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Epython Lab

@epythonlab
Гео и язык канала: Эфиопия, Английский
Категория: Технологии

Welcome to Epython Lab, where you can get resources to learn, one-on-one trainings on machine learning, business analytics, and Python, and solutions for business problems.
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Технологии
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Фильтр публикаций

Epython Lab

20 Dec, 20:02

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Epython Lab

20 Dec, 07:37

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📢Day 18/100: Labeling Amharic Text for NER

Labeling Amharic text for Named Entity Recognition is no small task.

Our algorithm identifies:

Prices using patterns like "ብር" (currency).

Locations from a predefined list.

Products through contextual analysis.

💡 Example: "ዋጋ 4800 ብር" -> "B-PRICE I-PRICE I-PRICE"

💡 Discussion: How can we simplify labeling entities in low-resource languages?

#NER #Amharic #DataLabeling #Ethiopia
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Epython Lab

19 Dec, 07:39

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📢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
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Epython Lab

18 Dec, 12:55

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📢Day 16/100: Tackling Amharic NLP Challenges

Amharic presents unique challenges in natural language processing (NLP), from its complex script to a lack of annotated datasets.



My approach: Fine-tune Large Language Models (LLMs) for Amharic Named Entity Recognition (NER) to extract product names, prices, and locations from Telegram messages.



💡 Discussion: What strategies can we adopt to make NLP more accessible for low-resource languages like Amharic?

#NLP #AI #Amharic #FintechEthiopia
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Epython Lab

17 Dec, 14:24

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📢Day 15/100: The Rise of Telegram E-Commerce in Ethiopia

Telegram is transforming e-commerce in Ethiopia, but its fragmented nature poses challenges. Vendors operate in silos, and customers struggle to navigate multiple channels.



EthioMart's Vision:



We aim to create a centralized platform aggregating data from Telegram channels, simplifying product discovery for customers and enhancing visibility for vendors.



💡 Question of the day: How can centralized platforms improve Ethiopia’s digital shopping experience?





#Ethiopia #ECommerce #DigitalTransformation #Telegram #FintechInnovation
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Epython Lab

16 Dec, 14:48

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Epython Lab

16 Dec, 08:31

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📢Day 14/100: Next Steps for the Credit Scoring Model

With the prototype complete, here’s what’s next:

1️⃣ Testing with real-world data: Partnering with fintechs to validate the model.

2️⃣ Incorporating mobile money data: Adding another dimension to the scoring process.

3️⃣ Monitoring and retraining: Ensuring the model stays relevant as new data comes in.

💡 Takeaway: A successful model is never truly done—it evolves with the market.

💡 Question: What’s your approach to maintaining machine learning models in production?

#CreditScoring #MachineLearning #FintechEthiopia #AI
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Epython Lab

15 Dec, 08:38

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📢Day 13/100: Real-World Prototype Deployment

The prototype for my credit scoring model is live! 🚀

Features:

1️⃣ Web dashboard: Enter customer details and get real-time risk classifications.

2️⃣ API integration: Seamless communication between the frontend and back end.

3️⃣ Explainable results: Each score is accompanied by a breakdown of contributing factors.

💡 Takeaway: Deploying a functional prototype provides valuable feedback for real-world usability.

💡 Question: How do you ensure user-friendly designs for fintech tools in emerging markets?

#Prototype #AI #FintechEthiopia #CreditScoring
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Epython Lab

14 Dec, 17:05

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Epython Lab

14 Dec, 12:28

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📢Day 12/100: Comparing Machine Learning Models

Today, I compared the performance of multiple machine learning models for credit scoring:

1️⃣ Logistic Regression: Simple and interpretable but less effective with complex data.

2️⃣ Random Forest: Excellent for feature importance but slower for large datasets.

3️⃣ Gradient Boosting: Best overall performance with high accuracy and recall.

💡 Finding: Gradient Boosting stood out with an ROC-AUC of 0.97.

💡 Question: Do you prioritize interpretability or accuracy when selecting a model for financial applications?

#MachineLearning #ModelSelection #CreditScoring #FintechEthiopia
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Epython Lab

13 Dec, 14:13

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📢Day 11/100: Integrating AI and ML in Credit Scoring

AI and machine learning are at the heart of my credit scoring model, but they require careful application. 🤖

Today’s focus:

1️⃣ Modeling approaches: Exploring supervised learning techniques like Gradient Boosting for risk prediction.

2️⃣ Bias mitigation: Addressing imbalances in transactional data to ensure fair outcomes.

3️⃣ Explainability: Building a model that’s transparent and interpretable to meet regulatory standards.

💡 Coming soon: Detailed performance metrics and insights from my initial experiments with AI-powered credit scoring!

#AI #MachineLearning #CreditScoring #ExplainableAI #FintechEthiopia
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Epython Lab

12 Dec, 16:43

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📢Day 10/100: Class Imbalance Challenges
Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨

In my dataset:

Fraudulent transactions are rare (
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Epython Lab

11 Dec, 10:05

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📢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
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Epython Lab

10 Dec, 08:25

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📢Day 8/100: Sketching My Credit Scoring Workflow

Today, I outlined the workflow for my credit scoring model. Here’s what it looks like:

1️⃣ Data collection: Leveraging transaction histories, behavioral metrics, and alternative data sources.

