Prepare for and practice interview questions from Equinix.

Equinix Interview Questions

Equinix Interview Guides

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

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

Machine Learning

Booking Regression

Missing Housing Data

Bank Fraud Model

Bagging vs Boosting

Spam Classifier 

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Subscription Overlap

Write a query to get the number of customers that were upsold

Upsell Transactions

Monthly Customer Report

Over-Budget Projects

A/B Testing

Button AB Test

Green Dot

Network Experiment Design

AB Test Ties

Testing Price Increase

Statistics

P-value to a Layman

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

What are the assumptions of linear regression?

Assumptions of Linear Regression

Using R Squared

Possibly Biased Coin

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>

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 have a table containing information about the time that employees have worked on a project.

Write a query to find how many unique calendar days each employee worked. Order your query by the <code>employee_id</code>.

Example:

An employee worked on two projects, and recorded their time like this:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1	</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-06</td>
</tr>

<tr>
<td>1	</td>
<td>2</td>
<td>2024-01-03</td>
<td>2024-01-07</td>
</tr>
</tbody>
</table>
The number of unique calendar days is therefore 7.

<h3>Schema</h3>

Input:

<code>projects</code> table

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

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

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

<tr>
<td><code>start_date</code>	</td>
<td>	DATE	</td>
</tr>

<tr>
<td><code>end_date</code>	</td>
<td>	DATE	</td>
</tr>
</tbody>
</table>
Output:

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

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

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

<h3>Example</h3>

Input:

<code>projects</code> table

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-15</td>
</tr>

<tr>
<td>1</td>
<td>1</td>
<td>2024-01-21</td>
<td>2024-01-31</td>
</tr>

<tr>
<td>1</td>
<td>2</td>
<td>2024-01-25</td>
<td>2024-02-28</td>
</tr>

<tr>
<td>2</td>
<td>3</td>
<td>2024-01-01</td>
<td>2024-01-31</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>unique_days_worked</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>54</td>
</tr>

<tr>
<td>2</td>
<td>31</td>
</tr>
</tbody>
</table>

Unique Work Days

Let’s say you work at Costco. Costco has a database with two tables. The first is <code>users</code> composed of user information, including their registration date, and the second is <code>purchases</code> that has the entire item purchase history (if any) for those users.

Write a query to get the total amount spent on each item in the ‘purchases’ table by users that registered in 2022.

Example:

Input:

<code>users</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>user_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>registration_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<code>purchases</code> table

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

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

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

<tr>
<td><code>item</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>

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

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

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

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

Total Spent on Products

Let’s say we have a very naive advertising platform. There is an audience of size A and a limited amount of impressions B.

Each of the impressions goes to just one user, defined at random. Every user in the audience has the same random chance of receiving each impression. 

<ol>
<li>Compute the probability that a user has exactly 0 impressions. </li>

<li>What’s the probability that every person has at least 1 impression?</li>
</ol>

Impression Reach

Probability

You are given two rectangles <code>a</code> and <code>b</code> each defined by four ordered pairs denoting their corners on the <code>x</code>, <code>y</code> plane. Write a function <code>rectangle_overlap</code> to determine whether or not they overlap. Return <code>True</code> if so, and <code>False</code> otherwise.

Note: If the two rectangles border one another, or share a corner like two diagonally adjacent positions on a chessboard, they are said to overlap.

Note: The lists of ordered pairs are in no particular order. The first entry in list <code>a</code> could be the top left corner while the first in list <code>b</code> is the bottom right.

Example:

Input:

<pre tabindex="0" class="chroma"><code>
a = [(-3,5), (-3,2),(0,5),(0,2)]
b = [(-1,4), (3,4), (3,1), (-1,1)]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rectangle_overlap(a, b) -&gt; True
</code></pre>

As point <code>(0,2)</code> is fully contained in rectangle <code>b</code> and point <code>(-1,4)</code> is fully contained in a rectangle <code>a</code>.

Write a function to determine whether or not two rectangles overlap.

