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πŸ‘¨β€πŸ’» I've embarked on the intriguing challenge of predicting Bitcoin prices πŸ“ˆ, delving into the world of bitcoin forecasting. Armed with historical data πŸ“Š sourced from "bitcoin.xlsx" spanning January 2019 to December 2020, I embarked on a data-centric journey πŸš€. Utilizing essential tools such as Pandas 🐼, NumPy πŸ”’, and Matplotlib πŸ“Š, I meticulously organized and visualized the data's insights. In my quest to solve this predictive puzzle, I harnessed the capabilities of scikit-learn 🧠, selecting Linear Regression πŸ“ˆ as my trusted companion. With caution, I partitioned the data into training and testing sets πŸš‚πŸ§ͺ, aiming to ensure the model's precision and its capacity to generalize. Post-training, I assessed its performance through the MSE metric πŸ“‰, striving to minimize forecasting errors. While the classification report veered off track somewhat, it's evident that in the ever-evolving realm of Bitcoin price prediction πŸͺ™πŸ“‰, precision and model refinement are our guiding lights. I'm all set for the thrilling journey that lies ahead! πŸŒŸπŸ’°πŸ’Ή

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