INNER CODE UNIT · Python

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NishkarshRaj/100DaysofMLCode · 10_Dimensionality_Reduction/Kernel_PCA/kernel_pca.py:16

def main():
    # Importing the dataset
    dataset = pd.read_csv('Social_Network_Ads.csv')
    X = dataset.iloc[:, [2, 3]].values
    y = dataset.iloc[:, 4].values

    # Splitting the dataset into the Training set and Test set
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

    # Feature Scaling
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)

    # Applying Kernel PCA
    kpca = KernelPCA(n_components=2, kernel='rbf')
    X_train = kpca.fit_transform(X_train)
    X_test = kpca.transform(X_test)

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