Exploring Healthcare Trends: A Python-Powered Analysis of Doctor Visits

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Mrs. N. Nagalakshmi
Sai Vishal
P. Shiva Sai
P. Karthik

Abstract

This project delves into an analysis of the “Dr.Visits” dataset using Python tools and libraries, aiming to uncover insights into patterns and relationships related to doctor visits and health conditions. Through data visualization techniques and statistical methods, the projectseeksto reveal key trends and correlations within the dataset. Initial steps involve importing the dataset and exploring its characteristics, including variables like gender, age, income, and illness distribution. The analysis focuses on understanding how these variables impact doctor visits and health-related activities. Notably, the project highlights gender-based variations in reduced activity due to illness, prompting further exploration of potential contributing factors. In summary, this project provides valuable insights into healthcare and patient behavior through the lens of the “Dr.Visits” dataset. 

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[1]
Mrs. N. Nagalakshmi, Sai Vishal, P. Shiva Sai, and P. Karthik , Trans., “Exploring Healthcare Trends: A Python-Powered Analysis of Doctor Visits”, IJDCN, vol. 4, no. 3, pp. 1–4, May 2024, doi: 10.54105/ijdcn.E9840.04030424.
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How to Cite

[1]
Mrs. N. Nagalakshmi, Sai Vishal, P. Shiva Sai, and P. Karthik , Trans., “Exploring Healthcare Trends: A Python-Powered Analysis of Doctor Visits”, IJDCN, vol. 4, no. 3, pp. 1–4, May 2024, doi: 10.54105/ijdcn.E9840.04030424.
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References

McKinney, Wes. (2018). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. https://www.oreilly.com/library/view/python-for-data/9781491957653/

VanderPlas, Jake. (2016). Python Data Science Handbook. O'Reilly Media https://jakevdp.github.io/PythonDataScienceHandbook/.

McKinney, Wes. (2012). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. https://www.oreilly.com/library/view/python-for-data/9781449323592/

McKinney, Wes. (2012). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. https://www.oreilly.com/library/view/python-for-data/9781449323592/

Hunter, John D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering. https://ieeexplore.ieee.org/document/4160265 https://doi.org/10.1109/MCSE.2007.55

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Vanderplas, J. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf https://doi.org/10.35940/ijitee.L3591.1081219

Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory Data Analysis using Python. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 12, pp. 4727–4735). https://doi.org/10.35940/ijitee.l3591.1081219

Biswas, B., & Mukherjee, Dr. T. (2021). How to Choose VLSI IC from E-Commerce Sites?: Sentiment Analysis with the help of Python Tools. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 6, pp. 1–7). https://doi.org/10.54105/ijainn.b3918.101621

Chanda, S. V., & A, A. (2020). Web Scraping in Finance using Python. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 255–262). https://doi.org/10.35940/ijeat.e9457.069520

Muqeeth, Mr. M. G., Kolhar, Dr. M., AlAmeen, Dr. A., & Rahmath, Dr. M. (2020). Data Science Techniques, Tools and Predictions. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 5661–5668). https://doi.org/10.35940/ijrte.f9887.038620

Sharma, Dr. K., & Garg, N. (2021). An Enhanced Data Storage Technique on Cloud Computing. In Indian Journal of Data Communication and Networking (Vol. 1, Issue 3, pp. 1–4). https://doi.org/10.54105/ijdcn.b5007.061321

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