Document Classification using Python and scikit-learn and nltk

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting

This is a weighted blending machine implemented using a neural network. The advantage of using a neural network is that the weights assigned to the models for the final result is assigned by the neural network based on backpropagation.

To design a predictive model using xgboost and voting ensembling techniques and extract insights from the data using pandas, seaborn and matplotlib

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

A repository covering all my work on various topics and algorithms while learning Data Science, Machine Learning and Deep Learning.

Classifying Audio to Emotion

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

Classification model with NLP to learn which subreddit a given post came from.

Built a classifier for evaluating quality of machine translation to predict best matching sentence to the reference sentence

Simple Codes

A credit card fraud detection kernel

This project implements an NLP-based solution for filtering out e-mails as ham or spam.

A collection of 3 deep learning models working together to predict people emotions through a voting classifier that comes with two strategies : "soft" and "hard".