somanathkshirsagar / -LeastSquare

LeastSquare is a web application developed with the objective of predicting the price of used cars. The project follows the life cycle of a data science project and incorporates various tools and techniques such as machine learning, regression analysis, linear regression, polynomial regression, Lasso regression, Ridge regression, and Streamlit.

Least Square

A simple Streamlit WebApp, which can predict Car Price with Machine Learning Models such as Linear Regression, Ridge Regression, and Lasso Regression.

Project Documentation

I've also trained this using Neural Network.

How to use?

Clone this repository

  git clone https://github.com/somanathkshirsagar/LeastSquare.git

Method 1 - Using Docker

Prerequisite - Docker

1. Build Docker Images

  docker build -t leastsquare:latest .

2. Run App in Docker Container

  docker run leastsquare:latest

Method 2 - Using Virtual Environment

Prerequisite - Conda, Python

1. Create a Conda Virtual Environment

  conda create -n leastsquare

2. Activate Virtual Environment

  conda activate leastsquare

3. Install Requirements.txt

  pip install -r requirements.txt

4. Run Streamlit App

  streamlit run app.py

About Dataset

This data was scraped from cars24.com.

I used Octoparse to scrape the data from the website.

• Name
• Variant
• Transmission
• km_driven
• Owner_Type
• Fuel
• Price
• Age (Derived from Year Purchased)

Dataset Link

Project Organization

Screenshots

Explore Page

Model Page

Predict Page