app.py

app.py

@@ -0,0 +1,35 @@
+# Create the dataset
+data = {
+    'num_students': [500, 600, 700, 800, 900],
+    'temperature': [20, 21, 22, 23, 24],
+    'num_rooms': [30, 35, 40, 45, 50]
+}
+
+# Split the dataset into features (X) and target (y)
+X = [[1, num_students, temperature] for num_students, temperature in zip(data['num_students'], data['temperature'])]
+y = data['num_rooms']
+
+# Calculate the coefficients using normal equations
+XTX = [[sum(x[i] * x[j] for x in X) for j in range(len(X[0]))] for i in range(len(X[0]))]
+XTy = [sum(X[i][j] * y[i] for i in range(len(X))) for j in range(len(X[0]))]
+
+coefficients = [0] * len(X[0])
+for i in range(len(X[0])):
+    coefficients[i] = sum(XTX[i][j] * XTy[j] for j in range(len(X[0])))
+
+# Print the coefficients
+print("Coefficients:", coefficients)
+
+# Predict the number of rooms required for a new scenario
+new_students = 750  # Number of students in the new scenario
+new_temperature = 20  # Temperature in the new scenario
+
+# Create the feature array for the new scenario
+new_scenario = [1, new_students, new_temperature]
+
+# Make the prediction
+predicted_rooms = sum(coefficients[i] * new_scenario[i] for i in range(len(new_scenario)))
+
+print("Number of students:", new_students)
+print("Temperature:", new_temperature)
+print("Predicted number of rooms:", predicted_rooms)