House-Price-Prediction

Table of Contents

1. Blog Post
2. Installation
3. Project Motivation
4. File Descriptions
5. Licensing, Authors, and Acknowledgements

Installation

All the librarires required to run the code are mentioned in requirements.txt.

To install Run: pip install -r requirements.txt

Project Motivation

To train a mahcine learning model for predicting house prices using various attributes of houses provided in the dataset.

File Descriptions

• Housing.ipynb : The Jupyter notebook that includes data exploration, code and visualizations
• Housing_Profile_Report.html : Profiling report of data set
• housing.csv : csv file containing house features
• Visualizations: Includes all plots generated from the training data
  • Correlation_Matrix.png : Correlation matrix
  • Pairplot.png : Seaborn Pairplot
  • Outlier_Detection.png : Outliers detected in features
  • Feature_Importance.png : Feature importance of the trained model

Screenshots

Screenshot 1: Correlation matrix to understand how various features relate with each other

Screenshot 2: Feature importance of Machine Learning model

Licensing, Authors, Acknowledgements

Author: Rahul Gupta Copyright 2020

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.