Doing a Loan Risk Profiling Dashboard in Power BI for my Business Intelligence assignment…

and my PC is already fighting for its life 😭

Data cleaning with Python vs Power BI…

Python: clean, simple, one line… done 😎
Power BI: click… transform… wait… click again 😭

I love Python fr, it just understands me

ahh, Drag and Drop

Finished everything…

✔️ Data Cleaning
✔️ DAX
✔️ KPIs
✔️ Dashboard

Now stuck at the hardest part 😭

👉 Making it look PROFESSIONAL in Power BI

Anyone really good at Power BI? 👀

I’m building a fully interactive dashboard and I want it to look professional 🔥

Any tips on:
• Clean design & layout
• Making it more interactive
• What separates a basic dashboard from a pro one

Drop your advice 👇

አይዞን gunners😔😁

The game is a game but Tikvah sport comment section is another level fr😂

@byte_philosopher

Classification Algorithms Quick Summary


The Big Idea

👉 Classification = predicting categories
(Spam / Not Spam, Yes / No, 0 / 1)


Models Breakdown

🔹 Logistic Regression
👉 Uses probability
👉 Simple & fast
👉 Great starting point


🔹 Naive Bayes
👉 Based on probability
👉 Very fast
👉 Great for text (spam detection)


🔹 K-Nearest Neighbors (KNN)
👉 Based on similarity
👉 “Follow your neighbors”
👉 Simple but slow for big data


🔹 Support Vector Machine (SVM)
👉 Finds the best boundary
👉 Focuses on important points
👉 Powerful but needs tuning


🔹 Decision Tree
👉 Step-by-step decisions
👉 Easy to understand
👉 Can overfit


🔹 Random Forest
👉 Many trees working together
👉 More accurate & stable
👉 Reduces overfitting


Simple Way to Remember

👉 Logistic → probability
👉 Naive Bayes → probability (fast)
👉 KNN → neighbors
👉 SVM → best boundary
👉 Tree → decisions
👉 Forest → teamwork


Real Truth

👉 There is no “best” model
👉 The best model = depends on your data

Final Tip

If you’re confused where to start:
👉 Start with Logistic Regression
👉 Try Random Forest
👉 Then explore others


Follow @DataMinds16 for more

#DataMinds #MachineLearning #Classification #Python #AI

Good morning Data Minds

Have a powerful Monday 🔥

New week, new goals… let’s get it 😤

@DataMinds16

#DataMinds_Opportunity 🚀

🚀 Calling All Graduates: Join the Teach For Ethiopia 2026 Paid Fellowship

✨ Ready to build your career while building your country?
This is a 2-year paid leadership opportunity for young Ethiopian graduates 👀

🎯 Who can apply?
🔸 Recent graduates (including Class of 2018 E.C.)
🔸 Age 18–30
🔸 Strong academic performance
🔸 Women are highly encouraged to apply

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🔹 Intensive leadership training
🔹 2-year paid fellowship
🔹 Real impact on Ethiopia’s education system
🔹 Join a strong network of future leaders

🔗 Apply here:
https://docs.google.com/forms/d/e/1FAIpQLSenFMNTlcn0FwGdKaTj0RAYsGMnWXNYRsSW71i4Lg3UlmRWbQ/viewform

⏳ Deadline: May 15, 2026

📢 If it’s not for you, share it with someone who needs it

📊 Follow @DataMinds16 for more opportunities

A. Linear Regression

It’s a model used for regression
👉 It predicts a number


Simple Idea:
👉 “Draw the best straight line through the data”


Example:
👉 Study hours → 80% score
👉 House size → price

Output
👉 A continuous value (number)


How it Works:
👉 Finds a line:
y = mx + b
👉 Minimizes the error between predicted & actual values


Key Insight:
👉 It assumes a linear relationship
(more input → proportional change in output)


When to Use It:
👉 Simple relationships
👉 Baseline model
👉 When data looks like a straight-line trend


Real Tip:
Always start with Linear Regression
👉 If it performs poorly → try more complex models


Want to go deeper?
👉 https://scikit-learn.org/stable/modules/linear_model.html


Follow @DataMinds16 for more

#DataMinds #MachineLearning #LinearRegression #Python #AI

B. Ridge Regression

It’s a model used for regression
👉 It improves Linear Regression by reducing overfitting

Simple Idea
👉 “Don’t let the model go too wild”


Example:
👉 Too many features → model becomes unstable
👉 Ridge keeps coefficients small → more stable


Output:
👉 A continuous value (number)


How it Works:
👉 Adds a penalty to large coefficients
👉 Keeps the model simple & controlled


Key Insight
👉 All features stay… but with smaller impact


When to Use It
👉 Many features
👉 Multicollinearity
👉 When Linear Regression overfits


