Machine Learning Projects

A collection of applied machine learning and data science projects implemented in Python. This repository showcases practical work across multiple domains including exploratory data analysis (EDA), image classification, natural language processing (NLP), and predictive modeling.

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📌 Overview

This repository contains independent Jupyter Notebook projects that demonstrate:

• Data preprocessing and exploratory data analysis (EDA)
• Supervised and unsupervised machine learning techniques
• Image classification using neural networks
• Natural language processing with NLTK
• Predictive modeling across different domains

Each notebook focuses on a specific problem and includes data analysis, modeling, and results.

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📁 Project Structure

├── notebooks/
│   ├── 01-career-transitions-analysis.ipynb
│   ├── 02-image-classification-nn.ipynb
│   ├── 03-nlp-nltk.ipynb
│   └── 04-financial-weather-prediction.ipynb
├── README.md

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📊 Projects

1. Career Transitions Analysis

• Exploratory data analysis on job satisfaction and career changes
• Statistical insights and visualization
• Focus: EDA, feature exploration

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2. Image Classification with Neural Networks

• Implementation of image classification models
• Neural network-based approach
• Focus: computer vision, deep learning basics

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3. Natural Language Processing with NLTK

• Text preprocessing and tokenization
• Feature extraction and analysis
• Focus: NLP fundamentals

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4. Financial & Weather Prediction

• Predictive modeling using structured data
• Analysis of trends and patterns
• Focus: regression / time-based prediction

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🛠️ Tech Stack

• Language: Python

• Libraries:

  • pandas, numpy
  • scikit-learn
  • matplotlib / seaborn
  • NLTK
  • PyTorch / TensorFlow (if applicable)

• Environment: Jupyter Notebook

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/mn-cs/machine-learning-projects.git
cd machine-learning-projects

2. Create environment

3. Install dependencies

4. Run notebooks

jupyter lab

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📈 Key Skills Demonstrated

• Data cleaning and preprocessing
• Exploratory data analysis (EDA)
• Machine learning model implementation
• Feature engineering
• Visualization and interpretation
• Problem decomposition and analysis

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📌 Notes

• Projects are independent and may use different datasets
• Some notebooks are exploratory and may evolve over time
• This repository is intended for learning, experimentation, and portfolio demonstration

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📄 License

This project is open-source and available under the MIT License.

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👤 Author

Michael Hayk GitHub: https://github.com/mn-cs

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This repository reflects ongoing learning and practical implementation of machine learning concepts.