Simi_ML_CARS-SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01278304",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas_profiling\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22edfb93",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('cars.ORG.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "481d3b80",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Dimensions.Height</th>\n",
       "      <th>Dimensions.Length</th>\n",
       "      <th>Dimensions.Width</th>\n",
       "      <th>Engine Information.Driveline</th>\n",
       "      <th>Engine Information.Engine Type</th>\n",
       "      <th>Engine Information.Hybrid</th>\n",
       "      <th>Engine Information.Number of Forward Gears</th>\n",
       "      <th>Engine Information.Transmission</th>\n",
       "      <th>Fuel Information.City mpg</th>\n",
       "      <th>Fuel Information.Fuel Type</th>\n",
       "      <th>Fuel Information.Highway mpg</th>\n",
       "      <th>Identification.Classification</th>\n",
       "      <th>Identification.ID</th>\n",
       "      <th>Identification.Make</th>\n",
       "      <th>Identification.Model Year</th>\n",
       "      <th>Identification.Year</th>\n",
       "      <th>Engine Information.Engine Statistics.Horsepower</th>\n",
       "      <th>Engine Information.Engine Statistics.Torque</th>\n",
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       "      <td>Audi 3.2L 6 cylinder 250hp 236ft-lbs</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>6 Speed Automatic Select Shift</td>\n",
       "      <td>18</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>25</td>\n",
       "      <td>Automatic transmission</td>\n",
       "      <td>2009 Audi A3 3.2</td>\n",
       "      <td>Audi</td>\n",
       "      <td>2009 Audi A3</td>\n",
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       "      <td>6</td>\n",
       "      <td>6 Speed Manual</td>\n",
       "      <td>21</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>30</td>\n",
       "      <td>Manual transmission</td>\n",
       "      <td>2009 Audi A3 2.0 T</td>\n",
       "      <td>Audi</td>\n",
       "      <td>2009 Audi A3</td>\n",
       "      <td>2009</td>\n",
       "      <td>200</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Dimensions.Height  Dimensions.Length  Dimensions.Width  \\\n",
       "0                140                143               202   \n",
       "1                140                143               202   \n",
       "2                140                143               202   \n",
       "\n",
       "  Engine Information.Driveline                Engine Information.Engine Type  \\\n",
       "0              All-wheel drive          Audi 3.2L 6 cylinder 250hp 236ft-lbs   \n",
       "1            Front-wheel drive  Audi 2.0L 4 cylinder 200 hp 207 ft-lbs Turbo   \n",
       "2            Front-wheel drive  Audi 2.0L 4 cylinder 200 hp 207 ft-lbs Turbo   \n",
       "\n",
       "   Engine Information.Hybrid  Engine Information.Number of Forward Gears  \\\n",
       "0                       True                                           6   \n",
       "1                       True                                           6   \n",
       "2                       True                                           6   \n",
       "\n",
       "  Engine Information.Transmission  Fuel Information.City mpg  \\\n",
       "0  6 Speed Automatic Select Shift                         18   \n",
       "1  6 Speed Automatic Select Shift                         22   \n",
       "2                  6 Speed Manual                         21   \n",
       "\n",
       "  Fuel Information.Fuel Type  Fuel Information.Highway mpg  \\\n",
       "0                   Gasoline                            25   \n",
       "1                   Gasoline                            28   \n",
       "2                   Gasoline                            30   \n",
       "\n",
       "  Identification.Classification      Identification.ID Identification.Make  \\\n",
       "0        Automatic transmission       2009 Audi A3 3.2                Audi   \n",
       "1        Automatic transmission  2009 Audi A3 2.0 T AT                Audi   \n",
       "2           Manual transmission     2009 Audi A3 2.0 T                Audi   \n",
       "\n",
       "  Identification.Model Year  Identification.Year  \\\n",
       "0              2009 Audi A3                 2009   \n",
       "1              2009 Audi A3                 2009   \n",
       "2              2009 Audi A3                 2009   \n",
       "\n",
       "   Engine Information.Engine Statistics.Horsepower  \\\n",
       "0                                              250   \n",
       "1                                              200   \n",
       "2                                              200   \n",
       "\n",
       "   Engine Information.Engine Statistics.Torque  \n",
       "0                                          236  \n",
       "1                                          207  \n",
       "2                                          207  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(dataset)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "500a36bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=df[['Dimensions.Height','Dimensions.Length','Dimensions.Width','Engine Information.Number of Forward Gears',\n",
    "          'Fuel Information.City mpg','Engine Information.Engine Statistics.Horsepower','Identification.Make']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e693f0d",
   "metadata": {},
   "outputs": [
    {
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       "   Dimensions.Height  Dimensions.Length  Dimensions.Width  \\\n",
       "0                140                143               202   \n",
       "1                140                143               202   \n",
