https://github.com/vyjayanthipolapragada/data_analytics_medical_appointments
Analyzing the data set which consists of medical appointments to draw insights about patient's no-show scenarios
https://github.com/vyjayanthipolapragada/data_analytics_medical_appointments
data-analysis data-analytics data-cleaning data-visualization data-wrangling jupyter-notebook matplotlib numpy pandas python seaborn
Last synced: 2 months ago
JSON representation
Analyzing the data set which consists of medical appointments to draw insights about patient's no-show scenarios
- Host: GitHub
- URL: https://github.com/vyjayanthipolapragada/data_analytics_medical_appointments
- Owner: VyjayanthiPolapragada
- Created: 2023-12-13T18:13:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-13T18:40:12.000Z (over 1 year ago)
- Last Synced: 2023-12-13T20:21:09.181Z (over 1 year ago)
- Topics: data-analysis, data-analytics, data-cleaning, data-visualization, data-wrangling, jupyter-notebook, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 156 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Used data analytics and visualization to draw valuable insights for the patients no-show scenarios
A medical appointments data set is used, which consists of patient's details (nearly 100k) and their presence based on the schedule
Data set can be downloaded here: https://www.kaggle.com/datasets/marwandiab/medical-appointment-no-shows-dataset
Libraries used: numpy, pandas, seaborn, matplolib (all are in-built with jupyter notebook)
Analysed the dataset to estimate the factors that affect patients to miss their appointment.
Following factors/columns are considered:
1. gender
2. age
3. time difference between schedule and appointment
4. sms sent/not sent
5. scholarship
6. neighbourhood
Processes involved:
1. Data wrangling
2. Data cleaning
3. Data analysis
While many conclusions were drawn from the above factors, yet data can be improved detailed to enhance detailed analytics.