Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

TÜBİTAK 2209-A funded academic research project comparing various ML/DL models for Bitcoin price prediction using financial indicators and cross-asset correlations.

Codebase for paper titled: "Using Machine and Deep Learning Models, On-Chain Data, and Technical Analysis for Predicting Bitcoin Price Direction and Magnitude"

In this project, analysis and prediction of the bitcoin price was carried out as part of a project to research artificial intelligence in finance in the scope of Interactive ML course at Augsburg University

Trading system comparing traditional ML (Random Forest) against biological neural networks (fruit fly brain connectome) for Bitcoin price prediction.

Predict Bitcoin prices with ML & DL models! 📈 Uses Ridge, Lasso, Random Forest, MLP, RNN & LSTM with hyperparameter tuning. 📊 Visualizes predictions & ROC curves. 🚀 Fetch data via yfinance, evaluate with MSE/R2. Perfect for crypto enthusiasts! 💸

Advanced Bitcoin price prediction system using AI and ML

Open-source Bitcoin price predictor with deep learning methods. Foundation for ML enthusiasts to learn and contribute to crypto prediction algorithms. Smart Bitcoin price prediction with sleek interface - 7 day analysis, neural networks, and 70% accuracy. Your playground for crypto AI experimentation.

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Hourly Bitcoin Price Prediction system using regression and LSTM to analyse market trends and forecast prices 30 days into the future.

Bachelor's thesis in Computer Science

Bitcoin price prediction using supervised ML algorithms such as linear regression, regularization(lasso and ridge) and Support Vector Machine with gridsearchCV

🚀 Bitcoin price prediction using RNN & LSTM neural networks with 30-day forecasting. Complete ML pipeline with data extraction, preprocessing, and time series analysis.

🔮 Bitcoin hourly price prediction using ML models (Linear Regression, Ridge, Decision Tree, Random Forest, KNN, SVM). Achieves 99.98% R² accuracy with technical indicators analysis.

A Streamlit-based web app for 14-day Bitcoin price prediction using LSTM+CNN and XGBoost models. The app visualizes real BTC prices, ML-generated forecasts, and a moving average baseline. Automated daily updates with GitHub Actions.

This project builds a Bitcoin (BTC) price prediction pipeline using classification and regression models to forecast next-hour price movements and values. It fetches hourly data, engineers features, applies multiple ML models, and prepares outputs for a Streamlit dashboard.

bitcoin price prediction

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bitcoin prices prediction

2 machine learning models with different amounts of features used to make future predictions on Bitcoin's price.

A machine learning-based project that predicts the short-term price movement of Bitcoin (BTC). Instead of predicting the exact price, the system forecasts whether the price will increase or decrease in the next hour or the next day.

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Bitcoin Price Prediction with a ML model.

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Bitcoin Price prediction using ML algorithms with Streamlit app using 4 features 'USDT_Close', 'USDT_Volume' , 'BNB_Close', 'BNB_Volume'

stock price prediction using these algorithms: 1 . Linear Regression(simple , 2 . Neural Network ( 3 Random forest (Classifier , regression)

The focus of our project is to investigate whether tweets can have a manipulative effect on the price of Bitcoin. To do this research, we aimed to develop a model using Multi-Layer Perceptron and BERT Transform.

Bitcoin price prediction using different ML Algorithms and Neural Networks with their comparative Study

Final year project (AI & Data Science by Saylani : Bitcoin price prediction using ML & Deep Learning with a Gradio UI, deployed on Hugging Face. https://huggingface.co/spaces/nadish1210/BITCOIN_PREDICTION