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https://github.com/alitekes1/machine_learning_project_btk_course
This repository contains my exercises, materials and projects from the BTK Akademi Machine Learning Course.
https://github.com/alitekes1/machine_learning_project_btk_course
Last synced: about 1 month ago
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This repository contains my exercises, materials and projects from the BTK Akademi Machine Learning Course.
- Host: GitHub
- URL: https://github.com/alitekes1/machine_learning_project_btk_course
- Owner: alitekes1
- Created: 2024-09-13T16:41:39.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-15T14:15:58.000Z (4 months ago)
- Last Synced: 2024-11-25T22:12:27.403Z (about 1 month ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 8.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Course on BTK Akademi
This repository contains exercises and materials from the BTK Akademi Machine Learning Course. In addition I have complete 2 project: Classification Project: [Airline Passenger Satisfaction
](https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction) and Regression Project: [Urban Air Quality and Health Impact Analysis](https://www.kaggle.com/datasets/abdullah0a/urban-air-quality-and-health-impact-dataset). I obtained dataset from Kaggle. Below, you will find details about the courses, as well as links to the course materials and certifications.
## Classification Project : Airline Passenger Satisfaction
### Overview
This project focuses on classifying categorical data using a Support Vector Classifier (SVC).
### Model Evaluation
#### Support Vector Machine (SVM)
precision recall f1-score support
0 0.93 0.95 0.94 14573
1 0.94 0.91 0.92 11403
accuracy 0.93 25976
macro avg 0.93 0.93 0.93 25976
weighted avg 0.93 0.93 0.93 25976
### Conclusion
The model achieved strong performance with a 90% accuracy. Further enhancements may include hyperparameter tuning or experimenting with alternative classification algorithms.
## Regression Project : Urban Air Quality and Health Impact Analysis
### Overview
This project involves predicting continuous target variables using Lineer Regression, Polinomial Regressior, Decision Tree Regressor, KNN Regressor, Random Forest Regressior models.
### Model Evaluation
| Model | MSE | R2 Score |
| -------------------------------------- | -------- | -------- |
| Lineer Regression (col=Severity_Score) | 0.271174 | 0.37570 |
| Polinomial Regressior(all 9 columns) | 0.029113 | 0.93297 |
| Decision Tree Regressior | 0.02529 | 0.94177 |
| KNN Regressor | 0.00629 | 0.98550 |
| Random Forest Regressior | 0.01683 | 0.96123 |
## Courses
### [Machine Learning Course (11+ hours)](https://www.btkakademi.gov.tr/portal/course/makine-ogrenmesi-30123)
This course provides a comprehensive introduction to machine learning concepts and techniques.
### [Machine Learning Hands-on with Python (8+ hours)](https://www.btkakademi.gov.tr/portal/course/python-ile-makine-ogrenmesi-uygulamalari-30284)
This course focuses on practical applications of machine learning using Python, offering hands-on experience with real-world projects.
## Certifications
