10 pages of word press , theme education
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I only need the introduction and background completed. 1 page, IEEE format. – ## GroupprojectIntroandbackground.docx – ## Groupprojectproposal.docx – ## breast-cancer.arff2.txt **## GroupprojectIntroandbackground.docx** **2. Project Objectives** The main goal of this project is to provide **hands-o…
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– ## GroupprojectIntroandbackground.docx
– ## Groupprojectproposal.docx
– ## breast-cancer.arff2.txt
**## GroupprojectIntroandbackground.docx**
**2. Project Objectives**
The main goal of this project is to provide
**hands-on research experience** in machine learning. Students will:
· Identify and define a research-worthy problem.
· Explore existing machine learning approaches.
· Develop, implement, and test their own solution.
· Critically evaluate their work in comparison with related methods.
**3. Project Topic Selection**
· Topics should be
**relevant to machine learning** in a domain that all group members agree on.
· Each group must submit a
**1-page project proposal**(12-point font, single spacing, 1-inch margins) that includes:
· Problem statement
· Dataset(s) selection
· Plan and methodology
**4. Data and Tools**
· Students may use
**publicly available datasets** (e.g., from Kaggle, UCI ML Repository, etc).
· Recommended tools include
**WEKA, Jupyter Notebook, PyTorch, Scikit-learn, etc.**
· Data preprocessing, cleaning, and feature engineering must be documented.
**5. Project Deliverables**
1.
**Proposal:** Problem definition, dataset selection, and plan.
2.
**Final Report (4-5 pages including references using** [**IEEE paper template**](https://www.ieee.org/conferences/publishing/templates) **):**
· Introduction & background
· Related work (literature review)
· Methodology & algorithms used (at least 5 different machine learning techniques need to be utilized)
· Experimental setup (datasets, evaluation metrics)
· Results and analysis (comparisons, graphs, tables)
· Conclusion and future work
3.
**Presentation/Demo:** 10–15 minutes per group.
**NOTE: Do not include WEKA screenshot results of your experiments in the report. Instead, summarize your findings in text and use tables, charts, or written explanations to clearly present your results.**
**NOTE: A 15% penalty will be applied to your grade if your paper is not formatted using the official IEEE paper template. Please make sure to carefully follow the template guidelines for structure, formatting, and references before submission**
**## Groupprojectproposal.docx**
2
**Predicting Breast Cancer Recurrence Using Machine Learning**
**Problem Statement**
Breast cancer is one of the most common cancers affecting women worldwide. Early detection is critical in improving patient outcomes. Structured datasets provide an opportunity to apply machine learning techniques to predict the likelihood of cancer recurrence or non-recurrence. The research problem is to determine if machine learning models can improve prediction accuracy and support early diagnosis and treatment planning.
**Dataset Selection**
[https://archive.ics.uci.edu/dataset/14/breast+cancer](https://archive.ics.uci.edu/dataset/14/breast+cancer)
We chose the breast cancer dataset from the UCI Machine Learning Repository. The dataset includes 286 instances with 9 attributes describing patient medical features. The target variable distinguishes between recurrence and no recurrence.
**Plan and Methodology**
We will use WEKA machine learning software to carry out the following steps:
1. Data Analysis and Preprocessing
a. Explore dataset characteristics and distributions
b. Encode categorical variables and check for missing values
2. Model Development
a. Establish a performance baseline using multiple algorithms:
i. Logistic Regression
ii. Decision Trees (J48)
iii. Random Forests
iv. Naïve Bayes
v. K-Nearest Neighbors (IBk)
3. Model Assessment
a. Evaluate models using 10-fold cross validation to ensure generalization
b. Compare model performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC
4. Feature Selection
a. Assess the impact of feature selection methods on performance
b. Evaluate if dimensionality reduction methods lead to stronger model performance
5. Critical evaluation
a. Provide a comparative analysis of algorithms
b. Discuss trade-offs, strengths, and weaknesses of each approach
c. Reflect on how these findings might apply in real-world clinical decision making
**## breast-cancer.arff2.txt**
% Breast Cancer Dataset (UCI ML Repository)
% ARFF for WEKA – corrected enums/underscores, no '?' in attribute values
@RELATION breast_cancer
@ATTRIBUTE class {no-recurrence-events,recurrence-events}
@ATTRIBUTE age {10-19,20-29,30-39,40-49,50-59,60-69,70-79,80-89,90-99}
@ATTRIBUTE menopause {lt40,ge40,premeno}
@ATTRIBUTE tumor_size {0-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59}
@ATTRIBUTE inv_nodes {0-2,3-5,6-8,9-11,12-14,15-17,18-20,21-23,24-26,27-29,30-32,33-35,36-39}
@ATTRIBUTE node_caps {yes,no}
@ATTRIBUTE deg_malig {1,2,3}
@ATTRIBUTE breast {left,right}
@ATTRIBUTE breast_quad {left_up,right_up,left_low,right_low,central}
@ATTRIBUTE irradiat {yes,no}
@DATA
no-recurrence-events,30-39,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,3,left,central,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,lt40,10-14,0-2,no,1,left,right_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,3,left,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,lt40,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,40-44,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,35-39,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,2,left,central,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,5-9,0-2,no,1,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,50-54,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,50-59,lt40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,70-79,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,right,central,no
no-recurrence-events,50-59,ge40,5-9,0-2,no,2,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,left,left_low,no
no-recurrence-events,20-29,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,right_low,no
no-recurrence-events,30-39,premeno,5-9,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,1,left,central,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,70-79,ge40,0-4,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,right_up,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,3,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,right,right_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,70-79,ge40,10-14,0-2,no,2,left,central,no
no-recurrence-events,30-39,premeno,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,30-39,premeno,25-29,6-8,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,25-29,0-2,yes,2,left,left_up,no
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,right_up,yes
no-recurrence-events,40-49,premeno,40-44,3-5,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,6-8,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,3-5,no,2,right,central,no
no-recurrence-events,30-39,premeno,40-44,3-5,no,3,right,right_up,yes
no-recurrence-events,40-49,premeno,5-9,0-2,no,1,left,left_low,yes
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,40-44,3-5,yes,2,left,left_low,no
no-recurrence-events,50-59,premeno,20-24,3-5,yes,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,45-49,0-2,no,2,left,left_low,yes
no-recurrence-events,60-69,ge40,45-49,6-8,yes,3,left,central,no
no-recurrence-events,40-49,premeno,25-29,0-2,?,2,left,right_low,yes
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,right,left_up,yes
no-recurrence-events,50-59,premeno,30-34,3-5,yes,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,0-2,no,3,left,central,no
no-recurrence-events,50-59,lt40,30-34,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,15-17,yes,3,right,left_up,no
no-recurrence-events,60-69,ge40,30-34,3-5,yes,3,left,left_low,no
no-recurrence-events,50-59,ge40,35-39,15-17,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,3,right,left_up,yes
no-recurrence-events,30-39,lt40,15-19,0-2,no,3,right,left_up,no
no-recurrence-events,60-69,ge40,40-44,3-5,no,2,right,left_up,yes
no-recurrence-events,50-59,ge40,25-29,3-5,yes,3,right,left_up,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,1,left,central,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,3-5,yes,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_low,yes
