Commit 27cddcbc authored by Sajana_it20194130's avatar Sajana_it20194130

Upload New File

parent 66137f67
# Importing the libraries
import numpy as np
import joblib
from sklearn.preprocessing import StandardScaler
# Load the trained model
loaded_model = joblib.load('random_forest_model.joblib')
scaler = joblib.load('standard_scaler.joblib')
# Accept user inputs for two numerical parameters
param1 = float(input("Enter the file size: "))
param2 = float(input("Enter the header size: "))
user_input = scaler.transform(np.array([[param1, param2]]))
# Make predictions based on the user inputs
prediction = loaded_model.predict(user_input)
# Display the prediction
print("Predicted class:", prediction[0])
## 205824 1024
## 241664 4096
## 405504 512
## 30208 512
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