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- W4362682081 abstract "Data insights are being thoroughly utilized by more innovative applications, which have an impact on every sector and field of study. The tools and processes powering this change, from analyzing the enormous amounts of data generated daily to learning from and taking appropriate action, are at its foundation. Python has been the most dominant choice for data science and machine learning (ML), and using low-level libraries in Python promotes both quality and productivity. ML is frequently viewed more widely as a branch of science that focuses on creating computer models and algorithms that can carry out particular tasks, often involving pattern recognition, without the need for explicit coding. This paper focuses on the interrelationship between the use of Python in conjugation with data science and machine learning algorithms. The Data Science tools such as data summary followed by exploratory data analysis (EDA) applied on Iris Data set to output train an optimized model for future prediction yielding remarkable accuracy." @default.
- W4362682081 created "2023-04-08" @default.
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- W4362682081 date "2022-12-23" @default.
- W4362682081 modified "2023-09-30" @default.
- W4362682081 title "Exploratory Data Analysis for Interpreting Model Prediction using Python" @default.
- W4362682081 cites W2576055109 @default.
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- W4362682081 doi "https://doi.org/10.1109/smartgencon56628.2022.10083533" @default.
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