Time Series Forecasting Analysis

Welcome to the Corporation Favorita Store Sales Forecasting project! This repository is dedicated to predicting store sales for Corporation Favorita, a prominent grocery retailer in Ecuador. Through advanced time series forecasting and analytics, we aim to develop a robust model that accurately predicts unit sales across various items in different Favorita stores.

Introduction

This project dives into the intriguing challenge of predicting store sales by leveraging time series forecasting techniques. We explore a diverse dataset containing information about dates, store details, product information, promotional activities, sales figures, and additional enriching data. The ultimate goal is to provide Corporation Favorita with data-driven insights for optimizing inventory management, promotional strategies, and overall business performance.

Project Description

Title: Time Series Forecasting Analysis Objective: Develop a robust time series forecasting model to predict store sales for Corporation Favorita, providing accurate predictions for unit sales across a diverse range of items in different Favorita stores.

Methodology

To achieve the objectives, we will follow a structured approach:

1. 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.

2. 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.

3. 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.

4. 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 forecasting accuracy.

5. 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.

6. 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.

Scope:

The project encompasses the exploration and analysis of a comprehensive dataset, including dates, store details, product information, promotional activities, and sales figures. Supplementary files containing additional information will be utilized to enhance the predictive capabilities of the models.

Data Overview

The project utilizes several datasets, including:

train.csv:

Training data with features such as store number, product family, onpromotion, and sales.

test.csv:

Test data for predicting target sales for the 15 days after the last date in the training data.

transaction.csv:

Information about transactions made on specific dates.

sample_submission.csv:

A sample submission file in the correct format.

stores.csv:

Store metadata, including city, state, type, and cluster.

oil.csv:

Daily oil prices, given Ecuador's dependency on oil.

holidays_events.csv:

Holidays and Events metadata, including information about transferred holidays.

Hypothesis and Analytical Questions

Hypothesis

Null Hypothesis (H0): There is no significant difference in sales between promoted and non-promoted items. Alternative Hypothesis (H1): Items that are promoted have significantly higher sales compared to non-promoted items.

Analytical Questions

1. Do items on promotion sell more, and is this difference statistically significant?
2. Are sales different between different types of stores?
3. Does the daily oil price correlate with overall sales?
4. Do sales change significantly during holidays?
5. Do some products consistently sell more during certain seasons?
6. Do sales patterns vary depending on the day of the week?

Project Description

Title: Enhancing Store Sales Forecasting for Corporation Favorita

Objective:

Develop a robust time series forecasting model to predict store sales for Corporation Favorita, providing accurate predictions for unit sales across a diverse range of items in different Favorita stores.

Scope:

The project encompasses the exploration and analysis of a comprehensive dataset, including dates, store details, product information, promotional activities, and sales figures. Supplementary files containing additional information will be utilized to enhance the predictive capabilities of the models.

Model Development

I will select appropriate time series forecasting models. Train the models using the prepared data and incorporate external factors if available. Optimize the models by tuning hyperparameters and selecting the best-performing model.

Model Evaluation

I will evaluate the trained models using appropriate metrics such as MAE, RMSE, MSE, or MAPE. Interpret the models to understand the factors driving store sales and their relative importance.

Project Deliverables

Exploratory Data Analysis (EDA) report. Data preprocessing and feature engineering documentation. Trained time series forecasting models. Model evaluation and performance analysis. Final project report summarizing the findings, insights, and recommendations.

Power Bi

https://app.powerbi.com/groups/me/reports/83908fcf-2c5f-4dff-9cb9-60e91da90dd8?pbi_source=desktop

Conclusion

The Regression Project aims to predict store sales accurately using time series forecasting techniques. By leveraging the provided datasets and following the outlined methodology, I aim to provide valuable insights to Corporation Favorita and develop a reliable forecasting model for store sales. The project will enable informed decision-making, optimize inventory management, and improve overall sales performance for the company.

The ultimate objective is to build a model that accurately predicts the unit sales for thousands of items.