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https://github.com/huseyincenik/auto_analytics_advanced_estimation_and_deployment

aws aws-cloudformation aws-ec2 decisiontreeregressor deployment lassoregression linearregression modeldeployment python3 randomforest regression regression-models ridgeregression streamlit streamlit-webapp xgboost

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Host: GitHub
URL: https://github.com/huseyincenik/auto_analytics_advanced_estimation_and_deployment
Owner: huseyincenik
Created: 2023-12-31T10:15:18.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-01-01T14:01:28.000Z (about 1 year ago)
Last Synced: 2024-01-02T11:24:03.357Z (about 1 year ago)
Topics: aws, aws-cloudformation, aws-ec2, decisiontreeregressor, deployment, lassoregression, linearregression, modeldeployment, python3, randomforest, regression, regression-models, ridgeregression, streamlit, streamlit-webapp, xgboost
Language: Jupyter Notebook
Homepage:
Size: 7.88 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # Auto Analytics: Advanced Estimation & Deployment 🛠️

![car_price_prediction](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14886202%2F40cc46b822bdbeec638a90743892cdd3%2Findir%20(3).jpeg?generation=1704037226194977&alt=media)

This project focuses on using machine learning algorithms to estimate car prices. Various regression algorithms were implemented, including:

- Linear Regression

- Lasso Regression

- Ridge Regression

- Decision Tree

- Random Forest

- XGBoost

Model evaluation, grid-search, and cross-validation were performed, resulting in the following scores:

| Model             | R2    | MAE    | RMSE   | MAPE   |

|-------------------|-------|--------|--------|--------|

| XGBoost           | 0.921 | 2123.94| 3373.07| 0.132  |

| Random Forest     | 0.921 | 2252.57| 3374.97| 0.150  |

| Lasso             | 0.831 | 2818.00| 4954.25| 0.192  |

| Linear Regression | 0.830 | 2818.65| 4957.25| 0.192  |

| ElasticNet        | 0.830 | 2817.18| 4959.12| 0.192  |

| Decision Tree     | 0.816 | 3467.44| 5157.75| 0.221  |

The final models chosen were Random Forest and XGBoost. Feature importance was determined separately for each model to reduce feature counts. The models were saved using Pickle and converted into a Streamlit file for deployment outside the notebook environment. The Streamlit file was published on both AWS EC2 instances and the Streamlit website, enabling users to make predictions interactively.

## Access Links

- [Streamlit Live](https://auto-price-deployment.streamlit.app/)

- [AWS EC2](http://54.227.111.162:8502/)

- [GitHub Notebook Link](https://github.com/huseyincenik/auto_analytics_advanced_estimation_and_deployment)

- [Kaggle Notebook Link](https://www.kaggle.com/huseyincenik/auto-analytics-advanced-estimation-deployment)

- [LinkedIn](https://www.linkedin.com/in/huseyincenik/)