Overview

I built an interactive Auto Sales Revenue Forecast Dashboard to empower finance teams with accurate revenue projections using historical auto sales data. Leveraging advanced time series modeling, this dashboard provides actionable insights through real-time scenario analysis, helping stakeholders make informed decisions in a dynamic market.

Key Features

• Accurate Forecasting: Achieved a 30% improvement in revenue projection accuracy by implementing a SARIMA model to capture seasonality and trends in auto sales data.
• Interactive Scenario Analysis: Integrated user inputs to adjust variables like growth rates, enabling stakeholders to explore "what-if" scenarios and visualize their impact on future revenue.
• User-Friendly Visualization: Designed with Plotly and hosted on Hex, the dashboard offers intuitive charts that make complex data accessible to non-technical users.

Technical Details

• Data Source: Historical auto sales data, cleaned and preprocessed to ensure quality inputs for modeling.
• Modeling: Used SARIMA (Seasonal ARIMA) to forecast revenue, fine-tuning parameters to account for seasonal patterns and market trends.
• Tools & Technologies: Python, SARIMA, Pandas, Plotly, Hex.
• Feature Engineering: Engineered features to capture seasonality, trends, and external factors, enhancing model performance.

Impact

This project demonstrates my ability to transform raw financial data into actionable insights. By improving forecast accuracy by 30% compared to baseline models, the dashboard enables finance teams to plan more effectively, optimize resource allocation, and respond to market changes with confidence. The interactive design ensures that stakeholders can explore scenarios in real time, bridging the gap between data science and business decision-making.