This repository contains code for predicting house sales prices using machine learning models. It includes data preprocessing, model training, evaluation, and prediction on test data.
The code provided here is for a project that aims to predict house sales prices based on various features. It involves data cleaning, handling missing values, feature selection, model training using Linear Regression and Random Forest Regression, model evaluation using Relative Absolute Error (RAE), and generating predictions on test data. The repository also includes data visualization using Matplotlib and Seaborn.
- Data Preprocessing
- Feature Engineering
- Model Training and Evaluation
- Data Visualization
To run the code, follow these steps:
- Clone the repository.
- Install the required dependencies using
pip install -r requirements.txt. - Run the Jupyter notebook or Python script.
- Allan Otieno
Feel free to contribute by opening issues, suggesting improvements, or submitting pull requests.