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.

Prediction module for Tumor Teller - primary tumor prediction system

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

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…

Different Techniques to Handle Imbalanced Data Set

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.

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

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. …

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

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

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

Apply machine learning to solve the challenge of credit risk

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

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

Analyze several machine learning algorithms to predict credit risk.

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

Built and evaluated variety of supervised machine learning algorithms to predict credit risk.

Use different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes

Train and evaluate models to determine credit card risk using a credit card dataset

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