https://github.com/ndomah/portfolio-projects

https://github.com/ndomah/portfolio-projects

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/ndomah/portfolio-projects
• Owner: ndomah
• Created: 2024-06-12T18:17:30.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-09-30T16:51:22.000Z (8 months ago)
• Last Synced: 2025-01-09T03:48:35.337Z (4 months ago)
• Language: Jupyter Notebook
• Size: 36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Portfolio-Projects

This repository contains my Data Analytics and Data Science portfolio projects to showcase my skills.

## Data Analytics

- [Employee Incentives and Wellness Analysis & Dashboard](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Analytics/Employee%20Incentives%20and%20Wellness%20Analysis%20%26%20Dashboard)

  - Tools Used: SQL, Power BI

- [Employee Retention Churn Model & Development](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Analytics/Employee%20Retention%20Churn%20Model%20%26%20Dashboard)

  - Tools Used: Google Cloud Platform (GCP), BigQuery, Google Colab, Pycaret, Looker Studio

- [Shopping Customer Segmentation & Clustering](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Analytics/Shopping%20Customer%20Segmentation%20%26%20Clustering)

  - Tools Used: Jupyter Notebook, Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn

- [World Bank Analytics & Dashboard](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Analytics/World%20Bank%20Analytics%20%26%20Dashboard)

  - Tools Used: Jupyter Notebook, Python, APIs, Tableau

## Data Science

- [Disease Classifier](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Disease%20Classifier)

  - Tools Used: Jupyter Notebook, Python, NumPy, Matplotlib, TensorFlow, Keras, FastAPI, Uvicorn, IO, Image, OS

- [IBM Capstone - Predicting SpaceX Falcon's First Stage Success](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Predicting%20SpaceX%20Falcon's%20First%20Stage%20Success%20(IBM%20Capstone))

  - Tools Used: Jupyter Notebook, Python, Pandas, Matplotlib, Seaborn, Folium, Plotly Dash, BeautifulSoup, APIs, Machine Learning (Classification), SQL, Microsoft PowerPoint

- [SQL Code Generator Web App](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/SQL%20Code%20Generator%20Web%20App)

  - Tools Used: Python, Google Gemini LLM & API, Streamlit

- [Software Developer Salary Prediction](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Software%20Developer%20Salary%20Prediction)

  - Tools Used: Jupyter Notebook, Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Machine Learning (Regression), Hyperparameter Tuning, Pickle, Streamlit

- [Taxi Time Series Analysis](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Taxi%20Time%20Series%20Analysis)

  - **Tools Used**:

    - Pandas for data manipulation and preprocessing.

    - Matplotlib and Seaborn for data visualization.

    - Statsmodels for statistical analysis and ARIMA modeling.

    - Fbprophet (Prophet) for advanced time series forecasting.

    - Scikit-learn for evaluation and performance metrics.

  -	**Jupyter Notebook**: For interactive analysis and visualization.

  -	**Data Source**: Public NYC taxi dataset.

- [Twitter Sentiment Analysis - Natural Language Processing](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Twitter%20Sentiment%20Analysis%20(Natural%20Language%20Processing))

  - **Libraries**: Scikit-learn, LightGBM, XGBoost, NLTK, TextBlob, Pandas, NumPy, Matplotlib

  - **Data Source**: Twitter Sentiment Analysis dataset from Kaggle

  - **Text Preprocessing**: TF-IDF Vectorizer, Count Vectorizer, NLTK stopwords, word stemming via TextBlob

  - **Machine Learning Models**: Logistic Regression, Gradient Boosting (LightGBM), XGBoost

- [Node Classification - Graph Neural Network](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Node%20Classification%20(Graph%20Neural%20Network))

  - **Libraries**: PyTorch Geometric, Torch, Scikit-learn

  - **Data Source**: Pubmed dataset from Planetoid

  - **Machine Learning Techniques**: Graph Neural Networks (GNNs), GraphSAGE algorithm

  - **Evaluation Metrics**: Accuracy, Cross-entropy loss

- [Image Classification - Computer Vision](https://github.com/ndomah/Portfolio-Projects/tree/main/Data%20Science/Image%20Classification%20(Computer%20Vision))

  -	**Framework**: PyTorch

  -	**Model**: ResNet-18 (pre-trained on ImageNet)

  -	**Libraries**: torchvision, cv2, matplotlib, PIL

  -	**Optimization**: Adam optimizer, Binary Cross-Entropy Loss

  -	**Data Processing**: Custom Dataset class, DataLoader

  -	**Deployment**: Google Colab for training