Cryptocurrency Price Prediction using Machine Learning Python

A Python app that forecasts Bitcoin, Ethereum and Dogecoin prices using an LSTM deep learning model. Users view predicted price trends, and the admin manages users.

Users connect a wallet such as MetaMask to the web front end. Each record (for example a product batch, certificate or vote) is written by a Solidity smart contract on an Ethereum test network. The contract checks who is allowed to add or update data, so only authorised accounts can write. Anyone with the record ID can verify its full history, which cannot be edited afterwards.

Records on a blockchain are shared across nodes, linked by cryptographic hashes and cannot be altered without consensus. This gives tamper-proof history and removes the need to trust one central administrator.

A program stored on the blockchain that runs automatically when its conditions are met. In Ethereum it is written in Solidity, compiled to bytecode and executed by every node, so its result cannot be changed by one party.

We compared a few suitable algorithms (for example logistic regression, decision tree, random forest and SVM) on the same train-test split and chose the one with the best balance of accuracy, recall and speed for this data.

Accuracy is correct predictions over all predictions. Precision is how many predicted positives are truly positive. Recall is how many actual positives were found. When classes are imbalanced, precision and recall matter more than accuracy.

Symmetric encryption (AES, DES) uses one shared key for encryption and decryption and is fast. Asymmetric encryption (RSA) uses a public key to encrypt and a private key to decrypt, which solves key sharing but is slower.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.