House Price Prediction Using Linear Regression

About the Project

This project was completed as part of my Machine Learning Internship at SkillCraft Technology.

The goal of this project is to predict house prices using a Linear Regression model based on factors such as square footage, number of bedrooms, and number of bathrooms. Through this project, I explored how machine learning can be used to analyze housing data and make price predictions.

What I Did

• Loaded and explored the housing dataset
• Cleaned and prepared the data for training
• Selected relevant features for prediction
• Trained a Linear Regression model using Scikit-Learn
• Evaluated the model's performance
• Generated house price predictions

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-Learn
• Jupyter Notebook

Features Used for Prediction

• Square Footage
• Number of Bedrooms
• Number of Bathrooms

What I Learned

This project helped me understand:

• The basics of supervised machine learning
• How Linear Regression works
• Data preprocessing and feature selection
• Model training and evaluation
• Making predictions using real-world data

Project Structure

• Dataset file
• Jupyter Notebook / Python script
• README file
• Supporting project files

Conclusion

Building this project gave me hands-on experience with machine learning and predictive modeling. It was a great opportunity to apply theoretical concepts to a real-world problem and strengthen my Python and data science skills.

Internship

Machine Learning Internship – SkillCraft Technology

Author: SharanRenukeshBharmshetty.