Rectangle Overlap

You are given a binary tree of unique positive numbers. Each node in the binary tree is represented as a dictionary with the following keys:

<ul>
<li><code>data</code>: integer value stored in the node</li>
<li><code>left</code>: reference to the left child (or <code>None</code>)</li>
<li><code>right</code>: reference to the right child (or <code>None</code>)</li>
</ul>

<pre tabindex="0" class="chroma"><code>node = {
 &#34;data&#34;: 6,
 &#34;left&#34;: {
 &#34;data&#34;: 3,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 },
 &#34;right&#34;: {
 &#34;data&#34;: 9,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 }
}
</code></pre>

Given two node values as input (<code>value1</code> and <code>value2</code>), write a function to return the value of the nearest common ancestor (lowest node in the tree that has both nodes as descendants).

Note: If one of the nodes doesn’t exist in the tree, return <code>-1</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code># Diagram of the binary tree
&#39;&#39;&#39;
 6 
 / \ 
 3 9 
 / \
 2 11
 / \
 5 8
&#39;&#39;&#39;
value1 = 8
value2 = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>common_ancestor(root,value1,value2) -&gt; 3
</code></pre>

Explanation:

<ul>
<li>Ancestors of <code>8</code>: <code>11 → 3 → 6</code></li>
<li>Ancestors of <code>2</code>: <code>3 → 6</code></li>
<li>Common ancestors: <code>3</code> and <code>6</code></li>
<li>The nearest common ancestor is <code>3</code>.</li>
</ul>

Write a function to return the value of the nearest node that is a parent to both nodes.

Nearest Common Ancestor

Write a Python function called <code>longest_increasing_subsequence</code> that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

Note: An increasing subsequence is a subset of a list where the elements are in strictly increasing order. The subsequence does not have to be contiguous or unique.

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 9, 2, 5, 3, 3, 7, 101, 18]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>4
</code></pre>

Explanation:

The longest increasing subsequence is <code>[2, 3, 7, 101]</code>, for a total length of 4.

Find the longest increasing subsequence in a list of integers.

Longest Increasing Subsequence

You are given two non-empty linked lists representing two non-negative integers. Each list contains a single number, where each item in the list is one digit. The digits are stored in reverse order.

Task: Add the two numbers and return the sum as a linked list, also with the digits in reverse order. You may assume the two numbers do not contain any leading zeros, except the number 0 itself.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>l1 = 2-&gt;4-&gt;3-&gt;null
l2 = 5-&gt;6-&gt;4-&gt;null
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>addTwoNumbers(l1, l2) = 7-&gt;0-&gt;8-&gt;null
</code></pre>

Explanation: 342 + 465 = 807.

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>l1 = 0-&gt;null
l2 = 0-&gt;null
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>addTwoNumbers(l1, l2) -&gt; 0-&gt;null
</code></pre>

Explanation: 0 + 0 = 0.

In this problem, we are given two linked lists representing two non-negative integers, with each item in the list holding one digit. The digits are stored in reverse order, and each of their nodes contains a single digit. We are required to add the two numbers and return the sum as a linked list.

Radix Addition

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

Let’s say you’re interning at a fintech startup that’s building a platform for online games of chance. During testing, one of your simulations flips a coin 10 times and gets 8 tails and 2 heads.

How would you determine if the coin is fair, and what statistical approach would you use to support your conclusion?

How would you assess if a coin is fair after observing 8 tails in 10 flips?

Fair Coin

Given a sorted list, create a function <code>convert_to_bst</code> that converts the list into a binary tree. <code>convert_to_bst</code> returns a <code>TreeNode</code> holding the root of the binary tree. A <code>TreeNode</code> is defined as the following:

<pre tabindex="0" class="chroma"><code>from dataclasses import dataclass

class TreeNode:
 pass

@dataclass
class TreeNode:
 value: int
 left: TreeNode = None
 right: TreeNode = None
</code></pre>

The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

Example:

Input:

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

Output:

<img src="/s3/questions/46a1a245-47bf-4b7e-8356-6019ef9af27b.png" alt="image"/>Converted Binary Tree.png

Note: The output in the test cases will return an in-order traversal of your tree.

