Benchmark – Ensemble Methods Project

Ensemble learning is a general approach where the combination of related methods provides better predictions or improves overall performance. Some real-world examples of its use include the Netflix Challenge, gene classification, image segmentation, and video retrieval.

In this assignment, you will implement ensemble learning, combining a variety of learning methods such as max voting, averaging, weighted averaging, bagging, boosting (gradient boosting, random forest, XGBoost, etc.), stacking, blending, and other variations.

You will have the freedom to choose between implementing classification or regression machine learning, or a combination of the two, so choose your ensemble techniques accordingly.

1. Access the “UCI Machine Learning Repository,” https://archive.ics.uci.edu/ . Note: There are about 120 data sets that are suitable for use in a clustering task. For this part of the exercise, you must choose one of these datasets, provided it includes at least 10 attributes and 10,000 instances
2. Ensure that the datasets are suitable for clustering using this method.
3. You may search for data in other repositories, such as Data.gov or Kaggle.

For your selected dataset, build an ensemble model as follows:

1. Explain the dataset and the type of information you wish to gain by applying an ensemble method.
2. Explain the ensemble components and how you will be using it in your analysis (list the steps, intuition behind the mathematical representation, and address its assumptions). Specifically, which of max voting, averaging, weighted averaging, bagging, boosting (gradient boosting, random forest, XGBoost, etc.), stacking, blending, and/or other variations have you chosen, and why.
3. Import necessary libraries, then read the dataset into a data frame and perform initial statistical exploration.
4. Clean the data and address unusual phenomena (e.g., normalization, feature scaling, outliers); use illustrative diagrams and plots and explain them.
5. Formulate two questions that can be answered by employing the ensemble learning
6. If appropriate and relevant to your model, split the data into training and testing sets.
7. Provide a diagram that illustrates how the ensemble components are combined into one learning model.
8. Implement and execute the ensemble learning model. Explain the intuition behind each mathematical step.
9. Answer the questions you formulated using the results obtained from executing the ensemble model.
10. Interpret the predictions made by the model in the context of the questions you asked.
11. Validate your model using relevant validation metrics such as a confusion matrix, accuracy score, ROC-AUC curves, and k-fold cross validation. Then, explain the results.
12. Explain how ensemble system reduced the variance.
13. Include all mathematical formulas used and graphs representing the final outcomes.

Prepare a comprehensive technical report as a Jupyter notebook, including all code, code comments, all outputs, plots, and analysis.

Make sure the project documentation contains

a) Problem statement

b) Algorithm of the solution

c) Analysis of the finding

d) References