Report Section
Data Science with Python Test
alex
| 1480448380787
Test Taker Details
a
alex
Email Address: demo@mettl.com
Test-Taker ID: - 9764284
Last Name:
Duggal
Date of birth:
Sep 15, 1994
Experience:
< 2 years
Recommendation
The candidate is recommended based on the test result. A one-on-one interview is also recommended for a final review.
Overall Assessment Score:
Values shown in above chart are percentages
Very Low (0 - 16)
Low (16 - 33)
Moderate (33 - 68)
High (68 - 84)
Very High (84 - 100)
Scores by Technical Competencies
Statistical and Data Interpretation Concepts:
Values shown in above chart are percentages
Very Low (0 - 16)
Low (16 - 33)
Moderate (33 - 68)
High (68 - 84)
Very High (84 - 100)
Scores by Technical Competencies
Data Mining Concepts:
Values shown in above chart are percentages
Very Low (0 - 16)
Low (16 - 33)
Moderate (33 - 68)
High (68 - 84)
Very High (84 - 100)
Scores by Technical Competencies
Machine Learning Concepts:
Values shown in above chart are percentages
Very Low (0 - 16)
Low (16 - 33)
Moderate (33 - 68)
High (68 - 84)
Very High (84 - 100)
Scores by Technical Competencies
Hands-on Coding:
Values shown in above chart are percentages
Very Low (0 - 16)
Low (16 - 33)
Moderate (33 - 68)
High (68 - 84)
Very High (84 - 100)
Question-Wise Details
Note: Some sections/questions are hidden by your Administrator. This may change the questions/sections ordering.
Section 1
Hands-on Coding
1
question(s)
8m 14s
Time taken
10.91/20
Marks Scored
Spam Detection
Spam messages are irrelevant or unsolicited messages sent typically to large numbers of users, for the purposes of advertising, phishing, spreading malware, etc.
You are required to design and train a spam detection model. Then with your trained model, you are needed to classify arbitrary unlabeled messages as "genuine" or "spam".
Training Data Set
You are given a TSV (Tab Separated Values) file with 5,000+ SMS messages. The first column in the file represents whether the given message is a normal message (genuine) or a spam message (spam). The second column is the message itself.
Examples:
For offline viewing, you can download the file here:
SMS Collection: https://s3-ap-southeast-1.amazonaws.com/mettl-prod/data-science/SMSCollection.txt
For training your model, you will have to read the file from the URL itself. A code snippet explaining how to do it is given below.
Python 2
Python 3
This corpus (collection of texts) will be your labeled training set. Using these genuine/spam messages, you are needed to train a machine learning model to learn to discriminate between genuine/spam messages automatically.
Program Structure
The program code already has an unimplemented function named
detectSpam. For the test cases, the input will be passed to this function and it should return the correct output.
Input
input1: a string message that can be a span message or a genuine message.
Output
The function
detectSpam should return "genuine" if the message is a genuine message or "spam" in case it is a spam.
Example 1
Input: Free outgoing calls to any number! Visit www.example.com to know more. Hurry!
Output: spam
Explanation: The input message is an advertisement and thus is classified as spam.
Example 2
Input: Tried reaching you but couldn't. Please call.
Output: genuine
Explanation: The input message looks like a genuine message.
Input: Tried reaching you but couldn't. Please call.
Output: genuine
Explanation: The input message looks like a genuine message.
❤
Compilation
Successful
Time Taken
8m
14s
Marks Scored
10.91
Out of
20
Language
PYTHON2
PYTHON2
00:00:00
/
00:00:00
Sample Test Case Timestamp
Graded Test Case Timestamp
Graded Test Case Code Compilation :
0
Successful
0
Attempt
Sample Test Case Code Compilation :
0
Successful
0
Attempt
Code complexity
:
0
Total no. of Testcase :
12
Total Passed :
6
Test Case
Marks
Cpu (ms)
Processing (ms)
Memory (KB)
Inputs
Expected output
Actual output
Error Message
Default 2
1.82
0
210
6628
Tried reaching you but couldn't. Please call.
genuine
genuine
NA
Default 1
0
0
167
3924
Free outgoing calls to any number! Visit www.example.com to know more. Hurry!
spam
genuine
NA
Basic 1
0
0
195
3548
Win a grand prize of an Apple Mac Book Pro. Register for the contest now. Give a missed call at 9876789876
spam
genuine
NA
Basic 2
1.82
0
204
3552
Hi! Whatssup. long time no see.
genuine
genuine
NA
Basic 3
0
0
177
3924
Experiance the impossible. The new 3D TV by Samsung. Visit your nearest store. Luck 1000 customers win a grand prize of euro 1000
spam
genuine
NA
Basic 4
1.82
0
168
3548
Talked with Clara, she has no idea :(
genuine
genuine
NA
Necessary 1
0
0
191
3548
The new running shoes by Nike. Self tieing and cleaning. For free demo, call on this number: 241412414
spam
genuine
NA
Necessary 2
1.82
0
181
3548
Did you recieve my package? do leme know
genuine
genuine
NA
Corner 1
1.82
0
173
3924
breakfst is free there, also some lucky winners are geting gft vouchrs. m going, wat abt u???
genuine
genuine
NA
Corner 2
0
0
170
3924
Say no to all your hiring problems, We are here to help. Visit hiring.example.com today
spam
genuine
NA
Time Complexity 1
0
0
190
3548
Cost effective photo albums. Sleek and stylish. For samples, whatsapp hello on 9988998878
spam
genuine
NA
Time Complexity 2
1.82
0
163
3924
She is not responing bro, i miss her a lot :(
genuine
genuine
NA
Test Log
Test Log
30 Nov,2016
-
Started the test with Statistical and Data Interpretation Concepts
-
Went to Data Mining Concepts of the test
-
Went to Machine Learning Concepts of the test
-
Went to Hands-on Coding of the test
-
Finished the test
About the Report
This Report is generated electronically on the basis of the inputs received from the assessment takers. This Report including the AI flags that are generated in case of availing of proctoring services, should not be solely used/relied on for making any business, selection, entrance, or employment-related decisions. Mettl accepts no liability from the use of or any action taken or refrained from or for any and all business decisions taken as a result of or reliance upon anything, including, without limitation, information, advice, or AI flags contained in this Report or sources of information used or referred to in this Report.