COVID-19 Vulnerability Index

this is my repository for the quick draw prediction model project

My solution for Quora's Question Pair contest on Kaggle.

this is my repository for Amazon review helpfulness prediction model

won silver medal, 164th of 5169

Animal State Prediction Dataset

Machine learning model built for IBM Hack 2020 challenge. ⚙️

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.

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.

The Complete Journey Dataset: Churn Prediction

Kaggle Airline Delay competition solution

Machine learning tutorial with examples

A host of data science + machine learning projects with Python, pandas, scikit-learn and more!

Detection and Prediction of Air quality Index

The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.

Codes and templates for ML algorithms created, modified and optimized in Python and R.

Machine Learning to Determine Auto-Insurance Premiums using Telematics

Personal Data Science Projects

This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R

By using feature engineering technique and XGBoost algorithm to predict house price

Identify question pair with the same intent using Convolutional Neural Network

A project of Kaggle which to predict the condition of selling in 6 weeks

A complete workshop on Machine Learning Algorithms

This project is specifically focused on data visualization and feature selection.

Predictive modeling

Practice project using Kaggle competition data on NYC Taxi Trips

A credit card fraud detection algorithm.

Machine learning using AWS sagemaker Python development

Machine Learning