Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).

Apple Stock Price Forecasting using Sentiment Analysis

An end-to-end plagiarism classification model, deployed to SageMaker.

Text classification with Machine Learning and Mealpy

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

Machine learning model Visualizer in web using streamlit

Repo on how to install and use thundersvm.

I participated in the Titanic ML competition where I used machine learning to create a model to predict which passengers survived the Titanic shipwreck.

Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmia), and heart defects you’re born with (congenital heart defects), among others.

Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmia), and heart defects you’re born with (congenital heart defects), among others.

Predicting house prices can help determine the selling price of a house in a particular region and can help people find the right time to buy a home.

This is a small project to classify the GTZAN dataset by applying multiple algorithms for training the models.

This repository consists of programming projects of my pattern recognition course in which I used libraries like sklearn, Keras, Tensorflow, etc. Also, I am working on MNIST dataset in these projects and using support vector, probabilistic generative model, neural network, etc as learning methods.

Credit Card Fraud Detection

Scraping data through Instagram and using the data to build a predictive model

Sentiment analysis of kinopoisk (https://www.kinopoisk.ru/) reviews. For analysis i used: classification methods (random forest, svc, k-neighbors) neural networks (lstm, mlp).

Classification Machine Learning project

Determining movie genres based on synopses, using various NLP methods.

This is a text classification for classifying the SMS as either spam message or non-spam message using Natural Language Processing.

Employee-Absenteeism-Project-Work