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Pradnya1208 / Telecom-Customer-Churn-prediction

Star 91

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

python machine-learning numpy sklearn plotly cross-validation pandas classification feature-engineering model-evaluation classification-algorithm roc-auc gridsearchcv customer-churn-prediction customer-churn-analysis
  • Updated Dec 29, 2021
  • Jupyter Notebook
easytorch

sraashis / easytorch

Star 30

EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.

deep-learning pytorch convolutional-neural-networks transfer-learning roc-auc fundus-image-analysis deep-learning-for-computer-vision k-fold-cross-validation pytorch-for-medical-images getting-started-with-pytorch deep-learning-example pytorch-google-colab fast-pytorch-training
  • Updated Dec 6, 2023
  • Python

sharmaroshan / Online-Shoppers-Purchasing-Intention

Star 17

In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

machine-learning tutorial cluster cross-validation eda data-visualization pca data-analysis support-vector-machines random-forest-classifier bar-plot hyper-parameter-tuning box-plot roc-auc violinplot distribution-plot
  • Updated Jun 5, 2019
  • Jupyter Notebook

mcabinaya / Bank-Marketing-Data-Analysis

Star 15

Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data

python random-forest pandas-dataframe histogram cross-validation data-visualization naive-bayes-classifier dimensionality-reduction logistic-regression matplotlib missing-data data-preprocessing class-imbalance svm-classifier multilayer-perceptron categorical-data roc-auc knn-classifier bank-marketing-analysis sklearn-library
  • Updated Jun 5, 2018
  • Python

aimaster-dev / default_loan_prediction

Star 12

This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

flask finance automation vuejs sklearn fintech xgboost classification lightgbm banking-applications fraud-detection roc-auc credit-risk loan-prediction-analysis shap explainability model-interpretability loan-prediction
  • Updated May 29, 2025
  • JavaScript

Pegah-Ardehkhani / Customer-Churn-Prediction-and-Analysis

Star 11

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

python data-science machine-learning telco sklearn machine-learning-algorithms plotly cross-validation classification accuracy feature-engineering churn-prediction imbalanced-data classification-algorithm feature-importance roc-auc gridsearchcv customer-churn-prediction customer-churn customer-churn-analysis
  • Updated Jan 5, 2023
  • Jupyter Notebook

AmirhosseinHonardoust / Customer-Churn-Prediction

Star 6

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

python data-science machine-learning random-forest classification logistic-regression predictive-modeling gradient-boosting portfolio-project feature-importance roc-auc customer-analytics precision-recall customer-churn
  • Updated Sep 11, 2025
  • Python

nilsdenter / text_based_link_prediction

Star 4

This project contains the data and code used in the paper: Denter, Nils M.; Aaldering, Lukas Jan; Caferoglu, Huseyin (2022): Forecasting future bigrams and promising patents: Introducing text-based link prediction. In Foresight ahead-of-print (ahead-of-print). DOI: doi.org/10.1108/fs-03-2021-0078.

machine-learning random-forest text scikit-learn economics networkx naive-bayes-classifier ensemble-learning logistic-regression patents uspto decision-tree link-prediction stochastic-gradient-descent linkprediction k-nearest-neighbors roc-auc poisson-regression
  • Updated Jul 11, 2022
  • Python

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