Credit Worthyness Analysis using Linear Regression

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.

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

This project trains and avaluates machine learning model to identify creditworthiness of borrowers and classify credit risk predictions for a peer-to-peer lending services company.

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.

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

Credit_Risk_Analysis using Machine Learning

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.

Credit Risk Classification

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

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

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.

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

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.

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

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

Analyze several machine learning algorithms to predict credit risk.