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Signed-off-by: Snehit Sah <snehitsah@protonmail.com>
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Snehit Sah
2025-06-20 19:00:37 +05:30
commit 536aeaeba0
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main.py
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import pandas as pd
import numpy as np
data= pd.read_csv('tren_complete_switched.csv')
X= data.iloc[:, [0,1,2]].values #All rows, all columns except the last
y= data.iloc[:, 3].values #All rows, last column (labels)
#data preprocessing
from sklearn.preprocessing import StandardScaler
scaler =StandardScaler()
X = scaler.fit_transform(X)
#training the svm model
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
#split data into training and testing sets
X_train, X_test, y_train,y_test =train_test_split(X,y,test_size=0.3, random_state=42) #70/30 split
models = ['rbf', 'linear', 'poly', 'sigmoid']
for m in models:
#SVM classifier
model=SVC(kernel=m)
#train the model
model.fit(X_train,y_train)
#make predictions
y_pred=model.predict(X_test)
#evaluate the model
accuracy =accuracy_score(y_test,y_pred)
print(f"Accuracy: {accuracy}, Model: {m}",)
output.txt
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Accuracy: 0.6095238095238096, Model: rbf
Accuracy: 0.6238095238095238, Model: linear
Accuracy: 0.4380952380952381, Model: poly
Accuracy: 0.5380952380952381, Model: sigmoid
tren_complete_switched.csv
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