Binary Tree Conversion

Blizzard Entertainment operates a global network of games and services, each generating massive volumes of real-time gameplay and infrastructure events, everything from player actions in Overwatch to server status updates for World of Warcraft. You are tasked with building a centralized system to reliably ingest, process, and distribute these events across the company for analytics, live operations, and player-facing features.

How would you architect the data ingestion and processing pipeline for this event system?

Design a scalable event data pipeline for real-time, multi-application ingestion and processing.

Centralized Event Ingestion

Data Pipelines

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

You are given a list of integers called <code>numbers</code>. Write a function to return any subset of <code>numbers</code> where the elements <code>sum</code> to <code>zero</code> and that does not contain the number <code>0</code>.

If there are no combinations of elements that sum to zero, return an empty list.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>numbers = [1,-2,6,7,1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,-2,1] # only possible combination
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>numbers = [0,0,1,3,6,-4,-1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,3,-4]
</code></pre>

or

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,-1]
</code></pre>

or any other subset that sums to zero and doesn’t include <code>0</code>.

Write a function to return any subset of the input list where the elements sum to zero and that does not contain the number 0.

Sum to Zero

Given a <code>ListNode</code> representing the head of a linked list, write a function <code>is_cyclic</code> which returns <code>True</code> if a linked list is cyclic and <code>False</code> if it is not. A linked list is cyclic when there is no tail to the linked list, and the supposed tail is attached to another node inside the list itself, creating a cycle.

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

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

Example:

Input:

<img src="/s3/questions/1a65651d-a483-4481-b073-2738d65ab9d7.png" alt="image"/>

Output:

<pre tabindex="0" class="chroma"><code>def is_cyclic(head) -&gt; True
</code></pre>

Cyclic Detection

Priority queues are an abstract data structure responsible for allowing enqueuing items with an attached priority. While typically implemented with a heap, implement a priority queue using a linked list.

The Priority Queue implementation should support the following operations:

<ul>
<li><code>insert</code>(element, priority): This operation should be able to insert an element into the Priority Queue, along with its corresponding priority.</li>
<li><code>delete()</code>: This operation should remove and return the element with the highest priority. If multiple elements share the same highest priority, the element first enqueued should be returned. In the case that the queue is empty, return None.</li>
<li><code>peek()</code>: This operation should return the element with the highest priority without removing it from the Priority Queue. Again, in the case of equal highest priorities, the element first enqueued should be returned. In the case that the queue is empty, return None.</li>
</ul>

Note: Smaller priority values imply that they have higher priority.

<h3>Input</h3>

<pre tabindex="0" class="chroma"><code>queue.insert(2, 1)
queue.insert(3, 3)
</code></pre>

<h3>Output:</h3>

<pre tabindex="0" class="chroma"><code>[2,3]
</code></pre>

Implementing a priority queue used linked lists.

Priority Queue Using Linked List

You’ve just launched a 7-day free trial paywall for Duolingo Plus and are monitoring the user journey through the funnel. You pull up a visualization and notice something odd.

What do you make of this funnel, and what next steps, if any, would you take?

<img src="/s3/questions/a2faad38-585f-43b3-af75-b540cfc69cb7.png"/>

Given a funnel with a bloated middle section, what actionable steps can you take?

Bloated Mid-Funnel

Product Sense & Metrics

Let’s say you’re a data engineer at Fidelity Investments, and you’re running a SQL query on a cloud-based data warehouse. All cluster resources and network health metrics look normal, but the query is still taking over 10 minutes to complete.

How would you go about diagnosing and improving the performance of this query?

How would you diagnose and speed up a slow SQL query when system metrics look healthy?

Slow SQL Query

Query Optimization

You’re given a list of sorted integers in which more than 50% of the list is comprised of the same repeating integer.

Write a function to return the median value of the list in \(O(1)\) computational time and space.

