Popular GitHub Repositories for Exploratory Data Analysis

Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

Ways of doing Data Science Engineering and Machine Learning in R and Python

R on Apache Spark (SparkR) tutorials for Big Data analysis and Machine Learning as IPython / Jupyter notebooks

The Google Advanced Data Analytics Certificate contains information on how to use machine learning, predictive modeling, and experimental design to collect and analyze large amounts of data, and prepare for jobs like Senior Data Analyst and Junior Data Scientist.

OlliePy is a python package which can help data scientists in exploring their data and evaluating and analysing their machine learning experiments by utilising the power and structure of modern web applications. The data scientist only needs to provide the data and any required information and OlliePy will generate the rest.

Association ruled based networks using graphical Hypergeometric Networks.

Anomaly detection and failure prognosis applied to industrial machines

Solutions for the Machine Learning Zoomcamp 2022 by DataTalks.Club.

Creating Regression Models Of Building Emissions On Google Cloud

Stock Prediction using LSTM, Linear Regression, ARIMA and GARCH models. Hyperparameter Optimization using Optuna framework for LSTM variants.

This repository is dedicated to the machine learning analysis on StockX prices. Blog ?

Neural network to predict eye gaze location from front-facing camera

Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

In this mini-project i have chosen to do sentiment analysis of social media websites such as twitter and reddit to gain insights into the people’s opinion towards prime ministerial candidates for the Lok Sabha election 2019. Social media provides a platform for people’s opinion of a person or event or topic to be heard from anywhere at any time and is the easiest and fastest way for them to do it. So, analysing these sentiments will be of immense use in knowing the trending topics and the mood of the people towards those topics among other things. The mini-project also aims at implementing and comparing contemporary machine learning text classification algorithms to predict the sentiment of a piece of text.

A project for the development of rich geospatial data from the city of São Paulo for use in Machine Learning models.

MOVIS: A Multi-Omics Software Solution for Multi-modal Time-Series Clustering, Embedding, and Visualizing Tasks, by Aleksandar An?el, Dominik Heider, and Georges Hattab

This project contains the file of my undergraduate Final Year Project. This project aims to expose cyberbullying in Twitter by using Machine Learning to classify whether the tweet is suspicious or not. A deployment has been created using streamlit.

A comprehensive collection of data analysis and machine learning projects, showcasing techniques and models for various data challenges. Dive in to explore code examples, analyses, and machine learning workflows.

A thorough analysis of opensource Fitbit data is performed. The key findings are highlighted and discussed. The analysis provided herein is performed using 940 data points collected from 33 distinct users. Machine Learning Models are used to solve a regression problem using Multiple Linear Regression, Random Forest and Extreme Gradient Booster