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Mentorkaya
--ใƒป1.5k views
โ€ข ๐Ÿ“… Date: January 16, 2023 Latest Prices: โ€ข ๐Ÿ“ˆ CoinDesk Market Index (CMI): 1,726 (+0.2%) โ€ข ๐ŸŸข Bitcoin (BTC): $42,878 (+0.6%) โ€ข ๐Ÿ”ด Ethereum (ETH): $2,529 (โˆ’0.3%) โ€ข ๐Ÿ“Š S&P 500: 4,783.83 (+0.1%) โ€ข ๐Ÿฅ‡ Gold: $2,044 (โˆ’0.5%) โ€ข ๐Ÿ“‰ Nikkei 225: $2,044 (โˆ’0.5%) Crypto Insights: โ€ข ๐Ÿ“‰ Bitcoin faces pressure after ETF launch in U.S., drops 12% from recent high. โ€ข ๐Ÿ“Š Binance traders led โ€œsell-the-factโ€ pullback in bitcoin, indicated by CVD.

โ€ข ๐Ÿ“… Date: January 16, 2023

Latest Prices:

โ€ข ๐Ÿ“ˆ CoinDesk Market Index (CMI): 1,726 (+0.2%)

โ€ข ๐ŸŸข Bitcoin (BTC): $42,878 (+0.6%)

โ€ข ๐Ÿ”ด Ethereum (ETH): $2,529 (โˆ’0.3%)

โ€ข ๐Ÿ“Š S&P 500: 4,783.83 (+0.1%)

โ€ข ๐Ÿฅ‡ Gold: $2,044 (โˆ’0.5%)

โ€ข ๐Ÿ“‰ Nikkei 225: $2,044 (โˆ’0.5%)

Crypto Insights:

โ€ข ๐Ÿ“‰ Bitcoin faces pressure after ETF launch in U.S., drops 12% from recent high.

โ€ข ๐Ÿ“Š Binance traders led โ€œsell-the-factโ€ pullback in bitcoin, indicated by CVD.

Disclaimer: Includes thrid-party opinions. No financial advice. May include sponsored content.ย See T&Cs.
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How you can build a simple AI model to predict the price of bitcoin Cryptocurrency, especially Bitcoin, has been a hot topic in recent years. With its volatile nature and massive potential for return, it has garnered the interest of investors worldwide. One of the major challenges, however, is predicting the future price of Bitcoin. With the help of AI and Machine Learning, we can tackle this problem more effectively. In this article, I will guide you through the process of building a simple AI model using a Long Short-Term Memory (LSTM) network. When it comes to predicting the price of Bitcoin, the LSTM model has been found to be quite effective. It is a type of recurrent neural network (RNN) that can capture long-term dependencies and patterns in time-series data, which makes it well suited for predicting sequences of values like a time series of Bitcoin prices. The LSTM model we've built works by taking a sequence of past Bitcoin prices as input and outputting a predicted price. The model is trained on a portion of the data (the training set), and its performance is evaluated on a portion of the data that it hasn't seen during training (the test set). The goal is to minimize the difference (or error) between the model's predictions and the actual prices in the test set. The LSTM layers in the model are responsible for learning the dependencies between the prices in the input sequence and the output prediction. The Dense layers following the LSTM layers are traditional fully connected neural network layers that generate the final output prediction. Despite its simplicity, this LSTM model can capture complex patterns in the time-series data, and with enough training data, it can make fairly accurate predictions. However, it's worth noting that because the price of Bitcoin is influenced by many factors (such as market demand, governmental regulations, and overall sentiment towards cryptocurrency), no model can predict the price with 100% accuracy. $BTC #TopCoinsJune2024
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