KNN_Sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN using Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
    "dataset = pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal-length  sepal-width  petal-length  petal-width        Class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9           4.9          3.1           1.5          0.1  Iris-setosa"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the feature data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0  8  2]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       1.00      0.80      0.89        10\n",
      " Iris-virginica       0.85      1.00      0.92        11\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.95      0.93      0.94        30\n",
      "   weighted avg       0.94      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Error Rate with the K value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean of error for predicted values  where k ranges from 1 to 40\n",
    "error = []\n",
    "for i in range(1, 40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Error')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the error values against k values\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}