Data Analyst Portfolio Project #2: Python Customer Segmentation & Clustering

This is a data analysis portfolio project that will allow you to perform customer segmentation on a specific group of mall customers. You will identify the best possible cluster using the KMeans unsupervised machine learning algorithm to find the univariate, bivariate, and multivariate clusters. Once these clusters are identified, summary statistics can be performed on these to identify the best marketing group. Book a one-on-one mentorship call: topmate.io/gae...
You can find the dataset here:
absentdata.com...
Get the code here:
github.com/Gae...
#datascience
#python
#dataanalysis
#porfolioproject
#kmeans
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[@aditideshpande5566]

Thank you so much for an exceptional well explained and clear video better than what I learnt in my masters degree!

[@nezzylearns]

This is an exceptional walkthrough, especially how you vividly explain the process of visualizing the data.

[@absentdata]

Lovely Feedback! Thanks. I am glad you enjoyed it.

[@janakiyeluripati6368]

I followed till 32 min as I am not into ML. I just loved it. Understood univariate, bivariate. Want more videos like this. Love from India. Stay blessed.

[@absentdata]

Thank you so much! I am glad you finished the video and understood the exploratory data analysis steps. You also stay blessed!

[@slacex]

for those who have an error in the following formula is ---> df.corr() ------> df.corr(numeric_only=True)

[@rishiraj1192]

Facing similar issue how to resolve it

[@vladthelad7298]

@@rishiraj1192 He literally said it in his comment

[@ai.simplified..]

More usefull than hours of clas, good job 😍

[@thanomnoimoh9299]

Python great way to analysis awesome!!! thank you for great clip.

[@mospher9253]

Better than most of the big channels around there Really good explanation and project step by step Can you do other video like this using other types of Clustering like GMM and others and do a more detailed analysis and conclusions as well thank you for the time you put on this video it was super helpful

[@absentdata]

Ibreally appreciate this. Sure I'll do more detail analysis on clustering

[@Maliiik804]

waiting for it from a long! Thanks for uploading this great content

[@absentdata]

Glad you're enjoying the content!

[@Yett1hhh]

for i in columns: plt.figure() sns.kdeplot(data=df, x=i, shade=True, hue='Gender')

[@muskanmodi724]

Hello.. for the kdeplot at 16:35, when I'm adding hue=df['Gender'], it is giving error The following variable cannot be assigned with wide-form data: `hue`

[@juancamilosanchez4693]

You can solve this by adding this to the code: x=df['Annual Income (k$)'], and then you put the hue and it works

[@mercyolaleye7502]

@@juancamilosanchez4693 Thanks, this helped.

[@iwojoseph]

@@juancamilosanchez4693 this worked! Thanks!

[@StrangeMemes52]

@@juancamilosanchez4693 yeah , thanks, this worked

[@kostantinaorselli1093]

@@juancamilosanchez4693 why does this solve that problem?

[@batoolalshareef9456]

Thanks alot, It's a great efforts,, keep on going, and share more videos like this 👍🌺🌺🌺

[@aramisfarias5316]

The end felt a little rushed and underwhelming, but overall very instructive. Good job. =)

[@javeda]

Please also tell how did you implemented code autocompletion in Jupyter notebook

[@RonaldPostelmans]

thanks for your great explanation

[@awaisanjum9023]

Amazing video. Kindly make more protfolio projects videos.

[@absentdata]

Thank you, I will

[@rachrach9871]

Awesome tutorial! I tried to download the dataset but I don’t where to begin. There’s an option for “raw” and “blame”. I’m new to data analytics so I would appreciate some help. Thank you very much

[@absentdata]

You can find the data here: absentdata.com/data-analysis/where-to-find-data/

[@rachrach9871]

@@absentdata thank you so much for your quick response! I’m already doing tutorial #1 and I’m hoping to learn as much from your tutorials

[@isaacetungu5215]

Worth watching and follwoing along. I completed the video and did my work alongside code. I needed more help on multivariate analysis of clustering. The last part of the video on it was not well explained. Any recoomendations or video on that @Absent Data??

[@adrianapanjiwijaya1520]

Hi, This is very helpful. I do have a question though, after df=df.drop('Customer ID'), I forgot to add the hashtag and continued on. From that point on, the Customer ID disappeared. But in your case, Customer ID value re-appear during clustering. How did that happen and how do I get it Customer ID back?

[@abrilgonzalez7892]

same question here!

[@emilioprill3373]

Learned a lot! Thank you

[@absentdata]

I'm glad to hear that. Please share it with anyone you think it helps

[@nazmussumon5105]

Thank you for this awesome tutorial. Learnt a lot.

[@brandonwarfield5611]

This is gold!!! I'm upset I'm just finding your channel!!!

[@absentdata]

I am glad that you found the channel. Share it with anyone you think it will help!

[@travelofftradition]

Hi! thank you for this video. I have a question. I want to segment bank customers. But the data is in multiple files like accounts.csv, customer_details.csv, transactions.csv How to approach this problem when we have data in multiple files to segment the customers? Thanks Mohit

[@absentdata]

You will need to merge them into a single dataset.

[@travelofftradition]

@@absentdata Ok. so basically i have to join them using any of joins like inner joins etc.? But how is it done when there are like 10-20 files? Is there any other way?