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_low,no
no-recurrence-events,40-49,ge40,40-44,15-17,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,ge40,30-34,0-2,no,2,left,left_up,yes
no-recurrence-events,30-39,premeno,20-24,3-5,yes,2,right,left_up,yes
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,3-5,yes,2,right,left_up,no
no-recurrence-events,50-59,premeno,25-29,3-5,yes,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_up,yes
no-recurrence-events,40-49,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,3-5,no,2,right,left_up,yes
no-recurrence-events,40-49,premeno,20-24,0-2,no,3,right,left_low,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,left_low,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_low,yes
no-recurrence-events,40-49,premeno,20-24,6-8,no,2,right,left_low,yes
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_up,yes
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,yes,2,right,left_up,no
no-recurrence-events,40-49,premeno,15-19,12-14,no,3,right,right_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,left_low,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,50-54,0-2,yes,2,right,left_up,yes
no-recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,10-14,3-5,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,yes
no-recurrence-events,50-59,ge40,15-19,0-2,yes,2,left,central,yes
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_low,no
recurrence-events,50-59,premeno,15-19,0-2,no,2,left,left_low,no
recurrence-events,40-49,premeno,40-44,0-2,no,1,left,left_low,no
recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
recurrence-events,50-59,ge40,30-34,0-2,no,3,left,?,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,left,right_up,no
recurrence-events,40-49,premeno,35-39,0-2,no,1,right,left_up,no
recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,right,central,no
recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,40-44,0-2,no,2,right,left_low,no
recurrence-events,40-49,ge40,20-24,0-2,no,2,right,left_up,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,left,left_up,no
recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,central,no
recurrence-events,30-39,premeno,15-19,0-2,no,1,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,right_up,no
recurrence-events,30-39,premeno,30-34,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,right_low,yes
recurrence-events,60-69,ge40,20-24,0-2,no,3,right,left_low,no
recurrence-events,30-39,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,ge40,20-24,3-5,no,3,right,left_low,yes
recurrence-events,40-49,premeno,30-34,15-17,yes,3,left,left_low,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,right,left_up,yes
recurrence-events,60-69,ge40,40-44,3-5,yes,3,right,left_low,no
recurrence-events,60-69,ge40,45-49,0-2,no,1,right,right_up,yes
recurrence-events,50-59,premeno,50-54,9-11,yes,2,right,left_up,no
recurrence-events,40-49,premeno,30-34,3-5,no,2,right,left_up,no
recurrence-events,30-39,premeno,30-34,3-5,no,3,right,left_up,yes
recurrence-events,70-79,ge40,15-19,9-11,?,1,left,left_low,yes
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,left_up,yes
recurrence-events,50-59,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,20-24,3-5,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,20-24,3-5,no,2,left,left_low,yes
recurrence-events,40-49,premeno,15-19,15-17,yes,3,left,left_low,no
recurrence-events,50-59,ge40,25-29,6-8,no,3,left,left_low,yes
recurrence-events,50-59,ge40,20-24,3-5,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,12-14,yes,3,left,left_up,yes
recurrence-events,30-39,premeno,30-34,9-11,no,2,right,left_up,yes
recurrence-events,30-39,premeno,15-19,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,30-34,9-11,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,35-39,6-8,yes,3,left,left_low,no
recurrence-events,30-39,premeno,20-24,3-5,yes,2,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,left_up,no
recurrence-events,40-49,premeno,50-54,0-2,no,2,right,left_low,yes
recurrence-events,30-39,premeno,40-44,0-2,no,1,left,left_up,no
recurrence-events,60-69,ge40,50-54,0-2,no,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,6-8,yes,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,yes
recurrence-events,40-49,premeno,20-24,3-5,yes,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,right_low,yes
recurrence-events,50-59,ge40,30-34,3-5,no,3,right,left_up,no
recurrence-events,60-69,ge40,25-29,3-5,no,2,right,right_up,no
recurrence-events,40-49,ge40,25-29,12-14,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_low,no
recurrence-events,30-39,premeno,35-39,9-11,yes,3,left,left_low,no
recurrence-events,40-49,premeno,30-34,3-5,yes,2,left,right_up,no
recurrence-events,60-69,ge40,20-24,24-26,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,3,left,right_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,3,right,left_low,yes
recurrence-events,40-49,premeno,15-19,0-2,yes,3,right,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,30-34,3-5,yes,2,left,central,yes
recurrence-events,40-49,premeno,25-29,9-11,yes,3,right,left_up,no
recurrence-events,30-39,premeno,25-29,6-8,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,10-14,6-8,yes,3,left,left_up,yes
recurrence-events,50-59,premeno,35-39,15-17,yes,3,right,right_up,no
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
recurrence-events,30-39,premeno,20-24,0-2,no,3,left,left_up,yes
recurrence-events,60-69,ge40,20-24,0-2,no,1,right,left_up,no
recurrence-events,40-49,ge40,30-34,3-5,no,3,left,left_low,no
recurrence-events,50-59,ge40,30-34,3-5,no,3,left,left_low,no
**## breast-cancer.arff2.txt**
% Breast Cancer Dataset (UCI ML Repository)
% ARFF for WEKA – corrected enums/underscores, no '?' in attribute values
@RELATION breast_cancer
@ATTRIBUTE class {no-recurrence-events,recurrence-events}
@ATTRIBUTE age {10-19,20-29,30-39,40-49,50-59,60-69,70-79,80-89,90-99}
@ATTRIBUTE menopause {lt40,ge40,premeno}
@ATTRIBUTE tumor_size {0-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59}
@ATTRIBUTE inv_nodes {0-2,3-5,6-8,9-11,12-14,15-17,18-20,21-23,24-26,27-29,30-32,33-35,36-39}
@ATTRIBUTE node_caps {yes,no}
@ATTRIBUTE deg_malig {1,2,3}
@ATTRIBUTE breast {left,right}
@ATTRIBUTE breast_quad {left_up,right_up,left_low,right_low,central}
@ATTRIBUTE irradiat {yes,no}
@DATA
no-recurrence-events,30-39,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,3,left,central,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,lt40,10-14,0-2,no,1,left,right_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,3,left,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,lt40,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,40-44,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,35-39,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,2,left,central,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,5-9,0-2,no,1,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,50-54,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,50-59,lt40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,70-79,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,right,central,no
no-recurrence-events,50-59,ge40,5-9,0-2,no,2,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,left,left_low,no
no-recurrence-events,20-29,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,right_low,no
no-recurrence-events,30-39,premeno,5-9,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,1,left,central,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,70-79,ge40,0-4,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,right_up,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,3,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,right,right_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,70-79,ge40,10-14,0-2,no,2,left,central,no
no-recurrence-events,30-39,premeno,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,30-39,premeno,25-29,6-8,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,25-29,0-2,yes,2,left,left_up,no
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,right_up,yes
no-recurrence-events,40-49,premeno,40-44,3-5,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,6-8,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,3-5,no,2,right,central,no
no-recurrence-events,30-39,premeno,40-44,3-5,no,3,right,right_up,yes
