Working And Logic.md

Linear Regression Model Evaluation

Importing Libraries

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

Reading the Dataset

df = pd.read_csv("D:\\UpSkillCampus Project11\\continuous_factory_process.csv")

The code reads the dataset from a CSV file and stores it in a pandas DataFrame called df.

Separating Features and Target

X = df.drop(['time_stamp'], axis=1)
y = df['Stage2.Output.Measurement14.U.Setpoint']

The code separates the features and the target variable from the DataFrame. The X variable contains the features by dropping the 'time_stamp' column, and the y variable contains the target variable 'Stage2.Output.Measurement14.U.Setpoint'.

Splitting the Dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

The code splits the dataset into training and testing sets using the train_test_split function. It takes the features (X) and target variable (y) as input and splits them into X_train, X_test, y_train, and y_test sets. The test set size is set to 20% of the total dataset, and a random state of 42 is used for reproducibility.

Creating and Training the Linear Regression Model

model = LinearRegression()
model.fit(X_train, y_train)

The code creates an instance of the LinearRegression model and fits it to the training data using the fit method. This step trains the model on the training set.

Making Predictions on the Test Set

y_pred = model.predict(X_test)

The code uses the trained model to make predictions on the test set by calling the predict method on the model with the test features (X_test) as input. The predicted values are stored in the y_pred variable.

Model Evaluation

mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)

mae = mean_absolute_error(y_test, y_pred)
print('Mean Absolute Error:', mae)

The code calculates the mean squared error (MSE) and mean absolute error (MAE) between the predicted values (y_pred) and the actual target values (y_test) using the mean_squared_error and mean_absolute_error functions, respectively. The computed error values are then printed to the console.

This code reads a dataset, splits it into training and testing sets, trains a linear regression model on the training set, and evaluates the model's performance by calculating the MSE and MAE on the test set.