Cryptocurrency Price Prediction Using Recurrent Neural Networks (RNNs). Requirements: 1. Introduction Provide an introduction to Cryptocurrency Price Prediction using Recurrent Neural Networks (RNNs).
$10-30 USD
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Cryptocurrency Price Prediction Using Recurrent Neural Networks (RNNs).
Requirements:
1. Introduction
Provide an introduction to Cryptocurrency Price Prediction using Recurrent Neural Networks (RNNs). Briefly explain the objective of this project and the significance of predicting cryptocurrency prices.
2. Input Dataset
- Description: Briefly describe the dataset you are using. Include information on the source of the data, the features available, and the time period covered by the data.
- Preprocessing: Explain any preprocessing steps you took to clean and prepare the data for modeling. This could include handling missing values, normalizing the data, or creating new features.
3. Split of Data
- Explain how you split the dataset into training and testing sets. Mention the ratio of the split and justify your choice.
4. Model: [5 marks]
- Architecture: Describe the architecture of the RNN or LSTM model you developed. Include information on the number of layers, the number of units in each layer, and any other relevant parameters.
- Justification: Justify your choice of architecture. Explain why you believe this architecture is suitable for the task of cryptocurrency price prediction.
5. Activation and Loss Function
- Explain the activation functions used in your model and justify your choices.
- Describe the loss function used during training and explain why it is appropriate for this task.
6. Output:
- Describe the output of your model. Is it predicting the price for the next day, the next week, or some other time period? How is the output interpreted?
7. Hyper-parameters Tuning with K-Folds
- Explain how you tuned the hyperparameters of your model using k-fold cross-validation. Describe the process and the results of the tuning.
Optimizer:
- Describe the optimizer used during training and justify your choice.
Solution to Gradient Vanishing:
- Explain any techniques you implemented to mitigate the vanishing gradient problem often encountered when training RNNs and LSTMs.
Print Model's Summary, Accuracy, and Loss:
- Include the summary of your model’s architecture.
- Report the accuracy and loss of your model on the testing set.
- Write the complete Python code for your proposed model with clear comments explaining each section of the code.
Projekt-id: #37293300
About the project
8 frilansare har lagt bud på i genomsnitt $27 för det här jobbet
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Cryptocurrency Price Prediction Using Recurrent Neural Networks (RNNs). Requirements: 1. Introduction Provide an introduction to Cryptocurrency Price Prediction using Recurrent Neural Networks (RNNs). My name is "Usam Mer
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This should be easy i built one like this last year i think i can easily help you with this and will explain you complete code and will help you github publish of this with good readme and seo, that will help long last Mer
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