Note: the lists are all non-empty

Example:

Input:

<pre tabindex="0" class="chroma"><code>li = [1,2,2]
</code></pre>

Output:

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

Median O(1)

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

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

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="/s3/questions/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

Implement Dijkstra’s shortest path algorithm for a given graph with a known source node.

A graph format is a dictionary of nodes as key and the value is a dictionary of neighbors to that node with the distance between them as follow.

Note: set the previous node of the source node to <code>None</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>
source_node = &#39;A&#39;

graph = {
 &#39;A&#39;: {
 &#39;D&#39;: 1,
 &#39;B&#39;: 6
 },
 &#39;B&#39;: {
 &#39;A&#39;: 6,
 &#39;D&#39;: 2,
 &#39;E&#39;: 2,
 &#39;C&#39;: 5
 },
 &#39;C&#39;: {
 &#39;B&#39;: 5,
 &#39;E&#39;: 5
 },
 &#39;D&#39;: {
 &#39;A&#39;: 1,
 &#39;B&#39;: 2,
 &#39;E&#39;: 1
 },
 &#39;E&#39;: {
 &#39;C&#39;: 5,
 &#39;B&#39;: 2
 }

 }


</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>
output_table = {
 &#39;A&#39;: {
 &#39;ShortestDistance&#39;: 0,
 &#39;PreviousVertex&#39;: None
 },
 &#39;B&#39;: {
 &#39;ShortestDistance&#39;: 3,
 &#39;PreviousVertex&#39;: &#39;D&#39;
 },
 &#39;C&#39;: {
 &#39;ShortestDistance&#39;: 7,
 &#39;PreviousVertex&#39;: &#39;E&#39;
 },
 &#39;D&#39;: {
 &#39;ShortestDistance&#39;: 1,
 &#39;PreviousVertex&#39;: &#39;A&#39;
 },
 &#39;E&#39;: {
 &#39;ShortestDistance&#39;: 2,
 &#39;PreviousVertex&#39;: &#39;D&#39;
 }
 }
</code></pre>

<img src="/s3/questions/785715d4-3305-4b9f-bdd7-33d5cf987bdb.png" alt="image"/>

Implement Dijkstra's shortest path algorithm for a given graph with a known source node.

Dijkstra implementation

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

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.

String Palindromes

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

Let’s say you’re in a supermarket.

You’ve found out that there is a huge discount on some items you want; unfortunately, you only have one bag with a certain <code>bag_capacity</code>. Each of the items has its own <code>value</code> and <code>capacity</code>, stored in lists called <code>values</code> and <code>capacities</code> contained in a dictionary called <code>items</code>.

Write a function to pick the items that maximize the total value without exceeding the bag’s capacity and return the total value.

Note: You can’t take more than one copy of each item home. Also, you do not need to fill the entire bag to capacity.

Example:

Input:

<pre tabindex="0" class="chroma"><code>items = {
 &#34;values&#34;:[2,3,1,4],
 &#34;capacities&#34;:[2, 4, 6, 5]
}

bag_capacity = 8
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>max_profit(items,bag_capacity) -&gt; 6
</code></pre>

We choose the first and last item with a total profit of 6 and a capacity of 7, which is less than the <code>bag_capacity</code>.

Write a function to pick the items that maximize the total value without exceeding the bag’s capacity

Filling Supermarket Bag

As a data scientist at a real estate analytics startup, you’re tasked with building a predictive model for home prices in a large metropolitan area. During your exploratory data analysis, you notice that the <code>home_prices</code> column in your dataset is heavily right-skewed (i.e., there are many lower-priced homes and a few extremely high-priced ones).

How might this skewness impact your modeling approach, and what steps would you consider to address it before training your model?

Bonus: If, instead, you discover that the target variable (<code>home_prices</code>) is heavily left-skewed (i.e., there are many high-priced homes and a few low-priced outliers), what would you do differently, if anything?

You're building a model to predict home prices and you see that the prices in your training data are right-skewed. How would you handle this situation?

Skewed Pricing

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.

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