Preprocess.py

import pandas as pd

dataset = pd.read_csv("dataset.csv")

X = dataset.iloc[:, :-1]
y = dataset.iloc[:, -1]

numeric_features = X.select_dtypes(
    include=['int64', 'float64']
).columns

categorical_features = X.select_dtypes(
    include=['object', 'string']
).columns

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder())
])

from sklearn.compose import ColumnTransformer

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ]
)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)

X_train = preprocessor.fit_transform(X_train)
X_test = preprocessor.transform(X_test)

print(X_train.shape)
print(X_test.shape)