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Omphemetse-ops / P1_DataSciences

This data science project focuses on leveraging predictive modeling techniques to forecast sales trends and optimize business strategies. By analyzing historical sales data and external factors influencing sales performance, this project aims to provide actionable insights for enhancing revenue generation and improving decision-making processes.
MIT License
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# Part 2: Data Preparation-Split data into Training and Tests #11

Open Omphemetse-ops opened 9 months ago

Omphemetse-ops commented 9 months ago
  1. Set the proportion: Decide on the ratio of data to allocate for training and testing (e.g., 70% for training and 30% for testing).
  2. Randomize data: Randomly shuffle the dataset to ensure that the training and testing sets are representative of the overall data.
  3. Split the data: Use libraries like scikit-learn in Python to split the data into training and testing sets based on the determined ratio.
  4. Verify the split: Check that both sets cover a diverse range of data points to avoid bias and ensure the model's generalization capability.
  5. Use the sets: Train the model on the training data and evaluate its performance on the testing data to validate the model's predictions and accuracy.

[Divide the data into two subsets(X and Y) for training the model and evaluating its performance.]