AdvancedRegression

Using Various Regression Algorithms to Predict House Sales

Overview

This project uses Machine Learning to predict house sale prices using previously available data. The following regression algorithms are used:

1. Linear Regression
2. Decision Tree Regression
3. Random Forest Regression
4. AdaBoost Regression
5. Gradient Boosting Regression
6. Support Vector Regression

It also uses PCA to reduce data dimensionality.

About the Dataset

The dataset used is available on Kaggle. It contains about 80 features belonging to over 1500 houses, which can be used to predict the sales of houses and the sale prices. The dataset is also available in the repository as train.csv.

To access the data from Kaggle, click on the following link

How to Run the Model on your System

1. Use this link to download the dataset and set the folder containing the downloaded data as the working directory.

2. Make sure you have all the libraries used in the Housesale.py file. In case you need to download any of the libraries, use this command on your Command Prompt:

pip install 'your library name'

3. Once you have all the libraries imported, copy the code from Housesale.py and run it.