Housing Price Prediction Project

This project focuses on predicting housing prices using a linear regression model on the California Housing dataset. The dataset comprises various features, including housing median age, total rooms, total bedrooms, population, households, median income, and ocean proximity.

Project Overview

The project aims to accomplish the following steps:

Data Preprocessing

• Load the housing dataset from a CSV file.
• Drop rows with missing values to ensure clean data.
• Encode categorical features using one-hot encoding.
• Split the data into features (X) and the target variable (y).

Model Building

• Split the data into training and testing sets (80% train, 20% test).
• Train a Linear Regression model using the training data.
• Make predictions on the test set and evaluate model performance using Mean Squared Error (MSE).

Visualization

• Visualize the relationship between the actual and predicted house values using a scatter plot.
• Display the distribution of median house values through a histogram.
• Create individual histograms for numerical features to understand their distributions.

Tools Used

• Python
• Pandas for data manipulation
• Matplotlib for data visualization
• Scikit-learn for machine learning modeling

Instructions

1. Clone or download this repository to your local machine.
2. Ensure you have Python installed along with necessary libraries (Pandas, Matplotlib, Scikit-learn).
3. Run the provided code in your Python environment to observe the housing price prediction analysis.