Supervised machine learning on lending and credit risk.
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Sep 5, 2020 - Jupyter Notebook
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balanced-accuracy-scores
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In this project, I developed a **binary classification model** to analyze clients’ past behavior and contract termination data to predict whether a customer is likely to leave the company.
data-science machine-learning numpy eda pandas class-imbalance roc-curve binary-classification churn-prediction imbalanced-data model-evaluation phyton random-forest-classifier roc-auc gridsearchcv xgboost-classifier lightgbm-classifier balanced-accuracy-scores model-preparation
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Apr 3, 2026 - Jupyter Notebook
This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.
ensemble-learning logistic-regression imbalanced-learning cluster-centroids imbalance-classification smoteenn smote-sampling balanced-accuracy-scores
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Mar 28, 2021 - Jupyter Notebook
Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.
machine-learning sklearn logistic-regression resampling predictive-modeling oversampling imbalanced-learning credit-risk loan-prediction-analysis classification-models imbalanced-classification smote-oversampler balanced-accuracy-scores undersampling-technique smoteenn-combination
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Jun 1, 2022 - Jupyter Notebook
Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the…
random-forest classification-report credit-risk cluster-centroids randomoversampler smote-sampling tp-fp balanced-accuracy-scores
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Nov 19, 2020 - Jupyter Notebook
Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con
python numpy scikit-learn sklearn pandas logistic-regression confusion-matrix classification-report imbalanced-learning imblearn train-test-split random-over-sampling balanced-accuracy-scores
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Oct 31, 2022 - Jupyter Notebook
Use Python to build and evaluate several machine learning models to predict credit risk for FinTech firms.
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Oct 19, 2020 - Jupyter Notebook
Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
python machine-learning python-library machine-learning-algorithms scikitlearn-machine-learning precision-recall-curve precision-recall imbalance-learning balanced-random-forest balanced-accuracy-scores
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Jan 4, 2023 - Jupyter Notebook
Performed feature engineering, cross-validation (5 fold) on baseline and cost-sensitive (accounting for class imbalance) Decision trees and Logistic Regression models and compared performance. Used appropriate performance metrics i.e., AUC ROC, Average Precision and Balanced Accuracy. Outperformed baseline model.
cross-validation performance-metrics logistic-regression decision-trees feature-engineering average-precision auc-roc-score balanced-accuracy-scores class-imbalance-problem
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Jul 24, 2023 - Jupyter Notebook
A predictive model to anticipate customer churn in telecom. Using supervised ML techniques, it identifies at-risk customers based on usage patterns and service plans. Proactively retaining customers, reducing attrition costs.
machine-learning class-imbalance decision-tree-classifier svm-classifier churn-prediction xgboost-classifier balanced-accuracy-scores
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Apr 9, 2024
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