All of the steps mentioned are correct and are typically done prior to building a Machine Learning Model. Here's a brief explanation of each:

1. Load the data: This is the first step where you load the dataset into your program to start working with it.

2. Explore the data: This step involves understanding the dataset, identifying the important features, and understanding the nature and distribution of the data.

3. Clean the data: This step involves removing or correcting any errors in the dataset, removing duplicates and irrelevant data, and dealing with missing values.

4. Preprocess the data: This step involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. This step may involve normalizing the data, handling categorical variables, and splitting the dataset into training and testing sets.

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All of the steps mentioned are correct and are typically done prior to building a Machine Learning Model. Here's a brief explanation of each:

1. Load the data: This is the first step where you load the dataset into your program to start working with it.

2. Explore the data: This step involves understanding the dataset, identifying the important features, and understanding the nature and distribution of the data.

3. Clean the data: This step involves removing or correcting any errors in the dataset, removing duplicates and irrelevant data, and dealing with missing values.

4. Preprocess the data: This step involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. This step may involve normalizing the data, handling categorical variables, and splitting the dataset into training and testing sets.

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All of the steps mentioned are correct and are typically done prior to building a Machine Learning Model. Here's a brief explanation of each:

1. Load the data: This is the first step where you load the dataset into your program to start working with it.

2. Explore the data: This step involves understanding the dataset, identifying the important features, and understanding the nature and distribution of the data.

3. Clean the data: This step involves removing or correcting any errors in the dataset, removing duplicates and irrelevant data, and dealing with missing values.

4. Preprocess the data: This step involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. This step may involve normalizing the data, handling categorical variables, and splitting the dataset into training and testing sets.

What are some of the steps that we do prior to building a Machine Learning Model? Select all that are correctPreprocess the dataLoad the dataExplore the dataClean the data

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