In this project, I worked on a classification problem using an imbalanced dataset which predicts ecological footprints. The aim of the project was not necessarily to build a classification model but to investigate the different methods of correcting an imbalanced dataset in order not to build a biased classifier

model is able to predict the type of Ultra or Smart plan that users need.

Different classification algorithms to determine whether or not an individual from the Pima group will have type 2 diabetes

Data Mining

Diabetes Classification with SVM and Random Forest Classifiers

Upsampling already available land cover raster layers using machine learning inside Google Earth Engine platform.

NLP Project using TF-IDF & RandomForestClassifier

ML model for rating prediction with NTLK and Scikit-learn.

Contribution to Titanic Project

Disaster Tweets Classifications by Machine Learning, which is a currently Kaggle Competition.

The goal is to find out the employees those who stay and those who leave the company in the upcoming year. Through the various process of selecting, manipulating, transforming data and build the ensemble models, we get a best accuracy for the employee turnover rate.

Create a model to predict if a customer will leave the bank.

Titanic Survival Prediction: Jupyter Notebook demonstrating Random Forest Classifier for survival prediction on Titanic dataset.

Machine Learning concepts and models like SMOTE, RandomForest Classifier, Decision Tree, K-NN, and Logistic Regression were first implemented without any ML libraries.

Evaluation of the Models (Regression and Classification)

Predicting Car prices using Kaggle's CarDekho Dataset

Different classification algorithms to predict the species of Iris flowers

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

How to predict tomorrow's S&P 500 index price using historical data

Predicting employee attrition using machine learning model