This project focuses on time series forecasting to predict store sales for Corporation Favorita, a large Ecuadorian-based grocery retailer. The objective is to build a model that accurately predicts the unit sales for thousands of items sold at different Favorita stores.
The goal of this project is to predict sales and optimize business strategies based on data provided. The data includes information on oil prices, holidays_events, store details, and sales transactions. The goal is to explore, preprocess, and develop regression models such as linear regression, XGBoost or ARIMA to predict and understand the factors influencing oil sales and store performance.
To achieve the objectives, we will follow a structured approach:
Data Exploration: Thoroughly explore the provided datasets to understand the available features, their distributions, and relationships. This step will provide initial insights into the store sales data and help identify any data quality issues.
Data Preparation: Handle missing values, perform feature engineering, and encode categorical variables as necessary. This step may involve techniques like imputation, scaling, and one-hot encoding.
Time Series Analysis: Analyze the temporal aspects of the data, including trends, seasonality, and potential outliers. This analysis will provide a deeper understanding of the underlying patterns in store sales over time.
Model Selection and Training: Select appropriate time series forecasting models and train them using the prepared data. Consider incorporating external factors like promotions, holidays, and oil prices, if available, to enhance the forecasting accuracy.
Model Evaluation: Evaluate the trained models using appropriate metrics, such as mean absolute error (MAE), root mean squared error (RMSE), or mean absolute percentage error (MAPE). Assess the models' performance and identify the most accurate and reliable forecasting model.
Model Deployment and Forecasting: Deploy the chosen model to predict store sales for future time periods, leveraging the provided test dataset. Generate forecasts for the target period and assess the model's ability to capture the sales patterns accurately.
By following this methodology, we aim to provide valuable insights to the telecom company and develop a reliable predictive model for customer churn.