California Housing Price Prediction

Project Overview

This project demonstrates a predictive analysis using a linear regression model to forecast median house values in California districts. The analysis is built using the well-known California Housing dataset from the scikit-learn library.

The main objective is to explore the dataset, build a linear model, and evaluate its performance in predicting housing prices based on several key features.

Key Features

• Data Loading & Exploration: Uses pandas to load and inspect the California Housing dataset.
• Data Cleaning: Checks for and confirms the absence of missing values.
• Model Training: Implements a LinearRegression model from scikit-learn.
• Model Evaluation: Assesses the model's predictive power using standard regression metrics.
• Visualization: Creates a scatter plot to visually compare actual vs. predicted values.

Dataset

The project utilizes the California Housing dataset, which contains data from the 1990 U.S. Census. Each row represents a single block group and includes the following features:

• MedInc: Median income in a block group.
• HouseAge: Median age of a house.
• AveRooms: Average number of rooms.
• AveBedrms: Average number of bedrooms.
• Population: Block group population.
• AveOccup: Average number of household members.
• Latitude: Latitude of the block group.
• Longitude: Longitude of the block group.

The model's target variable is MedHouseVal, which represents the median house value for California districts in hundreds of thousands of dollars ($100,000s).

Requirements

To run this notebook, you need to have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• scikit-learn You can install these using pip:

pip install pandas numpy matplotlib scikit-learn