How is ML project made through modular programming

In real-life machine learning (ML) projects, modular programming and version control using Git/GitHub are critical for building scalable, maintainable, and collaborative codebases.

Let’s break it down step by step, and then I’ll show you a simple project structure with modular code and explain how it would be used on GitHub.

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🧱 1. What is Modular Programming in ML Projects?

Instead of writing one giant Jupyter notebook, modular programming means:

• Splitting your project into logical modules/files
• Each module has a specific role: data processing, model training, prediction, etc.
• Makes the code reusable, testable, and cleaner

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🗂️ 2. Typical Project Folder Structure

ml-project/
│
├── data/                    # Raw or processed data
│   └── train.csv
│
├── notebooks/               # Jupyter notebooks for exploration or reports
│   └── EDA.ipynb
│
├── src/                     # Source code
│   ├── __init__.py
│   ├── data_loader.py       # Load and preprocess data
│   ├── model.py             # Train and evaluate model
│   ├── predict.py           # Make predictions with saved model
│   └── utils.py             # Utility functions
│
├── models/                  # Saved models
│   └── model.pkl
│
├── main.py                  # Run the pipeline end-to-end
├── requirements.txt         # Project dependencies
└── README.md                # Project overview

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🧑‍💻 3. Sample Modular Code: Titanic Prediction (Simplified)

🔹 src/data_loader.py

import pandas as pd
from sklearn.model_selection import train_test_split

def load_data(path):
    df = pd.read_csv(path)
    df = df.dropna()
    X = df[['Pclass', 'Age', 'Fare']]
    y = df['Survived']
    return train_test_split(X, y, test_size=0.2, random_state=42)

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🔹 src/model.py

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import joblib

def train_model(X_train, y_train):
    model = LogisticRegression()
    model.fit(X_train, y_train)
    joblib.dump(model, 'models/model.pkl')
    return model

def evaluate_model(model, X_test, y_test):
    preds = model.predict(X_test)
    return accuracy_score(y_test, preds)

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🔹 main.py

from src.data_loader import load_data
from src.model import train_model, evaluate_model
import joblib

def run():
    X_train, X_test, y_train, y_test = load_data('data/train.csv')
    model = train_model(X_train, y_train)
    accuracy = evaluate_model(model, X_test, y_test)
    print(f'Model Accuracy: {accuracy:.2f}')

if __name__ == '__main__':
    run()

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🔹 requirements.txt

pandas
scikit-learn
joblib

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🌐 4. Using GitHub

1. Initialize Git:

   git init
   git add .
   git commit -m "Initial commit with modular Titanic ML project"
   

2. Push to GitHub:

   git remote add origin https://github.com/yourusername/titanic-ml.git
   git push -u origin main
   

3. Best Practices on GitHub:
   • Write a clear README.md with:
     • Project goal
     • Setup instructions
     • Example commands
   • Use .gitignore to exclude large files like datasets or .pkl models.
   • Commit frequently with clear messages.
   • Use branches for experimentation (git checkout -b feature-model-tuning).

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🧠 Why This Approach?

• ✅ Easier to debug and update.
• ✅ Collaborators can work on separate modules without conflicts.
• ✅ GitHub enables version control, code reviews, and issue tracking.

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