Logistic Regression with Jupyter Notebook Link to heading

This part of the series demonstrates how to implement logistic regression on the Iris dataset using Jupyter Notebook. Jupyter Notebook provides an interactive environment for data analysis and visualization, making it a popular choice for data scientists.

Logistic Regression with Jupyter Notebook: Overview of the Series Link to heading

This series explores the implementation of logistic regression using a noised dataset based on the Iris dataset across four main sections:

  1. Dataset Preparation: Introduction to loading, exploring, and cleaning the Iris dataset, including handling missing values and data augmentation.

  2. Model Implementation: Step-by-step guide on setting up logistic regression, feature engineering, and training the model using scikit-learn.

  3. Hyperparameter Tuning: Optimization of model performance using GridSearchCV to find the best parameters.

  4. Model Deployment: Instructions on saving the model and creating a Flask API for real-time predictions.

Each section delves deeper into these crucial steps, providing practical code examples and best practices.

Monitoring and Updating Link to heading

Monitoring the API’s performance is crucial for maintaining its reliability and efficiency. This involves tracking metrics like response time and error rates. Regular updates may be needed to improve the model’s accuracy or adapt to new data, which requires retraining the model and redeploying the updated version to the server.