KNN (K-Nearest Neighbours Regressor).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6303a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4805924",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CarPrice_Assignment.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f930f980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0b04c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding the dataframe\n",
    "# One hot encoding is the process of converting the categorical data variables to be provided to machine and deep learning algorithms which in turn improve predictions as well as classification accuracy of a model\n",
    "data = pd.get_dummies(df, columns =['CarName','fueltype','aspiration','doornumber','carbody','drivewheel','enginelocation', 'enginetype' ,'cylindernumber','fuelsystem'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8548f7a1",
   "metadata": {},
   "outputs": [
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       "      <th>car_ID</th>\n",
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       "      <th>...</th>\n",
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       "      <th>cylindernumber_two</th>\n",
       "      <th>fuelsystem_1bbl</th>\n",
       "      <th>fuelsystem_2bbl</th>\n",
       "      <th>fuelsystem_4bbl</th>\n",
       "      <th>fuelsystem_idi</th>\n",
       "      <th>fuelsystem_mfi</th>\n",
       "      <th>fuelsystem_mpfi</th>\n",
       "      <th>fuelsystem_spdi</th>\n",
       "      <th>fuelsystem_spfi</th>\n",
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       "  </thead>\n",
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       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
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       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
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       "      <td>1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling  wheelbase  carlength  carwidth  carheight  curbweight  \\\n",
       "0       1          3       88.6      168.8      64.1       48.8        2548   \n",
       "1       2          3       88.6      168.8      64.1       48.8        2548   \n",
       "2       3          1       94.5      171.2      65.5       52.4        2823   \n",
       "3       4          2       99.8      176.6      66.2       54.3        2337   \n",
       "4       5          2       99.4      176.6      66.4       54.3        2824   \n",
       "\n",
       "   enginesize  boreratio  stroke  ...  cylindernumber_twelve  \\\n",
       "0         130       3.47    2.68  ...                      0   \n",
       "1         130       3.47    2.68  ...                      0   \n",
       "2         152       2.68    3.47  ...                      0   \n",
       "3         109       3.19    3.40  ...                      0   \n",
       "4         136       3.19    3.40  ...                      0   \n",
       "\n",
       "   cylindernumber_two  fuelsystem_1bbl  fuelsystem_2bbl  fuelsystem_4bbl  \\\n",
       "0                   0                0                0                0   \n",
       "1                   0                0                0                0   \n",
       "2                   0                0                0                0   \n",
       "3                   0                0                0                0   \n",
       "4                   0                0                0                0   \n",
       "\n",
       "   fuelsystem_idi  fuelsystem_mfi  fuelsystem_mpfi  fuelsystem_spdi  \\\n",
       "0               0               0                1                0   \n",
       "1               0               0                1                0   \n",
       "2               0               0                1                0   \n",
       "3               0               0                1                0   \n",
       "4               0               0                1                0   \n",
       "\n",
       "   fuelsystem_spfi  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 201 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f1ee3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.loc[:, data.columns != 'price']\n",
    "y = data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80b505dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3831341c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0.7518934216244982\n",
      "2\n",
      "0.7004847941285846\n",
      "3\n",
      "0.7254857868371443\n",
      "4\n",
      "0.7150761021535783\n",
      "5\n",
      "0.7091675646958264\n",
      "6\n",
      "0.698815509359165\n",
      "7\n",
      "0.6819580484590996\n",
      "8\n",
      "0.6580450889832494\n",
      "9\n",
      "0.6431806127186196\n",
      "10\n",
      "0.6187746308052828\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn.metrics import accuracy_score\n",
    "for i in range(1,11):\n",
    "    knn = KNeighborsRegressor(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    print(i)\n",
    "    print(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3aa32649",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import knearest neighbors Classifier model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "#Create KNN Classifier\n",
    "knn = KNeighborsRegressor(n_neighbors=2)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6943db13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preliminary model score:\n",
      "0.7004847941285846\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Preliminary model score:\")\n",
    "print(knn.score(X_test,y_test))\n",
    "\n",
    "no_neighbors = np.arange(1, 11)\n",
    "train_accuracy = np.empty(len(no_neighbors))\n",
    "test_accuracy = np.empty(len(no_neighbors))\n",
    "\n",
    "for i, k in enumerate(no_neighbors):\n",
    "    # We instantiate the classifier\n",
    "    knn = KNeighborsRegressor(n_neighbors=k)\n",
    "    # Fit the classifier to the training data\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    # Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "\n",
    "    # Compute accuracy on the testing set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)\n",
    "\n",
    "# Visualization of k values vs accuracy\n",
    "\n",
    "plt.title('k-NN: Varying Number of Neighbors')\n",
    "plt.plot(no_neighbors, test_accuracy, label = 'Testing Accuracy')\n",
    "plt.plot(no_neighbors, train_accuracy, label = 'Training Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f940cee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 20252917.70982079\n",
      "Root Mean Squared Error: 4500.324178303246\n",
      "R^2 Score: 0.7004847941285846\n"
     ]
    }
   ],
   "source": [
    "# Calculating the Accuracy\n",
    "# Accuracy of a linear model can't be calculated, the accuracy is predicted using error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Mean Squared Error\n",
    "print(\"Mean Squared Error:\",mean_squared_error(y_test, y_pred)) # tells you how close a regression line is to a set of points\n",
    "# squaring is necessary to remove any negative signs\n",
    "\n",
    "# Root Mean Squared Error\n",
    "print(\"Root Mean Squared Error:\", mean_squared_error(y_test, y_pred, squared=False))\n",
    "\n",
    "# R2 Score\n",
    "print(\"R^2 Score:\", r2_score(y_test,y_pred)) # correlation between actual and predicted value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84a07cb9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}