Great, let’s populate the sales-forecasting-predictive-model project with your initial README.md. This will formally describe the project, its purpose, structure, and usage—tailored to the Malawian company you've selected for the case study.
# Sales Forecasting Predictive Model
This project presents a time series-based predictive modeling solution designed to forecast future sales trends using historical data. The model incorporates ARIMA and Prophet techniques to generate reliable forecasts that help guide strategic planning and inventory management.
## Case Study: Rab Processors Limited (Malawi)
**Rab Processors Ltd** is one of Malawi’s largest agro-processing and manufacturing companies, producing maize flour, edible oils, packaged rice, beans, and other essential commodities. The company distributes its products nationwide through wholesalers, supermarkets, and retail outlets.
### Business Challenge
Rab Processors faces a common challenge in the FMCG (Fast-Moving Consumer Goods) sector: aligning production and distribution with unpredictable consumer demand. Periods of underproduction or overproduction lead to either stockouts or waste, both of which impact profitability.
With growing demand volatility, Rab Processors needs an intelligent forecasting tool to:
- Predict future sales patterns across product lines.
- Optimize inventory and reduce wastage.
- Align production schedules with market demand.
- Support data-driven decisions in procurement and logistics.
## Solution Overview
This project builds a time series forecasting pipeline to address Rab Processors’ demand prediction challenge. Two approaches are used:
1. **ARIMA (Auto-Regressive Integrated Moving Average)**
2. **Prophet (by Facebook) for trend + seasonality modeling**
These models are evaluated for their predictive accuracy, and the best-performing one is recommended for production.
## Project Structure
sales-forecasting-predictive-model/ │ ├── data/ # Raw and processed sales data │ ├── notebooks/ │ ├── 01_data_preparation.ipynb # Exploratory data analysis, data cleaning │ └── 02_modeling_and_forecast.ipynb # ARIMA and Prophet modeling and evaluation │ ├── src/ │ ├── preprocessing.py # Data formatting, transformation functions │ ├── forecasting_arima.py # ARIMA implementation │ ├── forecasting_prophet.py # Prophet implementation │ └── evaluation.py # Metrics: MAPE, RMSE, MAE │ ├── reports/ # Forecast outputs, charts, and evaluation summaries │ ├── app/ # Streamlit dashboard (optional for visual forecasts) │ ├── main.py # Entry script for running the full pipeline │ ├── requirements.txt # Python dependencies └── README.md # Project documentation
## Technologies Used
- Python 3.11+
- Pandas, NumPy
- Statsmodels (for ARIMA)
- Prophet
- Scikit-learn
- Matplotlib, Seaborn
- Streamlit (for optional dashboard)
## How to Run
1. Clone the repository:
```bash
git clone https://github.com/yourusername/sales-forecasting-predictive-model.git
cd sales-forecasting-predictive-model
-
Install required packages:
pip install -r requirements.txt
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Run the pipeline from the main script:
python main.py
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Or, explore the Jupyter notebooks in the
notebooks/directory to follow the step-by-step analysis.
- Generates monthly sales forecasts using robust time series techniques.
- Provides metrics to evaluate accuracy of different models.
- Visualizes forecast trends and confidence intervals.
- Offers exportable reports to guide operational decisions.
- Incorporate external factors such as weather and economic indicators.
- Enable real-time data ingestion and dashboard updates.
- Add product-specific models for granular forecasting.
This project is released under the MIT License.
Built for Rab Processors Ltd to demonstrate the power of data-driven forecasting in agricultural and FMCG sectors.
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Let me know if you want this customized further (e.g. include sample outputs, link to Streamlit app, etc.), or if you're ready to start filling in the `.py` or `.ipynb` files.