Exploratory Data Analysis with Diabetic Patient Dataset: Part 2 – Predictive Modeling & Insights [Video]

In Part 2 of our Exploratory Data Analysis (EDA) series, we build on the insights from Part 1 and dive into predictive modeling using the diabetic patient dataset. This video focuses on applying machine learning techniques to predict diabetes-related outcomes, such as hospital readmissions, and understanding the key factors contributing to these predictions.

🔍 What You’ll Learn in Part 2:

How to prepare your dataset for machine learning models.
Building classification models to predict diabetes-related outcomes (e.g., hospital readmissions).
Evaluating model performance using accuracy, precision, and recall.
Hyperparameter tuning to optimize model performance.
Insights into the key features affecting diabetes predictions.
💡 Key Steps Covered:

Preparing the Dataset: Encoding categorical features and splitting the data into training and testing sets.
Building Classification Models: Using algorithms like Logistic Regression, Random Forest, and XGBoost for prediction.
Model Evaluation: Understanding model metrics such as accuracy, precision, recall, and F1-score.
Hyperparameter Tuning: Optimizing models for better performance using GridSearchCV and RandomizedSearchCV.
Final Insights: Analyzing the model results to extract key factors for diabetes prediction.
👉 Timestamps: 00:00 – Introduction to Part 2 01:45 – Preparing the Dataset for Modeling 04:00 – Building and Training Classification Models 08:30 – Model Evaluation and Metrics 12:00 – Hyperparameter Tuning for Optimization 15:00 – Final Insights & Conclusion

Make sure to check out Part 1 if you haven’t already, and don’t forget to like, comment, and subscribe for more tutorials on data science and machine learning!

#DataScience #MachineLearning #DiabeticDataset #PredictiveModeling #Python #EDA #LogisticRegression #RandomForest #XGBoost