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Predicting car price using ML algorithm
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• Table of Contents
• About The Project
  • Steps involved
  • Tools used
  • Libraries
• Getting Started
  • Installation
  • Data Analysis and Model Selection
  • Application Development
  • Model Deployment
• Contact

• Reading data from source and performing EDA
• Performing feature engineering to feed desired data into ML model
• Finding best set of hyper-parameters using Randomized Search CV to train the model
• Evaluating various Regressor ML models using cross-validation and residual plot
• Creating web application using flask to predict car price based on various attributes.
• Finally, deploying the web application on cloud based platform (i.e. Heroku)

Following are the tools/frameworks used in developing the application:

• CSS
• Python
• Flask
• HTML
• Heroku

Below is the list of python libraries used in this project:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sklearn
import flask

To install any of the aforementioned libraries, below command can be written in the Anaconda prompt or terminal :

pip install <package_name>

Refer Car_Price_Prediction.ipynb to find details regarding data analysis and model selection.

To check model performance, Residual plot and scatter plot between y_test-y_pred are used.

Below are the final metrics obtained for various models that were considered in analysis.

After model selection, the web application is developed using flask which is a python based web-framework. For source code refer app.py.

Below are few snapshots of application in use:

• Step 1 Filling attributes related to car

  

• Step 2 Displaying predicted cost

  

Model is deployed on heroku which is a cloud based PaaS.

Application link : https://carprizpred.herokuapp.com/

Raj Praveen Pradhan - LinkedIn

Project Link: https://github.com/rppradhan08/Car_Price_Prediction