Bayesian Data Analysis Project for Bayesian Data Analysis (BDA) course 2018 at Aalto Univeristy

Introduction

The following data analysis project involves the design of a bayesian prediction model. In
particular, the project revolves around the task of forecasting the price of a house based on
certain features.

Objective

In this project, the goal is to construct a Stan model with a suitable prior value and make predictions
of the prices of houses based on the given dataset. Analysis of the data along with model creation,
evaluation and assessment of all possibilities of models has also been provided to go with the optimal
solution.

Dataset

The dataset has been taken from the following link: https://www.kaggle.com/harlfoxem/housesalesprediction/version/1
A total of 17 house features excluding the id and price columns. The total number of observations is 21613.
The label is supposedly the price of each house and the features of each house are as follows:

• id: Notation each house.
• date: Date house was sold.
• bedrooms: Number of bedrooms per house.
• bathrooms: Number of bathrooms per bedrooms.
• sqft_living: Square Footage of each home.
• sqft_lot: Square Footage of the lot.
• floors: Total number of floors (levels) in each house.
• waterfront: House which has a view to a waterfront.
• viewHas: Houses which has been viewed.
• condition: How good the condition is (Overall).
• grade: Overall grade given to the housing unit based on King County grading system.
• sqft_above: Square Footage of house apart from the basement.
• sqft_basement: Square Footage of the basement
• yr_built: The year in which the house was built.
• yr_renovated: The year when house was renovated.
• zipcode: Postal code of the house.
• lat: Latitude coordinate.
• long: Longitude coordinate.
• sqft_living15: Living room area in 2015 (implies — some renovations) This might or might not have affected the lotsize area
• sqft_lot15: Lot size area in 2015 (implies — some renovations)

Authors

1. Muhammad Abdullah Khan
2. Vasumathi Neralla