assignment_64.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b31b2e0b-c87b-4937-98ac-bb789b360359",
   "metadata": {},
   "source": [
    "Q1. Write a Python code to implement the KNN classifier algorithm on load_iris dataset in\n",
    "sklearn.datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d36c6bf-2d82-48a5-b975-1e5470148d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b3f363c-a0a3-4217-a27a-fa0e619a6431",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X = iris.data  # Features\n",
    "y = iris.target  # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b42579b-b7fa-4ec9-8c9c-b13e6ed4cfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "196a66ed-0ed9-480c-ab4e-bc5b53424180",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the KNN classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)  # Number of neighbors = 3 (can be tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9b5aa85-500b-4fe9-b7b1-a3162329b688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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=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-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=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the KNN classifier\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65d58d13-4416-4792-b4c4-a6ac0f2ab5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the classes for test set\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7bf6f06-77ab-4c21-bff2-1096a0176202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a23a15-4fa2-4d19-91a5-ecbd162a3702",
   "metadata": {},
   "source": [
    "Q2. Write a Python code to implement the KNN regressor algorithm on load_boston dataset in\n",
    "sklearn.datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a16eecac-83b2-4074-bf47-ecab1ea4ce78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3247fa7a-bc02-41f4-b1f2-f70b9bd274b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the California housing dataset\n",
    "housing = fetch_california_housing()\n",
    "X = housing.data  # Features\n",
    "y = housing.target  # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edc27189-199e-419e-9261-edd8193810b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "192b4eec-7be8-4eb6-a2a8-36b7f3206834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the KNN regressor\n",
    "knn_regressor = KNeighborsRegressor(n_neighbors=5)  # Number of neighbors = 5 (can be tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a8126fa-6870-4009-b381-812f3ed431e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the KNN regressor\n",
    "knn_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f79e983-46e5-4678-92e3-d0913218c013",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the target values for test set\n",
    "y_pred = knn_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbc56de6-06af-4084-a4e0-8eebc4d7616d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.1186823858768293\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean squared error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6821d9fe-689c-4ec4-94b8-e2b45faa4c5a",
   "metadata": {},
   "source": [
    "Q3. Write a Python code snippet to find the optimal value of K for the KNN classifier algorithm using\n",
    "cross-validation on load_iris dataset in sklearn.datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc6ad674-2c28-4ad2-8bf5-837536caeb89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal k: 13\n",
      "Optimal accuracy: 0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "X = iris.data  # Features\n",
    "y = iris.target  # Target variable\n",
    "\n",
    "# Define a range of k values to test\n",
    "k_values = list(range(1, 31))  # Test k values from 1 to 30\n",
    "\n",
    "# Perform 10-fold cross-validation for each k value\n",
    "mean_accuracy_scores = []\n",
    "for k in k_values:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
    "    mean_accuracy_scores.append(scores.mean())\n",
    "\n",
    "# Find the optimal k value with the highest mean accuracy score\n",
    "optimal_k = k_values[mean_accuracy_scores.index(max(mean_accuracy_scores))]\n",
    "optimal_accuracy = max(mean_accuracy_scores)\n",
    "\n",
    "print(\"Optimal k:\", optimal_k)\n",
    "print(\"Optimal accuracy:\", optimal_accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da933f6b-0430-4888-9f8d-ca174ecca902",
   "metadata": {},
   "source": [
    "Q4. Implement the KNN regressor algorithm with feature scaling on fetch_california_housing dataset in\n",
    "sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d70a1a09-c008-4ded-9716-c01aaad8b999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.4324216146043236\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Load the California housing dataset\n",
    "housing = fetch_california_housing()\n",
    "X = housing.data  # Features\n",
    "y = housing.target  # Target variable\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit and transform the training features\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "\n",
    "# Transform the testing features\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Initialize the KNN regressor\n",
    "knn_regressor = KNeighborsRegressor(n_neighbors=5)  # Number of neighbors = 5 (can be tuned)\n",
