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Deep Learning Approach

This section outlines the deep learning methodology used for the Store Sales - Time Series Forecasting project.

Methodology

  • Data Preprocessing & Cleaning:
    Raw sales, oil, holiday, and store data are merged, missing values are handled, and categorical variables are encoded using one-hot encoding.

  • Feature Engineering:
    Lag-based features, rolling statistics, and calendar-based features (such as day of week, holidays, and paydays) are generated to capture temporal patterns and external influences on sales.

  • Exploratory Data Analysis (EDA):
    Data distributions, trends, and correlations are visualized to inform feature selection and model design.

  • Model Development:
    The primary model is a feedforward neural network implemented in Keras/TensorFlow. The architecture consists of multiple dense layers with ReLU activations and dropout regularization to prevent overfitting.

  • Training Strategy:
    The Adam optimizer is used with a learning rate of 0.001. Early stopping is employed to halt training when validation loss stops improving, ensuring optimal generalization.

  • Evaluation:
    Model performance is monitored using Mean Squared Error (MSE) and Mean Absolute Error (MAE) on a validation set. The final model is saved and used for inference on the test set.

Model Architecture

  • Input Layer: Accepts engineered features for each store-date-family combination.
  • Hidden Layers: Two dense layers (96 and 64 units) with ReLU activation and dropout.
  • Output Layer: Single neuron with linear activation for sales prediction.

Training and Optimization

  • Optimizer: Adam (learning rate = 0.001)
  • Loss Function: Mean Squared Error (MSE)
  • Batch Size: 64
  • Epochs: Up to 20, with early stopping (patience = 4)
  • Regularization: Dropout (rate = 0.1) to reduce overfitting

Reproducibility & Automation

  • All steps, from data preparation to model training and inference, are automated via modular scripts.
  • Model checkpoints and logs are saved for transparency and reproducibility.

This deep learning approach is designed to capture both short-term and long-term sales patterns, leveraging rich feature engineering and modern neural network techniques to deliver accurate forecasts for the Kaggle Store Sales competition.