Heroku - https://california-house-price.herokuapp.com/
Google Cloud Platform - https://ai-california-house-prices.ue.r.appspot.com/
Azure - https://ml-california-house-price-predictions.azurewebsites.net/ [Free Tier Limit exceeded, Application might be shutdown]
California House Price App Predicts the cost of affording a home based on factors such as longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The data is collected from 1990 California census data
https://www.kaggle.com/camnugent/california-housing-prices
1. Flask
2. gunicorn
3. itsdangerous
4. Jinja2
5. MarkupSafe
6. Werkzeug
7. Pillow
8. Numpy
9. Scikit-learn
10. Pandas
11. Seaborn
12. Joblib
13. Matplotlib
14. HTML
15. CSS
16. Bootstrap
17. JavaScript
1. Exploratory Data Analysis(EDA)
2. Data Visualization and Cleaning
3. The total_bedrooms feature has 207 missing values. These missing values are filled by the mean of the entire feature
There was an outlier in the median house value feature which was removed
- Feature Engineering
- Feature Selection
- Trained many Machine Learning algorithms on the dataset
- Predicted all the trained models on the test dataset
- Model Evaluation(Calculated R2, Adjusted R2, MSE, RMSE, MAE and Accuracy)
- Accuracies graph of all the models' data was trained on is as below-
- Hyperparameter Tuning(GridSearch CV, Randomized Search CV) is done on the top-performing base ML algorithms(Catboost, RandomForest, LightGBM)
- Exported the model
- Developed Front End Web-based application and created a flask server
- App running successfully in Google Cloud Platform, Azure, and Heroku
Email - [email protected]
LinkedIn - https://www.linkedin.com/in/tejas-ta/
Blogs - https://tejasta.medium.com/