My Master Degree Project

To predict which customer is most likely to convert

This project demonstrates how to build a logistic regression model and understanding the evaluation metrics to determine if the model is performing well or not

This Prediction is a research analysis process on data using classification algorithms to compare the accuracy rate for each algorithm given below on this Monkey Pox data such as ( K-Neighbors Classifier, RandomForest Classifier, AdaBoost Classifier, Bagging Classifier, Gradient Boosting Classifier, Decision Tree Classifier )

💉 Vaccine Sentiment Classifier is a deep learning classifier trained on real world twitter data, that distinguishes 3 types of tweets: Neutral, Anti-vax & Pro-vax.

ROC visualization tool for multi-class data

Prediction of hospital length of stay at time of admission using support vector classifier

Credit Card Fraud Detection algorithm using smote , confusion matrix, correlation matrix, density plots and ROC-AUC curve . Model - Logistic Regression, Knn, Isolation Forest

Classification Model (End to End Classification of Heart Disease - UCI Data Set)

Clustering validation with ROC Curves

This project aims to create a classifier that can predict whether or not a candidate will change their job by using historical data on candidate demographics and details about their education and experience using different classification machine learning models.

Data Science : Youtube Vídeo Recommender on Python with youtube-dl. Predictive models with: DecisionTreeClassifier, RandomForestClassifier, TfidfVectorizer.

Maximizing Bank's Profitability

Naive Bayes, Confusion Matrix, and ROC Analysis were conducted using R to determine how different variables lead to a customer of a bank taking out a personal bank loan.

Built a logistic regression model and a classification tree model for predicting the final status of a loan based on various variables available. Confusion matrix and misclassification rate for each model for a test dataset. Variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four mo…

Projet de modélisation supervisée - SCORING

Wolfram Language (aka Mathematica) paclet for Receiver Operation Characteristic (ROC) functions.

Analyse the factors that lead to employee attrition at IBM.

Predicting success of Bank Telemarketing Calls: Implementing and Comparing various ML classification algorithms