📢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
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
👍3
📢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
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
📢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
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
📢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
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
❤2
📢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
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
👍3
📢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
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
📢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
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