1. Introduction
2. Deployment
3. Demo
4. Tools Used
5. Libraries
6. Algorithms Used
7. Results
8. Files Structure
9. Installation
10. Usage
11. Future Work
12. License

This project focuses on predicting car prices using machine learning techniques. The dataset used consists of various features such as mileage, brand, model, etc., and the target variable is the price of the car. The goal is to build a model that accurately predicts the price of a car given its features.

Deployment Link

• Python
• Streamlit

• scikit-learn (sklearn)
• Plotly
• Pandas
• NumPy

1. Decision Tree
2. Random Forest (yielding highest accuracy)
3. XGBoost
4. Linear Regression

The Random Forest algorithm yielded the highest accuracy among the algorithms tested. Here are the accuracy scores for each algorithm:

1. Clone the repository:

git clone https://github.com/neerajcodes888/Car-Price-Prediction
cd car-price-prediction

Train the model:

python  prediction.ipynb

Run the Streamlit app:

streamlit run app.py

Some potential areas for future improvement and expansion include:

• Incorporating additional features such as car age, fuel type, and condition.
• Experimenting with more advanced machine learning algorithms.
• Fine-tuning model hyperparameters for improved performance.
• Building a user interface for data input and displaying predictions in the Streamlit app.
• Handling outliers and missing data more effectively.
• Implementing feature engineering techniques to create more informative features.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.