"Data Cleaning in Python: Handle Missing Values & Normalize Data with Pandas & Sklearn!" 🚀

Task 2: Data Cleaning & Preprocessing
🧼 In this video, we clean and preprocess the World Happiness Report dataset for better analysis. We handle missing values, normalize numerical data, and save the cleaned dataset in CSV & JSON formats. These steps ensure high-quality data for future insights and machine learning models.

🔹 Topics Covered:
✔️ Handling missing values (drop or fill)
✔️ Normalizing numerical columns using MinMaxScaler
✔️ Saving cleaned data in multiple formats

🚀 A must-watch for anyone working with real-world datasets!
‪@Technohacksedutech‬
Mentor:- ‪@SandipGavit‬