This data science project series walks through the step-by-step process of building a House price prediction model. I first build a model using Sklearn and linear regression using the Bangalore home prices dataset. During model building, I cover almost all data science concepts such as data load and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, k-fold cross-validation, etc. Technology and tools this project covers
- Python
- Numpy and Pandas for data cleaning
- Matplotlib for data visualization
- Sklearn for model building
- Jupyter notebook
- Predict the home price by given of BHK, Bathrom number and location