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import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split import telebot from telebot.types import Message # Load the data into a Pandas DataFrame df = pd.read_csv('1XBetCrash.csv') # Extract the 'Multiplier' column from the DataFrame y = df['Multiplier'] # Drop the 'Time' and 'Multiplier' columns from the DataFrame X = df.drop(columns=['Time', 'Multiplier']) # Normalize the data using StandardScaler scaler = StandardScaler() X = scaler.fit_transform(X) # Split the data into training and test sets train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=123) # Train multiple models linear_reg = LinearRegression() linear_reg.fit(train_X, train_y) tree_reg = DecisionTreeRegressor(random_state=123) tree_reg.fit(train_X, train_y) forest_reg = RandomForestRegressor(n_estimators=100, random_state=123) forest_reg.fit(train_X, train_y) nn_reg = MLPRegressor(hidden_layer_sizes=(100,), max_iter=1000, random_state=123) nn_reg.fit(train_X, train_y) # Create a Telegram bot object bot = telebot.TeleBot('YOUR_TOKEN') # Define the handler function for the '/predict' command @bot.message_handler(commands=['predict']) def handle_predict(message: Message): # Get the chat ID of the user who sent the message chat_id = message.chat.id # Use the trained models to predict the next 10 values of the multiplier for model in [linear_reg, tree_reg, forest_reg, nn_reg]: predictions = [] for i in range(1, 11): next_X = X[-i].reshape(1, -1) next_y = model.predict(next_X)[0] predictions.append("Prediction {}: {}".format(i, next_y)) # Send a separate message for each model's predictions bot.send_message(chat_id=chat_id, text="Based on Model: {}".format(model.__class__.__name__)) bot.send_message(chat_id=chat_id, text='\n'.join(predictions)) # Start the bot bot.polling()
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