Tool for whitebox (binning + logreg) model development

Basic Fast API with Tf-Idf & logreg Sklearn model

Cancer risk classification pipeline: leakage detection, Stratified CV (macro-F1), model benchmarking (LogReg/RF/Calibrated SVM/XGBoost), and interpretability (permutation importance).

12h clinical deterioration early-warning baseline: tabular models (LogReg / HistGradientBoosting / optional XGBoost), simple probability ensemble, and cost-based threshold/policy tables exported for dashboards.

Machine learning model (LogReg) to predict the success rate of SME-education collaborations for MKB Werkplaats. Includes a local prediction tool/web app.

End-to-end P300 EEG classification pipeline including preprocessing, feature extraction, cross-validated modeling (LDA/LogReg), and visualizations. Built for demonstrating reproducible research and data analysis workflow.

End-to-end data science project predicting clinical trial completion using AACT data. Includes EDA, feature engineering, ML models (LogReg, XGBoost), SHAP interpretability, and Streamlit deployment.

Machine learning pipeline for ECG arrhythmia detection. Supports PTB-XL, PTB-DB, Chapman-Shaoxing & UCI datasets. Includes preprocessing, feature extraction, RFE, SMOTE, and models (LogReg, RF, XGBoost) with external validation.

AI-Driven Banking Insights - Banking Predictive Analytics: ML classification system predicting term deposit sign-ups. Features EDA, model comparison (LogReg 80.9% AUC), and deployment-ready pipeline. Built with Python/Sklearn.

ML model using logistic regression and python

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Identify fraud in Kaggle creditcard dataset.

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Classification models (LogReg, Decision Tree, RF) to predict term deposit campaign responses.

Predicting customer churn using behavioral data, ML models (LogReg, XGBoost), SHAP insights, and counterfactual simulations

Text Mining with Preprocessing Scheme, Lemmatization, TF-IDF, Using Model Machine Learning RF, KNN, LOGREG & Decision Tree

It is a project using pipline in which there is an implementation of LogReg and KNN models.

TF-IDF + LogReg/SVM/RNN/LSTM for CompLing classification; gensim LDA for topic modelling on arXiv CS.

Predicting loan default risk using machine learning on credit data. Includes data cleaning, EDA, class imbalance handling (SMOTE), and model evaluation (LogReg, Random Forest).

A full end-to-end Machine Learning project with ✓ Data Exploration ✓ Model Training (LogReg, RF, XGBoost) ✓ Threshold Optimization ✓ SHAP Explainability ✓ Professional Notebook Architecture

End-to-end credit default prediction focusing on imbalanced classification. Features behavioral feature engineering, threshold tuning, and model comparison (LogReg vs. GBM) evaluated on ROC-AUC.

Utilized pipelines and grid searches to build multiple models (LogReg/KNN/RandomForest) and select the best performing one for predicting whether a person was vaccinated or not

NLP-based ML pipeline classifying tweets into thematic categories. Includes preprocessing, TF-IDF vectorisation, model training (SVM/LogReg), evaluation metrics, confusion matrix, and reproducible research notebooks.

Four classification models (CART, C5.0, LogReg, and Random Forest) are built and compared in RStudio to predict student dropout rates based on demographic, macroeconomic, and socioeconomic factors.

End-to-end project for detecting insurance fraud using machine learning. Includes data preprocessing, model training (LogReg, RF, XGBoost), evaluation, and deployment via Flask web app. Model is serialized with pickle for real-time predictions.

Classifies AG News articles into World, Sports, Business, and Sci/Tech using TF-IDF and ML models (LogReg, RF, SVM). Includes preprocessing, prediction, and word cloud visualization. Built with Elevvo Pathways.

Classical ML network intrusion detection on NSL-KDD (KDDTrain+). Reproducible notebook with EDA, correlation analysis, robust preprocessing (log1p, scaling, one-hot), and tuned models (LogReg, DecisionTree, KNN, RandomForest). Metrics, ROC curves, and feature importance included.

Python codes (class notes and assignments) on Jupyter Lab with comments in "LogReg, KNN, SupportVectorMachines, Decision Trees, Random Forest, Bagging, Boosting, GradientBoost, Xboost, Stacking, model interpretation, imbalance tackling" for data sets such as food, smartphone, cancer, customer churn

Propensity & uplift modeling to grow caravan insurance via targeted cross-sell. Clean data, engineer features, handle imbalance, train/calibrate (LogReg, GBM, CatBoost), evaluate AUC/PR, Lift@K, Profit@Budget. Deliver ranked target list, reason codes, profit curves, and a reproducible scoring pipeline (R).