no-recurrence-events,40-49,premeno,5-9,0-2,no,1,left,left_low,yes
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,40-44,3-5,yes,2,left,left_low,no
no-recurrence-events,50-59,premeno,20-24,3-5,yes,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,45-49,0-2,no,2,left,left_low,yes
no-recurrence-events,60-69,ge40,45-49,6-8,yes,3,left,central,no
no-recurrence-events,40-49,premeno,25-29,0-2,?,2,left,right_low,yes
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,right,left_up,yes
no-recurrence-events,50-59,premeno,30-34,3-5,yes,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,0-2,no,3,left,central,no
no-recurrence-events,50-59,lt40,30-34,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,15-17,yes,3,right,left_up,no
no-recurrence-events,60-69,ge40,30-34,3-5,yes,3,left,left_low,no
no-recurrence-events,50-59,ge40,35-39,15-17,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,3,right,left_up,yes
no-recurrence-events,30-39,lt40,15-19,0-2,no,3,right,left_up,no
no-recurrence-events,60-69,ge40,40-44,3-5,no,2,right,left_up,yes
no-recurrence-events,50-59,ge40,25-29,3-5,yes,3,right,left_up,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,1,left,central,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,3-5,yes,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_low,yes
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_low,no
no-recurrence-events,40-49,ge40,40-44,15-17,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,ge40,30-34,0-2,no,2,left,left_up,yes
no-recurrence-events,30-39,premeno,20-24,3-5,yes,2,right,left_up,yes
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,3-5,yes,2,right,left_up,no
no-recurrence-events,50-59,premeno,25-29,3-5,yes,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_up,yes
no-recurrence-events,40-49,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,3-5,no,2,right,left_up,yes
no-recurrence-events,40-49,premeno,20-24,0-2,no,3,right,left_low,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,left_low,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_low,yes
no-recurrence-events,40-49,premeno,20-24,6-8,no,2,right,left_low,yes
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_up,yes
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,yes,2,right,left_up,no
no-recurrence-events,40-49,premeno,15-19,12-14,no,3,right,right_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,left_low,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,50-54,0-2,yes,2,right,left_up,yes
no-recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,10-14,3-5,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,yes
no-recurrence-events,50-59,ge40,15-19,0-2,yes,2,left,central,yes
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_low,no
recurrence-events,50-59,premeno,15-19,0-2,no,2,left,left_low,no
recurrence-events,40-49,premeno,40-44,0-2,no,1,left,left_low,no
recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
recurrence-events,50-59,ge40,30-34,0-2,no,3,left,?,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,left,right_up,no
recurrence-events,40-49,premeno,35-39,0-2,no,1,right,left_up,no
recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,right,central,no
recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,40-44,0-2,no,2,right,left_low,no
recurrence-events,40-49,ge40,20-24,0-2,no,2,right,left_up,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,left,left_up,no
recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,central,no
recurrence-events,30-39,premeno,15-19,0-2,no,1,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,right_up,no
recurrence-events,30-39,premeno,30-34,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,right_low,yes
recurrence-events,60-69,ge40,20-24,0-2,no,3,right,left_low,no
recurrence-events,30-39,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,ge40,20-24,3-5,no,3,right,left_low,yes
recurrence-events,40-49,premeno,30-34,15-17,yes,3,left,left_low,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,right,left_up,yes
recurrence-events,60-69,ge40,40-44,3-5,yes,3,right,left_low,no
recurrence-events,60-69,ge40,45-49,0-2,no,1,right,right_up,yes
recurrence-events,50-59,premeno,50-54,9-11,yes,2,right,left_up,no
recurrence-events,40-49,premeno,30-34,3-5,no,2,right,left_up,no
recurrence-events,30-39,premeno,30-34,3-5,no,3,right,left_up,yes
recurrence-events,70-79,ge40,15-19,9-11,?,1,left,left_low,yes
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,left_up,yes
recurrence-events,50-59,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,20-24,3-5,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,20-24,3-5,no,2,left,left_low,yes
recurrence-events,40-49,premeno,15-19,15-17,yes,3,left,left_low,no
recurrence-events,50-59,ge40,25-29,6-8,no,3,left,left_low,yes
recurrence-events,50-59,ge40,20-24,3-5,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,12-14,yes,3,left,left_up,yes
recurrence-events,30-39,premeno,30-34,9-11,no,2,right,left_up,yes
recurrence-events,30-39,premeno,15-19,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,30-34,9-11,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,35-39,6-8,yes,3,left,left_low,no
recurrence-events,30-39,premeno,20-24,3-5,yes,2,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,left_up,no
recurrence-events,40-49,premeno,50-54,0-2,no,2,right,left_low,yes
recurrence-events,30-39,premeno,40-44,0-2,no,1,left,left_up,no
recurrence-events,60-69,ge40,50-54,0-2,no,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,6-8,yes,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,yes
recurrence-events,40-49,premeno,20-24,3-5,yes,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,right_low,yes
recurrence-events,50-59,ge40,30-34,3-5,no,3,right,left_up,no
recurrence-events,60-69,ge40,25-29,3-5,no,2,right,right_up,no
recurrence-events,40-49,ge40,25-29,12-14,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_low,no
recurrence-events,30-39,premeno,35-39,9-11,yes,3,left,left_low,no
recurrence-events,40-49,premeno,30-34,3-5,yes,2,left,right_up,no
recurrence-events,60-69,ge40,20-24,24-26,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,3,left,right_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,3,right,left_low,yes
recurrence-events,40-49,premeno,15-19,0-2,yes,3,right,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,30-34,3-5,yes,2,left,central,yes
recurrence-events,40-49,premeno,25-29,9-11,yes,3,right,left_up,no
recurrence-events,30-39,premeno,25-29,6-8,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,10-14,6-8,yes,3,left,left_up,yes
recurrence-events,50-59,premeno,35-39,15-17,yes,3,right,right_up,no
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
recurrence-events,30-39,premeno,20-24,0-2,no,3,left,left_up,yes
recurrence-events,60-69,ge40,20-24,0-2,no,1,right,left_up,no
recurrence-events,40-49,ge40,30-34,3-5,no,3,left,left_low,no
recurrence-events,50-59,ge40,30-34,3-5,no,3,left,left_low,no
**## GroupprojectIntroandbackground.docx**
**2. Project Objectives**
The main goal of this project is to provide
**hands-on research experience** in machine learning. Students will:
· Identify and define a research-worthy problem.
· Explore existing machine learning approaches.
· Develop, implement, and test their own solution.
· Critically evaluate their work in comparison with related methods.
**3. Project Topic Selection**
· Topics should be
**relevant to machine learning** in a domain that all group members agree on.
· Each group must submit a
**1-page project proposal**(12-point font, single spacing, 1-inch margins) that includes:
· Problem statement
· Dataset(s) selection
· Plan and methodology
**4. Data and Tools**
· Students may use
**publicly available datasets** (e.g., from Kaggle, UCI ML Repository, etc).
· Recommended tools include
**WEKA, Jupyter Notebook, PyTorch, Scikit-learn, etc.**
· Data preprocessing, cleaning, and feature engineering must be documented.
**5. Project Deliverables**
1.
**Proposal:** Problem definition, dataset selection, and plan.
2.
**Final Report (4-5 pages including references using** [**IEEE paper template**](https://www.ieee.org/conferences/publishing/templates) **):**
· Introduction & background
· Related work (literature review)
· Methodology & algorithms used (at least 5 different machine learning techniques need to be utilized)
· Experimental setup (datasets, evaluation metrics)
· Results and analysis (comparisons, graphs, tables)
· Conclusion and future work
3.
