Abstract

The goal of this research is to develop an exploratory data analysis model in Python. Exploratory Data Analysis (EDA) is used to understand the nature of data. It helps to identify the main characteristics of data (patterns, trends, and relationships). The application of exploratory data analysis helps to build a solid foundation for more advanced analysis. The basic steps of exploratory data analysis are explained: importing libraries, reading data, displaying data, displaying general information, computing descriptive statistics, cleaning data (duplicates, missing values, and outliers), and analyzing data (univariate, bivariate, and multivariate). The developed model was tested on an experimental dataset. The model successfully performed the basic steps of exploratory data analysis and provided the required results.

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• Python: http://www.python.org
• Numpy: http://www.numpy.org
• Pandas: http://pandas.pydata.org
• Matplotlib: http://www. matplotlib.org
• Seaborn: http://seaborn.pydata.org
• NLTK: http://www.nltk.org
• SciPy: http://scipy.org
• SK Learn: http://scikit-learn.org
• Kaggle: http://www.kaggle.com
• Jupyter: http://www.jupyter.org