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

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

Employee attrition prediction with an interactive environment

Drawing Roc Curve

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

Predict rain the next day using daily observations of weather aspects in Australia regions for 10 years

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%)

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.

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.

Strategizing to maximize Customer Retention in Telecom Company

Insurance Cross Sell Opportunity Forecast through machine learning algorithm

Work from various classes

Classification using KNN

This employee attrition analysis project for XYZ Company identifies key factors driving turnover, like compensation and growth. Using data science, we built a model to predict attrition risks and provide actionable insights to improve employee retention and workforce stability.

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.

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.