💡 Real Tip

Ridge = control model complexity
👉 Helps generalize better on new data


Want to go deeper?
👉 https://scikit-learn.org/stable/modules/linear_model.html


Follow @DataMinds16 for more

#DataMinds #MachineLearning #RidgeRegression #Python #AI

C. Lasso Regression

It’s a model used for regression
👉 It improves Linear Regression by removing less important features


Simple Idea
👉 “Keep what matters… drop the rest”


Example

👉 Many features in data
👉 Lasso sets some coefficients to zero
→ Removes them automatically


Output
👉 A continuous value (number)


How it Works:
👉 Adds a penalty to coefficients
👉 Forces some of them to become zero


Key Insight:
👉 Feature selection happens automatically


When to Use It:
👉 Too many features
👉 You want a simpler model
👉 Feature selection is important


💡 Real Tip
Lasso = simpler model + fewer features
👉 Easier to interpret


Want to go deeper?
👉 https://scikit-learn.org/stable/modules/linear_model.html


Follow @DataMinds16 for more

#DataMinds #MachineLearning #LassoRegression #Python #AI

A. PCA (Principal Component Analysis)

It’s a technique used for dimensionality reduction
👉 It reduces features while keeping important information


Simple Idea
👉 “Less features… same meaning”


Example:

👉 Dataset has 100 features
👉 PCA reduces it to 2–3 features
→ still keeps most of the information


Output:
👉 New features (called principal components)


How it Works:
👉 Finds directions with maximum variance
👉 Projects data onto those directions


Key Insight
👉 Keeps what matters… removes redundancy


When to Use It

👉 Too many features
👉 Visualization (2D / 3D plots)
👉 Speeding up models


Real Tip

PCA = compression without losing much info
👉 Great before ML models


Want to go deeper?
👉 https://scikit-learn.org/stable/modules/decomposition.html


Follow Data @DataMinds16 for more

#DataMinds #MachineLearning #PCA #Python #AI

#DataMinds_Opportunity

A friend sent me this and I felt like I should share it here 👀

If you’ve been thinking about getting into data but didn’t know where to start… this might help

NITHUB has been doing a lot of trainings lately, and this one caught my attention 👇

🚀 The Career Shift

“Don’t just watch the future happen… build it.”
👉 https://lnkd.in/efJVnnWp

Spots are limited, so yeah… don’t wait too much

If it’s not for you, maybe send it to someone who needs it

Follow @DataMinds16 for more

B. ICA (Independent Component Analysis)

It’s a technique used for dimensionality reduction
👉 It separates mixed signals into independent sources


Simple Idea
👉 “Unmix the signals”


Example:
👉 Multiple people talking at once
👉 ICA separates each voice


Output
👉 Independent components (separate signals)


How it Works

👉 Finds underlying independent sources
👉 Assumes signals are statistically independent


Key Insight:
👉 PCA → keeps variance
👉 ICA → finds independent signals


When to Use It:
👉 Signal processing (audio, EEG)
👉 When data is mixed
👉 Source separation problems


Real Tip:
ICA is powerful when data is a mixture
👉 It helps you discover hidden sources


Want to go deeper?
👉 https://scikit-learn.org/stable/modules/decomposition.html


Follow @DataMinds16 for more

#DataMinds #MachineLearning #ICA #Python #AI

#DataMinds_Opportunity

🇪🇹 Something big just dropped for AI in Ethiopia 👀

Nexora Technology just launched NEXORA ACADEMY — Cohort 1, in partnership with Anthropic 🤯

They’re selecting only 9 people… yeah, just 9 😭

🎯 What you get:
• Paid training (monthly stipend 💰)
• Claude AI Certification
• Real project experience (not just theory)
• Priority hiring at Nexora

📚 Program (6 weeks):
• Weeks 1–2 → Anthropic courses
• Weeks 3–6 → Real-world AI projects for Ethiopia

👤 Who they want:
• Final year or recent CS/IT/SE grads
• Based in Ethiopia 🇪🇹
• Passionate about AI & building locally

🔗 Apply here:
https://forms.gle/814nem96XdpxT7fB9

⏳ They’ll close it once 9 people are selected… so don’t wait

📢 If this isn’t for you, send it to someone serious about AI

Follow @DataMinds16 for more

#DataMinds_Opportunity

🚀 Free 3-Month Web App Development Training

If you’re a graduate trying to build real skills and get opportunities… this one is worth checking

This program is hosted at FCA Creators Hub

🎯 What you’ll get:
• Hands-on web app development training
• Real-world experience + internship chances
• Mentorship from industry professionals
• Networking to grow your career

👤 Who can apply?
• Unemployed graduates (University, College, TVET)
• Age 18–34
• Refugees & persons with disabilities are encouraged

📍 Location: FCA Creators Hub
⏳ Duration: 3 months (May–July)
🗓 2 days/week (9:00 AM – 5:00 PM)

🔗 Apply here:
https://forms.gle/PM6Do2FJX7dCedXE6

📝 Deadline: April 30, 2026

📢 If it’s not for you, share it with someone who needs it

Follow @DataMinds16 for more