NewStats: 3,263,655 , 8,180,917 topics. Date: Saturday, 07 June 2025 at 05:38 AM 63l4r

Mrves111:

Hello Bibitayo2, please can you send me the excel e-book to [email protected].
God bless you immensely

could you please forward the excel ebook to my mail? [email protected]...

It was a very painful experience when I couldn't do my I.T at nestle for failure to perform simple computations with excel

Marveaux:
Sent

please also send to [email protected]

Please my laptop got damaged and i need 10k to fix it; pls help a brother 3032703376 first bank

Marveaux:

Sent

please also send to [email protected]
Or please if at all you can drop your whatsapp number so I can you

Started studying again after a long break.

Let's keep the thread alive,

"ALUMNIS" of this thread who have been employed should come and share their experience too o.

I'm a beginner for now.

Drybones:
Let's keep the thread alive,

"ALUMNIS" of this thread who have been employed should come and share their experience too o.

I'm a beginner for now.

I agree too..

Raalsalghul:
Started studying again after a long break.

I can relate to this. Meanwhile
I need your opinions on this topic
https://gootent.com/Should_I_Use_Preact_Over_Reactjs&6

Interesting!

This scope can be applied to virtually all forms of online conversations and social media platforms to understand patterns, styles and relationships in the communication process.

I recall a friend doing this to analyze trolls and their allies during the 2019 election. It was fascinating and sparked my interest in data analysis.

What tools did you use to mine and analyze the data?

Well done!

cochtrane:
Lately, I have been looking to mining relationships that may be existent in the network of relationships possible on a thread on Nairaland. If we collected data from any one thread, say this thread for example, we can define relationships between posters who quote each other in reply to other posts. If many more posters quote each other frequently than they do others, it is possible they could form a cluster of relationship independent from other relationships on a thread. I tried to look at this problem as a spectral partitioning problem and used linear algebra to examine possible solutions.

So, I scraped the data from this programming post on Nairaland and tried to look for clusters of relationships visualized as a directed graph. The direction of the edges indicate who quotes who. We can define this as a relationship between source and target. A in the source column has made a post and quotes the target. Multiple quotes have been separated into separate observations. The first picture shows the kind of graph we expect. The second picture shows a sparse matrix defining the relationship between any two node. This is after symmetrizing the resultant matrix we obtain from the directed graph. The last picture shows the cluster we form using the second smallest eigenvector of the Laplacian of the matrix. This is also known as the Fielder vector.

That last graph reveals the two groups inherent in the data based on interaction between posters. In this particular dataset, the main center of interaction is the original poster (OP) who is Paapii3d. The groups appear to be divided based on those who have or did not have interactions with the OP. When the individual groups are examined, we find that s clustered in the top left are less likely to have interacted with Paapii3d, while bulk of the other s are more likely to have interacted with the OP, or interacted with those who interacted with the OP. The average number of interactions in Group 0 is about 1.1, while the average number of interactions in Group 1 is much higher at 2.12.

This kind of solution can be used to identify communities in a graph, especially, as in this case, where there is a main center of attraction. I'm working on putting the notebook which goes into details about the solution approach on github. If anyone is interested, you can let me know and I'll send you the link.

Nice.

Abeg, where in Sango? It's far from me but I can take time to come over.

Also, what's the size of the tutorials?

Thanks

mcemmy0z:
If you reside around songo Ota I have these available
*Udemy - Beginner to Pro in Excel Financial Modeling and Valuation
*Udemy - SQL - MySQL for Data Analytics and Business Intelligence
*Tableau 10 A-Z Hands-On Tableau Training For Data Science!
*Tableau Hands-on Learn Data Visualization with Tableau
*Udemy - Power BI A-Z Hands-On Power BI Training For Data Science
*Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
*Udemy - Python for Financial Analysis and Algorithmic Trading

Sup bro, were you able to get tutorials from him?

doncafu:


I reside around Abule Egba but I can come to Ota to collect it... Thanks in advance

Hey guys look at the analysis I did with python and tableau
https://nairaland.unblockandhide.com/6342138/nairaland-2020-frontpage-analysis-visualization

Evanspaul:
Hey guys look at the analysis I did with python and tableau
https://nairaland.unblockandhide.com/6342138/nairaland-2020-frontpage-analysis-visualization

Solid work bro

randomShek:
Solid work bro

Thanks boss

Good evening house, please I’ve been following up on this thread. Started Data Science and got stuck in differentiating between Linear regression, polynomial regression, decision tress, logistic regression and other types of classifications with Random forest and so on. I’ll like when to use these classifications, or they give us the same result?

