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DS-U4-K&F

Creating a set of keywords, flashcards, and learning terms definitions can be highly effective for mastering the material in Unit 4: Predictive Data Analytics with Python. Here are some suggested flashcards and definitions:

Keywords and Flashcards

Flashcard 1: Essential Python Libraries

  • Front: What are the essential Python libraries for predictive data analytics?
  • Back: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

Flashcard 2: NumPy

  • Front: What is NumPy used for?
  • Back: NumPy is used for numerical operations, supporting large multi-dimensional arrays and matrices, and providing a collection of mathematical functions.

Flashcard 3: Pandas

  • Front: What are the primary functions of the Pandas library?
  • Back: Data manipulation and analysis, providing data structures like DataFrames for easy data cleaning, filtering, and aggregation.

Flashcard 4: Data Preprocessing

  • Front: What are common data preprocessing steps?
  • Back: Removing duplicates, transforming data using functions or mapping, replacing values, and handling missing values.

Flashcard 5: Handling Missing Values

  • Front: How do you handle missing values in a dataset using Scikit-learn?
  • Back: Using SimpleImputer to fill missing values with strategies like mean, median, most_frequent, or a constant value.

Flashcard 6: Predictive Analytics

  • Front: What is predictive analytics?
  • Back: Predictive analytics uses historical data to predict future events through statistical algorithms and machine learning techniques.

Flashcard 7: Association Rule Learning

  • Front: What is the Apriori algorithm?
  • Back: The Apriori algorithm is used to find frequent itemsets and generate association rules in large datasets.

Flashcard 8: Linear Regression

  • Front: What is linear regression?
  • Back: Linear regression models the relationship between a dependent variable and one or more independent variables to predict continuous outcomes.

Flashcard 9: Logistic Regression

  • Front: What is logistic regression used for?
  • Back: Logistic regression is used for binary classification problems, predicting the probability of a binary outcome.

Flashcard 10: Decision Trees

  • Front: How do decision trees work?
  • Back: Decision trees use a tree-like model of decisions and their possible consequences, where each node represents a decision based on a feature, and branches represent outcomes.

Flashcard 11: Scikit-learn

  • Front: What is Scikit-learn?
  • Back: Scikit-learn is a Python library for machine learning that provides simple and efficient tools for data mining and data analysis.

Flashcard 12: Loading Iris Dataset

  • Front: How do you load the Iris dataset using Scikit-learn?
  • Back: Using load_iris() from sklearn.datasets. 
    from sklearn.datasets import load_iris
data = load_iris()

Learning Terms and Definitions

  1. NumPy: A fundamental package for numerical computing in Python, providing support for arrays, matrices, and a collection of mathematical functions.

  2. Pandas: A powerful data manipulation and analysis library that provides data structures like DataFrames, allowing for easy manipulation, cleaning, filtering, and aggregation of data.

  3. Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.

  4. Seaborn: A Python data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

  5. Scikit-learn: A machine learning library in Python that offers simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.

  6. Data Preprocessing: The process of cleaning and transforming raw data before analysis, which includes removing duplicates, handling missing values, transforming data, and more.

  7. SimpleImputer: A class in Scikit-learn used to handle missing data by filling in missing values with a specified strategy such as mean, median, most_frequent, or a constant value.

  8. Predictive Analytics: The branch of data analytics focused on making predictions about future outcomes based on historical data and using statistical models and machine learning techniques.

  9. Descriptive Analytics: The branch of data analytics that focuses on summarizing historical data to understand what has happened in the past.

  10. Prescriptive Analytics: The branch of data analytics that recommends actions based on predictive analytics outcomes to help achieve desired results.

  11. Apriori Algorithm: An algorithm used in association rule learning to find frequent itemsets and generate association rules by iteratively identifying itemsets with high support.

  12. FP-Growth Algorithm: An algorithm for mining frequent itemsets in large datasets without candidate generation, using a compact data structure called the FP-tree.

  13. Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables, predicting continuous outcomes.

  14. Logistic Regression: A classification algorithm used to predict the probability of a binary outcome based on one or more predictor variables.

  15. Naive Bayes: A probabilistic classification technique based on Bayes' theorem, assuming independence between predictors.

  16. Decision Trees: A non-parametric supervised learning method used for classification and regression, which splits the data into subsets based on the value of input features.

  17. load_iris(): A function in Scikit-learn's datasets module that loads the Iris dataset, a classic dataset used for machine learning and data analysis practice.

By using these keywords, flashcards, and learning term definitions, you can enhance your understanding and recall of the key concepts in Unit 4: Predictive Data Analytics with Python.