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Car Price Prediction Project

This repository contains a Python project for predicting car prices using machine learning. In this project, we perform data preprocessing, data visualization, and build a machine learning model to predict car prices based on various features.

Table of Contents

• Project Overview
• Data Preprocessing
• Data Visualization
• Machine Learning Model
• Evaluation
• Usage
• Contributing

Project Overview

In this project, we aim to predict car prices using a dataset containing various car features. The project involves the following main steps:

1. Data Preprocessing: We first check for missing values and handle them appropriately. Fortunately, our dataset is clean and does not contain any missing data.

2. Data Visualization: We visualize the data to gain insights into the relationships between different features and the target variable (car prices). This helps us understand the data and make informed decisions during modeling.

3. Machine Learning Model: We build a machine learning model to predict car prices. The model is based on a Random Forest Regressor and is optimized using hyperparameter tuning.

4. Evaluation: We evaluate the model's performance using various metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2).

Data Preprocessing

We begin by checking for missing values in the dataset. Fortunately, there are no missing values in our dataset, so we can proceed with data visualization.

Data Visualization

We use data visualization techniques to better understand the dataset and its features. Some of the visualizations include:

• Histogram of Car Prices: To visualize the distribution of car prices.
• Scatter Plot of Engine Size vs. Horsepower: To understand the relationship between engine size and horsepower.
• Correlation Heatmap: To visualize the correlations between various car features.

These visualizations help us identify patterns and correlations in the data.

Machine Learning Model

We build a machine learning model to predict car prices. The model is based on a Random Forest Regressor, which is a powerful ensemble learning algorithm. We optimize the model's hyperparameters using Randomized Search and Grid Search.

Evaluation

The model's performance is evaluated using the following metrics:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-squared (R2)

The model achieves a high R-squared value, indicating its effectiveness in predicting car prices.

Usage

You can use this project as a template for your own car price prediction tasks. To get started, follow these steps:

1. Clone the repository to your local machine.
2. Install the required libraries using pip install -r requirements.txt.
3. Run the Python script to train and evaluate the model.

Feel free to customize the code and experiment with different machine learning algorithms and hyperparameters to improve the model's performance.

Contributing

If you'd like to contribute to this project, please fork the repository, create a new branch, and submit a pull request. We welcome contributions and improvements from the community.

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