Project Overview:

In this project, I utilized my data analysis expertise to construct a sales forecasting model for a major retail corporation. The fundamental objective was to accurately predict future sales figures which would enable the organization to make informed decisions in procurement, marketing, and stock management.

Data Collection and Preprocessing:

The initial part of the project involved gathering historical sales data along with other relevant data such as holidays, promotions, etc. Data preprocessing was conducted to clean the data, handle missing values, and convert categorical variables into numerical form using techniques like one-hot encoding.

Model Building:

After data preprocessing, I employed various predictive modeling techniques like ARIMA, Exponential Smoothing, and state-of-the-art machine learning models like XGBoost and LSTM. Model performance evaluation was carried out using metrics such as MAE, RMSE, and MAPE to select the best-performing model.

Results and Insights:

The most optimal model was utilized to predict future sales and these predictions were then validated with actual data. Various insights were obtained from the model including seasonal trends, the impact of promotions on sales, and more.

Presentation of Findings:

Results were presented to the stakeholders in a clear and easy-to-understand manner using data visualization tools like Tableau to demonstrate predicted vs actual sales, important features, etc.

Outcome:

As a result, the corporation was able to anticipate sales volumes with increased accuracy, allowing them to better manage inventory and adjust marketing strategies. This resulted in improved operational efficiency and increased profitability.

Conclusion:

This project successfully provided the corporation with a powerful tool in the form of a sales forecasting model, which could assist them in strategic decision making and planning for the future.