--migration php migrations orm:schema-tool:update --force
connect_error) { die("Connection failed: " . $conn->connect_error); } // Next, query the data from the database $sql = "SELECT age, income, purchased FROM customer_data"; $result = $conn->query($sql); // Then, preprocess the data as needed $data = array(); while($row = $result->fetch_assoc()) { $data[] = array($row["age"], $row["income"], $row["purchased"]); } // Scale the data for logistic regression analysis foreach($data as &$row) { $row[0] = $row[0] / 100; // Scale age between 0 and 1 $row[1] = $row[1] / 10000; // Scale income between 0 and 1 } // Split the data into training and testing sets shuffle($data); // Shuffle the data randomly $training_data = array_slice($data, 0, count($data) * 0.8); // Use first 80% of data for training $testing_data = array_slice($data, count($training_data)); // Use remaining 20% of data for testing // Use a logistic regression library (such as PHP-ML or Caret PHP) to build a logistic regression model use Phpml\Classification\LogisticRegression; $classifier = new LogisticRegression(); $X = array_column($training_data, array(0, 1)); // Input features (age and income) $y = array_column($training_data, 2); // Output labels (purchased or not) $classifier->train($X, $y); // Use the trained model to make predictions on new data $correct_predictions = 0; foreach ($testing_data as $row) { $age = $row[0]; $income = $row[1]; $predicted_label = $classifier->predict([$age, $income]); if ($predicted_label == $row[2]) { $correct_predictions++; } } $accuracy = $correct_predictions / count($testing_data); echo "Accuracy: $accuracy"; // Finally, close the MySQL connection $conn->close(); ?>