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Finished the implementation of the python code.

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Batuhan Berk Başoğlu 2025-09-18 20:58:02 -04:00
parent 5702c3c1b8
commit 455b48c89b
Signed by: batuhan-basoglu
SSH key fingerprint: SHA256:kEsnuHX+qbwhxSAXPUQ4ox535wFHu/hIRaa53FzxRpo
6 changed files with 540 additions and 159 deletions
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linear-regression-parkinsons.py
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@ -27,8 +27,8 @@ class LinearRegression:
class LinearRegression:
'''
Constructor for the Linear Regression with analytical. It uses bias. It also
initializes the weight, mean and std.
Constructor for the linear regression with analytical solution. It uses bias. It also
initializes the weight, mean and standard deviation.
'''
def __init__(self, add_bias):
self.add_bias = add_bias # bias to prepend a column of ones (the intercept term)
@ -60,7 +60,8 @@ class LinearRegression:
def fit(self, x: pd.DataFrame, y: pd.Series) -> "LinearRegression":
'''
Fit method to fit X and Y datas through pandas and train the linear model by analytical solution.
It uses pandas DataFrame for the X and Series for the Y.
It uses pandas DataFrame for the X and Series for the Y. It uses the linear regression formula
to calculate weight
'''
x = self.prepare(x)
y = pd.Series(y).astype("float64")
@ -84,7 +85,7 @@ class LinearRegression:
def predict(self, x: pd.DataFrame) -> pd.Series:
'''
Predict method is used to test trained data to do X prediction by multiplying X and weight vectors.
Predict method is used to test trained data to do Y prediction by multiplying X and weight vectors.
'''
if self.w is None: # if weight is empty, throw error
raise RuntimeError("Model is not fitted yet. Call `fit` first.")
@ -95,7 +96,7 @@ class LinearRegression:
def score(self, x: pd.DataFrame, y: pd.Series) -> float:
'''
This method is used to calculate coefficient of determination to assess the goodness
of fit from a regression model
of fit from the linear regression model
'''
y_pred = self.predict(x) # predicts Y value with X predict method.
y = pd.Series(y).astype('float64')
@ -127,7 +128,7 @@ if __name__ == "__main__":
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)}")
print(f"Rows 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)]
@ -157,12 +158,9 @@ if __name__ == "__main__":
# evaluation of the model
print("\nR² on training data:", model.score(x_train, y_train))
print("\nR² on testing data:", model.score(x_test, y_test))
print("R² on testing data:", model.score(x_test, y_test))
# predict Y values using the trained data
preds = model.predict(x_test)
print("\nFirst 5 predictions:")
print(preds.head())
print("\nWeights:")
print(model.w.round(4))
print("\nFirst 10 predictions:")
print(preds.head(10))