**Presentation/Demo:** 10–15 minutes per group.
**NOTE: Do not include WEKA screenshot results of your experiments in the report. Instead, summarize your findings in text and use tables, charts, or written explanations to clearly present your results.**
**NOTE: A 15% penalty will be applied to your grade if your paper is not formatted using the official IEEE paper template. Please make sure to carefully follow the template guidelines for structure, formatting, and references before submission**
**## Groupprojectproposal.docx**
2
**Predicting Breast Cancer Recurrence Using Machine Learning**
**Problem Statement**
Breast cancer is one of the most common cancers affecting women worldwide. Early detection is critical in improving patient outcomes. Structured datasets provide an opportunity to apply machine learning techniques to predict the likelihood of cancer recurrence or non-recurrence. The research problem is to determine if machine learning models can improve prediction accuracy and support early diagnosis and treatment planning.
**Dataset Selection**
[https://archive.ics.uci.edu/dataset/14/breast+cancer](https://archive.ics.uci.edu/dataset/14/breast+cancer)
We chose the breast cancer dataset from the UCI Machine Learning Repository. The dataset includes 286 instances with 9 attributes describing patient medical features. The target variable distinguishes between recurrence and no recurrence.
**Plan and Methodology**
We will use WEKA machine learning software to carry out the following steps:
1. Data Analysis and Preprocessing
a. Explore dataset characteristics and distributions
b. Encode categorical variables and check for missing values
2. Model Development
a. Establish a performance baseline using multiple algorithms:
i. Logistic Regression
ii. Decision Trees (J48)
iii. Random Forests
iv. Naïve Bayes
v. K-Nearest Neighbors (IBk)
3. Model Assessment
a. Evaluate models using 10-fold cross validation to ensure generalization
b. Compare model performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC
4. Feature Selection
a. Assess the impact of feature selection methods on performance
b. Evaluate if dimensionality reduction methods lead to stronger model performance
5. Critical evaluation
a. Provide a comparative analysis of algorithms
b. Discuss trade-offs, strengths, and weaknesses of each approach
c. Reflect on how these findings might apply in real-world clinical decision making
**## breast-cancer.arff2.txt**
% Breast Cancer Dataset (UCI ML Repository)
% ARFF for WEKA – corrected enums/underscores, no '?' in attribute values
@RELATION breast_cancer
@ATTRIBUTE class {no-recurrence-events,recurrence-events}
@ATTRIBUTE age {10-19,20-29,30-39,40-49,50-59,60-69,70-79,80-89,90-99}
@ATTRIBUTE menopause {lt40,ge40,premeno}
@ATTRIBUTE tumor_size {0-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59}
@ATTRIBUTE inv_nodes {0-2,3-5,6-8,9-11,12-14,15-17,18-20,21-23,24-26,27-29,30-32,33-35,36-39}
@ATTRIBUTE node_caps {yes,no}
@ATTRIBUTE deg_malig {1,2,3}
@ATTRIBUTE breast {left,right}
@ATTRIBUTE breast_quad {left_up,right_up,left_low,right_low,central}
@ATTRIBUTE irradiat {yes,no}
@DATA
no-recurrence-events,30-39,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,3,left,central,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,lt40,10-14,0-2,no,1,left,right_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,3,left,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,lt40,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,40-44,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,35-39,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,2,left,central,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,5-9,0-2,no,1,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,50-54,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,50-59,lt40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,70-79,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,right,central,no
no-recurrence-events,50-59,ge40,5-9,0-2,no,2,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,left,left_low,no
no-recurrence-events,20-29,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,right_low,no
no-recurrence-events,30-39,premeno,5-9,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,1,left,central,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,70-79,ge40,0-4,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,right_up,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,3,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,right,right_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,70-79,ge40,10-14,0-2,no,2,left,central,no
no-recurrence-events,30-39,premeno,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,30-39,premeno,25-29,6-8,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,25-29,0-2,yes,2,left,left_up,no
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,right_up,yes
no-recurrence-events,40-49,premeno,40-44,3-5,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,6-8,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,3-5,no,2,right,central,no
no-recurrence-events,30-39,premeno,40-44,3-5,no,3,right,right_up,yes
no-recurrence-events,40-49,premeno,5-9,0-2,no,1,left,left_low,yes
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,40-44,3-5,yes,2,left,left_low,no
no-recurrence-events,50-59,premeno,20-24,3-5,yes,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,45-49,0-2,no,2,left,left_low,yes
no-recurrence-events,60-69,ge40,45-49,6-8,yes,3,left,central,no
no-recurrence-events,40-49,premeno,25-29,0-2,?,2,left,right_low,yes
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,right,left_up,yes
no-recurrence-events,50-59,premeno,30-34,3-5,yes,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,0-2,no,3,left,central,no
no-recurrence-events,50-59,lt40,30-34,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,15-17,yes,3,right,left_up,no
no-recurrence-events,60-69,ge40,30-34,3-5,yes,3,left,left_low,no
no-recurrence-events,50-59,ge40,35-39,15-17,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,3,right,left_up,yes
no-recurrence-events,30-39,lt40,15-19,0-2,no,3,right,left_up,no
no-recurrence-events,60-69,ge40,40-44,3-5,no,2,right,left_up,yes
no-recurrence-events,50-59,ge40,25-29,3-5,yes,3,right,left_up,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,1,left,central,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,3-5,yes,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_low,yes
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_low,no
no-recurrence-events,40-49,ge40,40-44,15-17,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,ge40,30-34,0-2,no,2,left,left_up,yes
no-recurrence-events,30-39,premeno,20-24,3-5,yes,2,right,left_up,yes
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,3-5,yes,2,right,left_up,no
no-recurrence-events,50-59,premeno,25-29,3-5,yes,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_up,yes
no-recurrence-events,40-49,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,3-5,no,2,right,left_up,yes
no-recurrence-events,40-49,premeno,20-24,0-2,no,3,right,left_low,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,left_low,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_low,yes
no-recurrence-events,40-49,premeno,20-24,6-8,no,2,right,left_low,yes
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_up,yes
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,yes,2,right,left_up,no
