What it does
1. Pulls twitter and stock information
2. Clusters tweets based on influence
3. Builds both a unsupervised and supervised machine learning models with decent accuracy on validation sets
4. Output dataset and model saved
How I built it
Using several data science libraries in python, pandas carried me through all the csv formatting
For future expansions of this project, I would like to vastly increase the size of the dataset used, experiment with other dimensions such as graph theory based evaluation of the network, explore using more than one social media source, and just play with this concept on a larger scale. Along with this combine the datasets from the other models .
Built With
- css
- html
- pandas
- pelican
- python
- scikit-learn
- tflearn
- tweepy
This application is used for graphing the data of the stocks and displaying it .
Using panda.datareader the program downloads the desired stocks from the website choosen in this case it was "yahoo" and choose the stock of choice. Using matplotlib we graph the entire stock chart with various options .
*It must be noted that the there is another README in the this part of the application.