Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Boston this competition challenges you to predict the final price of each house. I have done detailed exploratory data analysis of House Prices dataset along with advance logistic regression model to predict house price in the future.
- The goal of this analysis is to predict the accrate SalePrice with given features.
- Predictive models are evaluated on the Root-Mean-Squared-Error (RMSE).
Dataset Description In this dataset, there are 14600 observations with 79 explanatory variables describing (almost) every aspect of residential homes in Boston. Among explanatory variables, there are 37 integer variables, such as Id, MSSubClass, LotFrontage, and 43 factor variables, such as MSZoning, Street, LotShape. Descriptive analysis and quantitative analysis will use subsets of it depending on models.
This is my submission to the Housing Prices Competition for Kaggle Learn Users. I have used Linear Regression Model for prediction. As I'm writing this , I am ranked among the top 5% of all Kagglers.
- First part of this report will focus on Descriptive and Exploratory Analysis
- Second part of this report include Predictive Analysis using Linear Regression Model