Machine Learning Portfolio 🚀

This repository documents my learning journey in machine learning, containing projects, data analysis, and code implementations. It is designed to bridge the gap between theoretical knowledge and practical "hands-on" experience.

📂 Repository Structure

Projects are categorized by machine learning subfields:

• 01_Supervised_Learning: Supervised learning algorithms (Classification, Regression, etc.)
• (Upcoming) 02_Unsupervised_Learning: Unsupervised learning (Clustering, etc.)

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🔬 Featured Project: Breast Cancer Classification with k-NN

This is the first project in this portfolio, addressing a medical diagnostic problem: Breast Cancer Classification.

🔗 View Project: 01_KNN_Breast_Cancer_Classification.ipynb

📌 Project Summary

The goal of this project is to develop a machine learning model to predict whether a tumor is Malignant or Benign using the Wisconsin Breast Cancer Dataset.

🛠️ Technologies & Libraries Used

• Language: Python 3.x
• Data Analysis: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn (sklearn)

📊 Project Steps

The following data science steps were implemented in this notebook:

1. Exploratory Data Analysis (EDA): Analyzed class balance, missing values, and feature correlations (Heatmap).
2. Data Preprocessing:
   • Split the dataset into Training (80%) and Test (20%) sets (using stratify).
   • Applied StandardScaler for feature scaling, as k-NN is a distance-based algorithm.
3. Model Training & Tuning:
   • Implemented the k-Nearest Neighbors (k-NN) algorithm.
   • Performed hyperparameter optimization by testing values from 1 to 20 to find the optimal 'k' neighbors.
4. Evaluation:
   • Evaluated model performance using Confusion Matrix and Classification Report.
   • Focused on Recall score (alongside Accuracy) to minimize false negatives (missed cancer cases), which is critical in medical diagnostics.

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2. Handwriting Recognition with SVM ✍️

Category: Supervised Learning File: SVM_Classification.ipynb

In this project, I built a Support Vector Machine (SVM) model to classify handwritten digits using the Scikit-learn Digits Dataset. The project focuses on image classification fundamentals and model evaluation.

• Objective: To correctly identify digits (0-9) from 8x8 pixel grayscale images.
• Methodology:
  • Loaded and visualized the dataset using matplotlib.
  • Preprocessed data by flattening 8x8 image matrices into 1D vectors.
  • Split the data into training and testing sets (80/20 split).
  • Trained a Support Vector Classifier (SVC) with a linear kernel.
• Results:
  • Evaluated performance using a Confusion Matrix (visualized with seaborn) and a Classification Report.
  • Achieved high accuracy across all digit classes.
• Tech Stack: Python, Scikit-learn, Matplotlib, Seaborn.

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(Buraya gelecekteki diğer projelerini aynı formatta ekleyebilirsin)

🛠️ Tools & Libraries

The projects in this portfolio primarily use the following tools:

• Python (NumPy, Pandas)
• Machine Learning: Scikit-learn
• Visualization: Matplotlib, Seaborn
• Environment: Jupyter Notebook / Anaconda

📬 Contact

Feel free to reach out for questions or feedback.

• GitHub: ErenBalkis
• LinkedIn: ErenBalkis