ML MODEL.py

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

data = pd.read_csv('datasets\Thermal_powerplant.csv')

X = data.iloc[:, :-1]
y = data.iloc[:, -1]

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

model = LinearRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

coefficients = model.coef_
intercept = model.intercept_
print(f"Coefficients: {coefficients}")
print(f"Intercept: {intercept}")

import numpy as np

x_input = [float(x) for x in input("Enter values for all features separated by spaces: ").split()]
x_input = np.array(x_input).reshape(1, -1)

y_output = model.predict(x_input)

print(f"The predicted output is {y_output[0]}")