Predicting Blood Donation

Checked Target Incidence using a Binary classifier Model which provides the output in ‘0 - the donor will not give blood’ and ‘1 - the donor will give blood’. Result is [‘0’ - 0.762] & [‘1’ - 0.238] Zeroed into LogisticRegression model using TPOT, a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming, giving us the AUC score of 0.7850 Improved the AUC score by 0.

May 23, 2020

Risk and Returns: The Sharpe Ratio

Loaded, Visualized, and summarized daily stock price data for ‘Amazon’, ‘Facebook’ and ‘S&P500’ Calculated daily stock returns data for ‘Amazon’, ‘Facebook’ and ‘S&P500’ Performed calculations for ‘Excess Returns: S&P 500 VS Stocks’ Computed ‘Mean’ and ‘Standard Deviations’ for Excess Returns Determined ‘Sharpe Ratio’ to help understand the return of an investment compared to its risk Link to GitHub Repository

May 23, 2020

Who's Tweeting - Trump or Trudeau ?

Loaded corpus of tweets from November 2017 into Pandas DataFrame, and passed it to scikit-learn for further processing Created vectorized representations of the tweets using ‘CountVectorizer’ and ‘TfidfVectorizer’ classes in order to apply machine learning after splitting the data into test and training sets Trained Multinomial Naive Bayes model with both the CountVectorizer and TfidfVectorizer data to check which model will perform better. Results are TF-IDF model performs better with 0.