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This project involves analyzing real-world medical appointment data through Time Series Analysis. The tasks include dataset cleaning, comprehensive analysis, and extracting insights using Python and MySQL.

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Time Series Analysis of Medical Appointments

The "Healthcare Appointments Analytics" project is a comprehensive data analysis initiative that delves into the intricacies of medical appointments and explores factors influencing patient attendance and no-show rates. By leveraging advanced analytical techniques, the project aims to uncover valuable insights that can positively impact patient engagement and resource allocation in healthcare settings.

Module 1 - Data Preprocessing using Python

Task 1. Load the data

Task 2. Find the duplicate values

Task 3. Find the null values

Task 4. Convert the data type

Task 5. Renaming the columns

Module 2 - Data Preprocessing using Python

Task 1. Drop the unwanted columns

Task 2. Creating the new column

Task 3. Drop the column

Task 4. Convert the Noshow

Task 5. Exporting the cleaned dataset

Task 6. Generate tables using the cleaned dataset

Module 3 - Analysing data using SQL

Task 1. How many values are there in the given dataset.

Task 2. Count the number of appointments for each day in the given dataset.

Task 3. Calculate the average number of appointments(Set to nearest whole number) per day in the given dataset.

Task 4. Find the day with the highest number of appointments in the given dataset.

Task 5. Calculate the monthly average number of appointments in the given dataset.

Task 6. Find the month with the highest number of appointments in the given dataset.

Task 7. Calculate the weekly average number of appointments in the given dataset.

Task 8. Find the week with the highest number of appointments in the given dataset.

Task 9. What is the distribution of appointments based on gender in the dataset?

Task 10. Calculate the number of appointments per weekday in the given dataset. Order the appointment counts in descending.

Task 11. Calculate the average time between scheduling and the appointment day in the given dataset. Set to nearest whole number.

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This project involves analyzing real-world medical appointment data through Time Series Analysis. The tasks include dataset cleaning, comprehensive analysis, and extracting insights using Python and MySQL.

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