1. Analysis for Predicting the Selling Price of Apartments Pratik Nikte
Feature Selection
Abstract
In our analysis we have used Ames Iowa Housing Dataset from the Kaggle website to analyze and predict the sale price of the house. We have used machine learning techniques like Linear Regression, Random Forest and XGBoost Algorithms.
Introduction
Using the dataset problem we are trying to solve here is to build models to predict house sale prices. Based on the dataset, we can understand how some variables have direct impact on the sale price of the house. We used in-built machine learning libraries in R-programming Language while working on this problem.
Figure 4: Multi-Correlation Matrix
Feature Selection
Parameters Impact on Sale Price
Model Selection & Comparison
46 Categorical Variables
33 Continuous Variables
79 Total Variables
Linear Regression
Linear Regression model assumes that the relationship between dependent (sale price) and independent (features) variable is linear
Project Plan
Random Forest
Random Forest does it own feature selection & create multiple decision trees which will help us to determine the accuracy to find the sale-price
XGBoost
We use the training data to predict target variable. With every iteration XGBboost minimizes the error rate compared to the first tree
Figure 1: Project Plan
Exploratory Data Analysis
Choose the parameters for the model
param <- list(colsample_bytree = .7,
subsample = .7,
booster = "gbtree",
max_depth = 10,
eta = 0.02,
eval_metric = "rmse",
objective="reg:linear")
Fixing Na’s -> 0 value
Data Formatting:
Categorical -> Numeric Variables
Transformed Sale Price to log(Sale Price+1) as our result analysis is based on RMSE log error
To reduce the effect of high-end outliers
Results
As our result showed us that XGBoost had the lowest RMSE score and it was more accurate than the other models
We evaluated XGBoost model by performing cross validation.
We implemented XGBoost model on our test dataset and predicted the Sale Price
Figure 2: Right Skewed Distribution
Figure 3: Normalized Distribution