[@absentdata]

@@travelofftradition append the files that are similar like all transactional files to create a single dataset and merge these with single customer details file which should also be result if an append.

[@nadil3230]

list object has no attribute mean , how to fix this error

[@faa_z]

Amazing video, thank you a lot, i only have question in 21:52 you said that from the graph seems like there is more femal than male, how did you know, is it because the median?

[@absentdata]

The value count function will count the number of males and females to give the actual number

[@forecaststatistics8496]

Good job!

[@anooppainuly5271]

Loved it

[@ericametta6964]

insightful

[@absentdata]

Glad you found it insightful

[@nadil3230]

why on the y axis it was density and can we change it with some other parameters.

[@absentdata]

Yes you can change the variables on the x and y axis. You can also use PCA techniques also to display the data

[@grhagandanap9912]

Thanks for the practice. But I got some problem when execute the n_clusters sensitivity analysis in 41:13. Do you know what the problem is?

[@44.kieutrang61]

Me too 😢

[@nayeem9358]

What is spending score ?

[@absentdata]

It is the score(out of 100) given to a customer by the mall authorities, based on the money spent and the behavior of the customer

[@TvsCar30]

The following variable cannot be assigned with wide-form data: `hue` someone can help me

[@sencxx6368]

sns.kdeplot(data=df, x='Annual Income (k$)', shade=True, hue='Gender')

[@parhatbazakov1091]

Hi, I am new to data, Can anyone answer my question please? If the correlation showed the most correlation with Age (-0.33) and no correlation with Annual income (0.0099), would it be better to cluster by age?

[@absentdata]

Low correlation doesn't necessarily mean low similarity. Clustering can still be useful to identify patterns even with low correlation. It depends on the goals of the analysis.

[@parhatbazakov1091]

@@absentdata Thanks!

[@alejandrosalgadolima3745]

Hi, great video.I can not understand why hue is not working in my computer. Could you please help me/

[@absentdata]

Whats your issue?

[@promise-abasi]

@@absentdata Hi, thank you so much for the video, I also have a challenge with the hue I can't seem to get pass' ValueError: The following variable cannot be assigned with wide-form data: `hue', from 17m, how do I solve this please, thank you

[@yusufbas035]

thank you

[@absentdata]

You're welcome

[@harryfeng4199]

Thnk uuuu

[@mahmoudemad8507]

i get an issue that fit_transform must get 2 arguments

[@absentdata]

try posting your code so we can see what's happening.

[@im4485]

Hi, is K means reliable at high dimensions?

[@absentdata]

I would say no. I would do some PCA to reduce some of your dimensions.

[@ai.simplified..]

15:00 practical &usefull

[@absentdata]

Yes loops are your friends. Saves tons of time :)

[@tejkumar9018]

----> 3 plt.figure() TypeError: 'module' object is not callable please help it cant execute because of error

[@KarthiKeyan-ci2yj]

I would like to learn Data Analytics , can I get your contact to get more information from you?

[@absentdata]

www.linkedin.com/in/gaelimholland

[@adishreepatra7330]

Hi, Loved your content! If possible please share the source code of this project

[@absentdata]

I added it in the description

[@EpicSharjeel]

everything changes in 4 year every syntax

[@sikkandarbasha-p8o]

can i put this project on my resume?

[@absentdata]

Of course you can!

[@zubairsultanate5660]

zub salute

[@hrsh3329]

👍🏽👍🏽👍🏽

[@vishnua5028]

How to download dataset

[@absentdata]

check the description 😊

[@vishnua5028]

@@absentdata I can't see any download option in GitHub

[@slacex]

df.groupby('Gender')['Age', 'Annual Income (k$)', 'Spending Score (1-100)'] ---> cannot subset columns with a tuple with more than one element. Use a list instead.

[@absentdata]

Is that your whole code? Because there is no aggregation function in your group by. Also you are adding two columns. So it should be df groupby('category')[['A','B']].mean()

[@slacex]

@@absentdata i have just resolve it ----> df.groupby(['Gender'])[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']].mean() min 30:41

[@mn4769]

sns.kdeplot(df['Annual Income (k$)'],shade =True,hue = df['Gender']); here i ValueError: The following variable cannot be assigned with wide-form data: `hue`. Can someone explain?

[@arindambhunia9862]

sns.kdeplot(x=df['Annual Income (k$)'],shade=True,hue=df['Gender']); write the code in this way, it will get resolved. I also had the same issue. Good Luck

[@forzahorizon4eliminator206]

and you got yourself a subscriber

[@absentdata]

Welcome to the family! I am happy to earn your subscription.

[@yashjajoria]

sns.kdeplot(df['Annual Income (k$)'],shade = True,hue= df['Gender']); - ValueError: The following variable cannot be assigned with wide-form data: `hue`

[@absentdata]

The updated version of sns.kdeplot may require you to make sure you have your Gender column in longform. so you need to melt the column like this. melted_df = df.melt(id_vars='Gender', value_vars=['Annual Income (k$)']) sns.kdeplot(data=melted_df, x='value', hue='Gender', shade=True)

[@yashjajoria]

thanks for response sir i'm your student @@absentdata