A data-driven project predicting used car prices through EDA and a Linear Regression model. Insights revealed strong effects of year, mileage, and engine size on price. Achieving an R² of 0.83, the study highlights how adding categorical features can further refine predictions and boost model performance.

🚕 Given a set of features such as the car brand, model, year of manufacture and other factors, we would be able to predict the price of the car in the next few years. We would be using different regression models in our process of predicting the prices of cars respectively.

A Machine Learning Project that uses Random Forest Regressor model to predict used cars price based on some attributes such as kilometers driven, age, number of previous owners etc.

A model for predicting used car prices in Saudi Arabia

This is a python project for building a linear regression model that is used to predict used car prices from a given dataset using machine learning. The dataset used for this project is taken from Kaggle. For the complete video explanation, check out the following link.

This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.

🚗 Predict car prices instantly with Linear & Lasso Regression! Built with Streamlit, scikit-learn, pandas & matplotlib. Compare models, explore data, and learn ML hands-on. Fast, open source, and easy to use for students & developers!

Technologies: Nutch 1.6, MapReduce in Java, Mahout. For selling a used car, its price can be predicted by giving some attributes. e.g. Car Model, total miles, engine type. Trained data will be provided to our system to predict the price for new data. Used Nutch to crawl car data from 52 states which is on craigslist.org. Over 0.3 million records were fetched. The content was then pruned using two MapReduce Jobs. The first MapReduce cleaned the data removing unwanted unicode symbols and incomplete data (information without car model or total miles or engine or price). The Second MapReduce extracted the required attributes and emitted in tsv format. This tsv was then provided to a Naïve Based Classifier in Mahout. A classification model was built from the training data. This models predicts the price when attributes like car model model, miles, engine was provided.

This project implements a regression model to predict the present price of a car based on various features from the dataset. The workflow includes data preprocessing, feature selection, training using Linear Regression, model evaluation, and result visualization.

This project is about helping buyers to make an informed purchase by predicting the price of used cars. Gradient Boosting Regressor turns out to be the best model with a Mean Squared Logarithmic Error (MSLE) of 0.033. Designed an interactive Web application for model deployment using the Flask framework and Hosted on AWS using Elastic Beanstalk service via Docker image.

The Car Price Prediction Model uses machine learning to predict the selling price of a car based on features like model, year, kilometers driven, fuel type, seller type, transmission, and more. Built using Python's scikit-learn and a Linear Regression model, it provides accurate predictions based on historical car data.

This repository offers a comprehensive analysis of the used car market using data cleaning, EDA, and machine learning to uncover trends and predict car prices. It features robust data preprocessing, insightful visualizations, and predictive models.

This project predicts the most probable car prices with the use of basic linear regression models. Vehicle dataset from cardekho : https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho

This project focuses on developing a robust and accurate model that can predict the price of used cars based on various features and attributes. The primary objective of this project is to leverage Lasso Regression, a powerful regularization technique, to build a predictive model that effectively handles high-dimensions

A predictive model to predict the prices of used cars in Nigeria

This project aims to predict the Price of an used Car by taking it's Company name, it's Model name, Year of Purchase, and other parameters.

🚗 A professional FastAPI-based REST API that predicts car prices using a Gradient Boosting machine learning model. Simply provide a car brand, and the API returns an intelligent price prediction along with the closest matching vehicle from a real user database.

An end to end machine learning model for predicting the price of used cars.

Used car price prediction tool was created for classifieds websites such as Craig’s list. Helps the seller set a price point using the Logistical Linear Regression model, helping the buyer and the seller.

Predicting the price of the car in the next few years using given features such as the car brand, model, year of manufacture and so on. We will be using different regression model for predicting the prices of cars respectively.

This repository contains a Jupyter Notebook that performs an analysis of car prices using machine learning regression models. The analysis aims to predict car prices based on various features such as make, model, mileage, and year.

A machine learning model that predicts the selling price of the car, the web app is built using streamlit and deployed on heroku

To explore and visualize the dataset, build a linear regression model to predict the prices of used cars, and generate a set of insights and recommendations that will help the businesses

This project focuses on predicting the price of used cars based on various features like brand, model, year, fuel type, transmission, and more. The dataset is sourced from Kaggle and includes a wide range of vehicles sold in India.

This app is a web-based tool for predicting the price of used cars. It uses a machine learning model trained on a dataset of used car listings to make predictions. Users can input information about a used car (e.g., make, model, year, mileage, etc.) and the app will display the predicted price of the car. The app is built using Flask and incorpor

The Regression model that predicts the price of the used cars

Car Price Predicting using Machine Learning Model and flask implementation

Predicting used car prices using machine learning models like LightGBM and CatBoost with real-world data preprocessing and model evaluation.

Predicting the price of a car based on key features using a Linear Regression model. This project involves data preprocessing, exploratory data analysis, model training, and evaluation to provide a simple yet effective car price predictor.

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.