Machine Learning Project on Imbalanced Data in R

Decision Trees, Random Forest and XGBoost: TITANIC SURVIVAL

My solution for Kaggle NYC Taxi Fare Prediction ( ranked 21st/1463)

Churn analysis on a sample E commerce data

Command line tool for data exploration.

White_wine_quality_data_exploration_in_R

Repository containing portfolio of data science projects completed as part of course work during masters and self learning.

It's a data visualisation project developed in D3.js which demonstrates how data can be visualised to find trends and support in decision making. This project uses IMDB data for 5000 movies (https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset) and is a small effort to help Movie creators (like producers or directors) to get insights befo…

This repository is dedicated to Data Science Topics, concepts and Projects

Walmart Store Sales Forecasting

This repository is demonstration of Pandas library of Python's super powers.

this is my version of creating a cluster and decision tree on FIFA Dataset

An simple example of data exploration and test-driving development

This repository discusses a research project on the employee database at Pewlett Hackard Corporation from the 1980s and 1990s. All that remains of the database of employees from that period is in six CSV files. On this project, a table was created that holds employees data in the CSVs, import the CSVs into a SQL database, and the data exploratio…

This repository saves my final project on NYC Restaurant Inspection data for MSIS502. Wondering how NYC restaurant inspection ratings are like? Do ethnic foods have lower ratings?

Electronic Sales Analysis using Pandas and Matplotlib.

Analysis, Exploration, and Prediction on Wine data

Use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions.

These are homework assignments from my Introduction to Machine Learning course for Fall 2020. In this class, I learned about the concepts of basic statistical machine learning and applying them to discover patterns and relationships in large data sets.