no-recurrence-events,40-49,premeno,15-19,12-14,no,3,right,right_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,left_low,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,50-54,0-2,yes,2,right,left_up,yes
no-recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,10-14,3-5,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,yes
no-recurrence-events,50-59,ge40,15-19,0-2,yes,2,left,central,yes
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_low,no
recurrence-events,50-59,premeno,15-19,0-2,no,2,left,left_low,no
recurrence-events,40-49,premeno,40-44,0-2,no,1,left,left_low,no
recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
recurrence-events,50-59,ge40,30-34,0-2,no,3,left,?,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,left,right_up,no
recurrence-events,40-49,premeno,35-39,0-2,no,1,right,left_up,no
recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,right,central,no
recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,40-44,0-2,no,2,right,left_low,no
recurrence-events,40-49,ge40,20-24,0-2,no,2,right,left_up,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,left,left_up,no
recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,central,no
recurrence-events,30-39,premeno,15-19,0-2,no,1,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,right_up,no
recurrence-events,30-39,premeno,30-34,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,right_low,yes
recurrence-events,60-69,ge40,20-24,0-2,no,3,right,left_low,no
recurrence-events,30-39,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,ge40,20-24,3-5,no,3,right,left_low,yes
recurrence-events,40-49,premeno,30-34,15-17,yes,3,left,left_low,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,right,left_up,yes
recurrence-events,60-69,ge40,40-44,3-5,yes,3,right,left_low,no
recurrence-events,60-69,ge40,45-49,0-2,no,1,right,right_up,yes
recurrence-events,50-59,premeno,50-54,9-11,yes,2,right,left_up,no
recurrence-events,40-49,premeno,30-34,3-5,no,2,right,left_up,no
recurrence-events,30-39,premeno,30-34,3-5,no,3,right,left_up,yes
recurrence-events,70-79,ge40,15-19,9-11,?,1,left,left_low,yes
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,left_up,yes
recurrence-events,50-59,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,20-24,3-5,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,20-24,3-5,no,2,left,left_low,yes
recurrence-events,40-49,premeno,15-19,15-17,yes,3,left,left_low,no
recurrence-events,50-59,ge40,25-29,6-8,no,3,left,left_low,yes
recurrence-events,50-59,ge40,20-24,3-5,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,12-14,yes,3,left,left_up,yes
recurrence-events,30-39,premeno,30-34,9-11,no,2,right,left_up,yes
recurrence-events,30-39,premeno,15-19,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,30-34,9-11,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,35-39,6-8,yes,3,left,left_low,no
recurrence-events,30-39,premeno,20-24,3-5,yes,2,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,left_up,no
recurrence-events,40-49,premeno,50-54,0-2,no,2,right,left_low,yes
recurrence-events,30-39,premeno,40-44,0-2,no,1,left,left_up,no
recurrence-events,60-69,ge40,50-54,0-2,no,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,6-8,yes,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,yes
recurrence-events,40-49,premeno,20-24,3-5,yes,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,right_low,yes
recurrence-events,50-59,ge40,30-34,3-5,no,3,right,left_up,no
recurrence-events,60-69,ge40,25-29,3-5,no,2,right,right_up,no
recurrence-events,40-49,ge40,25-29,12-14,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_low,no
recurrence-events,30-39,premeno,35-39,9-11,yes,3,left,left_low,no
recurrence-events,40-49,premeno,30-34,3-5,yes,2,left,right_up,no
recurrence-events,60-69,ge40,20-24,24-26,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,3,left,right_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,3,right,left_low,yes
recurrence-events,40-49,premeno,15-19,0-2,yes,3,right,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,30-34,3-5,yes,2,left,central,yes
recurrence-events,40-49,premeno,25-29,9-11,yes,3,right,left_up,no
recurrence-events,30-39,premeno,25-29,6-8,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,10-14,6-8,yes,3,left,left_up,yes
recurrence-events,50-59,premeno,35-39,15-17,yes,3,right,right_up,no
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
recurrence-events,30-39,premeno,20-24,0-2,no,3,left,left_up,yes
recurrence-events,60-69,ge40,20-24,0-2,no,1,right,left_up,no
recurrence-events,40-49,ge40,30-34,3-5,no,3,left,left_low,no
recurrence-events,50-59,ge40,30-34,3-5,no,3,left,left_low,no
**## GroupprojectIntroandbackground.docx**
**2. Project Objectives**
The main goal of this project is to provide
**hands-on research experience** in machine learning. Students will:
· Identify and define a research-worthy problem.
· Explore existing machine learning approaches.
· Develop, implement, and test their own solution.
· Critically evaluate their work in comparison with related methods.
**3. Project Topic Selection**
· Topics should be
**relevant to machine learning** in a domain that all group members agree on.
· Each group must submit a
**1-page project proposal**(12-point font, single spacing, 1-inch margins) that includes:
· Problem statement
· Dataset(s) selection
· Plan and methodology
**4. Data and Tools**
· Students may use
**publicly available datasets** (e.g., from Kaggle, UCI ML Repository, etc).
· Recommended tools include
**WEKA, Jupyter Notebook, PyTorch, Scikit-learn, etc.**
· Data preprocessing, cleaning, and feature engineering must be documented.
**5. Project Deliverables**
1.
**Proposal:** Problem definition, dataset selection, and plan.
2.
**Final Report (4-5 pages including references using** [**IEEE paper template**](https://www.ieee.org/conferences/publishing/templates) **):**
· Introduction & background
· Related work (literature review)
· Methodology & algorithms used (at least 5 different machine learning techniques need to be utilized)
· Experimental setup (datasets, evaluation metrics)
· Results and analysis (comparisons, graphs, tables)
· Conclusion and future work
3.
**Presentation/Demo:** 10–15 minutes per group.
**NOTE: Do not include WEKA screenshot results of your experiments in the report. Instead, summarize your findings in text and use tables, charts, or written explanations to clearly present your results.**
**NOTE: A 15% penalty will be applied to your grade if your paper is not formatted using the official IEEE paper template. Please make sure to carefully follow the template guidelines for structure, formatting, and references before submission**
**## Groupprojectproposal.docx**
2
**Predicting Breast Cancer Recurrence Using Machine Learning**
**Problem Statement**
Breast cancer is one of the most common cancers affecting women worldwide. Early detection is critical in improving patient outcomes. Structured datasets provide an opportunity to apply machine learning techniques to predict the likelihood of cancer recurrence or non-recurrence. The research problem is to determine if machine learning models can improve prediction accuracy and support early diagnosis and treatment planning.
**Dataset Selection**
[https://archive.ics.uci.edu/dataset/14/breast+cancer](https://archive.ics.uci.edu/dataset/14/breast+cancer)
We chose the breast cancer dataset from the UCI Machine Learning Repository. The dataset includes 286 instances with 9 attributes describing patient medical features. The target variable distinguishes between recurrence and no recurrence.