       "2                140                143               202   \n",
       "\n",
       "   Engine Information.Number of Forward Gears  Fuel Information.City mpg  \\\n",
       "0                                           6                         18   \n",
       "1                                           6                         22   \n",
       "2                                           6                         21   \n",
       "\n",
       "   Engine Information.Engine Statistics.Horsepower Identification.Make  \n",
       "0                                              250                Audi  \n",
       "1                                              200                Audi  \n",
       "2                                              200                Audi  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc8e17fc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>94</td>\n",
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       "      <td>6</td>\n",
       "      <td>17</td>\n",
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       "<p>5076 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "      Dimensions.Height  Dimensions.Length  Dimensions.Width  \\\n",
       "0                   140                143               202   \n",
       "1                   140                143               202   \n",
       "2                   140                143               202   \n",
       "3                   140                143               202   \n",
       "4                   140                143               202   \n",
       "...                 ...                ...               ...   \n",
       "5071                 13                253               201   \n",
       "5072                141                249               108   \n",
       "5073                160                249               108   \n",
       "5074                200                210               110   \n",
       "5075                200                 94               110   \n",
       "\n",
       "      Engine Information.Number of Forward Gears  Fuel Information.City mpg  \\\n",
       "0                                              6                         18   \n",
       "1                                              6                         22   \n",
       "2                                              6                         21   \n",
       "3                                              6                         21   \n",
       "4                                              6                         21   \n",
       "...                                          ...                        ...   \n",
       "5071                                           5                         18   \n",
       "5072                                           6                         12   \n",
       "5073                                           6                         12   \n",
       "5074                                           6                         17   \n",
       "5075                                           6                         17   \n",
       "\n",
       "      Engine Information.Engine Statistics.Horsepower  \n",
       "0                                                 250  \n",
       "1                                                 200  \n",
       "2                                                 200  \n",
       "3                                                 200  \n",
       "4                                                 200  \n",
       "...                                               ...  \n",
       "5071                                              250  \n",
       "5072                                              552  \n",
       "5073                                              552  \n",
       "5074                                              315  \n",
       "5075                                              315  \n",
       "\n",
       "[5076 rows x 6 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df_new.drop('Identification.Make',axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27aefce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Audi\n",
       "1              Audi\n",
       "2              Audi\n",
       "3              Audi\n",
       "4              Audi\n",
       "           ...     \n",
       "5071          Honda\n",
       "5072    Lamborghini\n",
       "5073    Lamborghini\n",
       "5074            BMW\n",
       "5075            BMW\n",
       "Name: Identification.Make, Length: 5076, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df_new['Identification.Make']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abd0c15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, train_size=0.75, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5d12b32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3807, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20d6075f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3807,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb4b0ab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1269, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0558682e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1269,)\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e86bed8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>62</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>142</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>135</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dimensions.Height  Dimensions.Length  Dimensions.Width  \\\n",
       "3810                223                137                64   \n",
       "3923                105                 72               227   \n",
       "390                 137                215                62   \n",
       "1245                142                200                90   \n",
