To get accurate and correct results of a machine learning model, you must prepare your data before its usage. Various applications like the DataPrep can prove to help complete such a tiresome work quickly and efficiently. Without making many efforts, with just a couple of lines of coding, the data can be prepared.
Applications like DataPrep assist the user to explore the attributes and the properties of the data in use. In the recent modifications of the application, advanced aspects like the EDA, short for Exploratory Data Analysis can be found which has been working like never before.
How to use DataPrep?
To make the best use of DataPrep, follow these simple tips.
- Import required libraries
The first and the foremost step to begin with DataPrep is to install necessary libraries. Generally, different features in DataPrep can be used through different functions and these functions need to be installed before getting started with preparing the data. Initially, a plot function needs to be downloaded which can be effectively used to visualize the properties and other statistical plots of the data under consideration. After this, you will have to import Plotly Express which is further required to download the datasets which you will be working on.
- Importing datasets
For importing the datasets, click on the option of import data sets by being on the flow page. For comparison or better presentation of the data, importing is paramount. You can import more than one data at the same time. This can be done by selecting ‘choose a file or folder’ and click the ‘pencil icon’ and insert the desired file. The files inserted can be renamed for a better understanding.
- Exploratory data analysis
To begin with, you need to do statistical data exploration and detailed analysis. You can make use of the plot function for this part of statistical data exploration. Generally, the whole data can be converted into a detailed analysis by just using a single line of coding.
After filling in the code you will be able to see the statistical properties, their frequency and their count. In case you wish to get a display of the dataset statistics, you may select the option of ‘Show Stats Info’ on the screen itself.
If you want to explore the data through its individual and separate attributes and not the whole together, it is possible and quite convenient. Exploring individual attributes of the data provides a clear idea about every aspect. Moreover, it supports various plots like the Box Plot etc.
- Plot correlation
In the next step, the plot needs to be imported and correlated so that a heat map for different attributes of statistical data can be created out of it. Heatmaps provide a lucid relationship between all the different attributes of the statistical data. DataPrep provides you with three variants of heatmaps.
- Finding the missing Data
Lastly, any missing data in the datasets must be searched so that a replacement can be made in case the data found is not required. For finding the data, use of advertising datasets can be made which can highlight at least some of the missing data.
Conclusion
DataPrep works efficiently with python. However, python is not an easy coding language to lay your hands on without having proper Python training.
You may consider Imarticus learning for getting professional assistance for the different subject matter. A python programming course can also be taken up at Imarticus for a deep insight into python.
