FastAPI for Data Science: Customer Churn Project (Part 3/5)@dc_1136

Data is messy—let's fix that! 🛠️ Welcome to Part 3 of our Customer Churn Analysis using FastAPI series.
In this session, we are bridging the gap between raw user input and a Machine Learning model. We dive deep into Data Validation using Pydantic and implement a custom Scaling Function to ensure our model receives perfectly formatted data for accurate predictions.
⏮️ Missing the previous steps?
Watch Part 1 (Introduction): https://youtu.be/qihtwejPe0E?si=us4Gdfh_O1Yddquf
Watch Part 2 (Basic Setup): https://m.youtube.com/watch?v=vhw2a82s6b4&list=PLf0Vxxcb2BDwLByA95dXgucoxuwNH-ec3&index=2&pp=gAQBiAQBsAQBsAgC

📍 What you will learn in this video:
✅ Advanced Pydantic Schemas: Defining every feature (Tenure, Monthly Charges, etc.) with strict types.
✅ The Scaling Logic: Implementing the scale_val() function to normalize numerical values.
✅ Mapping Categories: Organizing how we handle categorical inputs like InternetService and Contract.
✅ Input Validation: Why FastAPI + Pydantic is the best duo for preventing bad data from crashing your model.
This is the most critical step for ensuring your API is robust and production-ready!
🔔 Subscribe & Hit the Bell! In Part 4, we will finally write the prediction logic and get those Churn results!

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Keywords:-
FastAPI, Customer Churn, Data Science Project, Machine Learning API, Python FastAPI Tutorial, API Development, Churn Prediction, Data Analysis, Swagger UI, Web API for Data Science, Python Backend, Business Intelligence Project.

Hashtags:-
#FastAPI #DataScience #CustomerChurn #PythonProgramming #MachineLearning #WebDevelopment #API #DataAnalysis #TechTutorial #DataProject

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Видео FastAPI for Data Science: Customer Churn Project (Part 3/5)@dc_1136 канала Dynamic Coding