GitHub topics: over-sampling
ChaitanyaC22/Telecom-Churn-Prediction
In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.
Language: Jupyter Notebook - Size: 27.7 MB - Last synced at: 26 days ago - Pushed at: almost 4 years ago - Stars: 5 - Forks: 0

nickkunz/smogn
Synthetic Minority Over-Sampling Technique for Regression
Language: Python - Size: 730 KB - Last synced at: 7 months ago - Pushed at: about 1 year ago - Stars: 308 - Forks: 76

baibai25/MNDO
Multivariate Normal Distribution based Oversampling
Language: Jupyter Notebook - Size: 65.4 KB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 0 - Forks: 1

sharmaroshan/Fraud-Detection-in-Online-Transactions
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
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hanfei1986/Oversampling-of-imbalanced-data-with-RandomOverSampler--SMOTE-and-ADASYN
Imbalanced data commonly exist in real world, especially in anomaly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. RandomOverSampler, SMOTE, and ADASYN are useful oversampling tools to fabricate data for minority classes and make the dataset balanced.
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M-Hashemzadeh/RCSMOTE
RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem
Size: 6.28 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 0

NeonOstrich/Credit-Risk-Classification-using-Logistic-Regression
Trained and evaluated two supervised machine learning models using original and resampled data to identify 'healthy loan' and 'high risk loan' applicants from financial disclosures.
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alicevillar/student_admission_prediction
Predicting students admission with Logistic Regression, Decision Tree, SVM (SVC) and Random Forest
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chihangs/diabetes_classification
Use random forest, gradient boosting, neural network, with SMOTE-ENN and random over-sampling
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cbrito3/Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
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baibai25/MNDO-NC
Multivariate Normal Distribution Based Over-Sampling for Numerical and Categorical Features
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abhiram-ds/credit_card_fraud_detection
Credit Card Fraud detection based on anonymized data using multiple classification algorithms
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jabhinav/Data-Science-and-ML-for-Structured-Data-Classification
Repo contains scripts to perform data analysis on structure data. It also provides a comparison of various ML algorithms at different stages of data preparation.
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JalajVora/Text-Analytics-with-Multi-Class-and-Imbalanced-Learning
Genre Identification task along with Text Analytics with Multi-Class and Imbalanced Learning on Gutenberg Corpus
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