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Binary Classification on the S&P 500 using Naive Bayes and Logistic Regression in parallel for accuracy comparisons.

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CSCI 183 Final Project

Binary Classification on Stock Market (S&P 500) using Naive Bayes and Logistic Regression

Neil Prabhu, Mike Padden and Sutter Grune


Setup instructions

  1. Install Python 3.5+ from python.org or using 'sudo apt-get install python3'.
  2. Install pip:
  1. Ensure pip is up to date: 'pip install --upgrade pip'.
  2. Install Jupyter: 'pip install jupyter'.
  3. Install required dependencies: 'pip install pandas-datareader pip install fix_yahoo_finance pip install matplotlib pip install sklearn pip install pandas'.
  4. Download the data files using the code in the file CSProject.ipynb (In the google drive, you can find both original and processed data)
  5. Open the project notebook: CSProject.ipynb

External Dependencies

  • 'sklearn': Machine learning algorithm library
  • 'pandas-datareader': tool to capture data into a pandas dataframe
  • 'fix_yahoo_finance': tool to get Yahoo! Finance data
  • 'matplotlib': Data plotting tool
  • 'pandas': Data processing toolkits

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Binary Classification on the S&P 500 using Naive Bayes and Logistic Regression in parallel for accuracy comparisons.

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