$2$ Assignment Overview

$2\mathrm{.}1$ About Assignment:

In this assignment, you will perform a series of task $($explained in section $3)$ for Regression and Classification for a dataset and provide a rigorous rationale for your solutions. We will determine scores by judging both the soundness and cleanliness of your code, the quality of the write$-$up$($report$)$ and your ability to answer the question during viva. Here are examples of aspects that may lead to point deductions:

Use of misleading, unnecessary, or unmotivated graphic elements.

Unreadable code.

Missing or incomplete design rationale in write$-$up$.$

Ineffective encoding for your stated goal $($e$.$g$.,$ distracting colors, improper data transformation$).$

Tools and Python Package which can be used for this assignments $($listed but not limited to$)$:

Pandas library$($pd$)$

Numpy library$($np$)$

Matplotlib library$($plt$)$

Seaborn library$($sns$)$

sickit Learn$($sklearn$)$

$2\mathrm{.}2$ Learning Outcomes:

Learning outcomes can be following but not limited to:

Use Pandas as the primary tool to process structured data in Python with CSV files,

Extract various information from a given dataset using statistical and visualizing techniques.

To be able to build a Machine Learning Model, interpret the design choices for the model.

Be able to conduct various experiment on the model and interpret the result of the same.

$2\mathrm{.}3$ Data Selection:

Please feel free to pick any structured datasets in csv format that matches the task requirements. But please take pre$-$approval from your respected instructor and Module leader.

If you are not sure about which dataset to pick, select one from the options provided.

$2$

$5$CS$037$

Final Portfolio Project

Due:$13$ Feb

The best source to find datasets are but not limited to:

Kaggle Datasets:

Kaggle provides a high$-$quality dataset in different formats that we can easily find and download.

UCI Machine Learning Repository:

This repository contains databases, domain theories, and data generators that are widely used by the machine learning community for the analysis of $ML$ algorithms.

$3$ Tasks and Marks Division