Preparing Data for Machine Learning

It is very important to have a good and properly prepared data to execute through a machine learning pipeline. Sometimes you are lucky to get good data from the beginning. But at other times you need to prepare your data before feeding it your machine learning algorithm. In this article, you are going to learn how to prepare your data for your machine learning project.

Step 1: Get the Data

This is the first and obvious step. To prepare data for any machine learning project you will have to get your hands on the data first. There will be situations when you will have little to no data at the beginning. In that case you will have to find relevant sources to collect the data.

At other times, you will have a huge amount of data at hand. But that’s no relief as well. Why? You may ask. Because everytime more data is not good. It is very important to keep the scale and potential of the problem at hand.

You may need to ask some questions.

‘How big is the scope of the project?’

‘Do I need more or less data?’

‘Should I include all the data I have or do I need to collect more relevant data?’

Answering the above questions can really help a lot while moving further into the pipeline.

Step 2: Clean the Data

After you have selected the data, it is very important to clean the available data.

Sometimes you have lots of data, but all are features are not relevant. You will have to drop some of the data. All the time more data is not better. Irrelevant data can make the running process much slower may even give worse results. Therefore, it is very important to boil down the data to the required point.

Maybe you have some missing values. Then, you have to think of some ways to fill in those missing values. Dropping an entire section because of missing values may cause a lot of problems if it is an important feature. This part should be taken care of properly.

Step 3: Transforming and Scaling the Data

All the machine learning algorithms cannot just take data in any form. Most of the algorithms need numerical data specifically to work with.

It is very important to convert all the text data into numerical data so that the algorithm can properly understand the data and find patterns.

Sometimes, it may also help if you can do some feature engineering and mix two features to make them as one. Feature engineering can improve the results marginally in some cases where finding more data is not possible.

Scaling and normalizing the data are very important as well. If the numbers in data are very far apart but belong to the same feature, then it may not give good results. Scaling down the data and normalizing to bring all the data within an appropriate range is going to help a lot in this case.

Conclusion

This article gives some abstract idea about preparing data for machine learning. If you need more information, then you should really visit these links:

Data Preparation and Feature Engineering in ML

Data Mining and Predictive Analytics

Data Mining: Concepts & Techniques 

If you liked this article then comment, share and give a thumbs up. If you have any questions or suggestions, just Contact me here. Follow me on Twitter and Facebook to get regular updates.