/Car-Price-Prediction---ML-Project

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Car-Price-Prediction---ML-Project

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Car Dekho Price Prediction

Table of contents

  1. Objective
  2. Quick Demo
  3. Dataset Prview
  4. Description of variables in the dataset
  5. Car Price Prediction directory tree
  6. Installation
  7. Technologies Used

Objective

In this project, the objective is to predict Car Selling Price on various features like Car's Present_Price, Kms_Driven, Owner, Fuel_Type, Seller_Type, Transmission. We will use the CAR DEKHO dataset from Kaggle. This dataset contains information about used cars listed on website

Quick Demo

demo_gif


We can predict Car Selling Price by filling the data over UI and after that prediction will be displayed over UI.

Dataset Prview

A preview of top five rows of the Car Dekho dataset.
Car_Name Year Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type Transmission Owner
0 ritz 2014 3.35 5.59 27000 Petrol Dealer Manual 0
1 sx4 2013 4.75 9.54 43000 Diesel Dealer Manual 0
2 ciaz 2017 7.25 9.85 6900 Petrol Dealer Manual 0
3 wagon r 2011 2.85 4.15 5200 Petrol Dealer Manual 0
4 swift 2014 4.60 6.87 42450 Diesel Dealer Manual 0

Description of variables in the dataset

Above dataset contains information about used cars listed on website. This data can be used for a lot of purposes such as car price prediction using Machine Learning algorithms. The columns in the given dataset are as follows:

Car_Name: Name of Car sold

Year: Year in which car was bought

Selling_Price: Price at which car sold

Present_Price: Price of same car model in current year

Kms_Driven: Number of Kilometers Car driven before it is sold

Fuel_Type: Type of fuel Car uses

Seller_Type: Type of seller

Transmission: Gear transmission of the car (Automatic / Manual)

Owner: Number of previous owners

Car Price Prediction directory tree

├─ Templates
│  └─ index.html
│
├─ Procfile
│
├─ README.md
│
├─ Untitled.ipynb
│  
├─ app.py
│  
├─ car data.csv
│
├─ demo.gif
│
├─ main.py
│
└─ requirements.txt

Templates : contains templates for UI

app.py : Front and back end portion of the web application

Car Dekho Price Prediction.ipynb : conatains ipynb file (Jypiter Notebook file)

rf_regression_model.pkl : contains model for prediction

requirements.txt : required libraries

car data.csv : conatins raw data as csv file

Installation

  • Clone this repository and unzip it.

  • create new env with python 3 and activate it .

  • Install the required packages using pip install -r requirements.txt

  • Execute the command: python app.py

  • Open http://127.0.0.1:5000/ in your browser.

Technologies Used