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

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

Developed Machine Learning Models to Predict Credit Risk

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

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

Different Techniques to Handle Imbalanced Data Set

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…

Predict Health Insurance Owners who will be interested in Vehicle Insurance

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

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.

Credit Worthyness Analysis using Linear Regression

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. …

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

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.

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

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

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