Machine Learning Projects and Experiments Repository

Welcome to my GitHub repository for machine learning projects and experiments! This repository contains a variety of Jupyter Notebook files showcasing different machine learning techniques, data analysis, and data engineering tasks. Feel free to explore the projects and experiments I've worked on.

Table of Contents

1. Anomaly Detection Using AutoEncoder with Pytorch

   • Detect anomalies in data using an AutoEncoder model implemented with PyTorch.

2. Bubble Detection for the Crypto Market

   • Analyze and detect bubbles in the cryptocurrency market using data analysis and visualization techniques.

3. Data Engineering & Analysis

   • Perform data engineering tasks and exploratory data analysis to gain insights from various datasets.

4. Depth Estimation 2D to 3D

   • Convert 2D images to 3D depth maps using a machine learning approach.

5. Feature Importance with XGBoost vs ppscore

   • Compare feature importance techniques between XGBoost and ppscore libraries.

6. Fine-Tune LLaMA 2 with QLoRA for QA Datasets

   • Fine-tune the LLaMA 2 model using QLoRA for question-answering datasets.

7. Machine Learning Pipeline

   • Build a complete machine learning pipeline including preprocessing, model training, and evaluation.

8. McKinsey ProHack Challenge 2020 with XGBoost

   • Participate in the McKinsey ProHack Challenge 2020 using XGBoost with optimization techniques.

9. NLP Cases

   • Explore various natural language processing (NLP) cases and techniques.

10. Quantize Llama 2.7B Model and Fine-Tune on QA Datasets

    • Quantize the Llama 2.7B model and fine-tune it on question-answering datasets.

11. Quantum Method for Portfolio Optimization

    • Utilize a quantum approach for portfolio optimization.

12. Rule-Based Turkish Language Sentiment Analysis

    • Perform sentiment analysis on Turkish text using a rule-based approach.

13. SARIMA Model

    • Build a Seasonal Autoregressive Integrated Moving Average (SARIMA) model for time series forecasting.

14. Turkey Conflicts Data Mining

    • Mine and analyze data related to conflicts in Turkey.

15. Turkish Text Classification

    • Perform text classification on Turkish text using machine learning techniques.

16. Vector Databases Tutorial

    • Tutorial on working with vector databases in the context of machine learning.

17. Llama 2.13B Playground

    • Experiment and explore the Llama 2.13B model.

18. Medical Trends Analysis

    • Analyze trends in medical data.

19. Portfolio Optimization

    • Optimize investment portfolios using machine learning techniques.

20. Stroke Classification

    • Classify stroke and non-stroke cases using machine learning.

21. Tapu Kadastro Analysis

    • Analyze tapu (land registry) and kadastro (cadastre) data.

Requirements

The project notebooks are implemented using various libraries and frameworks. To reproduce the results or run the notebooks, please refer to the requirements.txt file for the necessary dependencies.

Feel free to reach out if you have any questions or suggestions. Happy exploring and learning!

Note: Some notebooks were created using Colaboratory, and others were developed locally. Make sure you have the required libraries and packages installed before running the notebooks.