Car Price Prediction This project aims to predict the price of cars using machine learning techniques. The goal is to develop a model that can accurately estimate the price of a car based on various features and attributes. By leveraging a dataset of car information such as make, model, year of manufacture, mileage, fuel type, and more, this project aims to provide an efficient and reliable solution for car price prediction. The project includes both a web application and a Jupyter Notebook component. The web application allows users to interact with the trained model and obtain predicted car prices based on provided input. The Jupyter Notebook component provides a detailed analysis of the data, model training, evaluation, and other exploratory tasks.
Utilizes a machine learning model trained on a comprehensive dataset of car information.
Provides accurate predictions of car prices based on various features and attributes.
Includes a user-friendly web application for easy interaction with the model.
Jupyter Notebook component allows for in-depth data analysis and exploration.
Python 3.7 o superior
Bibliotecas: flask, requests, pickle, sklearn, jsonify, numpy, pandas
warnings, seaborn, matplotlib