Master Degree Coursework: Econometrics II

A repository focused on using images of cars to complete various tasks using deep learning

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.

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

Python script to fetch cars data

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

Estudo para predição de preços de carros

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

Explanatory Data Analysis using Python

Web scraping car listings with Python and BeautifulSoup4

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…

Simple Implementation of many GAN models with PyTorch.

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.

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

Streamline your car rental experience with our effortless booking process.

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.

Project that detects the model of a car, between 1 and 196 models ( the 196 modelss of Stanford car file), that appears in a photograph with a success rate of more than 70% (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.

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