Paper: Fake_News_Capstone

Poster:

Github directory: https://github.com/mashres15/FakeNewsCapstone

The front end development part for my project is almost complete. The database setup and connection is completed. The integration of backend machine learning model with flask has worked. The flask prediction model has also been linked with the frontend. There are some issues with websites that are not blog and I am fixing the issue. Next step is to make a retrainable model.

Worked on the front end component of the project. With the sys admin telling me that they cannot host flask for my project. I started to look for alternatives. Heroku could not be used as it did not provide support for sci-kit the way I needed. Worked with Ajit to edit my first draft paper. Mades some figures of the architectures.

Finished the machine learning pipeline with actual experimentation. Having issues getting Flask setup and have been in touch with the SysAdmins. Halfway through the first draft of the paper. Made new design for the system architecture.

Build a rough machine learning pipeline for testing. Worked with Ajit to update timeline. Started with the first draft of paper.

Worked with setting up sci-kit learn and testing environment. Got Craig to give me access to Pollock and Bronte.

Started the project pipeline for Fake News Detection.

A Data Science and Machine Learning Project to explore the stock data of a particular stock exchange. The exploration will be focused on observing the repetitive trend in stock markets and relating it to the business cycles. Some questions that can be asked in this project is as follows:

• Is there any particular pattern that stocks market follow in between the end of December and start of January. This time period is said to be a speculative time for investors and trader. Particularly, it is observed that traders can benefit by buying in December and selling in January because of the closure of accounting books of firms.
• Another interesting phenomenon would be to ask if there is a trend in between bull market and bear market. That does a bull market always have to be followed by a bear market and vice versa.

The main resource for this project would be “Python for Finance” Analyze Big Financial Data by O’Reilly Media. Some other resources are as follows:

• https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
• https://pythonprogramming.net/machine-learning-pattern-recognition-algorithmic-forex-stock-trading/
• http://www.financial-hacker.com/build-better-strategies-part-4-machine-learning/

A portfolio tracker that keep tracks of investments in stocks in a particular market. Keeping in mind the time limitation, it would be better to focus on small markets for this project. The web-based application will provide different portfolios to users to keep track of their investments and to easily look at their best and worst investment.

In this project, the major component of research would be figuring about how to structure the database design for such a system as well as enforcing multiple levels of database transactions logging. A further investigation might be in mirroring the data for backup. Along with this, the project can have a data analysis research segment for any market that might suffice the need of this project.

The research component of this project will also lie in using Model View Controller design pattern to develop such a system. This project essentially has two part, the software design, and the data analysis research. If this project is taken, serious amount of planning has to be done to ensure that all both the component of the project is completed,