Lecture Notes: Using TensorFlow for Employee Exit Forecasting

Introduction

• Topic: Utilizing AI tools like TensorFlow for employee demand assessment and turnover forecasting.
• Focus: Creating a model to predict employee exits.

TensorFlow Overview

• TensorFlow: Open-source AI library by Google.
  • Offers extensibility and a range of features.

Data Preparation

• Data Source: CSV file with employee data.
  • Includes columns like work experience, competencies, salaries, job satisfaction.
  • Special column left indicates if an employee left (1) or stayed (0).

Model Architecture

• Neural Network Model:
  • Layers:
    • Input layer
    • Multiple hidden layers with ReLU activation function
    • Output layer with Sigmoid activation function (gives exit probability between 0 to 1)

Model Compilation and Training

• Model Compilation:

  • Uses model.compile() to define training configuration.
  • Parameters Defined:
    • Optimizer: Algorithm for updating network weights (e.g., Adam for speed and efficiency).
    • Loss Function: Measures forecast accuracy (Binary Cross Entropy for binary classification).
    • Metrics: e.g., Accuracy to gauge classification precision.

• Training Process:

  • Epochs: 50 complete iterations over the dataset.
  • Batch Size: 32 samples per batch for efficient training.
  • Data Split:
    • 80% for training
    • 20% for validation

Evaluating Model Performance

• Purpose of Data Splitting:
  • Training set: For model training.
  • Validation set: To assess model generalization to unseen data.
  • Detects overfitting (good training performance, poor validation performance).
• Hyperparameter Tuning:
  • Adjustments for optimal performance.
  • Examples: Learning rate and regularization strength.

Additional Concepts

• Learning Rate: Dictates speed of learning.
  • High rate = quick learning, less stable.
  • Low rate = slow learning, more stable.
• Regularization Strength: Penalizes model complexity to prevent overfitting.

Importance of Data Integrity

• Data Leakage: Ensuring validation set doesn’t influence training to prevent optimistic estimates.
• Conclusion: Proper data management ensures unbiased evaluation, prevents overfitting, and optimizes hyperparameters.

Summary

• Training Process:
  • Conducted over 50 epochs with a batch size of 32.
  • Utilizes validation set monitoring for performance.
• Goal: Effective training and selection of an appropriate predictive model.