Clustering validation with ROC Curves

Predict the winning probability of white player in a chess game on the basis of first move of white player and first move of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move

Supervised-ML---Logistic-Regression---Appointing-Attorney-or-not. EDA, Model Building, Model Predictions, Testing Model Accuracy, ROC Curve plotting and finding AUC value.

This project aim to predict the possibility of a hotel customer to cancel a booking he/she has made. I was using booking data from a hotel in Portugal that is used to take insight and design a simple machine learning model.

Credit risk is the borrower’s inability to repay a loan. Machine Learning models can predict risky customers and reduce lender losses. By analyzing behavior and demographics of past customers, these insights can apply to future customers for better loan decisions. This study aims to find the most suitable model for predicting loan defaults.

The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.

This research work summarized different machine learning algorithms to create models for predicting diabetes patients utilizing the Diabetes Dataset (PIDD) from the UCI repository. The classifiers were K-Nearest Neighbors, Naïve Bayes, Support Vector, Decision Tree, Random Forest, Logistic Regression and Ensemble Model using a voting classifier.

I build a model which can predict whether a user will re-listen to that song by evaluating given features of the user and songs.Our business objective is to recommend song which user already listen in past but user like to re-listen that song.

This is a binary classification problem. Given the data, one needs to predict that if a user is going to click an advertisement or not.