Feature Engineering for Machine Learning 1: Analysis of Missing Values in Titanic Datasets

Missing data, or missing values, occur when no data / no value is stored for certain observations within a variable. Incomplete data is an unavoidable problem in most data sources and may have a significant impact on the conclusions that can be derived from the data. Why is data missing? The source of missing data can be very different. These are just a few examples: A value is missing because it was forgotten, lost or not stored properly For a certain observation, the value does not exist The value can’t be known or identified In many organisations, information is collected into a form by a person talking with a client on the phone, or alternatively, by customers filling forms online. Often, the person entering the data does not complete all the fields in the form. Many of the fields are not compulsory, which may lead to missing values. The reasons for omitting the information can vary: perhaps the person does not want to disclose some information, for example, income, or they do not know the answer, or the answer is not applicable for a certain circumstance, or on the contrary, the person in the organization wants to spare the customer some time, and therefore omits to ask questions they think are not so relevant. There are other cases where the value for a certain variable does not exist. For example, in the variable ‘total debt as a percentage of total income’ (very common in financial data), if the person has no income, then the total percentage of 0 does not exist, and therefore it will be a missing value. It is important to understand how the missing data are introduced in the dataset, that is, the mechanisms by which missing information is introduced in a dataset. Depending on the mechanism, we may choose to process the missing values differently. In addition, by knowing the source of missing data, we may choose to take action to control that source and decrease the amount of missing information looking forward during data collection. ** Watch Full Playlist ** Feature Engineering in Python for Machine Learning    • Feature Engineering for Machine Learning 1...   ***------------------------------*** 💯 Read Full Blog with Code https://kgptalkie.com/missing-values-... 💬 Leave your comments and doubts in the comment section 📌 Save this channel and video for watch later 👍 Like this video to show your support and love ❤️ ~~~~~~~~ 🆓 Watch My Top Free Data Science Videos 👉🏻 Python for Data Scientist https://bit.ly/3dETtFb 👉🏻 Machine Learning for Beginners https://bit.ly/2WOVh7N 👉🏻 Feature Selection in Machine Learning https://bit.ly/2YW6ZQH 👉🏻 Text Preprocessing and Mining for NLP https://bit.ly/31sYMUN 👉🏻 Natural Language Processing (NLP) Tutorials https://bit.ly/3dF1cTL 👉🏻 Deep Learning with TensorFlow 2.0 and Keras https://bit.ly/3dFl09G 👉🏻 COVID 19 Data Analysis and Visualization Masterclass https://bit.ly/31vNC1U 👉🏻 Machine Learning Model Deployment Using Flask at AWS https://bit.ly/3b1svaD 👉🏻 Make Your Own Automated Email Marketing Software in Python https://bit.ly/2QqLaDy *********** 🤝 BE MY FRIEND 🌍 Check Out ML Blogs: https://kgptalkie.com 🐦Add me on Twitter:   / laxmimerit   📄 Follow me on GitHub: https://github.com/laxmimerit 📕 Add me on Facebook:   / kgptalkie   💼 Add me on LinkedIn:   / laxmimerit   👉🏻 Complete Udemy Courses: https://bit.ly/32taBK2 ⚡ Check out my Recent Videos: https://bit.ly/3ldnbWm 🔔 Subscribe me for Free Videos: https://bit.ly/34wN6T6 🤑 Get in touch for Promotion: [email protected] ✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: https://bit.ly/bert_nlp 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. Course Link: https://bit.ly/udemy95off_kgptalkie 📘 📙 Natural Language Processing (NLP) in Python for Beginners NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT Course Link: https://bit.ly/intro_nlp