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+# GPU Price Prediction Models
+
+## Overview
+This document provides a summary of the machine learning models used to predict GPU prices, including their performance metrics such as RMSE and R2 score.
+
+## Models Implemented
+
+1. **Linear Regression**
+2. **Ridge Regression**
+3. **Lasso Regression**
+4. **Decision Tree Regressor**
+5. **Random Forest Regressor**
+6. **Gradient Boosting Regressor**
+7. **XGBoost Regressor**
+8. **CatBoost Regressor**
+9. **Support Vector Regressor**
+10. **K-Nearest Neighbors Regressor**
+11. **Extra Trees Regressor**
+
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_0.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_1.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_2.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_3.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_4.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_5.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_6.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_7.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_8.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_9.png?raw=true)
+![results](https://github.com/adi271001/ML-Crate/blob/Computer-Hardware/Computer%20Hardware%20Analysis/Images/__results___32_10.png?raw=true)
+
+
+## Performance of the Models
+
+| Model                       | Train RMSE         | Test RMSE          | Train R2           | Test R2            |
+|-----------------------------|---------------------|---------------------|---------------------|---------------------|
+| Linear Regression           | 17.65               | 302016927576.74     | 0.9991              | -1.9900E+017        |
+| Ridge Regression            | 123.93              | 300.17              | 0.9580              | 0.8034              |
+| Lasso Regression            | 134.90              | 333.59              | 0.9502              | 0.7572              |
+| Decision Tree Regressor     | 17.65               | 302.87              | 0.9991              | 0.7999              |
+| Random Forest Regressor     | 151.01              | 353.12              | 0.9376              | 0.7280              |
+| Gradient Boosting Regressor | 105.99              | 307.28              | 0.9693              | 0.7940              |
+| XGBoost Regressor           | 38.36               | 328.19              | 0.9960              | 0.7650              |
+| CatBoost Regressor          | 81.89               | 330.35              | 0.9817              | 0.7619              |
+| Support Vector Regressor    | 626.45              | 696.85              | -0.0733             | -0.0594             |
+| K-Nearest Neighbors Regressor| 290.01              | 364.72              | 0.7700              | 0.7098              |
+| Extra Trees Regressor       | 17.65               | 359.22              | 0.9991              | 0.7185              |
+
+## Conclusion
+The evaluation of different models based on RMSE and R2 scores highlights their strengths and weaknesses. Models like Linear Regression and Decision Tree Regressor showed lower RMSE values, while XGBoost and Gradient Boosting Regressor had higher R2 scores, indicating better fit for the data.
+
+## Signature
+- **Name:** Aditya D
+- **Github:** [https://www.github.com/adi271001](https://www.github.com/adi271001)
+- **LinkedIn:** [https://www.linkedin.com/in/aditya-d-23453a179/](https://www.linkedin.com/in/aditya-d-23453a179/)
+- **Topmate:** [https://topmate.io/aditya_d/](https://topmate.io/aditya_d/)
+- **Twitter:** [https://x.com/ADITYAD29257528](https://x.com/ADITYAD29257528)