ML LAB

on March 05, 2025

PROGRAM 1

California Housing Analysis

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import fetch_california_housing
# Load the California Housing dataset
def load_data():
 data = fetch_california_housing(as_frame=True)
 df = data['data']
 df['Target'] = data['target'] # Add the target (house value) to the DataFrame
 return df
# Function to plot histograms for numerical features
def plot_histograms(df):
	numerical_features = df.select_dtypes(include='number').columns
	df[numerical_features].hist(bins=20, figsize=(15, 10), color='skyblue', edgecolor='black')
	plt.suptitle('Histograms of Numerical Features', fontsize=16)
	plt.tight_layout()
	plt.show()
# Function to generate box plots and identify outliers
def plot_boxplots(df):
	numerical_features = df.select_dtypes(include='number').columns
	for feature in numerical_features:
		plt.figure(figsize=(8, 6))
		sns.boxplot(x=df[feature], color='lightblue')
		plt.title(f'Box Plot of {feature}', fontsize=14)
		plt.xlabel(feature, fontsize=12)
		plt.tight_layout()
		plt.show()
		# Identify outliers using the IQR method
		Q1 = df[feature].quantile(0.25)
		Q3 = df[feature].quantile(0.75)
		IQR = Q3 - Q1
		lower_bound = Q1 - 1.5 * IQR
		upper_bound = Q3 + 1.5 * IQR
		outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)]
		print(f"{feature}: {len(outliers)} outliers found.")
# Main function to run the analysis
def main():
	df = load_data()
	print("Dataset loaded successfully!")
	print("\nGenerating histograms for numerical features...")
	plot_histograms(df)
	print("\nGenerating box plots and identifying outliers...")
	plot_boxplots(df)
if __name__ == "__main__":
 main()