The Bitcoin price prediction project leverages machine learning and deep learning techniques to forecast the future price movements of Bitcoin based on historical data. The primary objective of the project is to evaluate various predictive models, including traditional machine learning classifiers such as Logistic Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes, along with a deep learning model based on Long Short-Term Memory (LSTM) networks. The dataset used for the analysis includes Bitcoin's historical market data spanning from 2014 to 2022, which consists of features such as the closing price, volume, and other financial indicators. The project involves multiple stages, including data preprocessing, feature engineering, and the creation of lagged features to enhance model performance. For the LSTM model, time-series data is used to train a model that predicts the next day's closing price based on a sequence of previous prices. The classification models are tasked with predicting whether the price will go up or down on the following day. Evaluation metrics such as accuracy, precision, recall, and the F1 score are utilized to assess model performance, while the LSTM model’s effectiveness is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results of this project will provide valuable insights into the potential of using machine learning and deep learning algorithms for forecasting Bitcoin price movements. Additionally, The deployment of these models could aid investors, analysts, and traders in making informed decisions regarding Bitcoin investments.