I'm a junior Python developer learning machine learning, cloud technologies, and data analysis. My repositories showcase my learning journey from basic projects to ML models and cloud applications, demonstrating steady growth through hands-on practice.

• 🇷🇸 Location: Serbia
• 🎯 Status: Junior Python developer exploring ML, cloud automation, and data science
• 📚 Current Level: Junior developer focused on continuous learning
• 💪 Motto: "Learning by building, one project at a time!"

• 🤖 Machine Learning & NLP basics
• 🐍 Python development & OOP fundamentals
• ☁️ AWS cloud services & serverless basics
• 📊 Data analysis & visualization
• 🔄 ETL pipelines & data automation (learning)
• 🎮 Game development & interactive applications
• 🔧 System monitoring & automation tools

• Serverless architecture patterns and best practices
• AWS services integration and cloud fundamentals
• Building cloud-native applications step by step
• Event-driven automation workflows

• Master AWS fundamentals and get cloud certified
• Build more complex automation projects
• Learn advanced Python patterns and testing
• Contribute to open source learning projects

• 🎯 9 Repositories - Learning journey from basics to ML and cloud
• 🤖 ML Projects - Text classification, sentiment analysis, NLP learning
• 📚 Python Skills - Growing OOP knowledge, data science, and utilities
• 🔧 Development Journey - API integration, cloud services, ML pipelines
• ☁️ Cloud Learning - AWS serverless with Lambda and S3

DennisForge
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├── 🤖 ml-product-category-classifier ⭐ LATEST
│   └── 🧠 Machine learning model for product category classification using NLP.
│       Text classification with scikit-learn, TF-IDF vectorization, and
│       supervised learning for automated product categorization.
│
├── 💬 ml-product-reviews-project
│   └── 📊 Sentiment analysis and text analytics for product reviews.
│       NLP-powered insights using machine learning to analyze customer feedback
│       and extract meaningful patterns from review data.
│
├── ☁️ aws-atp-pipeline
│   └── 🚀 Serverless AWS Lambda ETL pipeline for ATP tennis data analysis.
│       Automated data processing with EventBridge scheduling, S3 storage,
│       and secure API integration using Kaggle API and Secrets Manager.
│
├── 🎬 movie-imdb-api-project
│   └── 📊 Movie dataset enrichment using OMDb API — fetches IMDb ratings, actors,
│       and vote counts, then exports to XML for comprehensive data analysis.
│
├── 🔧 system-check
│   └── 💻 Python utility for system performance monitoring and health checks
│       using psutil for comprehensive system administration tasks.
│
├── 🎮 python-games-collection
│   └── 🎯 Collection of Python games including terminal-based games, quizzes,
│       and interactive entertainment applications for learning and fun.
│
├── 📝 personal-diary-analyzer
│   └── 📖 Personal diary analysis tool for text processing, sentiment analysis,
│       and personal insights extraction from written content.
│
├── 📊 online_sales_analysis
│   └── 📈 Comprehensive data analysis project for online sales metrics,
│       trends analysis, and business intelligence insights.
│
└── 🔹 python-core-exercises
    └── 🐍 Foundational Python exercises covering core programming concepts,
        algorithms, and problem-solving techniques.

Learning AWS Serverless - My First Cloud Project

What I Learned:

• ☁️ First AWS Experience - Learning serverless architecture from scratch
• 📅 Event Scheduling - Understanding cron expressions and EventBridge
• 🔒 Cloud Security - Using AWS Secrets Manager for secure credential storage
• 🚀 Infrastructure as Code - Learning how to deploy cloud resources
• 📊 ETL Concepts - Extract from Kaggle API → Transform → Load to S3
• ⚡ Lambda Functions - Writing efficient, cloud-native Python code

🔗 View Project | 📖 Learning Notes

Learning Machine Learning - Text Classification Project

What I Learned:

• 🧠 Machine Learning Basics - First ML model using scikit-learn
• 📝 NLP Fundamentals - Text vectorization with TF-IDF
• 🎯 Classification - Supervised learning for categorization
• 📊 Model Evaluation - Understanding accuracy and performance metrics
• 🔄 ML Pipeline - Data preprocessing → Training → Prediction
• 📓 Jupyter Notebooks - Interactive ML development and experimentation

🔗 View Project | 📊 Notebook

Sentiment Analysis - Learning NLP

What I Learned:

• 💭 Sentiment Analysis - Analyzing customer review sentiments
• 📊 Text Analytics - Extracting insights from unstructured text
• 🔍 Feature Engineering - Creating meaningful features from text
• 📈 Data Visualization - Visualizing ML results and patterns
• 🧹 Data Cleaning - Preprocessing real-world messy data

🔗 View Project

Data Integration & Analysis - API-Powered Movie Database

Key Learning Points:

• 🌐 API Integration - Working with external REST APIs
• 📊 Data Processing - Enriching datasets with additional information
• 📁 Data Export - XML formatting and structured data output
• 🔄 Data Transformation - Converting and organizing movie information
• 📈 Analytics Ready - Preparing data for further analysis

System Monitoring & Performance Analysis

Key Learning Points:

• 💻 System Administration - Monitoring system resources and performance
• 📊 Performance Analysis - CPU, memory, and disk usage tracking
• 🔧 Utility Development - Creating practical system tools
• 📈 Real-time Monitoring - Live system status reporting
• 🛠️ Administrative Tools - Building utilities for system management

• 🔥 Learning Focus - Machine learning, cloud technologies, and Python development
• 💻 Primary Learning Path - Python → ML/Data Science → AWS Cloud
• 📚 Repository Count - 9 projects showing progression from basics to ML and cloud
• 🎯 Journey - Python fundamentals → ML & NLP → Cloud automation
• 🤝 Community - Learning from others and sharing junior developer experiences

• 🏗️ More AWS Services - Explore additional AWS services and cloud patterns
• � Better Data Processing - Learn more advanced data transformation techniques
• � Testing & Quality - Improve code testing and quality practices
• 📚 Cloud Fundamentals - Study for AWS certification

• 🔄 CI/CD Learning - Understand deployment pipelines and automation
• 🌐 Web Development - Learn Flask/Django for web applications
• 🤝 Open Source - Start contributing to projects and learning from others
• � Documentation - Improve technical writing and project documentation

🎯 "Become a skilled cloud developer who builds reliable, well-tested solutions"

• 📖 Advanced AWS Services - Step Functions, API Gateway, and advanced cloud patterns
• 🔧 Infrastructure as Code - CloudFormation, Terraform, and automated deployments
• 📊 Data Lake Architecture - Large-scale data processing and analytics platforms
• 🌐 Microservices Design - Distributed systems and event-driven architecture
• 📚 Cloud Security - Advanced security patterns and compliance frameworks

"Every problem is a learning opportunity and every project teaches something new."

I believe in learning by building real projects that solve actual problems. Each repository represents a step in my journey from basic Python to understanding cloud technologies and software engineering best practices.

💭 "Building my skills one project at a time! 🚀"

⭐️ From DennisForge - Learning cloud technologies and building Python skills through hands-on projects 🚀

📍 Serbia 🇷🇸 | 📬 github.com/DennisForge