The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict median housing price.
The dataset is obtained from Kaggle.
Link: https://www.kaggle.com/camnugent/california-housing-prices
The analysis and model creation can be found in the .ipynb file.
The main packages used are numpy, pandas, matplotlib, seaborn and sklearn.
The web app has been build using basic HTML, CSS, Javascript, Flask and Herkou.
Link: https://california-house-price-predict.herokuapp.com/
- Use multiple Algorithms
- Optimize Flask app.py
- Update the Front-End