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.
Using past Sport (Cricket) data to predict next win for Team India, in any format of the cricket.
A Flask web app which predicts whether it will rain tomorrow or not.
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.
Scraping data through Instagram and using the data to build a predictive model
Credit Card Fraud Detection
Classification Machine Learning project
Determining movie genres based on synopses, using various NLP methods.
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