batuhan-basoglu/Mini-Project-of-Machine-Learning
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Updated the mini batch as well.

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Batuhan Berk Başoğlu 2025-09-26 21:04:37 -04:00
parent 7268194bea
commit 1eb6609e9f
Signed by: batuhan-basoglu
SSH key fingerprint: SHA256:kEsnuHX+qbwhxSAXPUQ4ox535wFHu/hIRaa53FzxRpo
2 changed files with 70 additions and 17 deletions
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linear-regression-parkinsons.py
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@ -118,13 +118,13 @@ if __name__ == "__main__":
high_corr_features.append((col, high_corr.index.tolist()))
if high_corr_features:
print("correlated features (>0.95):")
print("\ncorrelated features (>0.95):")
for feature, correlated_with in high_corr_features:
print(f" {feature} AND {correlated_with}")
# check for weak correlation with target
target_corr = df.corr()['motor_UPDRS'].abs().sort_values(ascending=False)
print("Correlation with target variable descending order:")
print("\nCorrelation with target variable descending order:")
print(target_corr)
'''
@ -146,17 +146,17 @@ if __name__ == "__main__":
# Parkinson[Parkinson.columns[6:]] = normalize
# turn into array for regression
X = X.to_numpy()
x = x.to_numpy()
y = y.to_numpy()
# split data into train 80% / tests datasets 20%
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)
'''
for col in df:
df[col] = pd.to_numeric(df[col], errors='coerce') # convert columns to numeric values
df.dropna(inplace=True) # remove null values
print(f"Rows remaining after drop of the null values: {len(df)}\n")
print(f"\nRows remaining after drop of the null values: {len(df)}\n")
# sanity checks for data validity - realistic parkinson data range estimations
df = df[(df['age'] >= 18) & (df['age'] <= 95)]