🔬 AI Project Portfolio | NLP, Machine Learning, Computer Vision, Finance & Climate Change

Table of contents

• Background
• Domains & Projects
• Pipeline Examples
• Key Notebooks
• Datasets
• Technologies
• Getting Started
• Top-directory layout
• License
• Author

Background

This repository gathers a wide range of AI projects developed to address real-world problems through data-centric approaches and modern machine learning techniques. The projects span five major domains: Natural Language Processing (NLP), Machine Learning (ML), Computer Vision (CV), Finance, and Climate Change. The objective is to demonstrate practical solutions, predictive modeling, and intelligent systems using real-world datasets and state-of-the-art algorithms.

Domains & Projects

🧠 Natural Language Processing (NLP)

• Text classification, sentiment analysis, and topic modeling
• Named Entity Recognition (NER)
• Transformer-based models (BERT, GPT) for multilingual tasks

🤖 Machine Learning

• Supervised/unsupervised pipelines
• Model evaluation & interpretability
• Feature engineering, hyperparameter tuning
• Feature selection

👁️ Computer Vision

• Object detection (YOLOv5, SSD)
• Image classification with CNNs
• Image segmentation and real-time inference

💹 Finance

• Credit risk modeling: PD, LGD, EAD, EL
• Time series analysis and stock prediction
• Portfolio optimization and financial dashboards

🌍 Climate Change

• Rainfall prediction from satellite data
• Air quality forecasting and anomaly detection
• Spatiotemporal analysis of climate variables

Pipeline Examples

Here’s a typical pipeline structure used across projects:

Key Notebooks

Each domain contains Jupyter Notebooks illustrating:

• Data preprocessing and cleaning
• Model training, validation, and tuning
• Visualizations and interpretability
• Evaluation metrics and reporting

Datasets

Projects use open-source datasets from platforms such as:

• Kaggle
• UCI Machine Learning Repository
• NOAA (climate)
• Quandl (finance)
• Custom datasets collected or simulated for specific use-cases

Technologies

Project is built using:

• Languages: Python 3.8+
• Core Libraries:
  • scikit-learn, pandas, NumPy, matplotlib, seaborn
  • TensorFlow, PyTorch, XGBoost
  • Transformers, spaCy, NLTK
  • OpenCV, YOLO, fastai, Detectron2
• Tools: Jupyter Notebook, VSCode, Git, Docker, Streamlit
• Easily deployable via Google Colab

Getting Started

1. Clone the repository:

   git clone https://github.com/YOUR-USERNAME/YOUR-REPO-NAME.git
   cd YOUR-REPO-NAME

2. Install dependencies:

 ```sh
 pip install -r requirements.txt

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3. Explore the domain-specific folders and run the notebooks.

Top-directory layout

.
├── climate/         # Projects related to climate and environment
├── computer-vision/        # Image/video analysis projects
├── finance/                # Financial analytics and modeling
├── machine-learning/       # General ML projects (regression/classification/clustering)
├── nlp/                    # Natural Language Processing
├── requirements.txt        # Python dependencies
├── README.md               
└── LICENSE

License

Distributed under the MIT License. See LICENSE file for more information.

Author

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Made with ❤️ by Melvin FOKAM

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