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Meta AI Expands Reach to New Country. 🧠🤯

Meta AI, the free AI assistant already popular in many countries, is now available in a new location. Integrated into Facebook, Messenger, and Instagram, it aims to make daily life easier.

In its recent launch in India, Meta AI can help plan events, recommend restaurants, and even create mood boards for decorating. Students can use it to prep for exams, while social media users can learn more about interesting posts.

Meta AI can generate visuals from text using its "imagine" feature. You can even animate or modify these visuals for a unique touch.

Meta's latest language model, Meta Llama 3, makes Meta AI smarter, faster, and more engaging.

Meta AI is quickly becoming an essential tool for productivity and creativity.

#Meta #MetaAI #AI #Facebook #chatgpt
Python Script for Prediction of Next Closing Value of any Crypto PairProcess: Step 1: Copy this code snippet and paste it to any online/desktop #IDE , #MetaAI or #ChatGPT . Code Snippet: import pandas as pd from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error # Load the CSV file file_path = 'path_to_your_file.csv' # Replace with your file path column_names = [ 'timestamp_start', 'open', 'high', 'low', 'close', 'volume', 'timestamp_end', 'unknown1', 'unknown2', 'unknown3', 'unknown4', 'unknown5', 'unknown6' ] # Load the CSV file with correct column names notcoin_data = pd.read_csv(file_path, names=column_names, skiprows=1) # Convert the timestamp to a readable date format notcoin_data['timestamp_start'] = pd.to_datetime(notcoin_data['timestamp_start'], unit='ms') notcoin_data['timestamp_end'] = pd.to_datetime(notcoin_data['timestamp_end'], unit='ms') # Prepare the features and target variable notcoin_data['next_close'] = notcoin_data['close'].shift(-1) features = notcoin_data[['open', 'high', 'low', 'close', 'volume']].iloc[:-1] target = notcoin_data['next_close'].iloc[:-1] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42) # Train the Random Forest model model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Evaluate the model predictions = model.predict(X_test) mae = mean_absolute_error(y_test, predictions) print(f"Mean Absolute Error: {mae}"). Step 2: Download CSV file(.csv) from #Binance for the cryptocurrency you want to predict the next closing price. After downloading .csv file for the desired crypto, upload this file to MetaAI/ChatGPT or set location/path for IDE to access and use .csv file. Step 3: After setting up code snippet and .csv file, simply run program body to fetch the next closing price. Disclaimer: Prediction results depends upon the provided .csv file data. Prediction might have some difference of 0.001, between the prediction price and actual price. Always conduct research and be aware of crypto volatility. This post is completely unbiased and doesn't guaranteed any prediction outputs made by the program. Beware of potential hazards of cryptocurrencies before taking any investment decision. #StartInvestingInCrypto $NOT $PEPE $BTC Stay Tuned! Let me know in the comment section, If you want more relevant content.

Python Script for Prediction of Next Closing Value of any Crypto Pair

Process:
Step 1: Copy this code snippet and paste it to any online/desktop #IDE , #MetaAI or #ChatGPT .
Code Snippet:
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error

# Load the CSV file
file_path = 'path_to_your_file.csv' # Replace with your file path
column_names = [
'timestamp_start', 'open', 'high', 'low', 'close', 'volume',
'timestamp_end', 'unknown1', 'unknown2', 'unknown3', 'unknown4', 'unknown5', 'unknown6'
]

# Load the CSV file with correct column names
notcoin_data = pd.read_csv(file_path, names=column_names, skiprows=1)

# Convert the timestamp to a readable date format
notcoin_data['timestamp_start'] = pd.to_datetime(notcoin_data['timestamp_start'], unit='ms')
notcoin_data['timestamp_end'] = pd.to_datetime(notcoin_data['timestamp_end'], unit='ms')

# Prepare the features and target variable
notcoin_data['next_close'] = notcoin_data['close'].shift(-1)
features = notcoin_data[['open', 'high', 'low', 'close', 'volume']].iloc[:-1]
target = notcoin_data['next_close'].iloc[:-1]

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Train the Random Forest model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate the model
predictions = model.predict(X_test)
mae = mean_absolute_error(y_test, predictions)

print(f"Mean Absolute Error: {mae}").

Step 2: Download CSV file(.csv) from #Binance for the cryptocurrency you want to predict the next closing price.
After downloading .csv file for the desired crypto, upload this file to MetaAI/ChatGPT or set location/path for IDE to access and use .csv file.

Step 3: After setting up code snippet and .csv file, simply run program body to fetch the next closing price.

Disclaimer:
Prediction results depends upon the provided .csv file data.
Prediction might have some difference of 0.001, between the prediction price and actual price.
Always conduct research and be aware of crypto volatility.
This post is completely unbiased and doesn't guaranteed any prediction outputs made by the program.
Beware of potential hazards of cryptocurrencies before taking any investment decision.
#StartInvestingInCrypto
$NOT $PEPE $BTC

Stay Tuned!
Let me know in the comment section, If you want more relevant content.
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