Data Science and Analytics Tools - Time Series Forecasting for Sales

 Notes:

• What problem does it solve?
  It forecasts sales data to help businesses predict future sales and manage inventory better.

• How can businesses or users benefit from customizing the code?
  Businesses can adjust the model for different time frames, sales channels, or regions to create tailored forecasts.

• How can businesses or users adopt the solution further, if needed?
  The solution can be further enhanced with additional features like seasonality adjustments, event-based promotions, or marketing effects.

Actual Python Code:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from statsmodels.tsa.holtwinters import ExponentialSmoothing

from sklearn.metrics import mean_absolute_error

# Load your data (assumed to be daily sales data)

data = pd.read_csv('sales_data.csv', parse_dates=['Date'], index_col='Date')

sales = data['Sales']

# Train a Holt-Winters model for forecasting

model = ExponentialSmoothing(sales, trend='add', seasonal='add', seasonal_periods=12)

model_fit = model.fit()

# Forecast for the next 12 periods (months, weeks, etc.)

forecast = model_fit.forecast(12)

# Plot actual and forecasted values

plt.figure(figsize=(10, 6))

plt.plot(sales, label='Actual Sales')

plt.plot(forecast, label='Forecasted Sales', color='orange')

plt.legend()

plt.title('Sales Forecasting using Holt-Winters')

plt.show()

# Evaluate model accuracy

mae = mean_absolute_error(sales[-12:], forecast)

print(f'Mean Absolute Error: {mae}')