Hi there! I'm Εevval Demir, and this repository contains all the hands-on coding exercises I completed while following the BTK Academy Machine Learning Course.
Each topic is organized into folders and includes Python scripts and datasets used throughout the learning process.
| Folder | Description |
|---|---|
Bolum1/ |
Data preprocessing: handling missing values, encoding categorical data, and preparing datasets |
Classification/ |
Classification algorithms like KNN, Naive Bayes, SVM, and Decision Trees |
Clustering/ |
Customer segmentation using K-Means clustering |
Apriori/ |
Market basket analysis using the Apriori algorithm |
Regression/ |
Regression techniques: linear, polynomial, SVR, and Decision Trees |
Prediction/ |
Real-world prediction examples (e.g., salary, sales prediction) |
PCA/ |
Dimensionality reduction with Principal Component Analysis |
DL/ |
Deep Learning with Keras β e.g., ANN for churn prediction |
NLP/ |
Natural Language Processing β sentiment analysis on text data |
UCB/ |
Reinforcement learning β Upper Confidence Bound algorithm for ad selection |
- Python 3.x
- NumPy, Pandas, Matplotlib, Seaborn
- Scikit-learn
- Keras (TensorFlow backend)
- Jupyter Notebook / Spyder
- Google Colab (optional)
- Clone the repository:
git clone https://github.com/Sevval-Demir/MachineLearning.git