Data Cleaning on Training Dataset 🧹 || python for beginners
Hello Guys,
Welcome to the next installment of our Titanic Survival Prediction Model series! In this video, which marks Day 68 of our data science journey, we roll up our sleeves and dive into the crucial task of data cleaning on the training dataset of the Titanic.
Data cleaning is a vital step in the data preprocessing pipeline, ensuring that our dataset is pristine and ready for analysis and modeling. Join us as we meticulously clean the training dataset, addressing missing values and preparing the data for further exploration and modeling.
Here's what we'll cover in this tutorial:
Inspect Dataset Information: Begin by examining the information summary of the training dataset to identify any missing values and gain insights into the data types and features present.
Handle Missing Values: Implement strategies to handle missing values in the dataset, such as dropping irrelevant columns like "Cabin" and filling missing values in the "Age" column with the mean age of passengers.
Explore Categorical Data: Examine categorical features like "Embarked" to understand their distribution and fill missing values with the most common category, ensuring data integrity.
As we meticulously clean the training dataset, we pave the way for building a robust Titanic survival prediction model using logistic regression.
Join us as we embark on this crucial phase of our data science journey, ensuring that our dataset is primed and polished for predictive modeling.
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 delve deeper into the fascinating world of data analysis and modeling!
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