This project aims to predict house prices using machine learning techniques. It involves data cleaning, exploratory data analysis (EDA), feature engineering, model building, and evaluation.
The dataset used in this project is train.csv, containing information about various features of houses and their corresponding sale prices. The dataset is provided in the repository.
This project requires the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
You can install these dependencies using pip: