Project Overview This project aims to predict house prices using linear regression. The dataset contains historical data on house prices, which is used to train a predictive model. The project involves data preprocessing, exploratory data analysis (EDA), model training, and evaluation.
The primary programming language used for data manipulation, analysis, and building the predictive model.
For efficient data handling and manipulation.
To perform numerical computations.
Utilized for implementing the linear regression model and other machine learning algorithms.
For data visualization and to plot the regression line and scatter plots.
To create an interactive environment for coding and documentation Project Workflow
Gathered historical house price data.
Cleaned and prepared the data for analysis.
Visualized data trends and relationships.
Implemented and trained the linear regression model.
Assessed model performance using various metrics.
Made predictions on new data and visualized the results.
The linear regression model was trained with an R² score of 100%. Visualization of the regression line and scatter plots can be found in the images folder. Contributing Contributions are welcome! Please fork the repository and create a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or inquiries, please contact me at waqas56jb@gmail.com