Data Preparation : Overfitting and Underfitting in Machine Learning | Splitting the Data

📊 Introduction to Overfitting and Underfitting in Machine Learning
In this video, we explore the crucial concepts of overfitting and underfitting, especially within the context of credit risk models. Learn how these issues affect model performance, and how to avoid them through proper data preparation and evaluation techniques.

⏱️ Timestamps & Highlights:

00:00 – Introduction to overfitting and underfitting
00:24 – What overfitting and underfitting look like in predictive models
00:59 – Deep dive into overfitting: when the model learns too much from training data
01:44 – Causes of overfitting: complex models, too many features, and limited data
03:06 – Importance of splitting data: training (80%) vs testing (20%)
04:41 – What is underfitting and why does it happen?
06:00 – Visualizing model performance using logistic regression
07:03 – How overfit and underfit models appear on performance graphs
08:49 – Recap: the importance of model selection and evaluation
09:24 – Using sklearn for data splitting
09:48 – Preparing data using real-world loan data from GitHub
10:11 – Handling missing target values (loan status)
10:32 – Clarifying features (X) vs target (Y)
11:17 – Best practices for train-test split using random_state=42
11:39 – Code insights for data splitting with sklearn
12:02 – Verifying dataset structure post-split

🧠 Whether you're new to machine learning or refining your modeling approach, this video provides practical insights, visual explanations, and Python code examples to build robust, generalizable models.

🔗 Resources Mentioned:
👉 Code on GitHub (https://github.com/rajatkumar0308/PD_...)
data : https://github.com/rajatkumar0308/PD_...
👉 Python’s sklearn library for model training and testing

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#MachineLearning #Overfitting #Underfitting #DataScience #CreditRisk #Python #Sklearn #ModelEvaluation #LogisticRegression