Machine Learning (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "019eb2b1",
   "metadata": {},
   "source": [
    "# Implement a Linear Regression Model to predict the prices of houses based on square footages etc...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acd8b6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.5558915986952425\n",
      "R-squared: 0.5757877060324521\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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the California Housing dataset\n",
    "california_housing = fetch_california_housing()\n",
    "X, y = california_housing.data, california_housing.target\n",
    "feature_names = california_housing.feature_names\n",
    "\n",
    "# Convert the data into a DataFrame for easier manipulation\n",
    "california_df = pd.DataFrame(X, columns=feature_names)\n",
    "california_df['PRICE'] = y  # Add target variable (median house value)\n",
    "\n",
    "# Select features and target variable\n",
    "X = california_df[['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']].values\n",
    "y = california_df['PRICE'].values\n",
    "\n",
    "# Split data 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",
    "# Create and train the linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)\n",
    "\n",
    "# Plotting predicted vs. actual prices\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Actual Median House Value\")\n",
    "plt.ylabel(\"Predicted Median House Value\")\n",
    "plt.title(\"Actual vs. Predicted Median House Value\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824be0ed",
   "metadata": {},
   "source": [
    "# Create a K means clustering algorithm to group customers of a retail store based on their purchase history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2dbfe8e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer 1 is in cluster 2\n",
      "Customer 2 is in cluster 1\n",
      "Customer 3 is in cluster 3\n",
      "Customer 4 is in cluster 2\n",
      "Customer 5 is in cluster 1\n",
      "Customer 6 is in cluster 3\n",
      "Customer 7 is in cluster 2\n",
      "Customer 8 is in cluster 3\n",
      "\n",
      "Cluster Centroids:\n",
      "Cluster 1: [137.5  67.5  95.   45. ]\n",
      "Cluster 2: [88.33333333 38.33333333 65.         18.33333333]\n",
      "Cluster 3: [180.          90.         118.33333333  63.33333333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Sample customer purchase data\n",
    "customer_data = np.array([[100, 50, 75, 25],\n",
    "                         [150, 75, 100, 50],\n",
    "                         [200, 100, 125, 75],\n",
    "                         [75, 25, 50, 10],\n",
    "                         [125, 60, 90, 40],\n",
    "                         [180, 90, 110, 60],\n",
    "                         [90, 40, 70, 20],\n",
    "                         [160, 80, 120, 55]])\n",
    "\n",
    "# Define the number of clusters\n",
    "num_clusters = 3\n",
    "\n",
    "# Initialize the K-means algorithm\n",
    "kmeans = KMeans(n_clusters=num_clusters, random_state=0)\n",
    "\n",
    "# Fit the customer data to the K-means model\n",
    "kmeans.fit(customer_data)\n",
    "\n",
    "# Get the cluster assignments for each customer\n",
    "cluster_assignments = kmeans.labels_\n",
    "\n",
    "# Print the cluster assignments for each customer\n",
    "for i, cluster in enumerate(cluster_assignments):\n",
    "    print(f\"Customer {i+1} is in cluster {cluster+1}\")\n",
    "\n",
    "# Get the cluster centroids\n",
    "cluster_centroids = kmeans.cluster_centers_\n",
    "\n",
    "# Print the cluster centroids\n",
    "print(\"\\nCluster Centroids:\")\n",
    "for i, centroid in enumerate(cluster_centroids):\n",
    "    print(f\"Cluster {i+1}: {centroid}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1405f06e",
   "metadata": {},
   "source": [
    "# Design a decision tree classifier to predict whether a cusotmer will purchase a product or service based on their demographic and bhavioral data.Use a dataset sucha as Bank marketing dataset from the UCI machine learning repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d142b2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes of training and testing sets for 'bank.csv' dataset:\n",
      "Training set shape: (3616, 1)\n",
      "Testing set shape: (905, 1)\n",
      "\n",
      "Shapes of training and testing sets for 'bank-full.csv' dataset:\n",
      "Training set shape: (36168, 1)\n",
      "Testing set shape: (9043, 1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the 'bank.csv' dataset\n",
    "bank_data = pd.read_csv('bank.csv')\n",
    "\n",
    "# Load the 'bank-full.csv' dataset\n",
    "bank_full_data = pd.read_csv('bank-full.csv')\n",
    "\n",
    "# Split 'bank.csv' dataset into training and testing sets\n",
    "X_train_bank, X_test_bank = train_test_split(bank_data, test_size=0.2, random_state=42)\n",
    "\n",
    "# Split 'bank-full.csv' dataset into training and testing sets\n",
    "X_train_bank_full, X_test_bank_full = train_test_split(bank_full_data, test_size=0.2, random_state=42)\n",
    "\n",
    "# Print the shapes of the training and testing sets for both datasets\n",
    "print(\"Shapes of training and testing sets for 'bank.csv' dataset:\")\n",
    "print(\"Training set shape:\", X_train_bank.shape)\n",
    "print(\"Testing set shape:\", X_test_bank.shape)\n",
    "\n",
    "print(\"\\nShapes of training and testing sets for 'bank-full.csv' dataset:\")\n",
    "print(\"Training set shape:\", X_train_bank_full.shape)\n",
    "print(\"Testing set shape:\", X_test_bank_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c90e92c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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