Predictive Modelling with Data

Baseline Experiments: Train baseline models (e.g., Logistic Regression, Decision Trees) and evaluate performance.
Model Training and Optimization: Experiment with various ML models (Random Forest, XGBoost, SVM, ANN) and perform hyperparameter tuning.
Feature Analysis: Identify key features using advanced techniques (e.g., SHAP, RFE).
Class Imbalance Handling: Experiment with SMOTE or class-weighted models.
Performance Evaluation: Optimize models using metrics like precision, recall, F1-score, and AUC-ROC.
Validation: Use cross-validation and threshold adjustment for robustness.
Documentation: Log all experiments, ensure reproducibility, and provide visualizations (e.g., confusion matrices, ROC curves, feature importance plots).
Requirements:

Expertise in machine learning algorithms and tools (e.g., Python, scikit-learn, TensorFlow).
Experience with data preprocessing and imbalanced datasets.
Proven track record of working on high-quality projects.
Deliverables:

Comprehensive experimental results and comparisons.
Well-documented methodology and logs.
Visualizations and insights for publication.