Housing-Price-Predictions

Problem Statement

Help Alzar, the record keeper for finding lost details of 3.5k houses with the help of Machine Learning.

Installation

• Clone repository and run feature_extraction.py to create all_data.csv dataset. (Change the paths of all files accessed in feature_extraction.py to local paths on your machine first)
  • Dataset is given in form of text files so preprocessing is required to convert them into csv file
  • feature_extraction.py extracts data from text files and make all_data.csv.

Requirements

• This problem statement uses xgboost Regressor so it must be installed through either of these ways.
  • Using pip- pip install xgboost
  • Using conda- conda install -c py-xgboost
• Python2.7 is preferred for this project.

Usage

• Run feature_extraction.pyto create dataset from raw text files to processed csv files.
• Run feature_analysis.pyon Jupyter notebook to visualize dataset using functions of pandas dataframe.
• Run feature_analysis.py on Jupyter notebook to visualize relations between features and target value with the help of histogram, scatter plots and Heat Map.

• Run regression.py on Jupyter notebook for trying new features and feature selection and filling NaN values through interpolation.
  • After this data is ready to fit for different models.
• Running regression.py
  • This gives detail r2_score analysis after tuning hyperparameters of different types of regressions.
  • This will run cross validation across the training set on LinearRegression, LassoRegression, Ridge Regression and xgboost Regression and prints r2_score.

Results

• With the help of xgboost regressor we are able to achieve r2_score of 0.99512.
• Solution.csv is also given in repository to match results of test dataset.
• xgboost with tuned parameters gives final r2_score of 0.99553 on test dataset.

Note : For avoiding errors update the file's path accessed in feature_extraction.py, feature_analysis.py and regression.py