Project Title: Hybrid Machine Learning Model Development

???? Objective:

To build a hybrid machine learning model that combines multiple algorithms or techniques to improve prediction accuracy, robustness, or generalizability for a specific task (e.g., fraud detection, sentiment analysis, medical diagnosis).

???? Core Components:

Problem Selection:

Choose a complex task where single models underperform.

Examples: credit card fraud detection, customer churn prediction, fake news classification.

Data Collection & Preprocessing:

Use real-world datasets with noise or imbalance.

Clean data, handle nulls, encode categories, scale features.

Modeling Strategy:

Combine models in one of these hybrid methods:

Ensemble Methods: Bagging (Random Forest), Boosting (XGBoost, LightGBM), Stacking (meta-model combines predictions from base models).

Hybrid Deep Learning + ML: Use LSTM or CNN for feature extraction and feed outputs to ML classifiers.

Rule-Based + ML: Combine business rules with predictive models.

Implementation Steps:

Train multiple base models (e.g., SVM, Logistic Regression, Random Forest).

Use stacking/blending to combine their outputs using a meta-learner.

Optional: Neural network layers to integrate features from different sources (text + structured data).

Model Evaluation:

Metrics: Accuracy, AUC-ROC, Precision, Recall, F1-Score.

Use cross-validation and compare against individual base models.

Explainability:

Apply SHAP, LIME, or feature importance tools to explain model predictions.

Deployment (Optional):

Export model pipeline using joblib.

Build a simple API or interface using Flask or Streamlit.

???? Tools & Libraries:

Python, Scikit-learn, XGBoost, LightGBM

TensorFlow or PyTorch (if using neural networks)

SHAP, LIME (for interpretability)

Streamlit/Flask (for deployment)