Please help to solve the below cyber security for machine learning assignment:

A$.$ Objective: Build a machine learning model to detect phishing attacks using a dataset of emails and URLs.

B$.$ Dataset: Phishing Website Dataset from UCI Machine Learning Repository $($or any suitable phishing dataset or your choice$)$

C$.$ Tools: Python, scikit$-$learn $($sklearn$)$ library

D$.$ Process Steps:

$1)$ Data Preparation:

Download the phishing dataset and import it into your Python environment.

Explore the dataset to understand its structure, features, and target labels.

$2)$ Data Preprocessing:

Clean the data: Remove duplicates, handle missing values, etc.

Encode categorical variables: Convert categorical features into numerical format if needed.

Split the dataset into features $($X$)$ and target labels $($y$).$

$3)$ Feature Extraction:

Extract relevant features from URLs and email content.

Transform URLs and email content into numerical representations $($e$.$g$.,$ using TF$-$IDF$).$

$4)$ Model Selection:

Choose suitable machine learning algorithms for phishing detection $($e$.$g$.,$ Logistic Regression, Naive Bayes, Random Forest, etc.$).$

$5)$ Model Training:

Split the data into training and testing$/$validation sets.

Train the selected models on the training data.

$6)$ Model Evaluation:

Evaluate the models' performance on the testing$/$validation set.

Calculate metrics such as accuracy, precision, recall, F$1-$score, and confusion matrix.

E$.$ Submission Instructions:

A PDF document has to be uploaded on Canvas under assignment tab covering following:

$$ Overall process description & solution approach

$$ ML model used and reasons to use this specific model

$$ Details of each task done for each stage.

$$ Code listing and screen shot of the task stage

$$ Final output results and analysis of results

$$ Alternative model$($s$)$ which could have been used

F$.$ Expected Outcome:

Each submission must address at least $1$ of the following $4$ action areas.

$1.$ Develop an ML model to detect phishing website: Detects if a website is phishing website or not.

$2.$ Develop an ML model to detect a DDOS attack: Detects if the network traffic contains a DDOS attack vector

$3.$ Develop an ML Model to detect an email is a phishing attempt: Classifies an email into phishing or normal

$4.$ Develop an ML model to detect other malicious attack vectors: Detects if the network traffic contains any attack vector other than DDOS or phising