Predicting house prices using area type with linear regression.
This project uses machine learning to predict house prices based on the type of area (e.g., Built-up Area, Super Built-up Area, etc.). By exploring the relationship between area_type and price, I developed a predictive model that can estimate property prices with decent accuracy.
- Data preprocessing and cleaning
- Feature encoding for
area_type - Exploratory Data Analysis (EDA)
- Correlation analysis (Pearson r = 0.56)
- Model training using Linear Regression
- Model evaluation and interpretation
There is a moderate positive correlation (r = 0.56) between area_type and price, which means the area type of a house has a meaningful impact on its price. This insight was used to train a simple but effective regression model.
- Python
- Pandas
- Seaborn & Matplotlib (for visuals)
- Scikit-learn (for modeling)
This is an open project. Feel free to fork it, improve the model, or adapt it to other features!