{"id":11380,"date":"2020-02-08T06:03:06","date_gmt":"2020-02-08T00:33:06","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=11380"},"modified":"2020-02-04T11:17:32","modified_gmt":"2020-02-04T05:47:32","slug":"preparing-datasets-for-machine-learning","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/preparing-datasets-for-machine-learning\/","title":{"rendered":"Preparing datasets for machine learning"},"content":{"rendered":"

\"data<\/a>Data preparation is the process of correctly selecting the raw data for machine learning algorithms to generate accurate predictions and outcomes from the algorithm. Today, most of the datasets used for machine learning are flawed. Missing or incomplete data, data inconsistency, multiple data formats, and lack of data integration infrastructure are some of the principal challenges faced by data analysts in the data preparation stage. These problems are often hard to overcome. Hence, it comes as no surprise that data-related challenges are hindering 96% of organizations<\/a> from achieving success with machine learning and AI. Data scientists can implement the following steps for ensuring that data preparation for machine learning ends with fruitful results.<\/p>\n

Recommendations for data preparation for machine learning<\/h2>\n

To create a successful machine-learning program, organizations must train, test, and validate the data in the shortest time possible before deployment. Some key steps data scientists need to pay attention to in the data preparation process include:<\/p>\n

Understand the problem at the earliest<\/h3>\n

The desired outcome for the algorithm should be decided at the beginning. The data for the algorithm should be collected according to the desired output. While collecting the data, data scientists must think in terms of:<\/p>\n

classification<\/em>, whether the algorithm should answer yes or no;<\/p>\n

clustering<\/em>, classify objects in different classes;<\/p>\n

ranking<\/em>, to rank one object above or below another.<\/p>\n

Thus, the data for the algorithms should be collected according to the solution sought.<\/p>\n

Make the data consistent<\/h3>\n

The input format of the data should be the same across the entire dataset. Also, the consistency of the data should be ensured. For example, the input format for numbers should have consistency in decimal places. Additionally, the input format for multiple datasets should be the same too, i.e., $4.05 or four dollars and five cents, whatever the chosen format, should be consistent across the dataset. Also, ensure the ranges for the numbers are consistent throughout. If the dataset consists of whole numbers, then care should be taken that an integer isn\u2019t introduced in the data.<\/p>\n

Reduce the data<\/h3>\n

Sometimes, less is more. While gathering data for a machine learning algorithm, one must ensure that only the relevant data is gathered. Data scientists can use the attribute sampling approach wherein they can decide which values are critical to the output of the algorithm and discard values that won\u2019t contribute to predictive analysis. Thus, the data can be reduced significantly in training the machine learning algorithm.<\/p>\n

Ensure data cleaning<\/h3>\n

Another approach to streamlining the data preparation process is data cleaning. Missing values is a major issue as it can reduce prediction accuracy. Data scientists can adopt the following approaches to clean data used in machine learning:<\/p>\n

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