Don’t do Dirty Data - A step-by-step guide to Data Cleaning for Machine Learning
Data cleaning in machine learning is the process of preparing and preprocessing data before it is used for training a machine learning model. It is a crucial step in the machine learning pipeline as the quality of the data has a direct impact on the performance of the model. The data cleaning process typically involves several steps, including:
Data Exploration: The first step in the data cleaning process is to explore the data and get a sense of its overall quality. This typically involves reviewing the data for patterns, trends, and outliers, as well as identifying any missing values, errors, or inconsistencies. This step is important because it allows the data scientist to identify any problems with the data that will need to be addressed during the cleaning process.
For example, you have a dataset containing information about customers of an online retailer, including their age, gender, location, and purchase history. Before you start cleaning the data, you would want to explore it to get a sense of its overall quality. You might begin by looking at the summary statistics for each variable, such as the mean, median, and standard deviation for age, the frequency distribution for gender and location, and the total number of purchases made by each customer.
Data Validation: Once potential issues with the data have been identified, the next step is to validate the data. This typically involves checking the data against known values, such as ranges and constraints, to ensure that it is accurate and complete. For example, if the data includes a column for ages, the data scientist will check that all ages are within the valid range and that the data type of this column is an integer. For example, you might check the credit scores of customers in the dataset against the scores reported by a credit bureau, to ensure that the scores in your dataset are accurate and up to date.
Data Cleansing: After data has been validated, the next step is to cleanse the data. This typically involves correcting errors, filling in missing values, and removing any duplicate or irrelevant data. This step is important because it ensures that the data is accurate and complete and that it will not introduce bias into the model.
For example, suppose you have a dataset containing customer information for an e-commerce website, and you notice that some customers have entered their phone numbers in different formats, such as (555) 555-5555 or 555-555-5555. As part of the data cleansing process, you would need to standardize the phone number format to ensure consistency and accuracy in the data.
Data Transformation: After the data has been cleaned, the next step is to transform the data into a format that can be used for training the machine learning model. This typically involves normalizing the data, encoding categorical variables, and scaling the data. Normalization is important to ensure that all features are on the same scale and that they do not dominate the model training due to large numerical values. Encoding categorical variables is important to ensure that the model can understand and process the categorical data. Finally, scaling the data is important to ensure that the model can converge and converge faster during training.
For example, you have a dataset containing sales data for a retail company, and you want to calculate the total revenue for each product category. As part of the data transformation process, you would need to aggregate the sales data by category and calculate the total revenue for each category, which would involve grouping the data by category and summing the sales revenue for each group.
Data Reduction: After data has been transformed, the next step is to reduce the data to a manageable size. This step is important because a large amount of data can slow down the training process and make it more difficult for the model to find patterns and relationships in the data. Data reduction can be achieved by removing irrelevant data or by applying dimensionality reduction techniques such as principal component analysis or feature selection. For instance, you have a dataset containing daily stock prices for a company over the past decade, which contains thousands of data points. As part of the data reduction process, you could aggregate the daily stock prices into weekly or monthly averages to reduce the amount of data and simplify the analysis, which would involve reducing the number of data points and grouping the data into larger time intervals.
Handling Outliers: Outliers are data points that are significantly different from the other data points in the dataset. They can be caused by errors in data collection or measurement and can have a significant impact on the performance of the model. Therefore, it is important to detect and handle outliers. This can be achieved by using techniques such as z-score or interquartile range.
Split the dataset: After data cleaning, the next step is to split the dataset into training and testing datasets. This is important to ensure that the model can generalize well to new unseen data. The training dataset is used to train the model and the testing dataset is used to evaluate the performance of the model. A common practice is to split the dataset into 80% training and 20% testing.
Final Check: Once the data cleaning process is completed, the data scientist should perform a final check to ensure that the data is ready for training the machine learning model. This includes checking for any remaining errors, missing values, or inconsistencies in the data.
Thus, data cleaning is an essential step in the machine learning pipeline. It is important to ensure that the data is accurate, complete, and free of errors, inconsistencies, and outliers. Data cleaning also ensures that the data is in a format that can be used for training the machine learning model. A well-cleaned data leads to a better performance of the model and accurate predictions. Hope you enjoyed reading the blog!