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):    • FastAPI for Data Science: Customer Churn P...   Watch Part 2 (Basic Setup):    • FastAPI for Data Science: Customer Churn P...   📍 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! Please need your support =UPI id=sachintewari746@sbi The source code githublink will be shared only on the condition of 100 likes on each video + 100 subscribers 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 Follow me at:- 1. GitHub-- https://github.com/ring80master-commits 2. Linkdin -- https://www.linkedin.com/in/sachin-te...