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Machine Learning

Lasso vs Ridge

Bagging vs Boosting

Spam Classifier 

Decision Tree Evaluation

Random Forest Explanation

Data Structures & Algorithms

Find the missing integer from a array of consequtive integers

Find the Missing Number

Get Top N Frequent Words

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Sort Strings

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

Select the 2nd highest salary in the engineering department

2nd Highest Salary

7 Day Streak

Find the average yearly purchases for each product

Average Quantity

Employee Project Budgets

Payments Received

Statistics

P-value to a Layman

MLE vs MAP

When would you use metrics like the mean and median?

Best Measure

What is the difference between type I and type II errors?

Type I and II Errors

What are confidence intervals and how are they useful

Confidence Interval Explanation

Behavioral

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Describing a data project and its challenges

Hurdles In Data Projects

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

Given a table of <code>transactions</code>, write a query to find the average quantity of each product purchased per transaction each year.

The output should include the year, product_id, and average quantity of the product purchased per transaction that year. The output table should be sorted by year and product_id in ascending order. The value for avg_quantity should be rounded to two decimal places.

Example:

Input:
<code>transactions</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>year</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>avg_quantity</code></td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>

You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.

Note: Complexity of $O(n)$ required.

Example:

Input:

<pre tabindex="0" class="chroma"><code>nums = [0,1,2,4,5] 
missing_number(nums) -&gt; 3
</code></pre>

You’re given a string that may contain the characters <code>{</code>, <code>}</code>, <code>[</code>, <code>]</code>, <code>(</code>, and <code>)</code>.

Task: Verify that the string is balanced. A balanced string is one where every opening character, <code>{</code>, <code>[</code>, or <code>(</code>, has a corresponding closing character, <code>}</code>, <code>]</code>, or <code>)</code>.

Write a function called <code>is_balanced(string: str) -&gt; bool</code> which verifies the balance of a string.

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

You are tasked with keeping track of a customer’s addresses in your database.

However, customer’s addresses change whenever they move. We want to keep track of their moving history as well as the person that moves in afterwards.

How would you create or modify a schema to keep track of these address changes?

Create a schema to keep track of customer address changes

Address Schema

Data Modeling

We’re given two tables. One is named <code>projects</code> and the other maps employees to the projects they’re working on.

Write a query to get the top five most expensive projects by budget to employee count ratio.

Note: Exclude projects with 0 employees. Assume each employee works on only one project.

<code>projects</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>employee_projects</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>project_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>budget_per_employee</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If there is no index where this condition is satisfied return -1.

Note: the number that lies on the index is calculated to the left side of the list.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 7, 3, 5, 6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>equivalent_index(nums) -&gt; 2
</code></pre>

In this example, the sum of the elements to the left of index 2 (1 + 7) is equal to the sum of the elements to the right of index 2 (5 + 6).

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1,3,5]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>equivalent_index(nums) -&gt; -1
</code></pre>

In this example, there is no index where the sum of the elements to the left is equal to the sum of the elements to the right.

The task is to write a function that takes a list of integers as input and returns the index at which the sum of the integers to the left of the index is equal to the sum of the integers to the right. If no such index exists, the function should return -1.

Find the Index with Equal Left and Right Sum

Let’s say Verizon launches a wireless plan that offers unlimited data after 8 p.m. each night, and you notice some customers suddenly using 10x more data during that window than the average user.

How would you distinguish between fraud and product abuse in this scenario, and how might your findings shape future product or policy decisions?

How would you determine if high off-peak data usage is fraud or abuse, and what would you do about it?

Unlimited Plan Abuse

Product Sense & Metrics

Let’s say you’re a risk analyst at a telecom company, and you have access to transaction and billing data covering millions of call and data usage events each month.

Leadership suspects that revenue may be leaking due to mismatches between the number of usage events recorded and what customers are actually billed, but it’s unclear if the cause is system glitches, delayed reporting, pricing errors, or even fraud.

How would you go about analyzing the data to surface where revenue is leaking, and what patterns or signals would help you tell apart technical issues from intentional misuse?

How would you analyze telecom usage and billing data to identify and differentiate causes of revenue leakage?

Revenue Leakage Signals

Analytics

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on $O(n)$ time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

You’re given two tables, payments and users. The payments table holds all payments between users with the <code>payment_state</code> column consisting of either <code>&#34;success&#34;</code> or <code>&#34;failed&#34;</code>. 

How many customers that signed up in January 2020 had a combined (successful) sending and receiving volume greater than $100 in their first 30 days?

Note: The <code>sender_id</code> and <code>recipient_id</code> both represent the <code>user_id</code>.

<code>payments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>payment_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>sender_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>recipient_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>payment_state</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>amount_cents</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>users</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>num_customers</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics.

Return the ordered pair (precision, recall).

Example:

Input:

<pre tabindex="0" class="chroma"><code>
P = [[121, 9],
 [17, 144]]
</code></pre>

where P represents the following table

<table>
<thead>
<tr>
<th></th>
<th>Predicted =TRUE</th>
<th>Predicted =FALSE</th>
</tr>
</thead>

<tbody>
<tr>
<td>Actual =TRUE</td>
<td>121</td>
<td>9</td>
</tr>

<tr>
<td>Actual =FALSE</td>
<td>17</td>
<td>144</td>
</tr>
</tbody>
</table>
Output:

<pre tabindex="0" class="chroma"><code>def precision_recall(P) -&gt; (0.931, 0.877,)
</code></pre>

Write a function to calculate precision and recall metrics.

