Fake News analysis and prediction in R Script. Naive Bayes, Random Forest, SVM, NNET, ROC, Confusion Matrix, Accuracy, F1 score.

Employee attrition prediction with an interactive environment

The objective of this project is to predict the probability of loanee or borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date.

Identify and classify toxic online comments using NLP and Machine-Learning algorithms

ROC curve for any dataset, with 8 classification Algorithm in One.

Drawing Roc Curve

The code loops through the list of models and plots respective roc curves in a single plot

EC60091-Machine Intelligence & Expertise Systems Course,Autumn-2019

This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)

Classification using KNN

Here we will be firstly analysing the how different threshold values effect the area under the Curve in a Receiver Operating charcteristic(ROC) curve. And at last we will show how to define a function in python to calculate the most optimal threshold value for the logistic Regression.

Strategizing to maximize Customer Retention in Telecom Company

Insurance Cross Sell Opportunity Forecast through machine learning algorithm

Work from various classes

Explorartory Data Analysis with visualization. Use the supervised machine learning models to predict the customer segmentation. Use unsupervised learning model K Nearest Neighbors to create new clusters. Create Personas of the new clusters.

Given files with sequences containing signal peptides and sequences not containing signal peptides, multiple classifiers were tested to see which performs best in the classification of signal peptide sequences.

The client, a financial service institution, want to increase revenue streams and intents to target a segment of their customers who are most likely to default on the loans/Credit taken. We try to solve this problem using Logistic Regression Model.

Predicting the likelihood of patient to be at the risk of a heart disease

Machine Learning Algorithms