PHP 函数在数据分析中的实践经验

$data = ['name' => null, 'age' => 30, 'city' => 'Tokyo'];
$filtered_data = array_filter($data);
// 输出：['age' => 30, 'city' => 'Tokyo']

$text = "My name is John Doe. My age is 30.";
$replaced_text = str_replace('John Doe', 'Jane Doe', $text);
// 输出："My name is Jane Doe. My age is 30."

$csv_data = "John,30,Tokyo";
$exploded_data = explode(',', $csv_data);
// 输出：['John', '30', 'Tokyo']

$data = [10, 20, 30];
$sum = array_sum($data);
// 输出：60

$data = ['a', 'b', 'a', 'c', 'b'];
$counts = array_count_values($data);
// 输出：['a' => 2, 'b' => 2, 'c' => 1]

$data = ['name' => 'John Doe', 'age' => 30];
var_dump($data);
// 输出：array(2) { ["name"]=> string(7) "John Doe" ["age"]=> int(30) }

$data = [1, 2, 3, 4, 5];
$random_index = array_rand($data);
$random_element = $data[$random_index];
// 输出：例如，3

$data = [
    // 特征：年龄、收入、性别、是否忠诚会员
    ['age' => 30, 'income' => 50000, 'gender' => 'male', 'loyal' => true],
    // ... 其他客户数据
];

// 训练数据
$X_train = array_map(function($row) {
    // 移除目标变量（是否流失）
    unset($row['loyal']);
    return $row;
}, $data);

$y_train = array_column($data, 'loyal');

// 训练逻辑回归模型
$model = new \LogisticRegression();
$model->train($X_train, $y_train);

// 测试数据
$X_test = array_map(function($row) {
    // 移除目标变量
    unset($row['loyal']);
    return $row;
}, $data);

// 预测概率
$y_pred_proba = $model->predict_proba($X_test);

// 评估模型
$accuracy = metrics\accuracy_score($y_test, $y_pred_proba);