Encoding Categorical Columns on Titanic Dataset 🛠️ || python for beginners
Hello Guys,
Welcome back to our Titanic Survival Prediction Model series! In this captivating episode, marking Day 70 of our data science journey, we delve into the intricacies of encoding categorical columns in the Titanic dataset as we prepare our data for predictive modeling.
Data preprocessing is a crucial step in the data science pipeline, and encoding categorical variables is essential for transforming non-numeric data into a format that machine learning models can understand. Join us as we embark on this crucial stage of our journey towards building a predictive model for Titanic survival.
Here's what we'll cover in this tutorial:
Dataset Overview: Begin by examining the first few rows of the dataset to gain insights into its structure and features, laying the groundwork for data preprocessing.
Categorical Column Analysis: Explore the categorical columns "Sex" and "Embarked" to understand their unique categories and distribution within the dataset, providing valuable context for the encoding process.
Encoding Categorical Variables: Utilize the replace method to map categorical values to numerical equivalents for the "Sex" and "Embarked" columns, transforming object data types into int64 format to facilitate model training.
Data Verification: Validate the encoding process by inspecting the modified dataset, ensuring that categorical columns have been successfully encoded and are ready for further analysis and modeling.
As we navigate through the process of encoding categorical columns, we pave the way for building a robust predictive model that leverages the power of logistic regression to forecast Titanic survival probabilities.
Don't forget to like, share, and subscribe for more insightful tutorials on data science, machine learning, and predictive modeling. Stay tuned for our next video as we continue our quest to unlock the mysteries of the Titanic dataset!
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