Python is widely used in data science due to its extensive libraries, such as NumPy, Pandas, and Scikit-learn, which provide powerful tools for data manipulation, analysis, and machine learning.

Data preprocessing aims to clean, transform, and format raw data to enhance its quality and accuracy, making it more suitable for machine learning algorithms.

Linear Regression is a supervised machine learning algorithm that finds a linear relationship between a dependent variable and one or more independent variables.

Regularization techniques in machine learning aim to reduce overfitting, which occurs when a model learns the training data too well and loses its ability to generalize to new data.

Scatter Plot is a data visualization technique that displays the relationship between two variables by plotting data points on a two-dimensional plane.

Data Mining is the process of extracting hidden patterns, relationships, and insights from large amounts of raw data.

Pandas is a powerful Python library specifically designed for data manipulation and analysis, providing data structures and operations for manipulating numerical tables and time series.

Dimensionality reduction techniques aim to reduce the number of features in a dataset while retaining the most important information, making it easier to analyze and visualize.

K-Means Clustering is an unsupervised machine learning algorithm that groups data points into a specified number of clusters based on their similarities.

Model Validation is the process of assessing the performance of a machine learning model on unseen data to determine its generalization ability and robustness.

Clustering is a data science technique that groups similar data points together based on their features, allowing for the identification of patterns and trends in the data.

Model Tuning involves adjusting the hyperparameters of a machine learning model, such as the learning rate or the number of hidden units in a neural network, to optimize its performance.

Regression is a data science technique that models the relationship between a dependent variable and one or more independent variables, allowing for the prediction of continuous values.

Model Deployment is the process of integrating a trained machine learning model into a production environment, making it available for use by end-users.

Classification is a data science technique that assigns data points to predefined categories based on their features, allowing for the prediction of discrete values.