crypto0

Please open our files in sequence

Data cleaning( This notebook will give you the cleaned data to proceed to the next step)
Prediction
Classification
Sentiment Analysis ( specific data-clearning is included here)

3 research questions:

Prediction: Use models to check if certain cryptos are overpriced or underpriced.
Classification: identify trading opportunity to see if certain cryptos deviates from its category’s market behavior
Sentiment analysis: track twitter sentiments to imply market trends for certain cryptos, market’s attention and overall attitude

Datasets: For question 1,2: We are using datasets scraped from https://coinmarketcap.com/historical/20211001/ ( raw data: crypto.csv).

For question 3:https://data.world/dmikebishop/crypto-investment-fund-directory and https://www.kaggle.com/kaushiksuresh147/bitcoin-tweets In this question we separately analyzed different datasets. (The data is not included in the list of files because of its large size. The dataset can be easily downloaded in this link.) This data cleaning is in the same file with sentiment analysis.

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
1.Cleaning_and_EDA_final.ipynb		1.Cleaning_and_EDA_final.ipynb
2.Prediction_final.ipynb		2.Prediction_final.ipynb
3.Classification_crypto.ipynb		3.Classification_crypto.ipynb
4.Sentiment_Analysis_on_Bitcoin_Related_Tweets.ipynb		4.Sentiment_Analysis_on_Bitcoin_Related_Tweets.ipynb
BTC_tweets_daily_example.csv		BTC_tweets_daily_example.csv
General_PPT_Presentation.pdf		General_PPT_Presentation.pdf
README.md		README.md
Recitation_10_Model_Explainibility.ipynb		Recitation_10_Model_Explainibility.ipynb
WIT_COMPAS.ipynb		WIT_COMPAS.ipynb
crypto.csv		crypto.csv
crypto_cleaned_final.csv		crypto_cleaned_final.csv

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