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Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. CleaningData: Address any missing values or outliers that could skew results.
Datacleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaneddata and uncover patterns, trends, and relationships.
This step involves several tasks, including datacleaning, feature selection, feature engineering, and data normalization. It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model.
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