34 lines
966 B
Python
34 lines
966 B
Python
import pandas as pd
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import numpy as np
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data= pd.read_csv('tren_complete_switched.csv')
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X= data.iloc[:, [0,1,2]].values #All rows, all columns except the last
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y= data.iloc[:, 3].values #All rows, last column (labels)
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#data preprocessing
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from sklearn.preprocessing import StandardScaler
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scaler =StandardScaler()
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X = scaler.fit_transform(X)
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#training the svm model
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from sklearn.svm import SVC
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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#split data into training and testing sets
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X_train, X_test, y_train,y_test =train_test_split(X,y,test_size=0.3, random_state=42) #70/30 split
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models = ['rbf', 'linear', 'poly', 'sigmoid']
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for m in models:
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#SVM classifier
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model=SVC(kernel=m)
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#train the model
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model.fit(X_train,y_train)
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#make predictions
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y_pred=model.predict(X_test)
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#evaluate the model
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accuracy =accuracy_score(y_test,y_pred)
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print(f"Accuracy: {accuracy}, Model: {m}",) |