739             2.052   \n",
       "...           ...               ...            ...               ...   \n",
       "19530         902             4.117          4.981             4.347   \n",
       "19531         694             4.818         10.867             6.187   \n",
       "19532         684             2.673          4.927             1.904   \n",
       "19533         696             3.094          8.292             1.222   \n",
       "19534         504             3.775          3.962             2.039   \n",
       "\n",
       "       lub oil temp  Coolant temp  Engine Condition  \n",
       "0            84.144        81.632                 1  \n",
       "1            77.641        82.446                 0  \n",
       "2            77.752        79.646                 1  \n",
       "3            74.130        71.775                 1  \n",
       "4            78.397        87.000                 0  \n",
       "...             ...           ...               ...  \n",
       "19530        75.952        87.925                 1  \n",
       "19531        75.281        74.928                 1  \n",
       "19532        76.845        86.337                 1  \n",
       "19533        77.180        73.624                 1  \n",
       "19534        75.564        80.421                 1  \n",
       "\n",
       "[19069 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30158dbc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "10dfde87",
   "metadata": {},
   "source": [
    "### KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8825a0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_cleaned.drop(columns=[\"Engine Condition\"])\n",
    "Y = df_cleaned['Engine Condition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e151c633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>KNeighborsClassifier(n_neighbors=25)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=25)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "\n",
    "X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "x_test = scaler.transform(x_test)    \n",
    "\n",
    "\n",
    "knn = KNeighborsClassifier(\n",
    "    n_neighbors = 25\n",
    ")\n",
    "\n",
    "knn.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "056df0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(X_train.shape)\n",
    "#print(x_test.shape)\n",
    "Y_pred = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b16d9ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy KNN: 65.5742003146303\n"
     ]
    }
   ],
   "source": [
    "Knn_accuracy = accuracy_score(y_test, Y_pred)\n",
    "print(f\"Accuracy KNN: {Knn_accuracy*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "33eecf80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.35      0.42      1358\n",
      "           1       0.70      0.82      0.75      2456\n",
      "\n",
      "    accuracy                           0.66      3814\n",
      "   macro avg       0.61      0.59      0.59      3814\n",
      "weighted avg       0.64      0.66      0.64      3814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20a08ae3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, Y_pred)\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(cm, annot = True, fmt ='d' , cmap = \"binary\", cbar = False ,xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel(\"Predicated Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.title(\"K Nearst Neighbor\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9aacaad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Knn model saved succesfully\n"
     ]
    }
   ],
   "source": [
    "with open(\"knn_model.pkl\", 'wb') as file:\n",
    "    pickle.dump(knn, file)\n",
    "    \n",
    "print(\"Knn model saved succesfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58cf201",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "346bff8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(\n",
    "    kernel =\"linear\",\n",
    "    C = 1.0,\n",
    "    degree = 3, \n",
    "    gamma = \"scale\"\n",
    ")\n",
    "svm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "688cbd5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c89cd7dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy SVM: 66.59674882013634\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "svm_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy SVM:\", svm_accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e3d8b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.20      0.30      1358\n",
      "           1       0.68      0.92      0.78      2456\n",
      "\n",
      "    accuracy                           0.67      3814\n",
      "   macro avg       0.63      0.56      0.54      3814\n",
      "weighted avg       0.65      0.67      0.61      3814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "79a93b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(cm, annot = True, fmt ='d' , cmap = \"binary\", cbar = False ,xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel(\"Predicated Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.title(\"Support Vector Machine\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aead2e61",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d728d233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rdf = RandomForestClassifier(\n",
    "    n_estimators = 100, \n",
    "    random_state = 42\n",
    ")\n",
    "rdf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4f6bcf35",
   "metadata": {},
   "outputs": [],
   "source": [
    "Rfy_pred = rdf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72f34b59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy RandomForest:  66.28211851074987\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "rdf_accuracy = accuracy_score(Rfy_pred, y_test)\n",
    "print(\"Accuracy RandomForest: \", rdf_accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "17f848cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.41      0.47      1358\n",
      "           1       0.71      0.80      0.75      2456\n",
      "\n",
      "    accuracy                           0.66      3814\n",
      "   macro avg       0.62      0.61      0.61      3814\n",
      "weighted avg       0.65      0.66      0.65      3814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Rfy_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "997578fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, Rfy_pred)\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(cm, annot = True, fmt ='d' , cmap = \"binary\", cbar = False ,xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel(\"Predicated Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.title(\"Random Forest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "576eaa0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randon_F model saved succesfully \n"
     ]
    }
   ],
   "source": [
    "with open(\"randonF_model.pkl\", 'wb') as file:\n",
    "    pickle.dump(rdf, file)\n",
    "    \n",
    "print(\"Randon_F model saved succesfully \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4b8b27",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d63d4da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in ./anaconda3/envs/PythonCourse/lib/python3.10/site-packages (2.1.1)\n",
