7 ways to prep your Big Data for Machine Learning

Properly preparing Big Data for Machine Learning (ML) is essential to achieving accurate and reliable results because ML algorithms depend on the data that's input. As the saying goes, garbage in, garbage out!

How can you avoid feeding your ML algorithm garbage?

Here are 7 ways to prep and help your algorithms perform to the best of their abilities.

1. Collect and curate your data

The first step in preparing Big Data for Machine Learning is to collect and curate it. This includes selecting the right data sources, cleaning the data to remove any errors or inconsistencies, and ensuring the data is properly formatted and organized. You'd be surprised how often this simple step is overlooked!

2. Identify and handle missing data

Missing data can be a problem for ML algorithms; this is why you need to identify any missing data and determine how to handle it. This may involve imputing missing values or removing data points with too much missing data.

3. Normalize your data

Machine Learning algorithms perform better on data that is normalized — this involves scaling all features to the same range. This prevents any one feature from dominating the results of the algorithm and biasing the sample.

4. Feature engineering

Feature engineering is the process of selecting and transforming the features used in ML algorithms. This can involve selecting the most relevant features, transforming features to make them more relevant to the problem being solved, or creating new features from existing ones.

5. Select the right machine learning algorithm

Different Machine Learning algorithms are better suited to different types of data and problems. To properly prepare your big data for machine learning, you need to select the right algorithm for the task at hand.

6. Split your data into training and testing sets

To evaluate the performance of your Machine Learning algorithm, you need to split your data into training and testing sets. The training set is used to train the algorithm, while the testing set is used to evaluate its performance.

7. Evaluate and improve your model

Finally, once you've trained your Machine Learning model, it's important to evaluate its performance and identify areas for improvement. This may involve adjusting the hyperparameters of the algorithm, re-engineering features, or collecting more data to improve the accuracy of the model.

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