Objective: To understand the basic concepts of classification such as general process of a classification task, training/test data, model validation, overfitting, model selection, evaluation measures, etc.. By designing experiments and comparing the performance of different classifiers, to understand each advanced classification technique and its advantages.  

# Instructions

### 1. Select several classifiers that you think appropriate on this classification task. You are suggested to try at least one from each category: 
Category 1: KNN, Decision Trees, Naive Bayes
Category 2: SVM, AdaBoost, RandomForest, Neural Network

Don´t program the algorithms. Use Python, R, Matlab, etc..

### 2. Train the classifiers using the training data provided in this page. Predict labels for the testing set, and upload your predictions for the classifiers by December 8, 2024.

You will need to upload each classifier's predictions in a separate submission. Please title your submissions to reflect which algorithm was used, and specify the algorithm in the submission description.


### 3. Write project report (no less than 5 pages). 
You are suggested to use the following template (not obligated, but at least include all the following elements): 


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I.	Introduction 
This section includes introduction of classification, the algorithms you used in the project, evaluation metrics.  Remember mention the objective of the project. 
II.	Data sets description
In this section, characteristics of the data set such as size, number and type of attributes, classes should be clearly described to present the problem you are solving. 
III.	Classifiers
Introduce the models/algorithms you used to solve the classification problem. Additionally, explain the reason why you chose them.  
IV.	Experiments and Results 
Introduce the platform or toolbox you used. 
Experiment design, which includes parameter values, number of runs, evaluation measures, overfit problem addressing, etc.
Show experiment results, comparisons, analysis and discussions. It is necessary to illustrate the results and comparisons in different ways such as table, figure, screenshots, etc. Remember make explanation on the figures and tables, don´t just leave them there without any comments in the context. 
V.	Conclusion
Conclude the project, discuss the limitations and possible improving methods.
VI.	References
Put the references you took in the project design and report writing, including books, papers, websites of data source, software documents, etc. 
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### 4. Deliver the project report by December 8, 2024. 


### 5. Prepare a 10-minutes presentation and present your project in the class on December 9 and 11, 2024.