**Plan and Methodology**
We will use WEKA machine learning software to carry out the following steps:
1. Data Analysis and Preprocessing
a. Explore dataset characteristics and distributions
b. Encode categorical variables and check for missing values
2. Model Development
a. Establish a performance baseline using multiple algorithms:
i. Logistic Regression
ii. Decision Trees (J48)
iii. Random Forests
iv. Naïve Bayes
v. K-Nearest Neighbors (IBk)
3. Model Assessment
a. Evaluate models using 10-fold cross validation to ensure generalization
b. Compare model performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC
4. Feature Selection
a. Assess the impact of feature selection methods on performance
b. Evaluate if dimensionality reduction methods lead to stronger model performance
5. Critical evaluation
a. Provide a comparative analysis of algorithms
b. Discuss trade-offs, strengths, and weaknesses of each approach
c. Reflect on how these findings might apply in real-world clinical decision making
**## breast-cancer.arff2.txt**
% Breast Cancer Dataset (UCI ML Repository)
% ARFF for WEKA – corrected enums/underscores, no '?' in attribute values
@RELATION breast_cancer
@ATTRIBUTE class {no-recurrence-events,recurrence-events}
@ATTRIBUTE age {10-19,20-29,30-39,40-49,50-59,60-69,70-79,80-89,90-99}
@ATTRIBUTE menopause {lt40,ge40,premeno}
@ATTRIBUTE tumor_size {0-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59}
@ATTRIBUTE inv_nodes {0-2,3-5,6-8,9-11,12-14,15-17,18-20,21-23,24-26,27-29,30-32,33-35,36-39}
@ATTRIBUTE node_caps {yes,no}
@ATTRIBUTE deg_malig {1,2,3}
@ATTRIBUTE breast {left,right}
@ATTRIBUTE breast_quad {left_up,right_up,left_low,right_low,central}
@ATTRIBUTE irradiat {yes,no}
@DATA
no-recurrence-events,30-39,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,0-4,0-2,no,3,left,central,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,lt40,10-14,0-2,no,1,left,right_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,3,left,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,lt40,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,40-44,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,35-39,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,2,left,central,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,5-9,0-2,no,1,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,50-54,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,right_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,right,left_low,no
no-recurrence-events,50-59,lt40,15-19,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,70-79,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,70-79,ge40,40-44,0-2,no,1,right,right_up,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,right,central,no
no-recurrence-events,50-59,ge40,5-9,0-2,no,2,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,central,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,0-4,0-2,no,1,left,left_low,no
no-recurrence-events,20-29,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,15-19,0-2,no,2,right,right_low,no
no-recurrence-events,30-39,premeno,5-9,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,1,left,central,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,35-39,0-2,no,3,left,left_up,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,2,right,left_low,no
no-recurrence-events,70-79,ge40,0-4,0-2,no,1,left,right_low,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,right,left_up,no
no-recurrence-events,40-49,premeno,40-44,0-2,no,1,right,left_up,no
no-recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,2,right,left_up,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,premeno,10-14,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_low,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_up,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,left,right_up,no
no-recurrence-events,30-39,premeno,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_low,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,3,right,left_low,no
no-recurrence-events,50-59,premeno,25-29,0-2,no,2,right,right_low,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,right_low,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_low,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_up,no
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,right,right_up,no
no-recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,1,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,no,2,right,left_up,no
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,ge40,20-24,0-2,no,3,left,left_up,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,20-24,0-2,no,1,left,left_low,no
no-recurrence-events,50-59,ge40,15-19,0-2,no,2,right,right_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,left_up,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,right,left_low,no
no-recurrence-events,70-79,ge40,10-14,0-2,no,2,left,central,no
no-recurrence-events,30-39,premeno,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,30-39,premeno,25-29,6-8,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,25-29,0-2,yes,2,left,left_up,no
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,9-11,yes,2,right,right_up,yes
no-recurrence-events,40-49,premeno,40-44,3-5,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,6-8,no,2,left,left_up,no
no-recurrence-events,50-59,ge40,40-44,0-2,no,3,left,right_up,no
no-recurrence-events,60-69,ge40,30-34,0-2,no,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,3-5,no,2,right,central,no
no-recurrence-events,30-39,premeno,40-44,3-5,no,3,right,right_up,yes
no-recurrence-events,40-49,premeno,5-9,0-2,no,1,left,left_low,yes
no-recurrence-events,30-39,premeno,40-44,0-2,no,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,ge40,40-44,3-5,yes,2,left,left_low,no
no-recurrence-events,50-59,premeno,20-24,3-5,yes,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,1,left,left_up,no
no-recurrence-events,40-49,premeno,45-49,0-2,no,2,left,left_low,yes
no-recurrence-events,60-69,ge40,45-49,6-8,yes,3,left,central,no
no-recurrence-events,40-49,premeno,25-29,0-2,?,2,left,right_low,yes
no-recurrence-events,60-69,ge40,50-54,0-2,no,2,right,left_up,yes
no-recurrence-events,50-59,premeno,30-34,3-5,yes,2,left,left_low,yes
no-recurrence-events,30-39,premeno,20-24,0-2,no,3,left,central,no
no-recurrence-events,50-59,lt40,30-34,0-2,no,3,right,left_up,no
no-recurrence-events,50-59,ge40,25-29,15-17,yes,3,right,left_up,no
no-recurrence-events,60-69,ge40,30-34,3-5,yes,3,left,left_low,no
no-recurrence-events,50-59,ge40,35-39,15-17,no,3,left,left_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,3,right,left_up,yes
no-recurrence-events,30-39,lt40,15-19,0-2,no,3,right,left_up,no
no-recurrence-events,60-69,ge40,40-44,3-5,no,2,right,left_up,yes
no-recurrence-events,50-59,ge40,25-29,3-5,yes,3,right,left_up,no
no-recurrence-events,50-59,premeno,30-34,0-2,no,1,left,central,no
no-recurrence-events,50-59,ge40,30-34,0-2,no,1,right,central,no
no-recurrence-events,40-49,premeno,35-39,0-2,no,1,left,left_low,no
no-recurrence-events,40-49,premeno,25-29,0-2,no,3,right,left_up,yes
no-recurrence-events,40-49,premeno,30-34,3-5,yes,2,right,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_up,yes
no-recurrence-events,60-69,ge40,25-29,3-5,?,1,right,left_low,yes
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_up,no
no-recurrence-events,40-49,premeno,20-24,3-5,no,2,right,left_low,no
no-recurrence-events,40-49,ge40,40-44,15-17,yes,2,right,left_up,yes
no-recurrence-events,50-59,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,40-49,ge40,30-34,0-2,no,2,left,left_up,yes
no-recurrence-events,30-39,premeno,20-24,3-5,yes,2,right,left_up,yes
no-recurrence-events,30-39,premeno,15-19,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,30-34,6-8,yes,2,right,right_up,no
no-recurrence-events,50-59,ge40,20-24,3-5,yes,2,right,left_up,no
no-recurrence-events,50-59,premeno,25-29,3-5,yes,2,left,left_low,yes
no-recurrence-events,40-49,premeno,30-34,0-2,no,2,right,right_up,yes
no-recurrence-events,40-49,ge40,25-29,0-2,no,2,left,left_low,no
no-recurrence-events,60-69,ge40,10-14,0-2,no,2,left,left_low,no
no-recurrence-events,50-59,premeno,25-29,3-5,no,2,right,left_up,yes
no-recurrence-events,40-49,premeno,20-24,0-2,no,3,right,left_low,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_up,yes
no-recurrence-events,40-49,premeno,35-39,0-2,yes,3,right,left_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,1,right,left_low,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,9-11,?,3,left,left_low,yes
no-recurrence-events,40-49,premeno,20-24,6-8,no,2,right,left_low,yes
no-recurrence-events,50-59,ge40,25-29,0-2,no,1,left,right_low,no
no-recurrence-events,60-69,ge40,15-19,0-2,no,2,left,left_up,yes
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,right,left_up,no
no-recurrence-events,50-59,ge40,20-24,0-2,yes,2,right,left_up,no
no-recurrence-events,40-49,premeno,15-19,12-14,no,3,right,right_low,yes
no-recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_up,yes
no-recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,left_low,no
no-recurrence-events,30-39,premeno,10-14,0-2,no,2,left,right_low,no
no-recurrence-events,50-59,premeno,50-54,0-2,yes,2,right,left_up,yes
no-recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_up,no
no-recurrence-events,50-59,premeno,10-14,3-5,no,1,right,left_up,no
no-recurrence-events,40-49,premeno,10-14,0-2,no,2,left,left_low,yes
no-recurrence-events,50-59,ge40,15-19,0-2,yes,2,left,central,yes
no-recurrence-events,50-59,premeno,25-29,0-2,no,1,left,left_low,no
no-recurrence-events,60-69,ge40,25-29,0-2,no,3,right,left_low,no
recurrence-events,50-59,premeno,15-19,0-2,no,2,left,left_low,no
recurrence-events,40-49,premeno,40-44,0-2,no,1,left,left_low,no
recurrence-events,50-59,ge40,35-39,0-2,no,2,left,left_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,30-39,premeno,0-4,0-2,no,2,right,central,no
recurrence-events,50-59,ge40,30-34,0-2,no,3,left,?,no
recurrence-events,50-59,premeno,25-29,0-2,no,2,left,right_up,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,left,right_up,no
recurrence-events,40-49,premeno,35-39,0-2,no,1,right,left_up,no
recurrence-events,40-49,premeno,20-24,0-2,no,2,left,left_low,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,right,central,no
recurrence-events,40-49,premeno,30-34,0-2,no,3,right,right_up,no
recurrence-events,50-59,premeno,25-29,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,40-44,0-2,no,2,right,left_low,no
recurrence-events,40-49,ge40,20-24,0-2,no,2,right,left_up,no
recurrence-events,50-59,ge40,20-24,0-2,no,2,left,left_up,no
recurrence-events,40-49,premeno,15-19,0-2,no,2,left,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,central,no
recurrence-events,30-39,premeno,15-19,0-2,no,1,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,right_up,no
recurrence-events,30-39,premeno,30-34,0-2,no,1,right,left_up,no
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,right_low,yes
recurrence-events,60-69,ge40,20-24,0-2,no,3,right,left_low,no
recurrence-events,30-39,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,ge40,20-24,3-5,no,3,right,left_low,yes
recurrence-events,40-49,premeno,30-34,15-17,yes,3,left,left_low,no
recurrence-events,50-59,premeno,30-34,0-2,no,3,right,left_up,yes
recurrence-events,60-69,ge40,40-44,3-5,yes,3,right,left_low,no
recurrence-events,60-69,ge40,45-49,0-2,no,1,right,right_up,yes
recurrence-events,50-59,premeno,50-54,9-11,yes,2,right,left_up,no
recurrence-events,40-49,premeno,30-34,3-5,no,2,right,left_up,no
recurrence-events,30-39,premeno,30-34,3-5,no,3,right,left_up,yes
recurrence-events,70-79,ge40,15-19,9-11,?,1,left,left_low,yes
recurrence-events,60-69,ge40,30-34,0-2,no,3,right,left_up,yes
recurrence-events,50-59,premeno,25-29,3-5,yes,3,left,left_low,yes
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,right,left_low,no
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,20-24,3-5,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,20-24,3-5,no,2,left,left_low,yes
recurrence-events,40-49,premeno,15-19,15-17,yes,3,left,left_low,no
recurrence-events,50-59,ge40,25-29,6-8,no,3,left,left_low,yes
recurrence-events,50-59,ge40,20-24,3-5,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,12-14,yes,3,left,left_up,yes
recurrence-events,30-39,premeno,30-34,9-11,no,2,right,left_up,yes
recurrence-events,30-39,premeno,15-19,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,30-34,9-11,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,35-39,6-8,yes,3,left,left_low,no
recurrence-events,30-39,premeno,20-24,3-5,yes,2,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,3,left,left_up,no
recurrence-events,40-49,premeno,50-54,0-2,no,2,right,left_low,yes
recurrence-events,30-39,premeno,40-44,0-2,no,1,left,left_up,no
recurrence-events,60-69,ge40,50-54,0-2,no,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,yes,3,right,right_up,no
recurrence-events,40-49,premeno,30-34,6-8,yes,3,right,left_up,no
recurrence-events,40-49,premeno,30-34,0-2,no,1,left,left_low,yes
recurrence-events,40-49,premeno,20-24,3-5,yes,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,2,left,right_low,yes
recurrence-events,50-59,ge40,30-34,3-5,no,3,right,left_up,no
recurrence-events,60-69,ge40,25-29,3-5,no,2,right,right_up,no
recurrence-events,40-49,ge40,25-29,12-14,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,25-29,0-2,no,3,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_up,no
recurrence-events,50-59,lt40,20-24,0-2,?,1,left,left_low,no
recurrence-events,30-39,premeno,35-39,9-11,yes,3,left,left_low,no
recurrence-events,40-49,premeno,30-34,3-5,yes,2,left,right_up,no
recurrence-events,60-69,ge40,20-24,24-26,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,35-39,0-2,no,3,left,left_low,no
recurrence-events,40-49,premeno,25-29,0-2,no,2,left,left_low,yes
recurrence-events,50-59,ge40,30-34,6-8,yes,3,left,right_low,no
recurrence-events,50-59,premeno,25-29,0-2,no,3,right,left_low,yes
recurrence-events,40-49,premeno,15-19,0-2,yes,3,right,left_up,no
recurrence-events,60-69,ge40,30-34,0-2,yes,2,right,right_up,yes
recurrence-events,60-69,ge40,30-34,3-5,yes,2,left,central,yes
recurrence-events,40-49,premeno,25-29,9-11,yes,3,right,left_up,no
recurrence-events,30-39,premeno,25-29,6-8,yes,3,left,right_low,yes
recurrence-events,60-69,ge40,10-14,6-8,yes,3,left,left_up,yes
recurrence-events,50-59,premeno,35-39,15-17,yes,3,right,right_up,no
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,50-59,ge40,40-44,6-8,yes,3,left,left_low,yes
recurrence-events,30-39,premeno,30-34,0-2,no,2,left,left_up,no
recurrence-events,30-39,premeno,20-24,0-2,no,3,left,left_up,yes
recurrence-events,60-69,ge40,20-24,0-2,no,1,right,left_up,no
recurrence-events,40-49,ge40,30-34,3-5,no,3,left,left_low,no
recurrence-events,50-59,ge40,30-34,3-5,no,3,left,left_low,no
– 1
– 2
– 3
For this assignment, you will post your answers to the question below. **You are required to respond to at least two of your classmates post.** Please do not be late with this post because your classmates will be relying on you to post on time. Your follow-up posts can add additional insight to a cl…
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Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →For this assignment, you will post your answers to the question below. **You are required to respond to at least two of your classmates post.** Please do not be late with this post because your classmates will be relying on you to post on time. Your follow-up posts can add additional insight to a classmate's opinions or can challenge their opinions. Use examples from the readings, or from your own research, to support your views, as appropriate. For your follow-up posts this week, you may wish to visit a couple of the web sites contributed by your classmates and share your opinion of these sites with the class. Be sure to read the follow-up posts to your own posts and reply to any questions or requests for clarification. You are encouraged to conduct research and use other sources to support your answers. References are not required but you must tell us about your sources.
**Instructions:**
1. Discuss tools and techniques to remove Malware from infected machines; using a popular product to make your point.
2. Discuss the vulnerabilities that computer memory and computer process have that malware can take advantage of and exploit.
3. List any observations, tips or questions about this lab that would prove helpful to fellow students prior to midnight on Wednesday and comment on other student posts with value added comments (not simply agreeing) by midnight Sunday for full credit consideration
Using outsourced security personnel, what types of threats might this pose for an organization? What controls can be used to mitigate these threats? Cite all your sources, including the ones identified here (e.g., your course text), using APA format. If possible, cite external sources that help subs…
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Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →Using outsourced security personnel, what types of threats might this pose for an organization? What controls can be used to mitigate these threats?
Cite all your sources, including the ones identified here (e.g., your course text), using APA format. If possible, cite external sources that help substantiate your position. Provide any available scholarly research, directives, publications, or memorandums to support your discussion and provide references in APA format.
**The discussion assignment must have:**
1. Proper grammar and APA formatting.
2. Minimum 250 words for the first discussion post
**Week-2: Identify Cybersecurity Podcasts** Staying abreast of current cybersecurity incidents requires constant vigil. It is important to identify good online resources that you can use to stay current. Recorded podcasts have proven to be very popular and can contain important information. The valu…
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Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →**Week-2: Identify Cybersecurity Podcasts**
Staying abreast of current cybersecurity incidents requires constant vigil. It is important to identify good online resources that you can use to stay current. Recorded podcasts have proven to be very popular and can contain important information. The value of listening to a podcast is that it can be done as a “secondary” activity, such as while working out at the gym, riding public transit, driving a car, or taking a walk. For this activity, identify three unique cybersecurity-related podcasts.
The podcasts that you select must be unique and not that another learner has already used for this class.
Search the Internet for three cybersecurity-related podcasts.
Post your analysis (minimum of 200 words) of that podcast: who recorded the podcast, their credentials, what was the podcast's topic, and how long the podcast was.
Record your opinion of the podcast and assign the podcast a grade (A-F) regarding its value. Explain why the such a grade was assigned.
All assignments must be submitted in APA-7 format. follow the APA 7 style below is the link how to use it.
https://youtu.be/_fVv2Jt0o18
https://youtu.be/zeSIXD6y3WQ
Discuss Insecure External Software Components that may be the target of threat actors. In particular, explore the Application program interface (API), Device drives, and Dynamic link library (DLL). Specific examples must support your discussion responses. 200 words with references and citations apa …
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Order on TutorsGallery →Discuss Insecure External Software Components that may be the target of threat actors. In particular, explore the Application program interface (API), Device drives, and Dynamic link library (DLL). Specific examples must support your discussion responses.
200 words with references and citations apa 7 style
In-depth study guidance and analytical solution covering CCA 640: Cloud Computing Implementations and Migrations 2258.
Immediate access to the full model answer, formatted citations, and rubric breakdown.
Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →Full assignment prompt and step-by-step guidance are included in the downloadable solution file.
In-depth study guidance and analytical solution covering Explain cloud computing in simple terms.
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Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →Full assignment prompt and step-by-step guidance are included in the downloadable solution file.
– ## MMIS671_homework21.docx – hw2-1.q2.data1.zip **## MMIS671_homework21.docx** **Homework 2.** **Question 1. Decision Tree Classifier [10 Points]** Data: The zip file “ _hw2.q1.data.zip_” contains 3 CSV files: · “ _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘ _y_’…
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Order on TutorsGallery →– ## MMIS671_homework21.docx
– hw2-1.q2.data1.zip
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 | |
| 12 | |
| 13 | |
| 14 | |
| 15 | |
| 16 | |
| 17 | |
| 18 | |
| 19 | |
| 20 | |
| 21 | |
| 22 | |
| 23 | |
| 24 | |
| 25 | |
| 26 | |
| 27 | |
| 28 | |
| 29 | |
| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
| ID_011 | |
| ID_012 | |
| ID_013 | |
| ID_014 | |
| ID_015 | |
| ID_016 | |
| ID_017 | |
| ID_018 | |
| ID_019 | |
| ID_020 | |
| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 | |
| 12 | |
| 13 | |
| 14 | |
| 15 | |
| 16 | |
| 17 | |
| 18 | |
| 19 | |
| 20 | |
| 21 | |
| 22 | |
| 23 | |
| 24 | |
| 25 | |
| 26 | |
| 27 | |
| 28 | |
| 29 | |
| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
| ID_011 | |
| ID_012 | |
| ID_013 | |
| ID_014 | |
| ID_015 | |
| ID_016 | |
| ID_017 | |
| ID_018 | |
| ID_019 | |
| ID_020 | |
| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
**## MMIS671_homework21.docx**
**Homework 2.**
**Question 1. Decision Tree Classifier [10 Points]**
Data: The zip file “
_hw2.q1.data.zip_” contains 3 CSV files:
· “
_hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
_y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
· “
_hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
_x_1_, …, x
__25_.
**Task 1**. [4 points]
Use 5-fold cross-validation with the 10,000 labeled exampled from “
_hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
_DecisionTreeClassifier_.
_Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
_Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_
**Task 2**. [2 Points]
Train a
_DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q1.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q1.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 | |
| 12 | |
| 13 | |
| 14 | |
| 15 | |
| 16 | |
| 17 | |
| 18 | |
| 19 | |
| 20 | |
| 21 | |
| 22 | |
| 23 | |
| 24 | |
| 25 | |
| 26 | |
| 27 | |
| 28 | |
| 29 | |
| 30 | |
**Task 4.**[2 Points]
Of the 25 input variables which ones are relevant for this classification task?
The following … input variables are relevant for this classification task: …………………
Display your trained decision tree:
**Question 2. Supervised machine learning classifiers [10 Points]**
Data: The zip file “
_hw2.q2.data.zip_” contains 3 CSV files:
· “
_hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
_y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
· “
_hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
_ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
_x1_, …, x
_10_.
**Task 1**. [6 points]
Use 4-fold cross-validation with the 8,000 labeled exampled from “
_hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
_Scikit-Learn_ classifiers, including:
_GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:
**_Selected model with hyper-parameter values_** _:_
_Mean cross-validation accuracy: ………………………._(
_rounded to 4 decimal places_)
**Task 2**. [2 Points]
Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
_y_’ for the 2,000 examples in “
_hw2.q2.test.csv_”
_._ Report the following:
·
_Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)
·
_Classification report_ for the 2,000 test examples:
·
_Confusion matrix_ for the 2,000 test examples:
**Task 3.**[2 Points]
Use the model trained in Task 2 to predict the output class ‘
_y’_ for the 30 examples in “
_hw2.q2.new.csv_”. Specify the predicted classes in the table below:
| | |
| — | — |
| ID | predicted y |
| ID_001 | |
| ID_002 | |
| ID_003 | |
| ID_004 | |
| ID_005 | |
| ID_006 | |
| ID_007 | |
| ID_008 | |
| ID_009 | |
| ID_010 | |
| ID_011 | |
| ID_012 | |
| ID_013 | |
| ID_014 | |
| ID_015 | |
| ID_016 | |
| ID_017 | |
| ID_018 | |
| ID_019 | |
| ID_020 | |
| ID_021 | |
| ID_022 | |
| ID_023 | |
| ID_024 | |
| ID_025 | |
| ID_026 | |
| ID_027 | |
| ID_028 | |
| ID_029 | |
| ID_030 | |
**hw2-1.q2.data1.zip**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)
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