Confirmation Rate
<p>Table: <code>Signups</code></p>

<pre>
+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
+----------------+----------+
user_id is the column of unique values for this table.
Each row contains information about the signup time for the user with ID user_id.
</pre>

<p>&nbsp;</p>

<p>Table: <code>Confirmations</code></p>

<pre>
+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
| action         | ENUM     |
+----------------+----------+
(user_id, time_stamp) is the primary key (combination of columns with unique values) for this table.
user_id is a foreign key (reference column) to the Signups table.
action is an ENUM (category) of the type (&#39;confirmed&#39;, &#39;timeout&#39;)
Each row of this table indicates that the user with ID user_id requested a confirmation message at time_stamp and that confirmation message was either confirmed (&#39;confirmed&#39;) or expired without confirming (&#39;timeout&#39;).
</pre>

<p>&nbsp;</p>

<p>The <strong>confirmation rate</strong> of a user is the number of <code>&#39;confirmed&#39;</code> messages divided by the total number of requested confirmation messages. The confirmation rate of a user that did not request any confirmation messages is <code>0</code>. Round the confirmation rate to <strong>two decimal</strong> places.</p>

<p>Write a solution to find the <strong>confirmation rate</strong> of each user.</p>

<p>Return the result table in <strong>any order</strong>.</p>

<p>The result format is in the following example.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:</strong> 
Signups table:
+---------+---------------------+
| user_id | time_stamp          |
+---------+---------------------+
| 3       | 2020-03-21 10:16:13 |
| 7       | 2020-01-04 13:57:59 |
| 2       | 2020-07-29 23:09:44 |
| 6       | 2020-12-09 10:39:37 |
+---------+---------------------+
Confirmations table:
+---------+---------------------+-----------+
| user_id | time_stamp          | action    |
+---------+---------------------+-----------+
| 3       | 2021-01-06 03:30:46 | timeout   |
| 3       | 2021-07-14 14:00:00 | timeout   |
| 7       | 2021-06-12 11:57:29 | confirmed |
| 7       | 2021-06-13 12:58:28 | confirmed |
| 7       | 2021-06-14 13:59:27 | confirmed |
| 2       | 2021-01-22 00:00:00 | confirmed |
| 2       | 2021-02-28 23:59:59 | timeout   |
+---------+---------------------+-----------+
<strong>Output:</strong> 
+---------+-------------------+
| user_id | confirmation_rate |
+---------+-------------------+
| 6       | 0.00              |
| 3       | 0.00              |
| 7       | 1.00              |
| 2       | 0.50              |
+---------+-------------------+
<strong>Explanation:</strong> 
User 6 did not request any confirmation messages. The confirmation rate is 0.
User 3 made 2 requests and both timed out. The confirmation rate is 0.
User 7 made 3 requests and all were confirmed. The confirmation rate is 1.
User 2 made 2 requests where one was confirmed and the other timed out. The confirmation rate is 1 / 2 = 0.5.
</pre>
Database
# Write your MySQL query statement below
SELECT
    user_id,
    ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
FROM
    SignUps
    LEFT JOIN Confirmations USING (user_id)
GROUP BY 1;