       "3739                135                 53                24   \n",
       "\n",
       "      Engine Information.Number of Forward Gears  Fuel Information.City mpg  \\\n",
       "3810                                           6                         25   \n",
       "3923                                           5                         13   \n",
       "390                                            6                         21   \n",
       "1245                                           6                         14   \n",
       "3739                                           8                         16   \n",
       "\n",
       "      Engine Information.Engine Statistics.Horsepower  \n",
       "3810                                              182  \n",
       "3923                                              317  \n",
       "390                                               200  \n",
       "1245                                              556  \n",
       "3739                                              416  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03e205e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize Features\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd9d4f5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear\n",
      "0.3128447596532703\n",
      "poly\n",
      "0.5952193328079853\n",
      "rbf\n",
      "0.644602048857368\n",
      "sigmoid\n",
      "0.12897294457578146\n"
     ]
    }
   ],
   "source": [
    "for k in ('linear', 'poly', 'rbf', 'sigmoid'):\n",
    "    model = svm.SVC(kernel=k)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_train)\n",
    "    print(k)\n",
    "    print(accuracy_score(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "340b563a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the best model\n",
    "model = svm.SVC(kernel='rbf')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cbc3ec4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.6272655634357762\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on Testing Set\n",
    "X_test = scaler.transform(X_test)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1c76ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "               AMG       0.00      0.00      0.00         1\n",
      "             Acura       0.00      0.00      0.00         6\n",
      "      Aston Martin       1.00      1.00      1.00        13\n",
      "              Audi       0.43      0.18      0.25        17\n",
      "               BMW       0.67      0.49      0.56        37\n",
      "      BMW Motorrad       0.67      0.67      0.67        12\n",
      "           Bentley       1.00      1.00      1.00         3\n",
      "             Buick       0.91      0.50      0.65        20\n",
      "          Cadillac       0.59      0.90      0.71        48\n",
      "         Chevrolet       0.55      0.71      0.62       160\n",
      "          Chrysler       0.00      0.00      0.00         7\n",
      "Chrysler Group LLC       0.00      0.00      0.00         2\n",
      "             Dodge       0.72      0.73      0.72        63\n",
      "              Ford       0.68      0.76      0.72       166\n",
      "               GMC       0.49      0.43      0.46        99\n",
      "    Grand Cherokee       1.00      0.40      0.57        10\n",
      "             Honda       0.57      0.89      0.69        46\n",
      "           Hyundai       0.44      0.62      0.51        47\n",
      "          Infiniti       0.86      0.90      0.88        20\n",
      "            Jaguar       1.00      0.69      0.82        13\n",
      "              Jeep       0.90      0.88      0.89        32\n",
      "               Kia       0.30      0.26      0.28        31\n",
      "       Lamborghini       0.00      0.00      0.00         1\n",
      "        Land Rover       1.00      0.22      0.36         9\n",
      "             Lexus       0.56      0.50      0.53        10\n",
      "           Lincoln       0.00      0.00      0.00        13\n",
      "             Lotus       1.00      0.75      0.86         4\n",
      "              MINI       0.86      1.00      0.93        19\n",
      "          Maserati       1.00      0.50      0.67         4\n",
      "           Maybach       1.00      1.00      1.00         1\n",
      "             Mazda       0.45      0.57      0.51        40\n",
      "          Mercedes       0.79      0.88      0.83        17\n",
      "      Mercedes-AMG       0.00      0.00      0.00         2\n",
      "     Mercedes-Benz       0.00      0.00      0.00         1\n",
      "           Mercury       0.00      0.00      0.00         9\n",
      "        Mitsubishi       0.67      0.40      0.50        10\n",
      "            Nissan       0.87      0.83      0.85        66\n",
      "           Porsche       0.78      0.64      0.70        11\n",
      "       Rolls-Royce       0.00      0.00      0.00         2\n",
      "              Saab       0.42      0.36      0.38        14\n",
      "             Scion       0.00      0.00      0.00         6\n",
      "            Subaru       0.50      0.38      0.43        21\n",
      "            Suzuki       1.00      0.29      0.44        21\n",
      "            Toyota       0.55      0.61      0.58        85\n",
      "        Volkswagen       0.62      0.50      0.56        30\n",
      "             Volvo       0.92      0.55      0.69        20\n",
      "\n",
      "          accuracy                           0.63      1269\n",
      "         macro avg       0.56      0.48      0.50      1269\n",
      "      weighted avg       0.62      0.63      0.61      1269\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfdeb286",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f075a89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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