An end-to-end Machine Learning pipeline that uses Linear Regression to predict retail sales based on omnichannel advertising spend (TV, Radio, Newspaper). Built entirely in Python.
Un pipeline de Machine Learning de extremo a extremo que usa Regresion Lineal para predecir ventas minoristas basadas en el gasto en publicidad omnicanal. Construido enteramente en Python.
- Language: Python 3.12+
- Machine Learning:
scikit-learn - Data Manipulation:
pandas,numpy - Visualization:
matplotlib,seaborn
How do we allocate the marketing budget? This model analyzes historical data to quantify the exact return on investment (ROI) for each advertising channel, allowing the business to forecast total sales based on future ad spend.
¿Como asignamos el presupuesto de marketing? Este modelo analiza datos historicos para cuantificar el retorno de inversion (ROI) exacto para cada canal publicitario, permitiendo escalar las ventas futuras de acuerdo a la inversion en medios.
# Clone the repository
git clone https://github.com/JulianDataScienceExplorerV2/Omnichannel-Sales-Forecasting-ML.git
cd Omnichannel-Sales-Forecasting-ML
# Install requirements
pip install -r requirements.txt
# Run the model
python "Analisis de ventas con sklearn.py"- TV Spend is the strongest predictor of total sales volume.
- The Linear Regression model correctly captures the relationship with a high R-Squared (
$R^2$ ) value.
Data Analyst & Marketing Science Specialist