Logistic Regression: 8 Essential Steps to Build Your First Model
This is video one in my series on Logistic Regression. Here, I walk you through the 8 essential steps to set up a minimum viable model using logistic regression. Whether your an aspiring data scientist, or a seasoned data science professional looking for a refresher, this video should benefit you tremendously.
The 8 steps:
1. Data collecton: - thank you NIH
2. Preprocessing - dealing with null values and reducing skew.
3. Visualizing the distributions
4. Scaling the features to control for outliers and variance.
5. Splitting the data.
6. Training the model
7. Evaluating the model.
8. Making predictions.
Why Watch?
1. Step-by-Step Guidance: Follow along with clear and concise explanations.
2. Hands-On Example: Practical demonstration using the Pima Indians Diabetes dataset.
3. Visualization Techniques: Learn how to visualize data to better understand and interpret your model.
Who Should Watch?
1. Aspiring data scientists and machine learning enthusiasts.
Students and professionals looking to enhance their knowledge of logistic regression.
2. Anyone interested in learning how to build a robust logistic regression model from scratch.
Resources:
1. Pima Indians Diabetes Dataset - NIDDKD
2. Code - posted soon.
This is just the beginning! Subscribe to our channel and hit the bell icon to get notified about upcoming videos in this series, where we'll dive deeper into advanced topics, real-world applications, and more!
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