      "Requirement already satisfied: numpy in ./anaconda3/envs/PythonCourse/lib/python3.10/site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in ./anaconda3/envs/PythonCourse/lib/python3.10/site-packages (from xgboost) (1.11.1)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "478f0cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category = UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8245a478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=42, ...)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='logloss',\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier(\n",
    "    n_estimators = 100, \n",
    "    random_state = 42, \n",
    "    use_label_encoder=False, \n",
    "    eval_metric = 'logloss' \n",
    ")\n",
    "xgb.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f196aa0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbY_pred = xgb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ce108d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy XGBoost : 65.04981646565285\n"
     ]
    }
   ],
   "source": [
    "xgb_accuracy = accuracy_score(xgbY_pred, y_test)\n",
    "print(\"Accuracy XGBoost :\",xgb_accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "abff8414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.38      0.44      1358\n",
      "           1       0.70      0.80      0.75      2456\n",
      "\n",
      "    accuracy                           0.65      3814\n",
      "   macro avg       0.61      0.59      0.59      3814\n",
      "weighted avg       0.63      0.65      0.64      3814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, xgbY_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7fedb1f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, xgbY_pred)\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(cm, annot = True, fmt ='d' , cmap = \"binary\", cbar = False ,xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel(\"Predicated Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.title(\"XGBoost\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be678a37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XBoost Model saved successfully\n"
     ]
    }
   ],
   "source": [
    "with open(\"Xboost_model.pkl\", 'wb') as file:\n",
    "    pickle.dump(xgb, file)\n",
    "\n",
    "print(\"XBoost Model saved successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3ffba338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy: 65.5742003146303\n",
      "SVM Accuracy: 66.59674882013634\n",
      "Random Forest Accuracy: 66.28211851074987\n",
      "XGBoost Accuracy: 65.04981646565285\n"
     ]
    }
   ],
   "source": [
    "print(f\"KNN Accuracy: {Knn_accuracy*100}\")\n",
    "print(f\"SVM Accuracy: {svm_accuracy*100}\")\n",
    "print(f\"Random Forest Accuracy: {rdf_accuracy*100}\")\n",
    "print(f\"XGBoost Accuracy: {xgb_accuracy*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9b57bf0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"svm_model.pkl\", 'wb') as file:\n",
    "    pickle.dump(svm, file)\n",
    "\n",
    "print(\"Model saved successfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebad0c88",
   "metadata": {},
   "source": [
    "# ⚠️ Only for developers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6897bbec",
   "metadata": {},
   "source": [
    "### Testing with custom input data.. Engine health check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3fc5bd80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter Engine rpm: 1000\n",
      "Enter Lub oil pressure: 4\n",
      "Enter Fuel pressure: 5\n",
      "Enter Coolant pressure: 4\n",
      "Enter Lub oil temp: 78\n",
      "Enter Coolant temp: 80\n",
      "Enter model name: knn\n",
      "Engine is in good health\n",
      "Engine is in good health\n",
      "\n",
      "Input Log saved to 'engine_health_log.csv'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "try:\n",
    "    input_log = pd.read_csv('engine_health_log.csv')\n",
    "except FileNotFoundError:\n",
    "    input_log = pd.DataFrame(\n",
    "        columns=['Engine rpm',                    \n",
    "                 'Lub oil pressure',              \n",
    "                 'Fuel pressure',                        \n",
    "                 'Coolant pressure',                    \n",
    "                 'Lub oil temp',                \n",
    "                 'Coolant temp',               \n",
    "                 'Engine condition'                              \n",
    "])\n",
    "def predict_engine_health(input_data, model, scaler, log_df):\n",
    "    input_array = np.array(input_data).reshape(1, -1)\n",
    "    \n",
    "    input_scaled = scaler.transform(input_array)\n",
    "    \n",
    "    prediction = model.predict(input_scaled)\n",
    "    \n",
    "    if prediction[0] == 0:\n",
    "        Health_status = \"Engine is in good health\"\n",
    "    else:\n",
    "        Health_status = \"Engine is not in good health\"  \n",
    "        \n",
    "    print(Health_status)\n",
    "    \n",
    "    input_data.append(prediction[0])\n",
    "    log_df.loc[len(log_df)] = input_data\n",
    "    \n",
    "    return Health_status\n",
    "        \n",
    "input_data = [\n",
    "    float(input(\"Enter Engine rpm: \")),\n",
    "    float(input(\"Enter Lub oil pressure: \")),\n",
    "    float(input(\"Enter Fuel pressure: \")),\n",
    "    float(input(\"Enter Coolant pressure: \")),\n",
    "    float(input(\"Enter Lub oil temp: \")),\n",
    "    float(input(\"Enter Coolant temp: \"))\n",
    "]\n",
    "model_choice = input(\"Enter model name: \" ).strip().lower()\n",
    "\n",
    "if model_choice == \"knn\":\n",
    "    model = knn\n",
    "elif model_choice == \"svm\":\n",
    "    model = svm\n",
    "elif model_choice == \"randomforest\":\n",
    "    model = rdf\n",
    "elif model_choice == \"xgboost\":\n",
    "    model = xgb\n",
    "else:\n",
    "    print(\"Invalid model name\")\n",
    "    model = None\n",
    "\n",
    "if model:    \n",
    "    result = predict_engine_health(input_data, model, scaler, input_log)\n",
    "    print(result)\n",
    "    \n",
    "input_log.to_csv(\"Engine_health_log.csv\", index = False)\n",
    "print(\"\\nInput Log saved to 'engine_health_log.csv'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0e241e8d",
   "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>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>Lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
       "      <th>Engine condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0       800.0               3.4            4.0               4.0   \n",
       "1       800.0               4.0            5.0               6.0   \n",
       "2       800.0               5.0            6.0               4.0   \n",
       "3      1000.0               4.0            5.0               4.0   \n",
       "\n",
       "   Lub oil temp  Coolant temp  Engine condition  \n",
       "0          75.0          76.0               1.0  \n",
       "1          74.0          80.0               1.0  \n",
       "2          76.0          70.0               1.0  \n",
       "3          78.0          80.0               0.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1092c0",
   "metadata": {},
   "source": [
    "# 🏁  🏁  🏁  🏁  🏁  🏁  🏁 - FINISH - 🏁  🏁  🏁  🏁  🏁  🏁  🏁"
   ]
  }
 ],
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