One of the most common challenges in the restaurant and food service industry is demand forecasting. Restaurants often struggle to estimate how many customers they’ll serve or how many menu items they’ll sell on a given day. This uncertainty can lead to:
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Overproduction, resulting in waste and cost inefficiencies.
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Underproduction, leading to stockouts and poor customer experience.
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Staffing issues, affecting labor planning and scheduling.
To address this, we’ll build a machine learning workflow that predicts restaurant demand using both supervised and unsupervised learning techniques, while also leveraging Amazon Forecast for scalable, real-world deployment.
1. Data Collection and Preparation
Why It Matters:
Data quality determines model quality. Poor or missing data results in inaccurate models, regardless of the algorithm.
Data Sources:
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Internal: POS systems (sales, order times, item quantities)
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External: Holidays, weather, local events, promotions
Data Cleaning Techniques:
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Remove missing values:
df = df.dropna() -
Fix invalid values: Replace with mean/median or use interpolation.
df.fillna(df.mean(), inplace=True) -
Format categorical and date fields:
df['date'] = pd.to_datetime(df['date']) df['day_of_week'] = df['date'].dt.dayofweek
Feature Engineering:
Transform raw data into meaningful signals:
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Create lag features (e.g., sales 7 days ago)
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Encode special days like holidays
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Normalize or scale numerical features
2. Exploratory Data Analysis (EDA)
EDA helps identify patterns, outliers, and seasonality. For example:
import matplotlib.pyplot as plt
df['sales'].plot(title='Daily Sales Over Time')
Look for:
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Weekly/daily patterns
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Holiday spikes
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Outliers (data entry errors, extreme demand days)
3. Data Splitting
To avoid overfitting, split the dataset:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
features, target, test_size=0.2, random_state=42
)
Best Practice:
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Use time-based splits for time series data.
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Include a validation set if tuning hyperparameters.
Example split for time series:
train = df[df['date'] < '2023-01-01']
test = df[df['date'] >= '2023-01-01']
4. Model Training
Option A: Linear Regression (Baseline)
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train, y_train)
A simple, interpretable model that provides a baseline for more advanced ones.
Option B: KMeans Clustering (Segmentation)
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3).fit(features)
print(kmeans.labels_)
This helps group similar days or menu items based on demand patterns. It’s unsupervised and helps with exploratory analysis.
Option C: Amazon Forecast (Production-Ready)
Amazon Forecast automates model selection and tuning for time series forecasting.
Steps:
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Upload your data to Amazon S3.
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Define dataset schema (timestamp, item ID, target value).
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Create predictor.
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Generate forecasts.
Advantages:
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Managed infrastructure
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Supports quantile forecasting (p10, p50, p90)
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Scalable to thousands of SKUs/items
5. Model Evaluation
Regression Metrics:
| Metric | Description |
|---|---|
| R² | Explains variance between actual vs predicted |
| RMSE | Measures average prediction error |
| MAE | Mean absolute error (easier to interpret than RMSE) |
from sklearn.metrics import r2_score, mean_squared_error
r2 = r2_score(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
Classification Metrics (if predicting sold-out items):
| Metric | Use |
|---|---|
| Accuracy | % of correct predictions |
| Precision | Relevant positive predictions |
| Recall | Ability to find all positive cases |
| F1-Score | Balance between precision and recall |
6. Model Tuning
Tune hyperparameters to improve accuracy:
from sklearn.model_selection import GridSearchCV
params = {'fit_intercept': [True, False]}
grid = GridSearchCV(LinearRegression(), param_grid=params)
grid.fit(X_train, y_train)
For Amazon Forecast, you can configure forecast horizon, quantiles, and item grouping granularity.
7. Model Deployment
AWS Deployment Workflow:
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Connect Amazon Forecast to dashboards via Amazon QuickSight
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Automate retraining jobs using AWS Lambda + CloudWatch
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Set up batch inference pipelines for predictions
Track Inference:
Monitor how predictions compare with real outcomes:
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Log every prediction and actual value
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Evaluate weekly/monthly prediction accuracy
8. Updating and Re-training
Machine learning models degrade over time (data drift). Update your model by:
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Periodically retraining with new data
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Validating against recent actual demand
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Monitoring changes in accuracy or error
9. Performance Optimization
If your dataset is too large:
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Sample the data:
df.sample(n=1000, random_state=1) -
Use batch training
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Apply feature selection to reduce dimensionality
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Leverage cloud-based compute with GPUs if needed
Predicting restaurant demand is not only a technical problem—it’s a strategic advantage. By implementing a robust ML pipeline that integrates data collection, modeling, evaluation, and deployment, restaurants can significantly improve their operations, cut waste, and enhance customer experience.
Whether you're starting with scikit-learn models or deploying at scale with Amazon Forecast, the key is a clean, repeatable, and well-evaluated workflow.