The purpose of Explanatory Data Analysis (EDA) is to:

1. Summarise the main characteristics of data: EDA is used to understand the main attributes of the data, identify patterns, relationships, or anomalies to form assumptions. This includes using statistical figures and data visualization methods to represent the data in a way that is easier to understand.

2. Create data visualizations: EDA often involves creating visual representations of the data, such as graphs and charts. These visualizations can help to identify patterns, trends, and outliers that might not be apparent from looking at the raw data alone.

3. Clean the data: EDA also involves preparing the data for further analysis. This can include dealing with missing or inconsistent data, removing duplicates, and correcting errors.

However, EDA is not typically used to draw conclusions and make predictions. While it can provide valuable insights that can inform these processes, the goal of EDA is to understand the data, not to make predictions or draw definitive conclusions. That is usually the role of other types of data analysis or modeling.

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The purpose of Explanatory Data Analysis (EDA) is to:

1. Summarise the main characteristics of data: EDA is used to understand the main attributes of the data, identify patterns, relationships, or anomalies to form assumptions. This includes using statistical figures and data visualization methods to represent the data in a way that is easier to understand.

2. Create data visualizations: EDA often involves creating visual representations of the data, such as graphs and charts. These visualizations can help to identify patterns, trends, and outliers that might not be apparent from looking at the raw data alone.

3. Clean the data: EDA also involves preparing the data for further analysis. This can include dealing with missing or inconsistent data, removing duplicates, and correcting errors.

However, EDA is not typically used to draw conclusions and make predictions. While it can provide valuable insights that can inform these processes, the goal of EDA is to understand the data, not to make predictions or draw definitive conclusions. That is usually the role of other types of data analysis or modeling.

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The purpose of Explanatory Data Analysis (EDA) is to:

1. Summarise the main characteristics of data: EDA is used to understand the main attributes of the data, identify patterns, relationships, or anomalies to form assumptions. This includes using statistical figures and data visualization methods to represent the data in a way that is easier to understand.

2. Create data visualizations: EDA often involves creating visual representations of the data, such as graphs and charts. These visualizations can help to identify patterns, trends, and outliers that might not be apparent from looking at the raw data alone.

3. Clean the data: EDA also involves preparing the data for further analysis. This can include dealing with missing or inconsistent data, removing duplicates, and correcting errors.

However, EDA is not typically used to draw conclusions and make predictions. While it can provide valuable insights that can inform these processes, the goal of EDA is to understand the data, not to make predictions or draw definitive conclusions. That is usually the role of other types of data analysis or modeling.

What is the purpose of explanatory data analysis (EDA)?a) To draw conclusions and make predictionsTo summarise the main characteristics of dataTo create data visualisationsTo clean my data5:01

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