Used car price prediction using various machine learning models like Linear Regression, Decision Tree Regressor, and Random Forest Regressor with data preprocessing, feature engineering, and evaluation.

Hey everyone, this my ML data modeling full project

an ML regression model for predicting car sales prices

Analyzed over 500,000 records for car sales with 25 variables using regression and classification models in SAS. Established meaningful relationships between car price and its attributes and made prediction models, useful to sellers, online pricing services and buyers

This research forecasts U.S. used car retail sales from January 2018 to December 2019 using time-series data from 1992 to 2017. It applies four forecasting methods: Exponential Smoothing, Multiplicative and Additive Decomposition Models, and ARIMA. Models are evaluated using MAPE to determine the most accurate prediction technique.

Build a Car Sales Prediction Model using Machine Learning in Python. This project covers data cleaning, exploratory analysis, feature engineering, model training, and evaluation to forecast car sales. The model helps dealerships make data-driven decisions on inventory, pricing, and marketing.

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Main is a machine learning model that predicts the value of a used car based on relevant features. The model leverages regression algorithms to analyze data from past car sales and make informed predictions about a car's

Ensemble learning combines logistic regression and gradient boosting for car sales prediction. Python's ML libraries like SMOTE and Voting Classifier enhance model accuracy through balanced data and combining multiple models for robust predictions.

Car_Sales_Prediction is a comprehensive data analytics and machine learning project designed to analyze car sales data and predict car prices based on various features. The project combines powerful data visualizations created using Power BI with predictive modeling using Python and Scikit-learn.

Car Price Prediction is a predictive model that predicts the value of a car based on its characteristics. It uses data from past sales to build a model that can accurately estimate the price of a car given its features, such as model, year, mileage, Km's. This model can help buyers and sellers make informed decisions when buying and selling cars.

Collection of machine learning projects from CodeAlpha internship, including Sales Prediction, Car Price Estimator, and Iris Flower Classification, featuring predictive modeling, data visualization, and Flask web applications.

CodeAlpha Internship May 2025: Completed ML tasks including Iris Flower Classification (Random Forest), Car Price Prediction (based on features like brand, horsepower), and Sales Prediction (based on advertising data). Projects include data preprocessing, model training, and evaluation using Python.

Completed a Machine Learning project on BMW Car Sales using EDA, KNN, Logistic Regression, Naive Bayes, Linear & Polynomial Regression models for classification and price prediction. 📊 Built model evaluation with accuracy, confusion matrix, R² score and visualized results through graphs and dashboards.

The Car Sales Price Prediction project uses a Random Forest Regressor to predict car prices based on features like model, engine size, transmission, color, body style, dealer region, and annual income. It includes data preprocessing (missing values, scaling, encoding), hyperparameter tuning with Grid Search, and evaluation with R-squared

The prices of new cars in the industry are fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But due to the increased price of new cars and the incapability of customers to buy new cars due to the lack of funds, used cars sales are on a global increase (Pal, Arora, and Palakurthy, 2018). There is a need for a used car price prediction system to effectively determine the worthiness of the car using a variety of features. Even though there are websites that offer this service, their prediction method may not be the best. Besides, different models and systems may contribute to predicting power for a used car’s actual market value. It is important to know their actual market value while both buying and selling.

Car Sales Prediction Model

Car Sales Prediction Model

Car Sales Prediction Using ANN Model

Annual Car Sales Prediction Using ARIMA Model

Analyzed Honda Accord sales data with macroeconomic and behavioral indicators (unemployment, energy CPI, mileage, search queries)

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This project creates a Random Forest Regression model to predict the sales prices of cars using pre-processing steps to deal with missing data. It includes a data preprocessing pipeline model that handles categorical and numerical features.

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The ultimate goal of the project is to build a machine learning model that could predict the futuristic sales of a car given its current sales current advertising medium spends.

A model made in the coding language R which predicts how much a customer will spend on a vehicle based on various traits that they have.

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