Car Price Prediction:

Table of Content

• Demo
• Overview
• Motivation
• Technical Aspect
• Installation
• Deployement on Heroku
• Directory Tree
• Technologies used
• Bug / Feature Request
• Future scope of project
• Credits

Demo

Link: https://carpricepredictions-api.herokuapp.com/

Overview

This is a flask web app which predicts the price of used Cars trained on the top of Random Forest Regressor model. The dataset contains information about used cars is taken from Kaggle listed on www.cardekho.com. The trained model takes a data of used cars as a input and predict the Price of the Car as a output.

Motivation

What could be a perfect way to utilize unfortunate lockdown period? Like most of you, I spend my time in online games, web series and coding. Last Year, I started to learn Data Science and Machine Learning course from online platform. I came to know mathematics behind all the supervised/unsupervised model but it is important to work on real world application to actually makes a difference. It is just a small initiative towards this.

Technical Aspect:

This project is divided into two parts:

1. Trained a Machine Learning model using Random Forest Regressor(Code is available in this repo)
2. Deployed the model using Flask on Heroku Platform.

Installation

The Code is written in Python 3.7.9. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:

pip install -r requirements.txt

Deployement on Heroku

Login or signup in order to create virtual app. You can either connect your github profile or download ctl to manually deploy this project.

Our next step would be to follow the instruction given on Heroku Documentation to deploy a web app.

Directory Tree

├── static 
│   ├── styles.css
├── template
│   ├── index.html
├── CarPricePrediction.ipynb
├── Procfile	
├── README.md
├── app.py
├── car_data1.csv	
├── random_forest_regressor_model.pkl
├── requirements.txt

Technologies used

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result

Future Scope

• Use multiple Algorithms
• Optimize Flask app.py
• Front-End

Credits

• Dataset: The dataset contains information about used cars is taken from Kaggle listed on www.cardekho.com.
• Dataset Link: https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho