EDA stands for Exploratory Data Analysis. It is an approach to analyze and summarize the main characteristics of a dataset. EDA involves various techniques and tools to explore and understand the patterns, relationships, and trends in the data.
The main goal of EDA is to uncover important insights and findings that can inform further analysis or decision-making processes. Some of the common techniques used in EDA include data visualization, summary statistics, hypothesis testing, and machine learning algorithms. EDA is a critical step in the data analysis process, as it helps to identify any data quality issues, outliers, or missing values that may impact the validity of subsequent analyses. By exploring the data in a systematic and comprehensive way, EDA can also help to generate new research questions and hypotheses for further investigation. Linear regression is a type of supervised learning algorithm in machine learning that is used to predict continuous numerical values based on one or more independent variables. It is a statistical method that models the linear relationship between the dependent variable and one or more independent variables by finding the best-fit line or hyperplane that minimizes the sum of the squared errors between the predicted and actual values. Linear regression is a widely used algorithm in machine learning because it is simple, interpretable, and can be applied to a wide range of problems, such as sales forecasting, stock price prediction, and medical diagnosis. However, it assumes a linear relationship between the independent and dependent variables, which may not always be the case in real-world scenarios. Therefore, other more complex regression algorithms, such as polynomial regression and regression trees, may be used in these cases.