Flow_Output.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error

# Read the dataset
df = pd.read_csv("D:\\UpSkillCampus Project11\\continuous_factory_process.csv")

# Separate features and target
X = df.drop(['time_stamp'], axis=1)  # Remove the 'time_stamp' column
y = df['Stage2.Output.Measurement14.U.Setpoint']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate the mean squared error
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)

# Calculate the mean absolute error
mae = mean_absolute_error(y_test, y_pred)
print('Mean Absolute Error:', mae)