Thanks as this decision is affecting the project I want to work on

tobillionaire:
could you please forward the excel ebook to my mail? [email protected]...

It was a very painful experience when I couldn't do my I.T at nestle for failure to perform simple computations with excel

what kind of computation

Singingbae:
Good evening house, please I’ve been following up on this thread. Started Data Science and got stuck in differentiating between Linear regression, polynomial regression, decision tress, logistic regression and other types of classifications with Random forest and so on. I’ll like when to use these classifications, or they give us the same result?

Thanks as this decision is affecting the project I want to work on

To start with,do you know the difference between regression an classification?

Singingbae:
Good evening house, please I’ve been following up on this thread. Started Data Science and got stuck in differentiating between Linear regression, polynomial regression, decision tress, logistic regression and other types of classifications with Random forest and so on. I’ll like when to use these classifications, or they give us the same result?

Thanks as this decision is affecting the project I want to work on

To start with,do you know the difference between regression and classification?

Nastydroid:
what kind of computation

very simple o. Just addition, subtraction & count cos I googled it immediately I got home that day I never knew it's that simple, but like the saying "weytin you no know you".

Drybones:

To start with,do you know the difference between regression and classification?

go take statistics courses na

Raalsalghul:
Started studying again after a long break.

not really easy without motivation from ppl pursuing same career

Singingbae:
Good evening house, please I’ve been following up on this thread. Started Data Science and got stuck in differentiating between Linear regression, polynomial regression, decision tress, logistic regression and other types of classifications with Random forest and so on. I’ll like when to use these classifications, or they give us the same result?

Thanks as this decision is affecting the project I want to work on

Regression are used to predict continuous label. In simple term, they are used to predict the values (usually number) from given features(parameters).
Say you wanna predict the price of Garri (label) and you have historical data of t-fare to the market at different location, different kind of garri, and their prices. This kind of problem is regression Cus you are after a particular figure for your prediction.

linear regression, lasso, polynomial regression, and some classification regression like random forest regressor etc falls into this line


Classification as the name suggest help you to classify and predict an exact label you have in your dataset.
If you have prehistorical data of a weather condition(features) in your area that suggest if it will rain or not(your labels). Then classification model will help predict an output (it will rain) or (it will not rain) based on the weather data.

This is where the logistica regression(Binary classifier), Decision tree, Random feorest, SVM etc falls in.

The kind of data you deal with determine the machine learning type and algorithm to use


Get a Hands on Machine learning book if you really wanna dive deep into this.

@chilaka04, follow this thread from a to z and begin from here..

Drybones:

To start with,do you know the difference between regression and classification?

I won’t lie I don’t. I don’t mind you break it down for me

Singingbae:

I won’t lie I don’t. I don’t mind you break it down for me

Regression: predicting numerical stuffs like the price of a house, house prices are continuous (aren't specific). You can see a house of 2500, 7000,35667,12566,5322 and any amount (the prices are assumed).
Regression mostly answers the question "what is".
e.g what is the price of the car? what is the height of the man?

Classification: predicting whether something is a or b, 1 or 0.
As you can see, what you are predicting in classification is fixed unlike the classification when the values you are predicting are very different. Classification mostly answers the question "is it"
e.g Is the car price expensive or cheap? Is the man tall or short.

This database still look confusing to me o.... Pls can anyone groom me on how to create tables and link them in a database, should I just go and be creating tables with ms access or I will have to my sql to be able to create, query and modify tables

Drybones:

Regression: predicting numerical stuffs like the price of a house, house prices are continuous (aren't specific). You can see a house of 2500, 7000,35667,12566,5322 and any amount (the prices are assumed).
Regression mostly answers the question "what is".
e.g what is the price of the car? what is the height of the man?

Classification: predicting whether something is a or b, 1 or 0.
As you can see, what you are predicting in classification is fixed unlike the classification when the values you are predicting are very different. Classification mostly answers the question "is it"
e.g Is the car price expensive or cheap? Is the man tall or short.

Thank you, please also can I get machine learning materials, I mean pdf

Singingbae:

Thank you, please also can I get machine learning materials, I mean pdf

You can search this on Google.

It is easy to understand.

Starkid3010:
please also send to [email protected]
Or please if at all you can drop your whatsapp number so I can you

Sent

Juicethekid:
please also send to [email protected]

Sent

teewhydope:
just ed this, hoping to learn one or two things on excel

Please can you send the book to my email address. It's [email protected].
Thank you very much

Babitayo2 please sir can you send me the book on excel to my email address which is [email protected]