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BTC Price analysis using RNN(LSTM & GRU)

This project is based on RNN to compare the accuracy of prediction using GRU and LSTM model. This model takes in data for a given set of days(which can be changed) and then predicts the price on a particular given day. The main purpose of the project is to compare the accuracy of the two RNN models and to check which one works better.

This the Graph for LSTM model image

This is the Graph for GRU model image

BTC Sentiment Analysis

The sentiment analysis is performed using vader which helps the model to create and evaluate negative, positive, neutral sentiments. Then the sentments are shown using plots.

negative v positive

image

neutral v positive

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The model consists of 6 layers:

  1. Embedded Layer
  2. 1D Convolutional Layer
  3. MaxPooling1D Layer
  4. Bidirectional LSTM Layer
  5. Dropout Layer
  6. Dense Layer

image

Evaluation metrics used

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Result:

    1. Accuracy : 0.7872
    2. Precision : 0.8321
    3. Recall : 0.7458
    4. F1 Score : 0.7866 image

Confusion Matrix

image