AI-Powered Sales Forecasting

Project Overview

This project builds a robust sales forecasting pipeline using both deep learning (LSTM) and statistical models (ARIMA/SARIMA) to predict monthly sales revenue based on historical retail data. The solution visualizes trends, compares model accuracy, and integrates results into an interactive Streamlit dashboard for stakeholder analysis.

Objective & Business Context

Forecasting future sales is critical for resource planning, inventory management, and budgeting. This project uses time-series modeling to:

• Predict monthly revenue trends

• Visualize sales patterns over time

• Support data-driven planning with interpretable outputs

Business Value and Real-World Scope

Sales forecasting is a foundational use case in data-driven strategy:

• Retail Sector: Demand forecasting and promotion planning

• Supply Chain: Smarter inventory and distribution models

• Finance: Monthly revenue predictions, budgeting, and capital planning

• Executive Planning: Better stakeholder communication and investment forecasting

Implementation Flow

• Raw Dataset: Superstore.csv (original retail transaction data)

• Processed Datasets:

  • cleaned_Superstore.csv (cleaned and resampled for monthly sales)

  • processed_superstore.csv (used in dashboard visualizations)

  • forecast_results.csv (12-month forecast using ARIMA)

• Timeframe: Daily data resampled to monthly aggregates

• Target Variable: Sales

Dataset Overview

Data Preprocessing Workflow

Data Preparation:

• Parsed Order Date as datetime, sorted chronologically

• Resampled to monthly revenue (Sales) totals

• Exported cleaned dataset to cleaned_Superstore.csv

• Normalized sales values using MinMaxScaler

• Generated sequences of 12-month windows for LSTM input

Scaling for Deep Learning

Visual Analytics & Interpretations

• LSTM Forecast vs. Actual Plot: Overlaid comparison to assess model performance

• ARIMA Forecast Chart: Includes 12-month future trend + confidence bounds

• Monthly Sales Trends: Raw and normalized trend visualizations

• Interactive Dashboards: Empower end-users to explore trends and forecasts

LSTM Model Training

• 2-layer LSTM with dropout → dense output layer

• Trained for 100 epochs with validation split

• Predictions inverse-scaled for interpretability

LSTM Model Training

Evaluation & Comparison

Streamlit Dashboard

• auto_arima used to auto-tune best SARIMA hyperparameters

• Model trained on entire dataset, forecasted next 12 months

• Confidence intervals computed for visualization

• Metrics: MAE and RMSE for both models

• Side-by-side plots for predicted vs. actual sales

• Metric cards, historical/forecast plots (Plotly), CSV export

• Designed for intuitive review by non-technical users

✅ salesforecasting.py: Combined ML/statistical implementation

✅ processed_superstore.csv, forecast_results.csv: Cleaned & forecasted outputs

✅ Charts: Actual vs. predicted plots, confidence intervals

✅ Streamlit App: Dashboard with download and visual insights

Key Deliverables

Tools and Libraries Used

• pandas - Data transformation, aggregation

• matplotlib/seaborn - Visualize trends and accuracy

• tensorflow.keras - LSTM deep learning model

• pmdarima/statsmodels - Time-series forecasting

• scikit-learn - Scaling + metrics

• streamlit/plotly - Dashboard UI and charts

• Model Depth: Introduce ensemble models or hybrid models (e.g., LSTM + XGBoost)

• Business Dimensions: Include product-level or regional forecasting granularity

• Automation: Schedule automated retraining and deployment with new data

• Interactivity: Add filtering in dashboard for user-chosen time horizons or segments

Possible Next Steps & Conclusion

Conclusion

This project successfully demonstrates how modern AI techniques can turn historical sales data into actionable revenue forecasts. With both statistical transparency and deep learning’s power, it offers a strong foundation for enterprise forecasting needs — adaptable, interpretable, and production-ready.

Dive into the foundational concepts, algorithms, and real-world relevance behind this project. From machine learning principles to business strategy insights, this conceptual study bridges the gap between technical implementation and applied decision-making—helping you understand not just how it works, but why it matters.

Key Concepts

GitHub Repository

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