Data Science Cybersecuity Implementation

Predict payment fraud with machine learning on a payments fraud dataset from GitHub, using features like account age, transactions, local time, and payment methods, assess model accuracy amid data imbalance.

Model a logistic regression classifier, encode categorical features with get_dummies, split data into train and test sets, and evaluate accuracy, with an option to switch to a decision tree.

Apply logistic regression to payment fraud data, convert nominal data with one-hot encoding, and evaluate with train-test split and accuracy; swap to a decision tree for comparison.

Select 15 key features and train a random forest malware classifier, achieving 99.55% accuracy with cross-validated evaluation, and note it outperforms gradient boosting.

Develop a real-time phishing detector using logistic regression, leveraging a dataset from the ECI repository with 30 features and a label, then start coding to build the model.

Compare logistic regression and decision tree models for phishing detection, train on a labeled dataset, and report accuracy, with the decision tree achieving about 97.95 percent on testing.

Explore how pop3, imap, and smtp email servers manage spam and ham, from remote storage to user fetch, organization, and deletion, with data and feature selection guiding classification.

Download the ham and spam dataset, process with pandas, vectorize text using count vectorizer, train a naive bayes classifier, and achieve about 99% accuracy with strong recall and precision.

learn to build a twitter bot detector by framing it as a binary classification problem, download and inspect the 20-column, 2797-row dataset with nominal 0/1 values, and begin coding.

Apply machine learning to a cyber attacks dataset by preparing features, dropping categorical data, and evaluating decision tree and random forest models with cross validation for high accuracy.