Investigating Population Diversity, Sex Ratio Dynamics, and Resource Utilization: Insights into Lamprey Species within the Multispecies Ecosystem of the Great Lakes

Data analysis project on Titanic database using Python

Data analysis project on an e-mail database using Python

Survival probability of burn injury patients based on age, sex, race, hospital facility, and other significant facility nearing respondents.

Exploring of Diverse Plant Communities and Adaptation to Drought Conditions Based on Advanced Logistics Model with Variable Growth

Create, deploy and interact with a Machine Learning model in real time. Project involves training a Logistics Regression model for a binary classification task and deploying the trained model in a webserver to obtain real-time predictions via rest API using a simple web page

Efficient Modeling and Prediction of E-commerce Recommendation System: A Case Study on Amazon-Product Review Dataset

Python Pandas Program That Predicts Passengers That Survived The Titanic Sinking

Bank Customer Chun Rate

Analyzed the effectiveness of COVID vaccine on 8 different age groups and trained 5 classification machine learning models to check whether the the vaccine developed immunity in 100k people from an age group or not.

This repository contains all my learned and practiced Machine learning algorithms and data science approaches. Topics: *Banknote authentication model *Classification of iris by petal dimensions *Analysing credit risk by decision tree model *Fitbit data analysis *Prediction of housing price through linear regression

Using labelled classifed data to infer a learning algorithm in R

Project for predicting Heart_Disease using Logistics Regression

Applying different algorithms (RandomForest, K-NearestNeighbors and LogisticRegression) to predict Attrition for a fictional company.

Participate in a Kaggle-like machine learning competition and submit the model's predictions to your teacher/TA to be evaluated in an independent way

Applying different algorithms (RandomForest, K-NearestNeighbors and LogisticRegression) to predict target and compare their results.

Credit card Fraud Transaction Detection using various machine learning algorithm.

Delinquency Model to predict the loan repayment using Machine Learning. Visit https://yesdeepakmittal.github.io/delinquencymodel/

The following is one of the assignments for the Data Mining class that I took on the 6th semester. I was assigned to classify sanitation data in Surabaya using Logistics Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, and Support Vector Machine.