Cryptocurrency Price Prediction Project with ML Models

Tech Stack: Python | ML Algorithms: LogisticRegression, SVC, XGBClassifier
Tags: final year project btech, final year project mtech

Cryptocurrencies like Bitcoin, Ethereum, and Litecoin have taken the financial world by storm. Their unpredictable price movements offer both opportunities and challenges for investors. This final year project for BTech and MTech students focuses on predicting the price trends of cryptocurrencies using powerful machine learning models implemented in Python.

Project Overview

The Cryptocurrency Price Prediction Project aims to use historical market data and apply machine learning techniques to predict whether the price of a given cryptocurrency will increase or decrease in the near future. Unlike traditional stock markets, crypto markets are highly volatile and operate 24/7, making this a particularly challenging and exciting domain for machine learning applications.

Technology Used

• Programming Language: Python

• Libraries: pandas, numpy, sklearn, matplotlib, seaborn, xgboost

• Visualization Tools: matplotlib, seaborn

• Jupyter Notebook: For building and analyzing models interactively

Machine Learning Models Used

• LogisticRegression

  • Used as a baseline classification model.

  • Predicts binary outcomes: price increase or decrease.

  • Effective in identifying trends from features like closing price, volume, and market cap.

• SVC (Support Vector Classifier)

  • Finds the optimal hyperplane to separate two classes.

  • Performs well with high-dimensional data.

  • Requires tuning of kernel parameters for best results.

• XGBClassifier

  • Advanced model using gradient boosting and ensemble learning.

  • High performance and handles imbalanced datasets effectively.

  • Best suited for final predictions in the project.

Dataset Description

The dataset used in this project includes the following key features:

• Date

• Open, High, Low, Close prices

• Volume traded

• Market Cap

Additional technical indicators such as moving averages and RSI (Relative Strength Index) can be calculated to improve model accuracy.

Workflow of the Project

• Data Collection

  • Download cryptocurrency price data from sources like CoinMarketCap, Yahoo Finance, or APIs.

• Data Preprocessing

  • Clean null values in the dataset.

  • Convert date-time formats for consistency.

  • Perform feature engineering to extract meaningful insights.

• Feature Scaling

  • Use StandardScaler to normalize data for better model performance.

• Model Training

  • Train LogisticRegression, SVC, and XGBClassifier using a train-test split.

• Evaluation

  • Compare models based on accuracy, precision, recall, and F1-score.

• Visualization

  • Create graphs showing price trends, predictions vs actuals, and model comparison.

Why This Project?

• It showcases real-world application of machine learning in the financial domain.

• Cryptocurrency is a trending and highly relevant topic for final year project BTech and final year project MTech students.

• It involves both classification techniques and data analysis skills, making it an ideal blend of theory and practice.

• The use of multiple algorithms allows comparison and performance tuning.

Outcomes

By the end of the project, students will have:

• A fully working prediction system for cryptocurrency price trends.

• A well-documented Python codebase using Jupyter Notebooks.

• Clear understanding of how LogisticRegression, SVC, and XGBClassifier perform on financial time-series data.

• A complete report and visuals to showcase during project presentations or viva sessions.

Project Includes:

• PPT

• Synopsis

• Report

• Project Source Code

• Base Research Paper

• Video Tutorials

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