🚢 HANDLING MISSING VALUES IN TITANIC DATASET USING PYTHON & PANDAS

In this practical Python Data Science tutorial, we learn how to identify, analyze, and handle missing values in the famous Titanic dataset using Pandas and Jupyter Notebook. Missing values are one of the most common problems encountered during real-world data analysis and machine learning projects. Properly handling missing data is an important step in data cleaning and preprocessing. 📚 In this video, you will learn how to work with missing values in the Titanic dataset and apply different techniques for preparing data for further analysis and machine learning. 🔍 Topics Covered in This Tutorial ✅ Introduction to Missing Values ✅ Titanic Dataset ✅ Loading and exploring the dataset using Pandas ✅ Identifying missing values ✅ Counting missing values in columns ✅ Understanding NaN values ✅ Handling different representations of missing values ✅ Converting missing-value tokens into NaN ✅ Handling missing values in categorical columns ✅ Handling missing values in the Cabin column ✅ Replacing missing categorical values with "Unknown" ✅ Using fillna() to fill missing values ✅ Filling values using a single value ✅ Filling values using a dictionary ✅ Using Mean and Median for numerical data ✅ Using Mode for categorical data ✅ Data Cleaning and Preprocessing ✅ Preparing data for Machine Learning