I have actually 3 independent variable and 1 dependent variable i want to predict the unknown data that i need .I have done it with regression but i am facing errors in ANN.
first thing is that data is non-linear so we may not use Multiple linear regression .I had applied random forest but the pridiction is not accurate for all values.
3 independent variable are -hieght(100,300,500), for every single height -angle (0,30....180) and 112 pressure point for every single angle.
i actually want ton predict data for different angles with the interval of 5 degree. Bahadorreza Ofoghi Aparna Sathya Murthy
Bahadorreza Ofoghi
I do agree with you about SVR, in addition using some ensemble approaches can be effective
can you please send the svr code for this type of dataset ??
Parsa Kharazi Bahadorreza Ofoghi Aparna Sathya Murthy
it's a regression task
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
# Create the SVR model
svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)
# Assuming you have your training data and target variable
X_train = # training data
y_train = # target variable
# Preprocess the data (e.g., feature scaling)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
# Train the SVR model
svr.fit(X_train_scaled, y_train)
# Assuming you have your test data
X_test = # test data
# Preprocess the test data (using the same scaler)
X_test_scaled = scaler.transform(X_test)
# Make predictions
y_pred = svr.predict(X_test_scaled)
# You can then use the predicted values for evaluation or further analysis
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