Added the Linear Regression implementations.

mini-batch-sgd-logistic-regression-wdbc.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pandas as pd
+
+'''
+class LogisticRegression:
+    def __init__(self):
+
+    def prepare(self):
+
+    def fit(self):
+
+    def predict(self):
+
+    def score(self):
+'''
+
+if __name__ == "__main__":
+    columns = [
+        'ID', 'Diagnosis',
+        'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean',
+        'compactness_mean', 'concavity_mean', 'concave_points_mean', 'symmetry_mean', 'fractal_dimension_mean',
+        'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
+        'compactness_se', 'concavity_se', 'concave_points_se', 'symmetry_se', 'fractal_dimension_se',
+        'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst',
+        'compactness_worst', 'concavity_worst', 'concave_points_worst', 'symmetry_worst', 'fractal_dimension_worst'
+    ]
+
+    df = pd.read_csv('wdbc.data', header=None, names=columns, dtype=str)
+
+    df.drop(columns=['ID'], inplace=True) # drops id column
+
+    missing_rows = df[df.isin(['?', 'NA', 'na', '']).any(axis=1)]  # checks null values
+    print(f"Rows with null values: {len(missing_rows)}")
+
+    df.replace(['?','NA', 'na', ''], pd.NA, inplace=True) # replace null values with NA identifier
+
+    num_cols = df.columns.difference(['Diagnosis'])
+    for col in num_cols:
+        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)}")
+    for col in num_cols:
+        df = df[df[col] >= 0]
+
+    # check if there are still null values
+    assert df.isna().sum().sum() == 0, "There are still some null values."
+
+    df['Diagnosis'] = df['Diagnosis'].astype('category')