👩‍💻 Data Science Internship Projects (Developers Hub)

Welcome to the Data Science Internship Repository! This space is designed to help you gain real-world experience in applying machine learning and data science across multiple domains, such as HR analytics, NLP summarization, healthcare, and finance.

🧱 Key Components

Each task in this project includes:

• 📁 Dataset access (public or pre-downloaded)
• 📊 EDA & preprocessing notebooks
• 🧠 Model building and tuning
• 🧾 Explainability or interpretability techniques
• 📈 Performance metrics visualization
• 📋 Reports with actionable business/health insights

💡 Project Highlights

Area	Focus	Tools / Libraries
HR Analytics	Attrition prediction & SHAP analysis	pandas, sklearn, shap
NLP	Text summarization (extractive + abstractive)	spaCy, transformers, BERT/GPT
Healthcare	Disease classification & risk insights	XGBoost, seaborn, AUC-ROC
Finance	Loan default prediction + SMOTE	LightGBM, imbalanced-learn

🔍 Task-by-Task Breakdown

🧠 Task 1: Employee Attrition Prediction

Objective: Predict employee attrition and provide actionable retention strategies.

📦 Dataset: IBM HR Analytics Employee Dataset
📈 Algorithms: Random Forest
🔍 Explainability: SHAP
📊 Output: Interactive Dashboard / HR Insights Report

✅ Task Checklist

• Data Cleaning & EDA
• Model Training (RF)
• Model Explainability (SHAP, LIME)
• Insight Generation + Report

📰 Task 2: Text Summarization

Objective: Automatically summarize long texts using extractive and abstractive techniques.

📦 Dataset: CNN / Daily Mail
🧰 Tools: spaCy, HuggingFace Transformers (BERT, GPT)
📝 Output: Summary Generator + Model Evaluation

✅ Task Checklist

• Preprocessing (Tokenization, Cleaning)
• Extractive Summarization (spaCy)
• Abstractive Summarization (Transformers)
• Model Fine-Tuning
• Summary Evaluation Report

🩺 Task 3: Disease Diagnosis Prediction

Objective: Predict diabetes likelihood using medical features.

📦 Dataset: PIMA Indians Diabetes Dataset
📈 Models: Gradient Boosting
📊 Metrics: F1 Score, AUC-ROC
🧠 Output: Predictive model + Risk Profile Insights

✅ Task Checklist

• EDA & Feature Engineering
• Data Scaling
• Model Training & Evaluation
• AUC Curve + Feature Importance
• Healthcare Recommendations

💰 Task 4: Loan Default Prediction

Objective: Identify high-risk borrowers using financial data.

📦 Dataset: Lending Club Dataset
📈 Models: LightGBM, Logistic Regression
📊 Metrics: Precision, Recall, F1, AUC
📄 Output: Risk Classifier + Lender Report

✅ Task Checklist

• Data Preprocessing (Missing Values, Encoding)
• Handle Class Imbalance (SMOTE)
• Model Training (LightGBM)
• Evaluation & Confusion Matrix
• Risk Insights Dashboard

👥 Contributors

Project Lead: Rubaika Akhtar

Intern Team: Developers Hub