Project overview
Car Price Prediction using Machine Learning is especially helpful when the vehicle is used and not coming direct from the factory. With increase in demand for used cars more and more vehicle buyers are finding alternatives of buying new cars. There is a need of accurate price prediction mechanism for the used cars. Prediction techniques of machine learning can be helpful in this regard.
Front end: HTML, CSS, BootStrap Backend: Flask, Nodejs Languages: Python, Javascript Model : Random Forest Regression Ide: VS code Other libraries : Pandas, Numpy, Seaborn, Matplotlib, Sklearn Data set : https://www.kaggle.com/datasets/saisaathvik/used-cars-dataset-from-cardekhocom
Step1: Data Cleaning
Fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
Step2: Extracting Data Analysis
Extracting important parameters and relationships that hold between them and to test underlying assumptions
Step3: Feature Engineering
assigning attribute-value pairs to a dataset that's stored as a table.
Step4: Model Training
Identifying and Fitting the best combination of training data and providing good prediction range.
If you would like to experiment with the dataset yourself and the code at Github the web app is deployed Here .