JayaRaghavendra/California-Housing-Price-Prediction

California Housing Price Prediction: Used linear, Decision Tree, ensemble regression techniques (Random Forests), feature scaling and feature engineering using Principal component Analysis (PCA); achieved minimal RMSE with ensemble technique. Supervised learning, Machine Learning, Python, Jupyter Notebook.

California-Housing-Price-Prediction

Problem Statement

The purpose of the project is to predict median house values in Californian districts, given many features from these districts.

The project also aims at building a model of housing prices in California using the California census data. The data has metrics such as the population, median income, median housing price, and so on for each block group in California. 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.

link

https://jayaraghavendra.github.io/California-Housing-Price-Prediction/