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MACHINE LEARNING#1

A subset of artificial intelligence focused on building systems that learn from data to make predictions or decisions.

PYTHON#2

A versatile programming language widely used in data science and machine learning for its simplicity and extensive libraries.

REGRESSION#3

A statistical method for modeling the relationship between a dependent variable and one or more independent variables.

DATA PREPROCESSING#4

The process of cleaning and transforming raw data into a format suitable for analysis and modeling.

MODEL EVALUATION#5

The assessment of a model's performance using metrics to determine its accuracy and effectiveness.

FEATURE ENGINEERING#6

The process of selecting, modifying, or creating new features from raw data to improve model performance.

CROSS-VALIDATION#7

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

HYPERPARAMETER TUNING#8

The process of optimizing the parameters of a machine learning model to improve its performance.

LINEAR REGRESSION#9

A basic regression technique that models the relationship between two variables by fitting a linear equation.

ROOT MEAN SQUARE ERROR (RMSE)#10

A common metric for evaluating the accuracy of a model's predictions, measuring the average magnitude of errors.

MEAN ABSOLUTE ERROR (MAE)#11

A metric that measures the average absolute difference between predicted and actual values.

EXPLORATORY DATA ANALYSIS (EDA)#12

An approach to analyzing datasets to summarize their main characteristics, often using visual methods.

DATA NORMALIZATION#13

The process of scaling individual samples to have a mean of zero and a standard deviation of one.

RESIDUAL ANALYSIS#14

A technique used to assess the goodness of fit of a model by analyzing the residuals (errors) of predictions.

FLASK#15

A lightweight web framework for Python, often used to deploy machine learning models as web applications.

PREDICTIVE MODELING#16

The process of creating, testing, and validating a model to predict future outcomes based on historical data.

ETHICAL CONSIDERATIONS#17

Factors that address the moral implications of using machine learning, particularly in sensitive areas like housing.

DOCUMENTATION#18

The process of recording the steps, methods, and findings during model development for future reference.

VARIABLE SELECTION#19

The process of selecting a subset of relevant features for use in model construction.

USER INTERFACE (UI)#20

The means by which a user interacts with a machine learning model, often through a graphical interface.

DEPLOYMENT STRATEGIES#21

Methods for integrating a machine learning model into a production environment for real-world use.

MODEL IMPROVEMENT#22

The iterative process of refining a model based on evaluation metrics to enhance its predictive accuracy.

ADVANCED REGRESSION TECHNIQUES#23

Methods such as Ridge and Lasso regression that add regularization to improve model performance.

VISUALIZING PREDICTIONS#24

The practice of creating graphical representations of model predictions to analyze and communicate results.

GENERALIZATION#25

The ability of a model to perform well on unseen data, reflecting its effectiveness beyond the training set.