An end-to-end machine learning sales forecasting dashboard built with Python and Streamlit for analyzing and predicting consumer electronics sales performance.
https://electrotech-sales-forecasting-kbkw9ywxqcpoynm5p6ks3b.streamlit.app
https://github.com/Busayosage/electrotech-sales-forecasting
ElectroTech Sales Forecasting Dashboard is an interactive business intelligence and forecasting platform designed to help organizations analyze historical sales trends and generate predictive sales forecasts using machine learning models.
The system combines:
- Data analysis
- Forecast modeling
- Interactive visualizations
- KPI monitoring
- Business scenario simulation
- Cloud deployment
The dashboard enables users to explore how factors such as pricing, market trends, seasonality, and consumer confidence influence future sales performance.
Retail and consumer electronics companies require accurate forecasting systems to support:
- Inventory planning
- Demand forecasting
- Revenue estimation
- Pricing optimization
- Market strategy
- Seasonal sales analysis
Traditional spreadsheet forecasting methods are often limited, static, and difficult to scale.
This project provides a dynamic machine learning-powered forecasting solution with interactive analytics capabilities.
The project includes multiple forecasting and modeling approaches:
Used as the primary predictive forecasting model.
Used for trend-based time series analysis.
Used during exploratory model experimentation and performance evaluation.
- Total sales KPIs
- Average sales metrics
- Top-performing categories
- Model selection summary
Users can dynamically simulate forecasting scenarios using:
- Month
- Price
- Competitor activity
- Consumer confidence index
- Market trend index
- Seasonality
- Product specifications
- Historical sales trends
- Category performance analysis
- Seasonal sales comparison
- Feature importance charts
- Forecast prediction outputs
The dashboard automatically generates forecasting insights based on selected business conditions.
🏗️ Project Architecture
Raw CSV Dataset
↓
Data Cleaning & Preparation
↓
Exploratory Data Analysis
↓
Feature Engineering
↓
Machine Learning Model Training
↓
Model Serialization (.pkl)
↓
Streamlit Dashboard
↓
Cloud Deployment (Streamlit Cloud)
- Python
- Pandas
- NumPy
- Scikit-learn
- Statsmodels
- Plotly
- Matplotlib
- Streamlit Cloud
- GitHub
- Jupyter Notebook
- VS Code
The application is fully deployed online using:
- GitHub for version control
- Streamlit Cloud for hosting
Deployment workflow:
Local Development
↓
GitHub Repository
↓
Streamlit Cloud Deployment
↓
Public Live Dashboard
electrotech-sales-forecasting/
│
├── data/
│ └── raw/
│
├── notebooks/
│ ├── 01_data_understanding.ipynb
│ ├── 02_data_cleaning.ipynb
│ ├── 03_eda.ipynb
│ ├── 04_modeling.ipynb
│ ├── 05_arima_model.ipynb
│ └── rf_model.pkl
│
├── reports/
│
├── src/
│
├── app.py
├── requirements.txt
├── README.md
└── .gitignore
- End-to-end ML pipeline
- Interactive forecasting dashboard
- Cloud-hosted analytics application
- Time series forecasting
- Business intelligence reporting
- Scenario-based prediction system
- Real-time user interaction
Future production enhancements may include:
- AWS deployment
- Docker containerization
- CI/CD automation
- Real-time API integration
- Authentication system
- Database integration
- Automated retraining pipeline
- Advanced forecasting models
Operations and Performance Data Analyst focused on building practical business intelligence, forecasting, and machine learning solutions using Python, analytics, and cloud technologies.
✅ Completed
✅ Deployed Live
✅ Public Portfolio Project
✅ Cloud Hosted
✅ Machine Learning Integrated