Project Title: Machine Learning Classification on $[$Your Chosen Dataset$]$

Project Description:

In this final project, students will apply their knowledge of machine learning

classification methods to analyze a dataset of their choice. The project will involve

several key steps:

Dataset Selection: Students will choose a dataset from sources like Kaggle or

other relevant websites. The dataset should be suitable for classification tasks

and should contain features that can be used to predict a target variable.

Data Exploration: After selecting the dataset, students will thoroughly study its

characteristics. This includes understanding the features, data types,

distributions, and any potential challenges such as missing values or outliers.

Model Implementation: Using the scikit$-$learn library in Python, students will apply

various classification algorithms learned throughout the course, including:

Neural Network

Decision Tree

Random Forest

Logistic Regression

k$-$Nearest Neighbors $($kNN$)$

Evaluation Metrics: For each classification model, students will compute

evaluation metrics such as accuracy, precision, recall, F$1-$score, and confusion

matrix. These metrics will provide insights into the performance of each

algorithm and help in comparing their effectiveness.

Project Parts:

Code $(5$ points$)$: Students will write Python code to implement each

classification algorithm using the scikit$-$learn library. The code should

include:

Loading the dataset and preprocessing steps $($e$.$g$.,$ splitting data,

feature scaling$).$

Implementation of each classification algorithm with appropriate

hyperparameters.

Evaluation of each model using various evaluation metrics.

Report $(5$ points$)$: Students will prepare a detailed report documenting

their project. The report should include:

Introduction to the dataset and its relevance.

Data preprocessing steps.

Implementation details of each classification algorithm using

scikit$-$learn.

Evaluation metrics and analysis of results.

Conclusion and insights gained from the project.

Presentation Video $(5$ points$)$: Students will create a presentation video

summarizing their project. The video should effectively communicate the

key findings, challenges faced, and lessons learned during the project.

Project Objectives:

Apply machine learning classification algorithms to real$-$world datasets.

Gain hands$-$on experience in data exploration, preprocessing, model

implementation, and evaluation using the scikit$-$learn library.

Develop skills in analyzing and interpreting machine learning results.

Communicate findings effectively through written reports and presentation

videos.

Project Deliverables:

Code implementing classification algorithms using scikit$-$learn.

Report documenting the project details and analysis.

Presentation video summarizing the project findings.

Good luck with your project!