ChaitanyaC22/Fraud_Analytics_Credit_Card_Fraud_Detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

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paocarvajal1912/Supervised_Credit_Risk_Classification

Uses Logistic Regression and various machine learning techniques to train and evaluate models with imbalanced classes applied to identify the creditworthiness of borrowers.

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blleshi/Credit_Risk_Classification

Credit Risk Classification

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bhaskrr/restaurant-reviews-5-class-rating-prediction-

This repo contains the dataset and notebook for the kaggle restaurant reviews five class rating prediction

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Tobi1018/Credit_Risk_Analysis

Analyze several machine learning algorithms to predict credit risk.

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bhaskrr/amazon-books-4-class-rating-prediction

This repository holds the dataset and notebooks for the Amazon Books dataset 4 class Rating prediction

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DangCoop/Credit_Risk_Analysis

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MiracleOny/Credit-Risk-Analysis

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

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abdoghareeb46/NTI-Final-Assignment

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

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sjwedlund/Credit_Risk_Analysis

Apply machine learning to solve the challenge of credit risk

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caseygomez/Credit_Risk_Analysis

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

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Dhrumil-Zion/Sentiments-Prediction-Using-NLP

Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. I have found out the top 20 positive and negative feedback words from thousands how feedbacks. Also after processing this much I have automated the whole process with one function so that it can be used as generic for many machine learning algorithms. I have also tested another algorithm called DummyClassifier which gives an accuracy of around 84%. After that, I have executed the famous algorithm which is TF-IDF for NLP. I have combined TF-IDF with LogisticRegression which gives almost 93% accuracy but deep insights. Also, while working with data has solved the problem of imbalanced data through RandomOverSampler class from imblearn library.

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weihaolun/Credit_Risk_Analysis

Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.

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anjalysam/Health-Insurance-Cross-Sell-Prediction

Predict Health Insurance Owners' who will be interested in Vehicle Insurance

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abhijha3011/Techniques-To-Handle-Imbalanced-Data

Different Techniques to Handle Imbalanced Data Set

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Priya2216/Insurance_Claim_Prediction-Neural-Network

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Priya2216/DataScience-MachineLearning-Project

Data Science Major Project Completed in IT Vedant Institute using Machine learning algorithms

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helenaschatz/credit-risk-classification

Using various techniques to train and evaluate a model based on loan risk. Also, using a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

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Gsilvera24/Credit-Risk-Analysis

Credit Worthyness Analysis using Linear Regression

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NeonOstrich/Autism-Diagnosis-with-Linear-Regression-and-Neural-Networks-using-Random-Oversampling

We used a dataset that included birth and personal data as well as Autism Spectrum Quotient test scores to train machine learning algorithms to predict autism. We used Logistic Regression, Neural Network Models and Keras Tuner with Random Oversampling to train one with 90% accuracy.

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rutujahingankar/Health-insurance-cross-sell-prediction

Predict Health Insurance Owners who will be interested in Vehicle Insurance

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ashwinihegde28/Credit_Risk_Analysis

Credit_Risk_Analysis using Machine Learning

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jbalooshie/Credit_Risk_Analysis

Testing various supervised machine learning models to predict a loan applicant's credit risk.

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lingumd/Credit_Risk_Analysis

Machine learning models for predicting credit risk in LendingClub dataset.

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twigikit/regression-modelling

Logistic regression model with train_test_split data

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AlexGeiger1/Credit_Risk_Analysis

Python and sklearn are used to build and evaluate multiple machine learning models to predict credit risk.

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diercz/Credit_Risk_Analysis

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

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SohrabRezaei/Credit-Risk-Analysis

I am asked to resample the credit card data since it is not balanced. First, I start to split the data and perform oversampling with RandomOverSampler and SMOTE method, and I undersample with ClusterCentroids algorithm. Then, I utilize the SMOTEENN method to oversample and undersample the data. Finally, I used ensemble models.

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JennyJohnson78/Credit_Risk_Analysis

Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.

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Malvi1497/Credit_Risk_Analysis

To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.

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cedoula/Credit_Risk_Analysis

Build and evaluate several machine learning algorithms to predict credit risk.

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enj657/Credit_Risk_Analysis

Built and evaluated several machine learning algorithms to predict credit risk.

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Sanushi-Salgado/Tumor-Teller-Prediction-Module

Prediction module for Tumor Teller - primary tumor prediction system

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shaunwang1350/CreditLoans_MachineLearning

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 performance of these models and made a recommendation on whether they should be used to predict credit risk.

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nicoserrano/Credit_Risk_Analysis

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

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