Introducing the most comprehensive and up-to-date open source dataset on US car models on Github. With over 15,000 entries covering car models manufactured between 1992 and 2023, this repository offers valuable information for anyone looking to incorporate car data into their applications. Best of all, it's completely free to use!

Simple Implementation of many GAN models with PyTorch.

The models are trained on the Cars Dataset. Given an image of a car, the models can predict the vehicle's make, model and year of production.

Python script to fetch cars data

Master Degree Coursework: Econometrics II

Recognizing car models through pictures with deep machine learning

Scraped data from one of the most famous Arabic sites for selling cars [YallaMotors] Using requests-html library and stored it in CSV file to use it in ML projects.

Car Data Analysis notebook project. Created using Google collab

Analysis of cars in Germany and price prediction with regression model.

Analyze cars dataset through EDA using Python. Includes data preprocessing, visualization, and insights extraction. Technologies: Pandas, Seaborn, Matplotlib.

Determine what influences and drives car prices given technical specs and identify which car(s) are the most under/overpriced and why.

Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot cra…

Web scraping car listings with Python and BeautifulSoup4

Project on vehycle radar data to Udacity Nanodegree on Data Science. Started on Dec, 2020.

Welcome to ILikeThisCars dedicated to cars, where we provide all the latest news, reviews, and insights about the automotive industry. Our platform offers a wide range of resources for car enthusiasts, including in-depth car reviews, comparisons, and buying guides, as well as the latest industry news, trends, and developments.

Project that detects the brand of a car, between 1 and 49 brands ( the 49 brands of Stanford car file), that appears in a photograph with a success rate of more than 80% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer.

Data analytics tasks and solutions, featuring hands-on exercises for data cleaning, visualization, and analysis using Python libraries.

One of my very first exercises in Machine Learning in which I used Random Forest and SVM to predict used cars' prices