housing-rent-prediction

This is the github repository created for the End to End Machine Learning project done by: Aayushmaan Jain and Pratyush Patro

Which involves creating a web app which is able to take inputs and predict house prices in the following cities:

1. Mumbai
2. Delhi
3. Kolkata
4. Bangalore
5. Hyderabad
6. Chennai
7. Ahemdabad
8. Pune

Data Source

This project is an end to end implementation of a project where the goal is to predict house rent prices for various different cities
This project uses a machine leraning model in the backend to predict the rent prices in various cities using the inputs given to it
This project uses a XGBoost Regressor model (which turned out to be the best model)
The models were evaluated on the basis of two metrics

1. R²score
2. Mean Absolute Error

The models tried out in this project are

1. Linear Regression
2. Decision Tree Regression
3. Random Forest Regression
4. Adaboost Regression
5. Gradient Boost Regression
6. XGBoost Regression

Running the project on local machine
Step 1 - Clone the project

git clone https://github.com/aayush1036/housing-rent-prediction.git
cd housing-rent-prediction

Step 2 - Create an environment

pip install virtualenv
virualenv YOUR_ENVIRONMENT_NAME
YOUR_ENVIRONMENT_NAME\Scripts\activate

# or for linux/mac 
virtualenv YOUR_ENVIRONMENT_NAME 
source YOUR_ENV_NAME/bin/activate

Step 3 - Install the dependencies

pip install -r requirements.txt 
# for linux/mac 
pip3 install -r requirements.txt

Step 4 - Run the code

python main.py
# for linux/mac
python3 main.py

Directory structure

Home - Objects - Encoders(Contains all the label encoders and ordinal encoders for preprocessing)
               - Models (Contains the best selected model for all cities)
     - Results - Contains the results of all the models to aid in model selection
     - static - All the files needed to render flask app
     - templates - All the templates used in the flask app
     - _All_Cities_Cleaned.csv (data source)
     - .gitignore - Files to be ignored while uploading code on github
     - eda.ipynb - Python notebook used while doing Exploratory data analysis 
     - main.py - Python file containing the code for making the flask app
     - models.ipynb - Python notebook used while creating the models 
     - preprocessing.ipynb - Python notebook used while data preprocessing
     - README.md - Readme file for github repository 
     - requirements.txt - For installing the dependencies 
     - sql_files - The files containing insert queries for SQL (need to run these queries to create initial database)
     - wsgi.py - Entry point for deployment 
     - combine.py - The python script which retrains the model every month 

App structure

Home page

• Contains all the links to navigate through the page at the top
• Contains the information about all cities, where the headings are linked to the page for each city if the user is interested
• Contains basic overall analysis for the entire data at the bottom
• Contains contact details in the bottom

Pages for a particular city

• Contains all the links to navigate through the page at the top
• Contains a detailed analysis for the houses in that city to help the user in making an informed decision
• Contains a predict button at the bottom which takes the user to the predict page

Predict Page

• Takes the input from the user
• Submit button to display the results

Results Page

• Displays the result for the particular set of inputs

Contribute Page

• The user can provide information and contribute to the database if the user feels that the predictions are inaccurate or the inputs do not exist in the database

Thanks Page

• This page is displayed when the data contributed by the user is inserted in the database

This project is deployed on a production server by using gunicorn and nginx then connected to a domain via namecheap.
The machine learning models on the server are retrained on the first of every month by using the combine.py script

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