Car Price Prediction Project

This project aims to predict car prices based on a dataset obtained from Kaggle. The dataset contains information about various car attributes, including make, model, year, mileage, and more.

Dataset

The dataset used for this project can be found on Kaggle:

• Dataset URL: Car Dataset on Kaggle

Please download the dataset and place it in the /data directory before running the code. The code also uses the dataset url directly from my github repo

Project Overview

The goal of this project is to build a machine learning model that can predict the price of cars based on their features. The steps involved in this project include:

1. Data Preprocessing: Cleaning, exploring, and preparing the dataset for modeling.
2. Feature Engineering: Selecting and creating relevant features for the prediction task.
3. Model Selection: Choosing an appropriate machine learning algorithm for regression.
4. Model Training: Training the selected model on the dataset.
5. Model Evaluation: Evaluating the model's performance using appropriate metrics.
6. Prediction: Using the trained model to make price predictions for new data.

Requirements

• Python 3.x
• Jupyter Notebook (for running the code)
• Required Python libraries (specified in requirements.txt)

Usage

1. Clone this repository:

   git clone https:/ayyesu/car_price-prediction.git
   cd car_price-prediction
   

2. Install required Python Library

   pip install -r requirements.txt