Problem Statement: Building a predictive model to forecast sales based on historical data.
Detailed Scenario: Use a dataset of past sales to build a linear regression model that predicts future sales.
Usecase Approach: Use Python’s scikit-learn to create and train a linear regression model.
Tools and Modules: scikit-learn, numpy
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Approch:
- Prepare historical data (X = time, y = sales)
- Train a LinearRegression() model using fit()
- Predict future values using predict()
- (Optional) Plot actual vs predicted for clarity
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