batuhan-basoglu/Mini-Project-of-Machine-Learning
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Fixed the variable names.

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Batuhan Berk Başoğlu 2025-09-26 21:25:15 -04:00
parent bc377aa9fa
commit 670c0c9869
Signed by: batuhan-basoglu
SSH key fingerprint: SHA256:kEsnuHX+qbwhxSAXPUQ4ox535wFHu/hIRaa53FzxRpo
2 changed files with 8 additions and 8 deletions
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mini-batch-sgd-logistic-regression-wdbc.py
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@ -177,7 +177,7 @@ if __name__ == "__main__":
# cancer2.drop(cancer2[0], axis = 1, inplace = True)
# split data into train / tests datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
X_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
'''
missing_rows = df[df.isin(['?', 'NA', 'na', '']).any(axis=1)] # checks null values
print(f"Rows with null values: {len(missing_rows)}")
@ -256,13 +256,13 @@ if __name__ == "__main__":
x_test_raw = df_test.drop(columns=['Diagnosis']).values.astype(np.float64)
x_test_scaled = (x_test_raw - model.mean) / model.std
bias_test = np.ones((x_test_scaled.shape[0], 1), dtype=np.float64)
X_test = np.hstack((bias_test, x_test_scaled))
x_test = np.hstack((bias_test, x_test_scaled))
y_test = df_test['Diagnosis'].values.astype(int)
test_acc = model.score(X_test, y_test)
test_acc = model.score(x_test, y_test)
print(f"Mean accuracy on testing data: {test_acc:.4f}")
# predict Y values using the trained data
first_10 = X_test[:10]
first_10 = x_test[:10]
y_hat = model.predict(first_10)
print("\nFirst 10 predictions:", y_hat.ravel())