    "\n",
    "# Train the KNN regressor\n",
    "knn_regressor.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict the target values for test set\n",
    "y_pred = knn_regressor.predict(X_test_scaled)\n",
    "\n",
    "# Calculate mean squared error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd834183-b74c-401e-979a-3caa402007d3",
   "metadata": {},
   "source": [
    "Q5. Write a Python code snippet to implement the KNN classifier algorithm with weighted voting on\n",
    "load_iris dataset in sklearn.datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22e358fb-5af5-4a90-9718-deb201509626",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "X = iris.data  # Features\n",
    "y = iris.target  # Target variable\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the KNN classifier with weighted voting\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=5, weights='distance')  # Number of neighbors = 5, weighted voting\n",
    "\n",
    "# Train the KNN classifier\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predict the classes for test set\n",
    "y_pred = knn_classifier.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1aaa03-ea86-4fad-8d9c-3ea728ae7cde",
   "metadata": {},
   "source": [
    "Q6. Implement a function to standardise the features before applying KNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4594e27e-e6e9-45f3-b400-f551374dd143",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "def standardize_features(X_train, X_test):\n",
    "    \"\"\"\n",
    "    Standardize the features using StandardScaler.\n",
    "\n",
    "    Parameters:\n",
    "    X_train : array-like, shape (n_samples, n_features)\n",
    "        Training data.\n",
    "    X_test : array-like, shape (n_samples, n_features)\n",
    "        Testing data.\n",
    "\n",
    "    Returns:\n",
    "    X_train_scaled : array-like, shape (n_samples, n_features)\n",
    "        Standardized training data.\n",
    "    X_test_scaled : array-like, shape (n_samples, n_features)\n",
    "        Standardized testing data.\n",
    "    \"\"\"\n",
    "    # Initialize StandardScaler\n",
    "    scaler = StandardScaler()\n",
    "\n",
    "    # Fit and transform the training features\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "\n",
    "    # Transform the testing features\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    return X_train_scaled, X_test_scaled\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f821e0d8-b5c1-458b-948f-2a465bfd4573",
   "metadata": {},
   "source": [
    "Q7. Write a Python function to calculate the euclidean distance between two points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "53092bb0-95a4-4ac4-ab23-6c9306e1c972",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def euclidean_distance(point1, point2):\n",
    "    \"\"\"\n",
    "    Calculate the Euclidean distance between two points in n-dimensional space.\n",
    "\n",
    "    Parameters:\n",
    "    point1 : array-like\n",
    "        Coordinates of the first point.\n",
    "    point2 : array-like\n",
    "        Coordinates of the second point.\n",
    "\n",
    "    Returns:\n",
    "    distance : float\n",
    "        Euclidean distance between the two points.\n",
    "    \"\"\"\n",
    "    point1 = np.array(point1)\n",
    "    point2 = np.array(point2)\n",
    "    distance = np.sqrt(np.sum((point1 - point2) ** 2))\n",
    "    return distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16aae3d5-caca-47a4-ad0e-4a0e6fa4bfda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean distance between point1 and point2: 5.196152422706632\n"
     ]
    }
   ],
   "source": [
    "point1 = [1, 2, 3]\n",
    "point2 = [4, 5, 6]\n",
    "\n",
    "distance = euclidean_distance(point1, point2)\n",
    "print(\"Euclidean distance between point1 and point2:\", distance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8767fb-0771-4072-bc50-be4d0f8ee56f",
   "metadata": {},
   "source": [
    "Q8. Write a Python function to calculate the manhattan distance between two points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e8c4122-ca7a-45c7-8987-7fd6bdad8ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def manhattan_distance(point1, point2):\n",
    "    \"\"\"\n",
    "    Calculate the Manhattan distance between two points in n-dimensional space.\n",
    "\n",
    "    Parameters:\n",
    "    point1 : array-like\n",
    "        Coordinates of the first point.\n",
    "    point2 : array-like\n",
    "        Coordinates of the second point.\n",
    "\n",
    "    Returns:\n",
    "    distance : float\n",
    "        Manhattan distance between the two points.\n",
    "    \"\"\"\n",
    "    point1 = np.array(point1)\n",
    "    point2 = np.array(point2)\n",
    "    distance = np.sum(np.abs(point1 - point2))\n",
    "    return distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb51c683-7047-4e21-be98-57cd96d92289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan distance between point1 and point2: 9\n"
     ]
    }
   ],
   "source": [
    "point1 = [1, 2, 3]\n",
    "point2 = [4, 5, 6]\n",
    "\n",
    "distance = manhattan_distance(point1, point2)\n",
    "print(\"Manhattan distance between point1 and point2:\", distance)\n"
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}