INSAID Assignment to create a ML model to detect fraud transactions for a financial company.

Analytical understanding and applying parameter optimization, regression with gradient descent to predict water quality levels across Indian waters.

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

We build a chatbot by implementing machine learning and natural language processing.

Credito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.

[CIKM 2021] Code and dataset for "Label-informed Graph Structure Learning for Node Classification"

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

Project is about predicting Class Of Beans using Supervised Learning Models

Feature Engineering with Python

WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.

This repository covers my code using regression models to predict if a customer would be exiting a bank or not. It also capture the use classification models to classify if a customer has left the bank or not (binary classification).

This is an implementation for a DataCamp project: A Visual History of Nobel Prize Laureates. We try to answer the proposed questions and visualize the results.

Unofficial but extremely useful Label and One Hot encoders.

the code uses KNN, Gaussian Naive Bayes & SVM to classify images. It preprocesses, normalizes data, applies PCA , computes accuracy, precision etc. It evaluates k-NN using Euclidean distance & cosine similarity, visualizing results with line plots, 3D scatter plots, & confusion matrices to demonstrate classifier performance.

Database management and data analytics from a car-sharing dataset. The dataset contains information about the customers' demand rate between January 2017 and August 2018.

Repo houses the predictive NN model and its associated .py modules

Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features

This sentiment analysis model utilizes a Transformer architecture to classify text sentiment into positive, negative, or neutral categories with high accuracy. It preprocesses text data, trains the model on the IMDB dataset, and effectively predicts sentiment based on user input.

This classification task is specifically dependent on a video dataset that includes video clips of kill and death scenes from the first-person shooting game “CS Go”. I have used the ResNet-50 model for image classification and then turn it into a more accurate video classifier by employing the rolling averaging method.