Predictive Modeling with Python

Overview

This repository is a comprehensive guide for implementing predictive algorithms in Python from scratch. It is ideal for learners and practitioners in data science and machine learning.

Contents

1. 01. Understand your data with Descriptive Statistics.ipynb: Introduction to statistical analysis for data understanding.
2. 02. Understand your data with Visualization.ipynb: Demonstrates data visualization techniques for better data interpretation.
3. 03. Prepare Data for Machine Learning.ipynb: Guides on preprocessing and data preparation for machine learning.
4. 04. Feature Selection for Machine Learning.ipynb: Techniques for selecting the most relevant features in a dataset.
5. 05. Evaluate the Performance of Machine Learning Algorithms with Resampling.ipynb: Discusses resampling methods to evaluate machine learning models.
6. 06. Machine Learning Algorithms Performance Metrics.ipynb: Overview of different performance metrics for machine learning algorithms.
7. 07. Spot Check Classification Algorithms.ipynb: Techniques for quick testing of various classification algorithms.
8. 08. Spot Check Regression Algorithms.ipynb: Methods for evaluating different regression algorithms.
9. 09. Compare Machine Learning Algorithms.ipynb: Strategies for comparing and selecting the best machine learning algorithms.
10. 10. Automate Machine Learning Workflows with Pipelines.ipynb: Demonstrates how to use pipelines to streamline machine learning workflows.
11. 11. Improve Performance with Ensembles.ipynb: Techniques for boosting model performance using ensemble methods.
12. 12. Improve Performance with Algorithm Tuning.ipynb: Discusses methods to fine-tune machine learning algorithms for enhanced performance.
13. 13. Predictive Modeling Project Template.ipynb: A template for structuring predictive modelling projects.
14. 14. Iris Flower Classification Project.ipynb: A case study on classifying Iris flower species.
15. 15. Boston House Price Prediction.ipynb: Predictive modelling project for Boston house price prediction.
16. 16. Sonar Mines vs Rocks Prediction.ipynb: Application of predictive modelling for classifying sonar signals.

License

This project is licensed under the MIT License.

Acknowledgements

Special thanks to the Python and data science communities for their invaluable resources and support.