![]() ![]() We can also create our dataset by gathering data using various API with Python and put that data into a. For real-world problems, we can download datasets online from various sources such as, etc. Here we will use a demo dataset for data preprocessing, and for practice, it can be downloaded from here, ". It is useful for huge datasets and can use these datasets in programs. What is a CSV File?ĬSV stands for " Comma-Separated Values" files it is a file format which allows us to save the tabular data, such as spreadsheets. However, sometimes, we may also need to use an HTML or xlsx file. To use the dataset in our code, we usually put it into a CSV file. So each dataset is different from another dataset. The collected data for a particular problem in a proper format is known as the dataset.ĭataset may be of different formats for different purposes, such as, if we want to create a machine learning model for business purpose, then dataset will be different with the dataset required for a liver patient. To create a machine learning model, the first thing we required is a dataset as a machine learning model completely works on data. ![]() ![]() Splitting dataset into training and test set.Data preprocessing is required tasks for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model. Why do we need Data Preprocessing?Ī real-world data generally contains noises, missing values, and maybe in an unusable format which cannot be directly used for machine learning models. So for this, we use data preprocessing task. And while doing any operation with data, it is mandatory to clean it and put in a formatted way. When creating a machine learning project, it is not always a case that we come across the clean and formatted data. It is the first and crucial step while creating a machine learning model. Next → ← prev Data Preprocessing in Machine learningĭata preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. ![]()
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