Comprehensive Exam Performance Analysis

Flashcards covering key concepts related to Python programming, data analytics, machine learning, statistics, and business analytics, highlighting strengths, areas for improvement, and actionable study recommendations.

Python Fundamentals

Basic principles of Python programming, including data types, operators, lists, dictionaries, and functions.

False Positives/Negatives

Classification concepts that describe errors in predicting class assignments.

CRISP-DM

A widely used data mining process model consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Regression Evaluation Metrics

Metrics such as R², RMSE, and MAE used to measure the accuracy and performance of regression models.

Supervised vs. Unsupervised Learning

Supervised learning uses labeled data for training, while unsupervised learning finds patterns without labeled outcomes.

Parameters vs. Arguments

Parameters are the variables in a function definition, while arguments are the actual values passed to those parameters during function calls.

Multi-Class vs. Multi-Label Classification

Multi-class classification involves categorizing instances into one of multiple classes, while multi-label classification allows instances to belong to multiple classes.

Pandas Operations

Operations using the pandas library for data manipulation and analysis, including indexing, sorting, and aggregation of dataframes.

Time Series Decomposition Components

The components of a time series, including Trend, Seasonality, and Residual, used for forecasting and analysis.

KNN and K-Means

Machine learning algorithms that rely on Euclidean distance and are sensitive to feature scaling.