Ayoola T.

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Data Science Pipeline Script PRODUCTION-READY TEMPLATE • REFERENCE IMPLEMENTATION Overview: This script defines an end-to-end Machine Learning pipeline optimized for tabular classification. It handles basic dataset preprocessing, trains a RandomForestClassifier ensemble, evaluates test accuracy, prints validation reports, and finalizes with binary serialization utilizing joblib. import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, accuracy_score import joblib def load_and_preprocess_data(filepath): """ Loads a dataset, handles basic missing values, and splits features from the target variable. """ print("Loading dataset...") df = pd.read_csv(filepath) # Handle missing values df = df.dropna() # Assuming 'target' is the column name for the label X = df.drop(columns=['target']) y = df['target'] return X, y def train_pipeline(): """ Main data science pipeline: Data generation, splitting, training, evaluation, and model serialization. """ print("Generating synthetic data for demonstration...") # Generate random synthetic dataset Page 1 np.random.seed(42) X_dummy = pd.DataFrame(np.random.randn(200, 5), columns=[f'feature_{i}' for i in range(5)]) y_dummy = pd.Series(np.random.choice([0, 1], size=200)) # Split into train and test sets X_train, X_test, y_train, y_test = train_test_split( X_dummy, y_dummy, test_size=0.2, random_state=42 ) print(f