Answer the following in the provided document titled machine$\_$learning.ipynb.

$1.$ For each of the following examples describe at least one possible input and

output. Justify your answers:

$1\mathrm{.}1.$ A self$-$driving car

$1\mathrm{.}2.$ Netflix recommendation system

$1\mathrm{.}3.$ Signature recognition

$1\mathrm{.}4.$ Medical diagnosis

$2.$ For each of the following case studies, determine whether it is appropriate

to utilise regression or classification machine learning algorithms. Justify

your answers:

$2\mathrm{.}1.$ Classifying emails as promotional or social based on their content and

metadata.

$2\mathrm{.}2.$ Forecasting the stock price of a company based on historical data and

market trends.

$2\mathrm{.}3.$ Sorting images of animals into different species based on their visual

features.

$2\mathrm{.}4.$ Predicting the likelihood of a patient having a particular disease based

on medical history and diagnostic test results.

$3.$ For each of the following real$-$world problems, determine whether it is

appropriate to utilise a supervised or unsupervised machine learning

algorithm. Justify your answers:

$3\mathrm{.}1.$ Detecting anomalies in a manufacturing process using sensor data

without prior knowledge of specific anomaly patterns. $3\mathrm{.}2.$ Predicting customer lifetime value based on historical transaction data

and customer demographics.

$3\mathrm{.}3.$ Segmenting customer demographics based on their purchase history,

browsing behaviour, and preferences.

$3\mathrm{.}4.$ Analysing social media posts to categorise them into different themes.

$4.$ For each of the following real$-$world problems, determine whether it is

appropriate or inappropriate to utilise semi$-$supervised machine learning

algorithms. Justify your answers:

$4\mathrm{.}1.$ Predicting fraudulent financial transactions using a dataset where

most transactions are labelled as fraudulent or legitimate.

$4\mathrm{.}2.$ Analysing customer satisfaction surveys where only a small portion of

the data is labelled with satisfaction ratings.

$4\mathrm{.}3.$ Identifying spam emails in a dataset where the majority of emails are

labelled.

$4\mathrm{.}4.$ Predicting the probability of default for credit card applicants based on

their complete financial and credit$-$related informationMachine Learning

Compulsory task

For each of the following examples describe at least one possible input and output. Justify your answers:

$1\mathrm{.}1$ A self$-$driving car

$1\mathrm{.}2$ Netflix recommendation system

$1\mathrm{.}3$ Signature recognition

$1\mathrm{.}4$ Medical diagnosis

Answer here

For each of the following case studies, determine whether it is appropriate to utilise regression or classification machine learning algorithms. Justify your

answers:

$2\mathrm{.}1$ Classifying emails as promotion or social based on their content and metadata.

$2\mathrm{.}2$ Forecasting the stock price of a company based on historical data and market trends.

$2\mathrm{.}3$ Sorting images of animals into different species based on their visual features.

$2\mathrm{.}4$ Predicting the likelihood of a patient having a particular disease based on medical history and diagnostic test results.

Answer here

$2\mathrm{.}1$

$2\mathrm{.}2$

$*2\mathrm{.}3$

$*2\mathrm{.}4$

For each of the following real$-$world problems, determine whether it is appropriate to utilise a supervised or unsupervised machine learning algorithm. Justify

your answers:

$3\mathrm{.}1$ Detecting anomalies in a manufacturing process using sensor data without prior knowledge of specific anomaly patterns.

$3\mathrm{.}2$ Predicting customer lifetime value based on historical transaction data and customer demographics.

$3\mathrm{.}3$ Segmenting customer demographics based on their purchase history, browsing behaviour, and preferences.

$3\mathrm{.}4$ Analysing social media posts to categorise them into different themes.

Answer here

$3\mathrm{.}1$

$3\mathrm{.}2$

$3\mathrm{.}3$

$3\mathrm{.}4$

For each of the following real$-$world problems, determine whether it is appropriate to utilise semi$-$supervised machine learning algorithms. Justify your

answers:

$4\mathrm{.}1$ Predicting fraudulent financial transactions using a dataset where most transactions are labelled as fraudulent or legitimate.

$4\mathrm{.}2$ Analysing customer satisfaction surveys where only a small portion of the data is labelled with satisfaction ratings. ${?}^{**}4\mathrm{.}3ld$ intifying spam emails in a

dataset where the majority of emails are labelled.

$4\mathrm{.}4$ Predicting the probability of default for credit card applicants based on their complete financial and credit$-$related information.

Answer here

$4\mathrm{.}1$

$4\mathrm{.}2$

$4\mathrm{.}3$

$4\mathrm{.}4$