Machine Learning Project on Imbalanced Data in R
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Updated
Feb 24, 2018 - R
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.
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 focuses on using simple lines of code with several Python packages(pandas-profiling, sweetviz, dtales) that does the extensive detailed Exploratory Data Analysis by producing several uni-variate statistics
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.
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