Course Files for Complete Python 3 Bootcamp Course on Udemy

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then s…

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to…

Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying …

EEG data processing and it's convolution using AutoEncoder + CNN + RNN

The project uses EEG signals from the DEAP Dataset to classify emotions into 4 classes using Ensembled 1-D CNNs, LSTMs and 2D , 3D CNNs and Cascaded CNNs with LSTMs.

The project provides an application for face recognition using convolutional neural network.

This repository contains the code for emotion recognition using wavelet transform and svm classifiers' rbf kernel.

This project classifies female facial images into 5 different face shapes using Convolutional Neural Networks.

Detection of human emotions from eeg signals using the amigos dataset

Project to recognize hand gesture using state of the art neural networks.

This project aims to detect the anomalies in Web-Traffic using a C-LSTM architecture.

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

The classification goal is to predict if the client will subscribe a term deposit (variable y).

Data science project

This project focuses on using the Semantic Segmentation Deep Learning architecture DeepLAbV3+ on the Agriculture-Vision dataset. We focus on improving the architecture's performance by solving the …

An EEG-based emotion recognition system using Simple Recurrent Units(SRU) in Pytorch library. It identifies three emotions: positive, neutral and negative. It uses SEED dataset which consist of EEG…

Project on Pattern Detection and Recognition using Deep Learning

Used TensorFlow to build a neural network that can predict fraudulent credit card transactions.

Final project for ECE239AS Deep Learning and Neural Network at UCLA, Winter 2019

Developed an best EEG Channel Selection Model Using Technique like MIMR

Bank Marketing Classification using scikit-learn library to train and validate classification models like Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Neural Network and Support …

This is my first Deep Learning project, which is a MNIST hand-written digits classifier. The model is implemented completely from scratch WITHOUT using any prebuilt optimization like Tensorflow or …

Analyzing bank telemarketing data to improve marketing campaigns

The Portuguese Bank had run a telemarketing campaign in the past, making sales calls for a term-deposit product. Whether a prospect had bought the product or not is mentioned in the column named 'r…

Increase the effectiveness of the bank's telemarketing campaign.

Use of machine learning to predict whether a customer would subscribe to a term deposit.

B.Sc THESIS