https://github.com/codewithdark-git/ai-ml-internship
This repository contains four diverse AI/ML projects designed to provide hands-on experience in anomaly detection, multi-label classification, satellite image analysis, and credit risk assessment. These tasks focus on real-world datasets and cutting-edge machine learning techniques.
https://github.com/codewithdark-git/ai-ml-internship
Last synced: 7 months ago
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This repository contains four diverse AI/ML projects designed to provide hands-on experience in anomaly detection, multi-label classification, satellite image analysis, and credit risk assessment. These tasks focus on real-world datasets and cutting-edge machine learning techniques.
- Host: GitHub
- URL: https://github.com/codewithdark-git/ai-ml-internship
- Owner: codewithdark-git
- License: mit
- Created: 2025-01-25T04:08:27.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-09T19:17:12.000Z (about 1 year ago)
- Last Synced: 2025-02-09T20:25:13.794Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 395 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **AI/ML Engineering Internship Projects**
## **Overview**
This repository contains four diverse AI/ML projects designed to provide hands-on experience in anomaly detection, multi-label classification, satellite image analysis, and credit risk assessment. These tasks focus on real-world datasets and cutting-edge machine learning techniques.
---
## **Table of Contents**
- [**AI/ML Engineering Internship Projects**](#aiml-engineering-internship-projects)
- [**Overview**](#overview)
- [**Table of Contents**](#table-of-contents)
- [**Project Details**](#project-details)
- [**Task 1: Financial Time-Series Anomaly Detection**](#task-1-financial-time-series-anomaly-detection)
- [**Task 2: Multi-Label Emotion Recognition from Text**](#task-2-multi-label-emotion-recognition-from-text)
- [**Task 3: Satellite Image Analysis for Deforestation Monitoring**](#task-3-satellite-image-analysis-for-deforestation-monitoring)
- [**Task 4: Credit Risk Analysis**](#task-4-credit-risk-analysis)
- [**Technologies Used**](#technologies-used)
- [**Installation and Setup**](#installation-and-setup)
- [**Contributing**](#contributing)
- [**License**](#license)
---
## **Project Details**
### **Task 1: Financial Time-Series Anomaly Detection**
- **Objective**: Detect anomalies in stock price trends to identify unusual market activity or potential manipulations.
- **Dataset**: Yahoo Finance Stock Market Dataset.
- **Approach**:
1. Preprocess historical stock price data and calculate financial indicators (SMA, EMA, RSI, Bollinger Bands).
2. Use **Isolation Forest** or **DBSCAN** for anomaly detection.
3. Build a time-series forecasting model (LSTM/Prophet) to identify deviations.
4. Visualize anomalies on stock price trends.
- **Outcome**: A tool that highlights anomalies in stock prices for better market insights.
---
### **Task 2: Multi-Label Emotion Recognition from Text**
- **Objective**: Classify multiple emotions (e.g., joy, sadness, anger) from text data.
- **Dataset**: [GoEmotions Dataset by Google](https://github.com/google-research/goemotions).
- **Approach**:
1. Preprocess the dataset and handle imbalances.
2. Fine-tune a transformer model like **BERT** for multi-label classification.
3. Evaluate the model using metrics like Hamming Loss and F1 Score.
4. Test on real-world textual data (e.g., customer feedback or social media posts).
- **Outcome**: A robust emotion detection system for text analysis applications.
---
### **Task 3: Satellite Image Analysis for Deforestation Monitoring**
- **Objective**: Detect and monitor deforestation using satellite imagery.
- **Dataset**: [Planet: Understanding the Amazon from Space](https://www.kaggle.com/competitions/planet-understanding-the-amazon-from-space).
- **Approach**:
1. Preprocess satellite images for land cover analysis.
2. Train **CNNs** for land classification.
3. Perform change detection using sequential images to identify deforestation areas.
4. Visualize environmental changes over time.
- **Outcome**: A system that identifies and monitors deforestation trends.
---
### **Task 4: Credit Risk Analysis**
- **Objective**: Predict creditworthiness and flag high-risk customers for financial institutions.
- **Dataset**: [Give Me Some Credit Dataset](https://www.kaggle.com/c/GiveMeSomeCredit).
- **Approach**:
1. Preprocess the dataset, handling missing values and imbalances (e.g., SMOTE).
2. Engineer features like income, debt, and repayment history.
3. Train models (Random Forest, Gradient Boosting, XGBoost) and evaluate using precision and recall metrics.
4. Optimize models to reduce default rates and flag high-risk customers.
- **Outcome**: A credit risk assessment tool to assist financial decision-making.
---
## **Technologies Used**
- **Python**: Programming language.
- **Libraries**:
- **Data Preprocessing**: `pandas`, `numpy`, `scikit-learn`.
- **Modeling and Training**: `TensorFlow`, `PyTorch`, `transformers`, `xgboost`, `imbalanced-learn`.
- **Visualization**: `matplotlib`, `seaborn`, `plotly`.
- **Specialized Libraries**: `ta-lib`, `rasterio`, `prophet`.
---
## **Installation and Setup**
1. Clone this repository:
```bash
git clone https://github.com/codewithdark-git/AI-ML-Internship.git
cd AI-ML-Internship
```
2. Install additional libraries if needed for specific tasks.
---
## **Contributing**
Contributions are welcome! Please create a pull request or submit an issue if you encounter bugs or have suggestions for improvements.
---
## **License**
This project is licensed under the [MIT License](LICENSE).