Data Analytics Python Portfolio

By Mohammad Sayem Chowdhury

🌐 Live Portfolio: https://saayeeem.github.io/Data_Analytics_Python/

Welcome to my comprehensive data analytics and machine learning portfolio! This repository showcases my journey and expertise in data science, featuring projects spanning data analysis, machine learning, natural language processing, and data visualization using Python.

🚀 Overview

This portfolio demonstrates my proficiency in various data science domains through hands-on projects and implementations. Each section represents a different aspect of the data science pipeline, from data collection and preprocessing to advanced machine learning and visualization techniques.

📁 Repository Structure

🔍 Data Analysis

Location: Data_Analysis/

Core data analysis projects focusing on exploratory data analysis, statistical modeling, and predictive analytics:

• House Sales Analysis: Comprehensive analysis of King County house sales data
• Automotive Data Analysis: Statistical modeling and prediction for automobile datasets
• Data Wrangling Workflows: Advanced data cleaning and preprocessing techniques
• Model Development & Evaluation: End-to-end machine learning pipeline implementation

🤖 Machine Learning (ML)

Location: ML/

Advanced machine learning implementations across multiple domains:

• Classification Models: Decision trees, SVM, logistic regression, and ensemble methods
• Clustering Algorithms: K-means, hierarchical clustering, and DBSCAN implementations
• Regression Analysis: Linear, polynomial, and advanced regression techniques
• Recommender Systems: Content-based and collaborative filtering approaches
• Model Optimization: Feature selection, hyperparameter tuning, and performance evaluation

📝 Natural Language Processing (NLP)

Location: NLP/

Comprehensive NLP projects and implementations:

• Text Preprocessing Pipeline: Custom tweet preprocessing and text cleaning workflows
• Sentiment Analysis: Advanced sentiment classification using various algorithms
• Word Embeddings: Word2Vec, GloVe, and contextual embeddings manipulation
• Classification Models: Naive Bayes, logistic regression for text classification
• Feature Engineering: TF-IDF, bag-of-words, and advanced text vectorization

📊 Data Visualization

Location: Visualization/

Creative and insightful data visualization projects:

• Interactive Dashboards: Dynamic visualizations using Plotly and Dash
• Statistical Plots: Comprehensive statistical visualization library
• Geospatial Analysis: Location-based data visualization and mapping
• Custom Visualization Tools: Waffle charts, word clouds, and advanced plotting techniques

🛸 Capstone Projects

SpaceX Falcon 9 Analysis (Capstone_Data_Science_SpaceY/)

End-to-end data science project analyzing SpaceX Falcon 9 launch success patterns:

• Data Collection: API integration and web scraping
• Data Wrangling: Comprehensive data cleaning and feature engineering
• Exploratory Analysis: Statistical analysis and pattern discovery
• Predictive Modeling: Machine learning models for launch success prediction
• Interactive Dashboard: Real-time launch data visualization

Stack Overflow Developer Survey Analysis (Capstone_StackOverflow_Survey/)

Comprehensive analysis of developer trends and insights:

• Survey Data Analysis: Large-scale survey data processing
• Trend Analysis: Multi-year developer trend identification
• Interactive Visualizations: Dynamic dashboard for survey insights
• Statistical Reporting: Comprehensive analytical reports and findings

🐍 Python Development

Location: Python/

Advanced Python programming concepts and implementations:

• Object-Oriented Programming: Advanced OOP concepts and design patterns
• Data Structures & Algorithms: Custom implementations and optimizations
• API Development: RESTful API creation and integration
• Testing & Documentation: Comprehensive testing frameworks and documentation

🗄️ SQL & Database Management

Location: SQL/

Database management and SQL analytics projects:

• Database Design: Relational database modeling and optimization
• Complex Queries: Advanced SQL operations and analytics
• Data Pipeline Creation: ETL processes and data integration
• Performance Optimization: Query optimization and database tuning

🛠️ Technologies & Tools

Programming Languages

• Python 3.x: Primary language for all implementations
• SQL: Database queries and data manipulation
• JavaScript: Interactive visualizations and web components

Data Science Libraries

• Data Manipulation: Pandas, NumPy, Dask
• Machine Learning: Scikit-learn, TensorFlow, PyTorch
• Visualization: Matplotlib, Seaborn, Plotly, Bokeh
• Statistical Analysis: SciPy, Statsmodels
• NLP: NLTK, spaCy, Transformers, Gensim

