This project focuses on predicting real estate prices using a linear regression model. It involves analyzing historical housing data, extracting relevant features, and applying machine learning techniques to estimate property prices. The goal is to build a reliable model that helps in making accurate predictions based on property attributes.
Data Preprocessing: Cleaning, handling missing values, and encoding categorical data.
Exploratory Data Analysis (EDA): Visualizing trends and patterns in the dataset.
Model Building: Training a linear regression model for price prediction.
Model Evaluation: Assessing performance using evaluation metrics like Mean Squared Error (MSE) and R² Score.
Programming Language: Python
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Real-world dataset usage for training and testing.
Insightful data visualizations for better understanding.
Feel free to fork the repository, create feature branches, and submit pull requests. Contributions are always welcome!
For questions, suggestions, or collaborations, please contact me at:
Email: [email protected]