2️⃣ Feature engineering: Creating features like transaction recency, frequency, and value tailored to BNPL behavior.

3️⃣ Model selection: Comparing Gradient Boosting, Random Forest, and Logistic Regression.

4️⃣ Evaluation: Balancing precision, recall, and ROC-AUC to ensure the model is reliable in the Ethiopian context.

💡 Tips needed: What’s your go-to feature engineering strategy for financial datasets?

#AI #CreditScoring #ModelDevelopment #BNPL #DataScienceEthiopia
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Epython Lab

9 Dec, 10:03

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📢Day 7/100: Tackling Data Availability in Ethiopia

Building a credit scoring model in Ethiopia has challenges, especially regarding data. 📊

Key hurdles I’m exploring:

1️⃣ Data scarcity: Formal credit histories are rare, but eCommerce and mobile money data offer untapped potential.

2️⃣ Local partnerships: Collaborating with fintechs to access anonymized transaction data.

3️⃣ Privacy compliance: Ensuring data protection laws are adhered to while innovating responsibly.

💡 Question of the day: Are there alternative sources of financial data that have worked well in other emerging markets?

#DataChallenges #Ethiopia #FintechInnovation #AlternativeData #PrivacyByDesign
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Epython Lab

8 Dec, 10:03

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I am excited to share with you the Python Programming for Beginners roadmap

Basic Python Programming: https://youtu.be/ISv6XIl1hn0

Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok

OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw

Join #epythonlab https://t.me/epythonlab

Join https://t.me/epythonlab for more learning resources
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Epython Lab

8 Dec, 09:49

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📢Day 6/100: BNPL Risks in Emerging Markets

While Buy-Now-Pay-Later (BNPL) services are revolutionizing access to credit, they come with risks—particularly in emerging markets like Ethiopia. ⚠️

Key risks I’m addressing in my project:

1️⃣ Credit risk: Developing a robust scoring system to predict default probabilities.

2️⃣ Behavioral risk: Educating users to avoid overspending, especially first-time borrowers.

3️⃣ Operational challenges: Adapting BNPL models to Ethiopia’s infrastructure and regulatory environment.

💡 Discussion point: How can BNPL providers balance convenience with responsible lending practices?

#RiskManagement #BNPL #CreditScoring #FinancialInclusion #EthiopiaFintech
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Epython Lab

7 Dec, 09:07

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📢Day 5/100: Understanding Ethiopian Fintech

Ethiopia's fintech ecosystem is a mix of challenges and opportunities. 📈🌍

From low formal banking penetration to an increasingly digital population, it’s clear that innovation in financial services is critical.

Key insights from my research today:

1️⃣ Low banking penetration but high mobile adoption: Over 75% of transactions are cash-based, yet mobile payment systems like Telebirr are gaining traction.

2️⃣ Regulatory frameworks: Ethiopia’s regulatory approach emphasizes financial inclusion but poses innovation challenges, especially for BNPL services.

3️⃣ Unique consumer behaviors: Ethiopians' dominance of informal financial systems and cash reliance shape their engagement with digital financial services.

💡 Question of the day: How can fintech drive financial literacy in Ethiopia to accelerate digital adoption?

#FintechAfrica #Ethiopia #BNPL #FinancialLiteracy #DigitalTransformation
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Epython Lab

6 Dec, 07:17

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📢 Day 4/100: The Role of Data in Credit Scoring

Data is the fuel for any credit scoring engine. 🔍

However, in Ethiopia, traditional credit data is scarce.

Today, I'll dive into:

Types of alternative data (e.g., mobile money, e-commerce behavior).

Ethical challenges in data collection.

I plan to build a framework that respects privacy while being effective.

#DataDriven #CreditScoring #AlternativeData #Fintech #EthicalAI #Ethiopia
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Epython Lab

5 Dec, 08:20

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📢 Day 3: Using RFM Scoring to Classify Customer Risk
📊 Before diving into machine learning or deep learning models, I started with a simpler yet powerful approach: RFM (Recency, Frequency, Monetary) scoring to classify customers into high-risk or low-risk groups.
Why RFM? In the context of Ethiopian BNPL services, where traditional credit histories are scarce, RFM provides a practical starting point by analyzing customer behavior:
1️⃣ Recency: How recently did the customer make a purchase?
2️⃣ Frequency: How often do they shop?
3️⃣ Monetary: How much do they spend?
This method helped me:
✅ Identify behavioral patterns to differentiate reliable customers from risky ones.
✅ Create a foundation for more advanced models
✅ Address data scarcity by leveraging transactional and engagement data.
RFM scoring is simple and interpretable, making it easier to communicate results to stakeholders early on.Next, I’ll integrate these insights into machine learning models to refine predictions and enhance scalability. 🚀
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