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1. Twitter-moods-as-stock-price-predictors-on-Nasdaq Twitter-moods-as-stock-price-predictors-on-Nasdaq Public

   An attempt to predict next day's stock price movements using sentiments in tweets with cashtags. Six different ML algorithms were deployed (LogReg, KNN, SVM etc.). Main libraries used: Pandas & Numpy

   Jupyter Notebook 24 6

2. Analysing-IMDB-reviews-using-GloVe-and-LSTM Analysing-IMDB-reviews-using-GloVe-and-LSTM Public

   Using the IMDB data found in Keras here a few algorithms built with Keras. The source code is from Francois Chollet's book Deep learning with Python. The aim is to predict whether a review is posit…

   Jupyter Notebook 7 3

3. K-means-clustering-on-US-crime-data K-means-clustering-on-US-crime-data Public

   Unsupervised machine learning using U.S. crime data and k-means clustering. Crime categories: murder, assault & rape in all 50 states in 1973.

   Jupyter Notebook 3 9

4. Predicting-Nordea-stock-price-using-an-LSTM-neural-network- Predicting-Nordea-stock-price-using-an-LSTM-neural-network- Public

   Using an 80/20 split in the historical data daily closing prices where predicted using a LSTM network based on data observed in the past 30 days for each prediction

   Jupyter Notebook 2 2

5. Multivariate-Regression---King-County-House-Prices Multivariate-Regression---King-County-House-Prices Public

   Supervised Machine Learning Using Regression Analysis

   Jupyter Notebook 2 1

6. Predicting-the-Popularity-of-Online-News Predicting-the-Popularity-of-Online-News Public

   Building a model which can predict the number of online 'shares' an article will get based on a set of variables attached to it. (Python)

   Jupyter Notebook 5 3