Sales Forecasting Problem

Forecasting sales based on weekends, holidays, and weather conditions is a classic time series problem with external regressors. Given your setup with MySQL and Python, here’s a step-by-step approach:

1. Data Collection:

• Sales Data: Extract historical sales data from your MySQL database. This data should ideally be daily sales figures.
• Weather Data: Use APIs like OpenWeatherMap, Weather Underground, or any other reliable source to fetch historical weather data for your restaurant’s location.
• Holidays Data: You can manually create a list of holidays or use libraries like holidays in Python to generate a list for your country.
• Weekends: Using Python’s datetime library, you can quickly determine which days are weekends.

2. Data Preprocessing:

• Merge Data: Combine sales, weather, holidays, and weekend data into a single data frame.
• Handle Missing Values: Use interpolation or imputation methods to handle missing values.
• Feature Engineering: Create binary columns like is_weekend, is_holiday, and weather-related features like is_rainy, is_sunny, etc.

3. Exploratory Data Analysis (EDA):

• Visualize sales trends over time.
• Check for seasonality and trends in the data.
• Analyze sales on holidays and weekends vs. weekdays.
• Investigate the impact of different weather conditions on sales.

4. Model Selection:

Given the nature of the problem, here are some models you can consider:

• Linear Regression with Regressors: Use weekends, holidays, and weather conditions as regressors.
• Time Series Models: ARIMA, SARIMA, or even Prophet by Facebook. Prophet is particularly useful as it can handle holidays and other special events.
• Tree-based Models: Random Forest or gradient-boosted trees can capture non-linear relationships.
• Deep Learning: LSTM or GRU, which are recurrent neural networks, can be used for time series forecasting. However, they might require a larger dataset and more tuning.

5. Model Evaluation:

• Split your data into training and testing sets.
• Train your model on the training set and evaluate its performance on the testing set.
• Use metrics like RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error) to evaluate the model’s forecasting accuracy.

6. Model Deployment:

Once satisfied with the model’s performance, you can deploy it using frameworks like Flask or FastAPI in Python. This way, you can input the current or forecasted weather conditions, whether it’s a holiday or weekend, and get a sales forecast in return.

7. Continuous Monitoring and Updating:

• Regularly monitor the model’s predictions against actual sales.
• Re-train the model periodically with new data to ensure its accuracy remains consistent.

Tools & Libraries:

• Data Handling & Analysis: pandas, numpy
• Visualization: matplotlib, seaborn
• Modeling: scikit-learn, statsmodels, fbprophet, TensorFlow, or PyTorch
• APIs for Deployment: Flask, FastAPI

Remember, while the model provides a forecast, it’s essential to consider other factors and use business acumen to make final decisions.

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