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COVID-19 Vulnerability Index
<img alt="octocat" src="https://github.githubassets.com/images/icons/emoji/octocat.png?v8" /> Detection and Prediction of Air quality Index <img alt="octocat" src="https://github.githubassets.com/images/icons/emoji/octocat.png?v8" />
this is my repository for the quick draw prediction model project
LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data
Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python
Web application for earthquake prediction in a window of few future days. live data collection from https://earthquake.usgs.gov/
this is my repository for Amazon review helpfulness prediction model
My solution for Quora's Question Pair contest on Kaggle.
This is a Liver Disease Machine Learning Classification Capstone Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to deploy a machine learning model from scratch. The files and documentation with experiment instructions needed for replicating the project, is provided for you.
Codes and templates for ML algorithms created, modified and optimized in Python and R.
Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.
World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases.
Advance Time Series Analysis using Probabilistic Programming, Auto Regressive Neural Networks and XGBoost Regression.
The Complete Journey Dataset: Churn Prediction
Classifying audio files using ML algorithms.
By using feature engineering technique and XGBoost algorithm to predict house price
Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.
I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.