Predicting cryptocurrency price movements is a well-known problem of interest. In this modern age, social media represents the public sentiment about current events. Twitter especially has attracted a lot of attention from researchers who are studying the public sentiments. Recent studies in natural language processing develop automatic techniques in analyzing sentiment in social media information. This research is directed towards predicting volatile price movement of cryptocurrency by analyzing the sentiment on social media and finding the correlation between them. Machine learning algorithms including support vector machine and linear regression will be used to predict the prices. The most efficient combination of machine learning algorithms and the datasets being used will be determined.

Software Architecture Diagram

Link to video tutorial: https://youtu.be/ml4Tc-Xr7bc

Link to senior paper: https://drive.google.com/file/d/1S2TUrBGu8VMmVX1iES8osTWCS6J3PfOG/view?usp=sharing

Cryptocurrency price prediction

This would predict crypto currency prices using deep learning. As cryptocurrency popularity is increasing in the modern age and the money flow is increasing this is making cryptocurrencies more volatile and the patterns are changing. Some of the problems faced are as unlike the stock market cryptocurrencies are dependent on factors such as its technological progress and internal competition etc. I plan to get data from news agencies about specific tokens and also data of all the price changes in crypto from 2012 to predict the future prices. As per my research this would require the use of LSTM neural networks. Some of the many places this data can be found is on Cryptocompare API: XEM and IOT historical prices in hour frequency, Pytrends API: Google News search frequency of the phrase “cryptocurrency”, Scraping redditmetrics.com: Subreddit “CryptoCurrency,” “Nem,” and “Iota” subscription growth. We can predict the price by  Identifying the Cointegrated Pair. This is a popular method used to stationarize time series. It can be used to remove trends and seasonality. Taking the difference of consecutive observations is used for this project.

Gender and age detection using deep learning

This would predict the age and gender of a person using a picture of a person or live view using a webcam. The predicted gender may be one of ‘Male’ and ‘Female’.  It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions.I will be using the Adience dataset; the dataset is available in the public domain. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. As there are already multiple studies done on this topic, factors which affect the efficiency of the program can be worked on.

Forest fire detection using k-clustering

This model would detect forest fires using the Keras Deep Learning library. As seen recently around the world in places such as the Amazon rainforest and a prominent part of Australia, wildfires are increasing in this era. These disasters are damaging to the ecosystem like damaging habitat and releasing carbon dioxide. This project can be built using k-means clustering. This model would be able to identify any forest fires hotspots along with the intensity of the fire at that particular spot which would result in either the model detecting if it’s a wildfire or not. There is another way of making this using the neural network MobileNet-V2 or U-net which is more efficient and I will be researching more on this. There is a data set compiled with over 1300 images that would be used to detect wildfires.  The data for this project can be found at https://drive.google.com/file/d/11KBgD_W2yOxhJnUMiyBkBzXDPXhVmvCt/view.