text: import pandas as pd
  import numpy as np
  # import matplotlib
  # matplotlib.use("TKagg")
  # import matplotlib.pyplot as plt
  from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
  from sklearn.linear_model import LinearRegression
  
  def predictPrice(stock, days):
      # make data frame
      df = pd.read_csv("csv/" + stock + ".csv", sep='\s*,\s*', header=0, encoding='ascii', engine='python')
      # Store the "Low" in the x var, and the "High" in the y var
      # We want to use the Low as the predictor, the independent variable
      # Stock's High is the dependent variable, what we want to predict
      X = df['Low'].values.reshape(-1,1)
      y = df['High'].values.reshape(-1,1)
      # Here we split the data: 80% for training, 20% for testing in test_size
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      # Here we train our algorithm, "regressor"
      regressor = LinearRegression()
      regressor.fit(X_train, y_train)
      # Forecast out inputted # of days
      # Shift data frame columns up to predict "days" amount into the future
      df['Prediction'] = df['High'].shift(days)
      # Drop unessential columns and remove "days" amount of bottom rows
      y_forecast = df.drop(['Date', 'Open', 'High', 'Low'],1)[days:]
      prediction = regressor.predict(y_forecast)
      # regressor_confidence = regressor.score(X_test, y_test)
      return prediction[days]
  