To create a basic AI without scientific training,follow these steps:

1. Generate a Simple Dataset:

   • Create a dataset with known features and labels. For example, age and income, with a binary label indicating purchase preference.
   • Use a simple rule, such as if a person's income is greater than their age, they like the product.

2. Split the Dataset:

   Divide the dataset into training (8%) and testing (2%) sets. This ensures the model generalizes well to unseen data.

3. Implement Linear Regression:

   • Use a simple linear regression model to predict the binary label based on features.
   • Initialize weights and bias to zero.
   • Use an optimization method (e.g., gradient descent) to minimize prediction error.

4. Training:

   • For each data point, compute the predicted value.
   • Calculate the error and adjust weights and bias using the learning rate.

5. Testing:

   Test the model on the testing set to evaluate accuracy, which is the number of correct predictions divided by total predictions.

6. Evaluation and Application:

   • Use the model to predict on new data to assess its performance.
   • This provides insight into how well the AI generalizes.

Summary: By generating a controlled dataset, splitting it, and implementing a simple linear regression model, you can create a basic AI that learns from a controlled environment. This approach provides a foundation for understanding AI applications and their limitations.