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What are the different steps of data cleaning for a machine learning project?
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1. Inspecting the data – checking the available data types, and identifying potential missing values.
2. Treating missing values – to identify the missing values and ways to handle them.
3. Formatting the data – rearranging the available data and transforming it into a suitable format for learning models.
4. Outlier analysis – creating numeric variables for rare and extreme values, and understanding their impact on the models.
5. Data standardization – getting all the data into a unified and consistent format.
6. Data normalization – converting the values of a certain feature into a common range.
7. Feature engineering – creating new features from the existing ones for achieving better accuracy.