Precision and Recall

Write a query to get the cost of all transactions by user ordered by total cost descending.

Example:

Input:

<code>transactions</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>products</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<code>users</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>total_cost</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Note: Round the total cost to 2 decimal places.

Total Transactions

<ol>
<li>Given an example paragraph string and an integer <code>N</code>, write a function <code>n_frequent_words</code> that returns the top <code>N</code> frequent words in the posting and the frequencies for each word.</li>

<li>What’s the function run-time?</li>
</ol>

Example:

Input:

<pre tabindex="0" class="chroma"><code>posting = &#34;&#34;&#34;
Herbal sauna uses the healing properties of herbs in combination with distilled water. 
The water evaporates and distributes the effect of the herbs throughout the room. 
A visit to the herbal sauna can cause real miracles, especially for colds. 
&#34;&#34;&#34; 
n = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>n_frequent_words(posting,N) = [
 (&#39;the&#39;, 6), 
 (&#39;herbal&#39;, 2), 
 (&#39;sauna&#39;, 2),
]
</code></pre>

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

Using the <code>employees</code> table, get the total salary of all employees.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>manager_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>total_salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Total Salary

Given a list of strings, write a function, <code>sorting</code> from scratch to sort the list in ascending alphabetical order.

Notes:
- Do NOT use the built-in <code>sorted</code> function
- Return the new sorted list, rather than modifying the list in-place

Bonus: Have your solution be $O(n log(n))$.

Example:

Input:

<pre tabindex="0" class="chroma"><code>array = [&#34;apple&#34;, &#34;cat&#34;, &#34;banana&#34;, &#34;zoo&#34;, &#34;football&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sorting(array) -&gt; [&#34;apple&#34;, &#34;banana&#34;, &#34;cat&#34;, &#34;football&#34;, &#34;zoo&#34;]
</code></pre>

Let’s say that you are working on analyzing salary data. You are tasked by your manager with computing the average salary of a data scientist using a recency weighted average, and you are provided data from the past ‘n’ years.

The recency weighting should ensure that the salaries from more recent years are weighted more heavily than the less relevant salaries of years that are farther in the past.

Write the function to compute the average data scientist salary given a mapped linear recency weighting on the data. In this case the input <code>previous_salaries</code> is a list of the salaries of the last <code>n</code> years and the list is ordered chronologically, with the most recent year coming last.

Round the result to two-digit numbers.

Example:

<pre tabindex="0" class="chroma"><code>#input
previous_salaries = [64000,66000,75000,88000,90000]

#output
recency_weighted_salaries(previous_salaries) -&gt; 81533.33
</code></pre>

Explanation: We have the <code>5</code> salaries from the <code>5</code> years.
We should give weight <code>1</code> to the first-year salary, <code>2</code> to the second one and so on till we reach the end and give weight <code>5</code> to the most recent year salary at the end.

$result = \frac{64000 * 1 + 66000 * 2 + 75000 *3 + 88000 *4 +90000 *5}
 {1+2+3+4+5} = 81533.33 $

Write the function to compute the average data scientist salary given a mapped linear recency weighting on the data.

Recency Weighted Salaries

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Given a string <code>sentence</code>, return the same string with an addendum after each character of the number of occurrences a character appeared in the <code>sentence</code>.

Note: don’t treat the spaces <code>&#34; &#34;</code> as characters. Also; do not return the addendum for those characters that also appear in the <code>discard_list</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>sentence = &#34;Interview Query&#34;

discard_list = [&#39;I&#39;,&#39;e&#39;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>inject_frequency(sentence, discard_list) -&gt; &#34;In1t1er2v1i1ew1 Q1u1er2y1&#34;
</code></pre>

Append Frequency

Given a table with event logs, find the percentage of users that had at least one seven-day streak of visiting the same URL.

Note: Round the results to 2 decimal places. For example, if the result is 35.67% return 0.35.

Example:

Input:

<code>events</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>url</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>percent_of_users</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

Given the head of a singly linked list represented as a <code>ListNode</code>, and two zero-indexed positions <code>x</code> and <code>y</code>, write a function <code>swap_node</code> which swaps the positions of nodes <code>x</code> and <code>y</code> and returns the new head. Note that you cannot simply swap the values of these nodes, as these nodes do not have values. Instead, you must swap these two using pointer manipulation (i.e., modifying the <code>next</code> references of the involved nodes inside the list).

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
 next: ListNode = None
</code></pre>

Example:

Input:

<pre tabindex="0" class="chroma"><code>linked_list = ListNode(0x01) -&gt; ListNode(0x02) -&gt; ListNode(0x03) 
# Here, imagine that &#39;0x0_&#39; refer to their memory locations.
head = 0x01
x = 1
y = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def swap_node(head, x, y) -&gt; ListNode(0x01) -&gt; ListNode(0x03) -&gt; ListNode(0x02)
</code></pre>

Swapping Nodes

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Say you work for an e-commerce website that receives clickstream data from Kafka on a daily basis. You’re asked to design a data analytics solution for storing and querying raw data, with a two-year retention period.

The current data volume is around 600 million events daily.

What kind of data analytics solution would you design, keeping costs in mind?

Design a solution to store and query raw data from Kafka on a daily basis.

Clickstream Data

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>join_type</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_rows</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Size of Joins

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

Testing Price Increase

A/B Testing

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

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