Create a script in R to implement a machine learning model for predicting stock prices

Here's a basic script in R for implementing a machine learning model to predict stock prices: ``` # Load necessary libraries library(tidyverse) library(caret) library(xts) library(quantmod) # Define the ticker symbol and the time period for prediction ticker <- "AAPL" start_date <- as.Date("2015-01-01") end_date <- as.Date("2023-03-10") # Import stock data getSymbols(ticker, from = start_date, to = end_date) stock_data <- as.data.frame(AAPL) # Create new columns for technical indicators stock_data$MA_50 <- SMA(Cl(AAPL), n = 50) stock_data$RSI_14 <- RSI(Cl(AAPL), n = 14) # Define training and testing data sets train_size <- floor(0.75 * nrow(stock_data)) train_set <- stock_data[1:train_size, ] test_set <- stock_data[(train_size + 1):nrow(stock_data), ] # Create input and output variables for the model input_vars <- c("MA_50", "RSI_14") output_var <- "AAPL.Adjusted" # Train the model using caret package model <- train(train_set[, input_vars], train_set[, output_var], method = "lm", trControl = trainControl(method = "cv")) # Use the trained model to make predictions on the test set predictions <- predict(model, test_set[, input_vars]) # Calculate mean squared error (MSE) of predictions mse <- mean((predictions - test_set[, output_var])^2) # Print the MSE cat("Mean Squared Error:", mse) ``` Note that this is just a basic example and there are many ways to improve the accuracy of the model, such as adding more technical indicators, using different machine learning algorithms, or incorporating external factors like news or economic data.