Hands-On Sales Forecasting with Python: Analysis of Multiple Linear Regression Model

This article will explore how to build a sales forecasting model using Python, analyze the relationship between TV, radio, and newspaper advertising investments and sales data, and establish an accurate prediction system. The core goal is to help enterprises develop marketing strategies, optimize advertising budget allocation, and replace traditional experience-based judgment with data-driven machine learning methods.

In today's highly competitive business environment, accurately forecasting sales is crucial for enterprises to develop marketing strategies and optimize advertising budget allocation. Traditional experience-based judgment struggles to cope with complex market changes, while machine learning technology provides a data-driven scientific approach for sales forecasting. This project presents a complete sales forecasting solution, building a model to help enterprises plan marketing resources by analyzing the relationship between historical advertising investments and sales.

The project uses a classic advertising dataset, which includes key features: TV (TV advertising investment), Radio (radio advertising investment), Newspaper (newspaper advertising investment), and Sales (sales volume, target variable). The three-dimensional features reflect multi-channel marketing scenarios, and different media have different influence mechanisms: TV has wide coverage but high cost, radio is highly targeted, and newspapers have influence among specific groups.

Built based on the Python ecosystem, core components:

• Pandas: Responsible for preprocessing tasks such as loading CSV data, checking missing values, and converting data types.
• Scikit-learn: Provides data splitting (train_test_split), linear regression model selection, evaluation metrics like R²/MSE, and feature engineering support.

Process steps:

1. Data preparation: Load Advertising.csv, check integrity and distribution (using describe()).
2. Feature-target separation: TV/Radio/Newspaper as features X, Sales as target y.
3. Data splitting: Divide into training/test sets at a ratio of 70/30 or 80/20, fix random_state to ensure reproducibility.
4. Model training: Linear regression fits parameters by minimizing the sum of squared residuals, learning the weight coefficients of each channel.

Evaluation metrics:

• R² (Coefficient of Determination): Measures the model's ability to explain data variation; the closer to 1, the better.
• MSE (Mean Squared Error): Reflects the average deviation between predictions and true values.
• RMSE (Root Mean Squared Error): Consistent with the original unit, intuitively shows the scale of error. Feature importance: TV advertising usually contributes the most, followed by radio; the effect of newspapers varies by industry and audience.

Application value:

• Marketing budget optimization: Simulate expected sales for different budget allocations to find the combination that maximizes ROI.
• Channel effect comparison: Analyze the coefficients and significance of each channel to guide budget reallocation. Expansion directions:
• Non-linear modeling: Try polynomial regression, decision trees, etc., to capture non-linear relationships.
• Time series analysis: Introduce the time dimension, use ARIMA/Prophet to handle seasonal trends.
• Feature expansion: Add external factors such as holidays, promotions, and competitor dynamics.

This project demonstrates a complete data science process: data loading → exploratory analysis → model construction → result evaluation. As a benchmark model, linear regression provides interpretable results to help understand the contribution of each channel. For beginners, sales forecasting is an ideal practice project (moderate dataset, intuitive scenario, complete process); after mastering it, you can explore more complex algorithms and application scenarios.