Assignment $3$

Teamwork

Topics: Classification

Deadline: June $9$ @ $11$:$59$ pm

You are given a dataset named "happydata.xlsx$"$ containing information about

individuals' perceptions of various factors $($info availability, house cost, school

quality, police trust, street quality, events$)$ and whether they are happy $(0$: No$,1$:

Yes$).$ Your task is to perform a classification analysis using one of the following

algorithms: K$-$Nearest Neighbors $($KNN$),$ Logistic Regression, or Support Vector

Machine $($SVM$).$ Submit a report that includes the following information:

$1.$ A brief description of the problem and the dataset.

$2.$ Describe your selected classification method $($like KNN$,$ Logistic Regression, or

SVM$)$ and its underlying concepts.

$3.$ Data preparation steps.

$4.$ Explain your training approach and parameter selections.

$5.$ Present your model evaluation results using relevant metrics $($e$.$g$.,$ accuracy,

precision, recall, F$1-$score$).$

$6.$ Discuss the findings of the study and any results regarding the model's

performance.

$7.$ Create visualizations to illustrate your chosen algorithm's performance.

$8.$ Conclude by summarising your finding and the general lesson from the

assignment.

Submission guidelines:

$1.$ The main submission document, containing your analysis, figures,

interpretations, and conclusions, must be in Microsoft Word format$(.$docx$).$

$2.$ Please use a clear and concise naming convention for your files, including

your name and the assignment number $($e$.$g$.,$

"YourName$\_$Assignment$3.$docx"$).$

$3.$ Ensure that key results are presented clearly in the Word document.

$4.$ If calculations were performed using Excel, Tableau, or Python, submit the

respective files as supplementary materials alongside the Word document.