[SOLVED] 代写 python network FINA 5240: FinTech Analytics Assignment 5

FINA 5240: FinTech Analytics Assignment 5
Halis Sak October 17, 2019
Question. In the previous homework assignment we used tf-idf method to make predictions on labels based on the content of whitepapers of ICOs (Initial Coin Offerings). Now we want to use feed-forward neural networks for the same classification problem. Please use Python to do the following tasks.
a) We need a tokenizer to split the content of the documents into words. Please use nltk package (follow the steps at https://pythonspot.com/tokenizing- words-and-sentences-with-nltk/ to download all the required packages). Af- ter completing the installation process for nltk package, create a new Pandas dataframe having columns [¡°tok_content¡±,¡°label¡±]. Tokenize the content of whitepa- pers in ¡°ICOData.csv” using ¡°word_tokenize¡± function of nltk package and store them in ¡°tok_content¡± column of the new dataframe. The column ¡°label¡± should
be the label of documents in ¡°ICOData.csv¡±.
b) We can use gensim package for creating vectors for the tokens of our whitepapers. (We used this package for news articles in Mandarin in Lecture 3 and 4.) First, train a word2vec model for tokens of our whitepapers using gensim package. Then, find the most similar words to ¡°Bitcoin¡±.
c) Construct a mapping for tokens in our dictionary to integers as we did in our lecture notes. Then, split the new dataframe into two groups; training and testing (¡°df_train¡± and ¡°df_test¡±). The first 130 rows of the data should be in ¡°df_train¡± and the rest should be in ¡°df_test¡±.
d) We want to fit a two-layer feed-forward neural network to our data as we did in Lecture 3. Please set the parameters of the model. The maximum number of tokens, ¡°mlen¡±, can be assigned to 3000. You are welcome to experiment with the hyperparameters of the model.
e) Finally, we want to train the model and compute the classification accu- racy. The Python code that I wrote for Lecture 3 can be used mostly without any change. However, data and target lines of the code should be changed.
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Please make these changes appropriately and report the classification accuracy.
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