As a data scientist 70-80 percent of your time spending on data cleansing. If you have given data which contains special characters and you may need to avoid those special characters, what methods do you use to avoid it?
https://youtu.be/qL7lX5lCfgw

Unpacking variables is very efficient way yo write clean code in Python
Take a look at the below links to get understand about iterable unpacking

Extended Iterable Unpacking in Python

#python #Subscribe #youtubechannel

https://youtu.be/BluC5ByjiJI

3 most commonly used string formatting
https://youtu.be/d6K5u5S9wn4

What are the concepts behind list, tuple, and dictionary?

This tutorial will give you an insight about them
https://youtu.be/YYzOGQCBUjo

The hidden costs of data quality issues in Machine Learning
https://youtu.be/TdMu-0TEppM

Day 1: Introduction to the Challenge
📢 Day 1/100: The Journey Begins!
I'm embarking on a 100-day challenge to share insights, progress, and lessons learned as I build a data-driven credit scoring model tailored for Buy-Now-Pay-Later (BNPL) services in Ethiopia's fintech space. 🚀

Why this topic? BNPL is reshaping financial inclusion, and robust credit scoring is the backbone of sustainable lending. Follow along as I explore data, algorithms, and strategies to make this happen!

hashtag#Fintech hashtag#DataScience hashtag#CreditScoring hashtag#BNPL hashtag#FinancialInclusion hashtag#Ethiopia hashtag#100DaysChallenge

📢Day 9/100: Feature Engineering Deep Dive
Feature engineering is where raw data turns into actionable insights! 🛠
In my credit scoring project, key features include:
1️⃣ Recency, Frequency, Monetary (RFM): Critical for understanding customer behavior.
2️⃣ Fraud indicators: High-value transactions flagged based on outlier analysis.
3️⃣ Categorical encodings: Using Weight of Evidence (WoE) to transform qualitative data like product categories.
💡 Takeaway: Good features are the foundation of any successful model. They ensure the patterns we observe are meaningful and actionable.
💡 Discussion point: What’s your go-to method for handling highly skewed data in financial datasets?
#FeatureEngineering #DataScience #CreditScoring #FintechEthiopia

📢Day 17/100: From Data to Insights



My journey started with collecting and cleaning data from Telegram channels, a hub for Ethiopian e-commerce.



Key steps:

1️⃣ Scraping Telegram messages to capture product details.

2️⃣ Preprocessing Amharic text to handle non-text characters and normalize content.

3️⃣ Tokenizing text for labeling.



💡 Takeaway: High-quality data preparation is the backbone of effective machine learning models.


#DataScience #AmharicNLP #FintechEthiopia

As a Developer, the best practice is writing clean, simple, concise, and readable code.

Learn about how to write clean code  https://youtu.be/upe7v7dhv0Y

Sharing is caring 🙏

💡 Researchers & Beginners in Python!

This step-by-step guide walks you through installing and setting up Python on Windows using the Microsoft Store, along with VS Code setup to get you coding in no time!

🔗 https://www.youtube.com/watch?v=EGdhnSEWKok

Like & share if you found this helpful!

#PythonForResearch #PythonSetup #DataScience #AI #MachineLearning #CodingForBeginners #ResearchTools #Academia #PythonOnWindows