🤍 Selam Güzelim

Tez Analiz Bir Şeyler Platformu

Tam tezde yazdığı şekilde makine öğrenmesi ve veri seti kullanıyor. Tam tezde yazan sonuçları da analiz ediyor. Grafik ve değer olarak gösteriyor.

1
📁

Data Import

Upload your cybersecurity dataset CSV files. Support for CICIDS, KDD, and other standard formats.

2
🤖

Algorithm Selection

Choose from state-of-the-art supervised, unsupervised, and anomaly detection models.

3
⚙️

Advanced Options

Enable Grid Search, Cross Validation, and Data Augmentation for optimal results.

1

Dataset Configuration

📤
Drop your CSV file here or click to browse
Supports CICIDS2017, KDD, and other cybersecurity datasets
2

Algorithm Selection

🌳
Decision Tree
Fast, interpretable classification model with excellent speed.
Lightning Fast 86-90%
🌲
Random Forest
Ensemble method with high accuracy and robustness.
Fast 92-96%
🚀
XGBoost
Extreme Gradient Boosting for state-of-the-art performance.
Moderate 94-98%
🧠
Neural Network
Deep learning model with automatic feature learning.
Slow 90-95%
👁️
Isolation Forest
Unsupervised anomaly detection for identifying outliers.
Very Fast 80-88%
🎯
K-Means
Clustering algorithm for pattern discovery.
Very Fast 75-82%
3

Advanced Configuration

🔄

SMOTE Data Augmentation

Balance imbalanced datasets with synthetic minority oversampling

⚙️

Grid Search Optimization

Automatically find optimal hyperparameters for models

📊

Cross Validation

5-fold stratified cross-validation for robust evaluation

📋

Analysis Summary

Selected Algorithms
✓ Random Forest
✓ XGBoost
✓ Neural Network
Estimated Analysis Time
~42 seconds
Waiting for dataset upload...

Analysis in Progress

Real-time monitoring of your machine learning pipeline

Execution Pipeline

Activity Log

Results

🏆

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