🤖 Minimal Model Training Demo

A streamlined example demonstrating how to train a simple machine learning model using Python, scikit-learn, and pandas.

📋 Table of Contents

• Setup
• Running the Scripts
• Project Structure
• Using the Trained Model
• Troubleshooting
• Next Steps

⚙️ Setup

First, ensure you have Python 3.11+ installed on your system. Install the required dependencies:

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

requirements.txt:

pandas
scikit-learn

🚀 Running the Scripts

Train the Model

Train the logistic regression model by running:

python train.py

This script performs the following operations:

• Loads the data from data/sample.csv
• Preprocesses the features and target variables
• Trains a logistic regression model on the data
• Saves the trained model as model.pkl under the model folder

📁 Project Structure

ml-project/
├── data/
│   └── sample.csv
├── train.py
├── requirements.txt
├── model/
│   └── model.pkl (generated after training)
└── docs/
    ├── README.md
    └── LICENSE

🔍 Using the Trained Model

After training, you can use the model in your applications:

import pickle
import pandas as pd

# Load the trained model
with open('model/model.pkl', 'rb') as f:
    model = pickle.load(f)

# Prepare your data (ensure it has the same format as training data)
new_data = pd.read_csv('path/to/new_data.csv')

# Make predictions
predictions = model.predict(new_data)
print(predictions)

❓ Troubleshooting

• Missing dependencies: Ensure all packages are installed via pip install -r requirements.txt
• File not found errors: Check that your data file exists in the data/ directory
• Version conflicts: Verify your Python version is 3.11+ and package versions match requirements
• Memory issues: For large datasets, consider batch processing or increasing system resources

🔮 Next Steps

• Add cross-validation to improve model robustness
• Experiment with different ML algorithms beyond logistic regression
• Implement hyperparameter tuning to optimize model performance
• Add data visualization to better understand your dataset