Unsa4

ABOUT-ME

APRIORI-ALGO-PYTHON

My first data mining algorithm implementation

Credit-Card-Fraud-Detection-Task5-

This was my Task5 during my internship at Codsoft. i used random forests classifier with accuracy of 94% for fraud detection.

DATA-ANALYSIS-UNSA

Fashion-Compatibilty

Fashion_Compatability

Used CNN and multi layered network in order to calculate the outfit compatabilty score as well as pair compatible clothing items in the dataset.

Healthcare-Translation-APP-using-Generative-AI

A web-based prototype that enables real-time, multilingual translation between patients and healthcare providers. This application should convert spoken input into text, provide a live transcript, and offer a translated version with audio playback.

Hibernate-App

A single entity application developed using hibernate that implements the concept of ORM in Java and the db i used is MySql.

Iris-Flower-Classification-Task3-

This was my Task3 during my internship at Codsoft. I Leveraged deep learning algorithms, including Neural Networks to create an accurate classification model. The activation function i used is sigmoid. The model achieved an impressive 90% accuracy on classifying iris flowers.

Laundry-MS-

I developed this project using springboot as backend, netbeans for frontend and MongoDB as a no sql db.

Unsa4/ABOUT-ME

Unsa4/APRIORI-ALGO-PYTHON

My first data mining algorithm implementation

Unsa4/Credit-Card-Fraud-Detection-Task5-

This was my Task5 during my internship at Codsoft. i used random forests classifier with accuracy of 94% for fraud detection.

Unsa4/DATA-ANALYSIS-UNSA

Unsa4/Fashion-Compatibilty

Unsa4/Fashion_Compatability

Used CNN and multi layered network in order to calculate the outfit compatabilty score as well as pair compatible clothing items in the dataset.

Unsa4/Hibernate-App

A single entity application developed using hibernate that implements the concept of ORM in Java and the db i used is MySql.

Unsa4/Iris-Flower-Classification-Task3-

This was my Task3 during my internship at Codsoft. I Leveraged deep learning algorithms, including Neural Networks to create an accurate classification model. The activation function i used is sigmoid. The model achieved an impressive 90% accuracy on classifying iris flowers.

Unsa4/Laundry-MS-

I developed this project using springboot as backend, netbeans for frontend and MongoDB as a no sql db.

Unsa4/LoginSignup-

Login Signup Pages using Java Swing and MySql DB.

Unsa4/Moving-Rating-Task2-

This was my Task 2 during my internship at Codsoft. I utilized state-of-the-art Decision Tree Classifier machine learning model for predictions. Achieved an impressive output {Mean Squared Error (MSE): 0.84 Mean absolute error: 0.6170794192997439 R2 score: 0.5464928722394091} on our predictions.

Unsa4/MVC-FRAMEWORK

My 2nd C# project using MVC architecture and ASP.NET framework

Unsa4/PYGAME-ALIEN-INVASION

My first project !

Unsa4/Recipe-to-Cuisine-Classification

Used Logistic Regression and some model fine tuning techniques in order to achieve maximum accuracy.

Unsa4/REST-API-using-Nodejs

This is my 5th semester project that i developed using HTML, CSS ,MONGO DB and Node JS.

Unsa4/Sales-Prediction-Task4-

This was my Task4 during my internship at Codsoft. I used Random Forest Regressor to make accurate predictions.

Unsa4/SHOPPING-CART

Using OOP (py)

Unsa4/Titanic-Survival-Prediction-Task1-

This was my Task1 during my internship at Codsoft in which i utilized a Random Forest Classifier with hyperparameter tuning to build a predictive model. Achieved an impressive accuracy rate of 100% on the predictions

Unsa4/WEB-API

This is my first Asp.Net project in which i developed REST API's for crud operations.