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Retail Sales Analysis and Forecast using Machine Learning

Introduction

Retail Sales Forecast employs advanced machine learning techniques, prioritizing careful data preprocessing, feature enhancement, and comprehensive algorithm assessment and selection. The streamlined Streamlit application integrates Exploratory Data Analysis (EDA) to find trends, patterns, and data insights. It offers users interactive tools to explore top-performing stores and departments, conduct insightful feature comparisons, and obtain personalized sales forecasts. With a commitment to delivering actionable insights, the project aims to optimize decision-making processes within the dynamic retail landscape.


Table of Contents

  1. Key Technologies and Skills
  2. Installation
  3. Usage
  4. Features
  5. Contributing
  6. License
  7. Contact

Key Technologies and Skills

  • Python
  • Scikit-Learn
  • PostgreSQL
  • Numpy
  • Pandas
  • Plotly
  • Matplotlib
  • Seaborn
  • Streamlit

Installation

To run this project, you need to install the following packages:

pip install scikit-learn
pip install xgboost
pip install psycopg2
pip install numpy
pip install pandas
pip install plotly
pip install matplotlib
pip install seaborn
pip install streamlit

Usage

To use this project, follow these steps:

  1. Clone the repository: git clone https://github.com/gopiashokan/Retail-Sales-Forecast.git
  2. Install the required packages: pip install -r requirements.txt
  3. Run the Streamlit app: streamlit run app.py
  4. Access the app in your browser at http://localhost:8501

Features

Data Preprocessing:

  • Data Understanding: The dataset comprises store, sales, and features data, offering details on store attributes like name, department, date, type, size, weekly sales, and environmental factors such as holiday status, temperature, fuel price, multiple markdowns, CPI, and unemployment. The primary focus is on predicting weekly sales, serving as the target variable for our modeling endeavors. This initial exploration forms the basis for subsequent data preprocessing and model development.

  • Encoding and Data Type Conversion: The process involves preparing categorical features for modeling by transforming them into numerical representations, considering their inherent nature and relationship with the target variable. Simultaneously, data types are converted to align with the modeling process requirements, ensuring seamless integration and compatibility. This step facilitates the effective utilization of categorical information in the subsequent stages of the project.

  • Handling Null Values: Notably, the 'MarkDown' columns present a challenge with over 50% null values, while other columns exhibit minimal null values. To address this, we employ machine learning models to predict and impute the missing values, ensuring a more complete and robust dataset for subsequent analysis and modeling. This strategic approach allows us to mitigate the impact of missing data on the overall quality of our dataset.

  • Feature Improvement: Emphasizing enhanced modeling effectiveness, we concentrate on refining the dataset. This involves creating new features to extract deeper insights and enhance overall dataset efficiency. Evaluation, conducted through Seaborn's Heatmap, reveals that aside from Size and Type with correlation values of 0.21 and 0.17 (absolute value) respectively, no other columns exhibit a strong correlation with weekly sales. This underscores the need for a strategic feature enhancement to bolster the predictive power of our model.

Machine Learning Regression Model:

  • Multiple Models: Recognizing the challenge posed by over 50% null values in the 'MarkDown' columns, we adopt a comprehensive approach. Two separate machine learning models are trained to predict weekly sales – one leveraging the 'MarkDown' features and another excluding them. This dual-model methodology enables a thorough examination of the influence of 'MarkDown' columns on predictive accuracy, shedding light on the optimal approach for incorporating this information into the modeling process.

  • Algorithm Assessment: In the realm of regression, our primary objective is to predict the continuous variable of weekly sales. Our journey begins by splitting the dataset into training and testing subsets. We systematically apply various algorithms, evaluating them based on training and testing accuracy using the R2 (R-squared) metric, which signifies the coefficient of determination. This process allows us to identify the most suitable base algorithm tailored to our specific data.

  • Algorithm Selection: After a thorough evaluation, two contenders, the Extra Trees Regressor and Random Forest Regressor, demonstrate commendable testing accuracy. Upon checking for any overfitting issues in both training and testing, both models exhibit strong performance without overfitting concerns. I choose the Random Forest Regressor for its ability to strike a balance between interpretability and accuracy, ensuring robust performance on unseen data.

  • Model Accuracy and Metrics: Upon optimizing parameters, model1 and model2 exhibit impressive accuracies of 97.4% and 97.7%, respectively. Opting for model1 (with MarkDowns) ensures robust predictions for unseen data. Additional evaluation includes key metrics like mean absolute error, mean squared error, root mean squared error, and the coefficient of determination (R-squared), offering a comprehensive assessment of the model's performance and reliability.

  • Model Persistence: We conclude this phase by saving our well-trained model to a pickle file. This strategic move enables us to effortlessly load the model whenever needed, streamlining the process of making predictions on weekly sales in future applications.

Exploratory Data Analysis (EDA) - Streamlit Application:

Migrating to SQL:

  • Following the weekly sales predictions, the data is migrated to a PostgreSQL database. Leveraging PostgreSQL as a Relational Database Management System allows us to store structured data in a tabular format.
  • Utilizing SQL queries, we seamlessly access the required data from the database, facilitating efficient retrieval and analysis of pertinent information.

Top Sales:

  • Explore the Top Stores option to discover the top 10 stores based on weekly sales, with the flexibility to select a specific date and either an overall or department-specific view.
  • Additionally, the Top Departments option reveals the top 10 departments by weekly sales, allowing users to customize their analysis by choosing a date and either an overall or store-specific perspective.
  • These features offer a dynamic way to gain insights into the highest-performing stores and departments under various criteria.

Comparison:

  • Gain a detailed understanding of weekly sales dynamics by analyzing the influence of key features such as type, size, holiday, temperature, fuel price, CPI, unemployment, and multiple markdowns.
  • This section allows users to assess feature impact through comparisons of current and previous weeks, exploration of top and bottom-performing stores, and manual evaluations of two distinct stores with departments.
  • These comparisons provide a comprehensive view of how different factors contribute to store sales, offering valuable insights into performance trends and patterns.

Features:

  • Embark on a detailed analysis to understand how the different ranges of features affect weekly sales. Explore the impact of various feature ranges on sales, considering both date-wise and store-wise perspectives.
  • This examination covers features such as type, size, holiday, temperature, fuel price, CPI, unemployment, and multiple markdowns. The comprehensive study ensures a thorough grasp of how each feature uniquely influences the data, offering valuable insights into sales patterns.

Prediction:

  • Users provide input parameters such as date, store, department, holiday, temperature, fuel price, CPI, unemployment, and multiple markdowns. Leveraging the pre-trained Random Forest Regressor model stored in a pickle file, the system predicts weekly sales based on the user's input.
  • Users can experiment with various combinations of input values, allowing our machine learning regression model to dynamically forecast weekly sales tailored to the provided data parameters. This interactive feature enhances user engagement and facilitates personalized predictions.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.


License

This project is licensed under the MIT License. Please review the LICENSE file for more details.


Contact

📧 Email: gopiashokankiot@gmail.com

🌐 LinkedIn: linkedin.com/in/gopiashokan

For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.