House Valuator

A lot of factors contribute to the overall value of a house e.g median household income, location. etc

To put this to the test, we have the dataset housing.csv on which we shall run our analysis using multiple regression models to find out which one works the best in predicting the value of the house
In this task we shall try to estimate the value of a house based on multiple factors.

The inspiration behind this project was from Zillow's Challenge on their dataset with a prize of $1.2 Million

Plotting the data, we can clearly see a correlation exists

We shall try out a bunch of regression models to see which one works best

1. LinearRegression by sklearn.linear_model

2. KNeighborsRegressor from sklearn.neighbors

3. RandomForestRegressor from sklearn.ensemble

Feature Selection

We can see that most important two features are median_income and ocean_proximity_inland
To further improve our model, we can select the top-N important features and re-do the regression.
The purpose of feature selection is to remove the features which are irrelevant to the task