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PORTFOLIO.md
README.md

README.md

Portfolio

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Scikit-Learn Tutorial: Predictive Analytics with Python

Link to Github Repository: https://github.com/Sharp-Data/Scikit-Learn-Tutorial

In this tutorial, you'll see how you can easily load in data from a database with sqlite3, how you can explore your data and improve its data quality with pandas and matplotlib, and how you can then use the Scikit-Learn package to extract some valid insights out of your data.

Part 1: Predicting MLB Team Wins per Season

In this project, you’ll test out several machine learning models from sklearn to predict the number of games that a Major-League Baseball team won that season, based on the teams statistics and other variables from that season. The following scikit-learn models are featured in this project:

  • K-means clustor
  • Linear Regression
  • Ridge Regression

Link to Post: https://www.datacamp.com/community/tutorials/scikit-learn-tutorial-baseball-1

Part II: Predicting Hall of Fame Careers

In this project, you'll see how to use classification models to predict which players make it into the MLB Hall of Fame. The following scikit-learn models are featured in this project:

  • Logistic Regression
  • Random Forest
Link to Post: https://www.datacamp.com/community/tutorials/scikit-learn-tutorial-baseball-2

Results

The Random Forest model predicted 50 of 61 (82%) Hall of Fame careers with only 2 false positives out of 6,239 total players.

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Project: Predicting Bike Rentals

Link to Github Repository: https://github.com/Sharp-Data/Predicting-Bike-Rentals.git

In cities across the US, bike sharing programs are becoming increasingly popular. Anyone can rent a bike by the hour or by the day from any of the communal bike sharing stations. Washington D.C. has one of these programs. Hadi Fanaee-T (http://www.liaad.up.pt/area/fanaee) at the University of Porto compiled this data into a CSV file, which contains 17,380 rows. Each row represents the bike rental count "cnt" for a single hour of a single day.

We will be trying to predict the bike rental count for one hour time periods using the following models to see which one is most accurate:

  • Linear Regression
  • Decision Tree
  • Random Forest

Results

We were able to reduce our error metric (mean squared error) from ~17,054 with a linear regression model to ~2,149 with Random Forest.

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Project: Predicting the Stock Market

*Link to Github Repository: https://github.com/Sharp-Data/Predicting-the-Stock-Market.git

The S&P500 index aggregates the stock prices of 500 large companies. Every day investors make (or lose) money by trading Exchange Traded Funds, which allow you to buy and sell indexes like they were stocks. Creating a predictive model to predict the S&P500 could allow traders to make money from their investments.

Trading stocks is a risky endeavor. Nothing in this project constitutes stock trading advice!

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Blog Posts: Data-Science-Poker-Projects

Link to Blog Post: https://medium.springboard.com/how-i-used-professional-poker-to-become-a-data-scientist-e49b75dfe8e3
Link to Github Repository: https://github.com/Sharp-Data/Data-Science-Poker-Projects

April 15th, 2011, is referred to as Black Friday in the poker community. It’s the day that the United States Government shut down the top three online poker sites. About 4,000 US citizens played online poker professionally back then, and thus the exodus began. Canada and Costa Rica were popular destinations. I’m from Southern California, so I’m no stranger to Baja California. I decided to set up shop south of the border in a town called Rosarito, Mexico...

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Additional Projects: https://github.com/Sharp-Data/Portfolio/blob/master/PORTFOLIO.md

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