A Regression based ML model for prediction of car prices in market

Car Price Prediction: Machine Learning (Data Science) Project using CarDekho.com dataset predicting prices of cars

Data visualisation using seaborn

car price predication with machine learning. The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. Car price prediction is one of the major research areas in machine learning.

Machine learning Algorithms

belajar machine learning

Machine Learning Project

Car Price Prediction Using Multiple Regression

🚗 Solving the problem of predicting the price of a used car using Sklearn's supervised machine learning techniques.

Car price prediction flask app using linear regression and a sample dataset from kaggle

The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. Car price prediction is one of the major research areas in machine learning

Car Price Prediction Using Machine Learning (Regression Use Case)

These are the tasks which I have performed during my Data Science Internship by Oasis Infobyte

Built a car price prediction from Kaggle dataset solution using Linear Regression and also Lasso Regression

Predict Car Prices with Random Forest (top 7% ranked model)

A ML Algorithm Driven Car Price Prediction Project

Utilizing pandas and sklearn, this machine learning project applies linear regression for precise prediction of upcoming car prices, incorporating vital ML concepts. Data is sourced from a CSV file.

The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. Car price prediction is one of the major research areas in machine learning

Car price prediction system to effectively determine the worthiness of the car using a variety of features.

In this project, we tried to predict the prices of other houses according to the values ​​of these features with the model we obtained by training a data set containing some features and prices of real houses with linear regression and decision tree regression methods