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This repository offers a complete project on predicting used car prices using machine learning. It features data cleaning, model selection, training, and evaluation in a Jupyter Notebook, along with a Streamlit app for interactive predictions. Also included are a project report, user guide, and resources like datasets and joblib files in a ZIP.
23 Νοε 2020 · Essentially, the company wants to know: Which variables are significant in predicting the price of a car. How well those variables describe the price of a car. Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the American market.
Used car pricing today is often varied, based off a relatively small feature set, and is some variant of linear regression. Here, we try various deep learning architectures that incorporate more features in order to better predict used car prices than other models in existing literature today. Specifically, we compare performance between
🚗 Car price prediction using machine learning. Demo app built with Streamlit for easy interaction.
This research study analyzes car price prediction utilizing machine learning models such as LightGBM, Random Forest, XGBoost, and CatBoost regressors with a comprehensive dataset containing diverse car attributes.
This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that users input. The model building process involves machine learning and data science.
30 Αυγ 2023 · In this series of two articles we will utilize Python and Machine Learning to analyze a dataset with cars and build a price predictive model based on the available features (variables). The goal is to provide some general guidelines and ways of thinking when working on a Machine Learning project. We will describe in more detail:
