Credit Risk Analysis with Machine Learning
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A full ML pipeline for customer churn prediction in telecom, banking, or SaaS. Includes robust data cleaning, automatic feature engineering, model training/tuning (Logistic Regression, RF, XGBoost), interpretability, and interactive dashboards for actionable business retention insights.
python machine-learning random-forest scikit-learn pandas xgboost churn-prediction shap optuna lightbgm
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4 Boosting Algorithms You Should Know – GBM, XGBoost, LightGBM & CatBoost
data-science random-forest decision-trees boosting-algorithms gradient-boosting xgboost-algorithm lightbgm catboost-algorithm
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SaleFore AI: Ultra-accurate sales forecasting using ensemble ML (XGBoost, LightGBM, CatBoost) with RTX 4060 GPU optimization. Achieves 88-95% accuracy with advanced hyperparameter tuning.
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This project detects AI-generated text using an ensemble of classifiers: Multinomial Naive Bayes, Logistic Regression, LightGBM, and CatBoost. It includes robust data preprocessing, model development, and evaluation, ensuring accurate identification of AI-generated content from a diverse text dataset.
numpy tokenizer naive-bayes scikit-learn transformers pandas logistic-regression catboost sgd-classifier tfidf-vectorizer lightbgm
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Flask API for real-time intrusion detection using a threshold-optimized LightGBM model.
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predicting a continuous target (regression) based on number of anonymous features columns given in the data.
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Architected production ML platform: 420K+ retail records → XGBoost forecasting (99.82% R²) → DP inventory solver (O(n×W)) → FastAPI service (50 stores, 95% SLA). AWS deployment with PostgreSQL, Docker, 40+ unit tests, interactive dashboards. Custom
python git docker aws tensorflow numpy scikit-learn chartjs postgresql keras pandas pytest xgboost tableau mlflow fastapi lightbgm dsa-algorithm
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Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
graphviz supervised-learning logistic-regression decision-tree-classifier gradient-boosting-classifier random-forest-classifier ensemble-models bagging-trees lightbgm xgboost-classifier randomizedsearchcv adaboostclassifier
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All Possible Machine Learning algorithms implementation in jupyter notebook with csv file.
random-forest linear-regression machine-learning-algorithms jupyter-notebook naive-bayes-classifier logistic-regression handwritten-digit-recognition svm-classifier gradient-boosting id3-algorithm mini-project xgboost-algorithm knn-classifier lightbgm svm-mode cat-boost find-s-algorithm
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🔍 Predict customer churn with a machine learning system that identifies at-risk clients and recommends tailored retention strategies for better ROI.
python docker data-science machine-learning random-forest svm pandas saas xgboost softmax-regression churn-prediction smote mlops shap churn-analysis fastapi optuna lightbgm streamlit
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Machine Learning pipeline for financial transaction fraud detection. Incorporates SMOTE, ensemble models, neural networks.
python machine-learning data-mining kaggle neural-networks xgboost autoencoder ensemble-learning smote fraud-detection imbalanced-learning catboost lightbgm arizona-state-university financial-security
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Analytics Vidhya AmExpert ML Competition
python machine-learning cross-validation classification boosting analytics-vidhya-competition lightbgm
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AI loan underwriting platform for credit-invisible rural India. Docker microservices with FastAPI, LightGBM, 45-feature pipeline from SMS/contact/location data. 99% faster (14 days → 60s), 98% cheaper (₹450 → ₹8). Production-ready with PostgreSQL, Redis, Prometheus monitoring.
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Stellar objects classification from sensor data taken by the SDSS (Sloan Digital Sky Survey) using some basic machine learning models.
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Data Storm 1.0 - Business Problem - Team Fixzels
python data-science machine-learning neural-network jupyter random-forest numpy machine-learning-algorithms jupyter-notebook credit-card kaggle kaggle-competition colab business-solutions svm-classifier xgboost-algorithm knn-classifier lightbgm
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Este proyecto desarrolla un modelo de clasificación binaria cuyo objetivo es estimar la probabilidad de que un cliente realice al menos un pago, a partir de información financiera y comportamental consolidada a nivel cliente.
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