The project aims at building a model of housing prices to predict median house values in California using the provided dataset. This model should learn from the data and be able to predict the median housing price in any district, given all the other metrics.

Districts or block groups are the smallest geographical units for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). There are 20,640 districts in the project dataset.

https://www.kaggle.com/aakashjoshi123/o-reilly-solution-with-my-observations-notebook/data

• Exploratory data analysis
• Feature engineering
• Applying ML algorithms:
  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • XGBoost Regressor
• Conclusion

• MSE
• RMSE
• MAE