Iris_Flower_Classification_Using_K_Nearest_Neighbours.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard scaler for scaling the data and using the KNeighborsClassifier for classification.\n",
    "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\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "data = iris.data\n",
    "target = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting data to pandas data frame\n",
    "df = pd.DataFrame(data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = iris.target_names\n",
    "df['species'] = target_names[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                5.1               3.5                1.4               0.2   \n",
      "1                4.9               3.0                1.4               0.2   \n",
      "2                4.7               3.2                1.3               0.2   \n",
      "3                4.6               3.1                1.5               0.2   \n",
      "4                5.0               3.6                1.4               0.2   \n",
      "\n",
      "  species  \n",
      "0  setosa  \n",
      "1  setosa  \n",
      "2  setosa  \n",
      "3  setosa  \n",
      "4  setosa  \n",
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "145                6.7               3.0                5.2               2.3   \n",
      "146                6.3               2.5                5.0               1.9   \n",
      "147                6.5               3.0                5.2               2.0   \n",
      "148                6.2               3.4                5.4               2.3   \n",
      "149                5.9               3.0                5.1               1.8   \n",
      "\n",
      "       species  \n",
      "145  virginica  \n",
      "146  virginica  \n",
      "147  virginica  \n",
      "148  virginica  \n",
      "149  virginica  \n"
     ]
    }
   ],
   "source": [
    "#print first five and last five data items from the data set\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>KNeighborsClassifier(n_neighbors=3)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the KNN classifier\n",
    "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Train the model on the training data\n",
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        10\n",
      "  versicolor       1.00      1.00      1.00         9\n",
      "   virginica       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test data\n",
    "predictions = knn_model.predict(X_test)\n",
    "\n",
    "# Calculate and print the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Print a classification report\n",
    "report = classification_report(y_test, predictions, target_names=iris.target_names)\n",
    "print(\"Classification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list to store accuracy scores\n",
    "accuracy_scores = []\n",
    "\n",
    "# Changing hyperparameter k over a range of 1 to 25 to see how accuracy varies with the change of value k.\n",
    "for k in range(1, 25):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn_model.fit(X_train, y_train)\n",
    "    predictions = knn_model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, predictions)\n",
    "    accuracy_scores.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot to visualize the accuracy scores\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1, 25), accuracy_scores, marker='o')\n",
    "plt.title(\"Accuracy vs. Number of Neighbors (K)\")\n",
    "plt.xlabel(\"Number of Neighbors (K)\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xticks(np.arange(1, 25))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}