smote-oversampler

loan-default

Analytics-and-Data-mining

Credit_Risk_Analysis

CreditCard_Fraud_Detection_UsingML

Detect Fraudulent Credit Card transactions using different Machine Learning models

Battery-Degradation-Analysis-Project-code

Battery analysis project

Neural_Network_Charity_Analysis

A Deep Learning analysis to predict success of charity campaigns

Credit_Risk_Analysis

Supervised Machine Learning Project

Credit_Risk_Analysis

using machine learning to assess credit risk

Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

Credit-Risk-Modelling

Built a model to determine the risk associated with extending credit to a borrower. Performed Univariate and Bivariate exploration using various methods such as pair-plot and heatmap to detect outliers and to monitor the behaviour and correlation of the features. Imputed the missing values using KNN Imputer and implemented SMOTE to address the imbalanced data. Trained the model using KNN, Decision Trees, Logistic Regression and Random Forest to achieve the best accuracy of 93%.

Credit_Risk_Analysis

Data preparation, Statistical reasoning, Machine Learning

Credit_Risk_Analysis

Creating various machine learning models to create the most accurate model to predict credit risk

Credit_Risk_Analysis

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

credit-risk-predictor

Uses several machine learning models to predict credit risk.

Heart_Disease_Prediction

Predict heart disease classification problem

imbalanced_data

This notebook will walk you through the steps for dealing with an imbalanced dataset using an example of a real project that I recently completed.

Credit_Risk_Analysis

Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn

HeartDisease-Analysis-and-Prediction

Data analysis, visualization and prediction for the prevention of heart disease

Risky_Business

Credit Risk Analysis utilizing imbalanced classification machine learning models

Comparative-Principal-Component-Analysis

In this analysis, I will demonstrate how PCA works in different tasks and how much time and resources we save in our daily analysis.

Travel-Customer-Churn-with-Oversampling

The aim of this post is to identify and visualize factors that contribute to customer churn of a travel company.

Solution-for-HackerEarth-Machine-Learning-challenge

HackerEarth Machine Learning challenge: Of Genomes And Genetics

Credit_Risk_Analysis

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

Credit_Risk_Analysis

The objective of this analysis was to use machine learning models to accurately predict credit risk.

Credit_Risk_Analysis

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

Credit_Risk_Analysis

This project uses different techniques to train and evaluate models with unbalanced classes using credit card dataset to predict low-risk and high-risk credit cards.

Credit_Risk_Analysis

Extract data provided by lending club, and transform it to be useable by predictive models.

lyrics_classifier

This project uses web scraping to download song text and uses Natural Language Processing (NLP) to predict an artist based a line of song text

Risky_Business

Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appears to be the model of best fit for the given data.