California-House-Price-Prediction

This is a regression problem to predict california housing prices.

The dataset contains 20640 entries and 10 variables.

Longitude Latitude Housing Median Age Total Rooms Total Bedrooms Population Households Median Income Median House Value Ocean Proximity Median House Value is to be predicted in this problem.

1) EDA and Data Cleaning

I have done the exploratory data analysis and done following manipulations on data.

Creating new features

Removing outliers

Transforming skewed features

Checking for multicoliniearity

2) Training machine learning algorithms:

Here, I have trained various machine learning algorithms like

Linear Regression

Ridge Regression

Support Vector Regression

Gradient Boosting Regression

Stacking of various models