Database & Big Data

• Databases: SQLite, PostgreSQL, MongoDB
• Big Data: Apache Spark, Hadoop ecosystem
• Cloud Platforms: AWS, Google Cloud Platform, Azure

Development Tools

• IDEs: Jupyter Notebook, VS Code, PyCharm
• Version Control: Git, GitHub
• Containerization: Docker
• Testing: pytest, unittest

🎯 Key Features

Advanced Analytics

• Statistical Modeling: Comprehensive statistical analysis and hypothesis testing
• Predictive Analytics: Time series forecasting and predictive modeling
• Feature Engineering: Advanced feature selection and creation techniques
• Model Interpretation: SHAP values, LIME, and model explainability

Interactive Dashboards

• Real-time Visualization: Dynamic data visualization with Plotly Dash
• Geospatial Analysis: Interactive maps and location-based insights
• Custom Widgets: Specialized visualization components
• Responsive Design: Mobile-friendly dashboard interfaces

Production-Ready Code

• Modular Architecture: Clean, maintainable code structure
• Error Handling: Robust error management and logging
• Documentation: Comprehensive docstrings and README files
• Testing: Unit tests and integration testing

📈 Project Highlights

🚀 SpaceX Launch Prediction

Advanced machine learning model achieving 85%+ accuracy in predicting Falcon 9 landing success, incorporating:

• Real-time API data integration
• Advanced feature engineering
• Ensemble modeling techniques
• Interactive prediction dashboard

📊 Developer Survey Analytics

Comprehensive analysis of 100,000+ developer responses, revealing:

• Technology adoption trends
• Salary prediction models
• Geographic insights
• Career progression patterns

🤖 Advanced NLP Pipeline

Custom text processing pipeline featuring:

• Multi-language support
• Sentiment analysis with 92% accuracy
• Named entity recognition
• Topic modeling and clustering

🚀 Getting Started

Prerequisites

Python 3.8+
pip package manager
Jupyter Notebook
Git

Installation

# Clone the repository
git clone https://github.com/saayeeem/Data_Analytics_Python.git

# Navigate to project directory
cd Data_Analytics_Python

# Install required packages
pip install -r requirements.txt

# Launch Jupyter Notebook
jupyter notebook

Quick Start

1. Explore Data Analysis: Start with Data_Analysis/review-introduction.ipynb
2. Try Machine Learning: Check out ML/Classification/ for classification examples
3. NLP Experiments: Begin with NLP/Sayem_Tweet_Preprocessing_Showcase.ipynb
4. Interactive Dashboards: Run Capstone_Data_Science_SpaceY/spacex-launch-dashboard-app.py

📚 Learning Journey

This portfolio represents my continuous learning and development in data science. The projects here demonstrate practical applications of theoretical concepts, showcasing my ability to:

• Solve Real-World Problems: Each project addresses genuine business or research questions
• Implement Best Practices: Following industry standards for code quality and documentation
• Communicate Insights: Clear visualizations and comprehensive analysis reports
• Continuous Learning: Incorporating latest techniques and technologies in data science

🙏 Acknowledgments

This portfolio has been developed through extensive learning and practice. I would like to acknowledge the valuable educational resources and inspiration from:

• Educational Platforms: Various online learning platforms that provided foundational knowledge in data science and machine learning
• Open Source Community: The incredible Python data science community for developing and maintaining the tools that make this work possible
• Industry Practitioners: Data scientists and researchers whose published work and methodologies have influenced my approach
• Academic Resources: Universities and educational institutions that provide high-quality data science curricula and research

While this portfolio represents my personal implementations and understanding, the knowledge has been built upon the collective wisdom of the data science community. All code implementations are my own work, created to demonstrate understanding and practical application of data science concepts.

📞 Contact

Mohammad Sayem Chowdhury

• 📧 Email: m.sayem.c@gmail.com
• 💼 LinkedIn: Mohammad Sayem Chowdhury
• 🌐 Portfolio: Personal Website

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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"Data is the new oil, but analytics is the refinery." - Mohammad Sayem Chowdhury

Thank you for exploring my data analytics portfolio! Feel free to reach out for collaborations, discussions, or any questions about the projects showcased here.