Today, I am going to share
results of an exercise that I carried out recently for a start-up. Intention of
the study was to extract those major attributes that are generally driving less/in
experienced (or) re-skilled data miners towards the given objective and to
understand where they are failing back. Herein, twist is majority of them have
given same conclusion or explanation for the given objective. Results highlight
or comment on, those important aspects of the practice where most of them failed
to cognize for the sake of quick answer/solution.
Sample Observed:
All members of the sample had experience
both with R and data mining solutions; either through course projects
(free/paid/part-of-curriculum) or through industry experience, however, industry
experienced sample have been limited between minimum of 1 year to maximum of 3 years
from whatever domain. Details of sample are as below:
- 17 -
Fresher’s from various engineering background (both Graduates and Post-Graduates)
- 12 -
Fresher’s from various quantitative background (Maths, Stats, MBAs, Econometrics,
etc.)
- 18 - Experienced
from different industry background (data management related, programming, consulting,
etc.)
- All members of the sample belong to two major
cities of India.
About Test Data:
Bank data of customers belonging
to a particular city branch having around 17000 observations for a period of one
month, which as information about customer’s age, few demographics, no of transactions
they did in that month, whether they visited branch in that month, etc., total
of 12 variables.
Infrastructure Provided:
Computing machine with a pre-installed latest R (3.1.1)
& RStudio that has 8GB RAM and Intel Core i7 Processor.
Objective:
“Comment about the variables ‘visiting branch’ and ‘age’ relationship”.
Time Limit:
A time limit of 20 minutes was given, which was almost two
and half times more than average time of experienced people, took to give their
comments.
Highlights from the Exercise:
- As mentioned earlier, almost all except few has given same
inference that ‘numbers of visits to branch’ have positive relationship with ‘age’
of the customers. In other words, as age is increasing, customers are preferring
to visit the branch. Not to forget to mention, interestingly most of them are comfortable with R programming except few typo errors, kudos to all developers making it more user friendly.
- Astonishingly, only 21% of the sample, has done some data
understanding after reading the data, i.e. looking into descriptive stats
either through summary functions or plots before moving to the modeling part. In
these 21%, not even a single sample member is from engineering background (by
saying this I am not generalizing it, nor against engineering background, but commenting
from sample perspective). Also, perceptibly, another 15% came back to data
understanding after fitting at least one or two models.
- One more astonishment is, type of techniques employed by
participants went onto deep learning methods. Average number of models applied
by all participants was near to 3, herein, there are few participants, who didn’t
even fitted a single technique/model.
- Only 15% of the sample, had clearly mentioned that result
may be spurious or declined to comment on relationship due to noise in the data;
however, only half of them came out with explanations for the same.
- Notable fact from our exercise is that, many of
them directly applied the techniques they are aware (few among them directly
fitted neural networks, and then came back to machine learning classification
techniques as they need to comment on relationship). And, more than half of the
sample first directly test with a variant of Generalized Linear Model and then
went to applications of other techniques as they found explanatory power of the
model was low and they were behind all data mining techniques till time limit
ends.
What was wrong in the data?
When this data was originally received, I observed that due
to a machine/man-made mistake, column ‘age of the customer’ in the data was
having representation of an additive nature, for instance, if customer has
visited the branch twice in the month and his original age is 25, it appeared
as 50. Hence, positive relationship as age increased, however, it was not the
case after the noise removal.
Summary:
Data Mining is a process of many stages as depicted in CRISP-DM1
and data understanding is key of them, I always suggest process your data
incrementally, if you want efficient analytical solution, ignoring it, and employing
which fits the data well practice, may not work in all situations.
Author thank management of start-up for allowing
to publish exercise highlights. He undertook several programs towards
analytical talent development, views expressed here are from his industry
experience. He can be reached at mavuluri.pradeep@gmail for more details.
