Predictive Sales Dashboard for E-Commerce Retaile

May 18 / Artur Bine
Predictive Sales Dashboard for E-Commerce Retail is a data analytics capstone project focused on forecasting sales trends and delivering actionable insights for strategic decision-making in a digital retail context. The project simulates a real-world business scenario, where an e-commerce company seeks to optimize its inventory, marketing strategies, and revenue forecasts using historical sales data.
The main objective is to transform raw data into an interactive, predictive dashboard that business stakeholders can use to understand key metrics like monthly revenue, top-selling products, customer segments, and future sales performance. The solution blends traditional analytics with machine learning techniques to support data-driven planning.

Business Questions Addressed

  • What are the seasonal trends and revenue patterns across product categories?
  • Which products and customer segments drive the most sales?
  • What are the forecasted monthly sales for the next 6 months?
  • How can the company reduce stockouts and overstocking based on predicted demand?

Tools & Technologies Used

The project utilized a combination of data analysis, modeling, and visualization tools:

Data Preparation & Modeling
: Python (Pandas, NumPy, scikit-learn, statsmodels), Excel
Database & Querying: SQL (for extracting structured datasets)
Dashboard & Visualization: Power BI (interactive reports), Matplotlib, Seaborn
Prediction Techniques: Linear regression, ARIMA time series modeling
Project Management: Trello (task tracking), GitHub (version control)

Project Workflow

  • Data Collection
    Imported and cleaned historical e-commerce sales data (CSV/SQL), including customer orders, returns, product details, and timestamps.
  • Data Wrangling & EDA
    Handled missing values, normalized categories, engineered features (e.g., customer lifetime value, product popularity), and explored trends using Python and visual tools.
  • Predictive Modeling
    Built time series models (ARIMA and Prophet) and regression models to predict future monthly sales and revenue. Evaluated model accuracy using RMSE and cross-validation.
  • Dashboard Development
    Created a fully interactive dashboard in Power BI with drill-down capabilities, KPI cards, filters by region/product, and embedded forecast visuals
  • Final Insights
    Provided actionable recommendations for inventory planning, product bundling, and campaign targeting based on forecasted demand and historical trends.


Key Insights & Results

  • Identified Q4 as the peak sales season, with electronics and home goods showing the strongest growth.
  • Predicted a 15% increase in revenue over the next 6 months with upcoming campaigns.
  • Highlighted underperforming product categories and suggested bundling strategies.
  • Reduced inventory misalignment risks by simulating demand per warehouse location

Learning Outcomes

  • Gained hands-on experience in full-cycle data analytics: from data ingestion to actionable insights.
  • Learned to integrate Python-based predictive models into business dashboards.
  • Improved skills in SQL, data storytelling, and KPI design for stakeholder reporting.
  • Practiced agile project delivery, version control, and presenting findings to non-technical audiences

Project Assets

📊 Power BI Dashboard Preview (Demo)

🧾 GitHub Repository with Code & Dataset

📄 Project Report PDF
Artur Bine - Data Analyst Student
Created with