https://github.com/ayushsiloiya619/brain-stroke-analysis

Data Analytics with Python
https://github.com/ayushsiloiya619/brain-stroke-analysis

data-analysis matplotlib-pyplot python3 seaborn seaborn-python

Last synced: about 2 months ago
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Data Analytics with Python

• Host: GitHub
• URL: https://github.com/ayushsiloiya619/brain-stroke-analysis
• Owner: ayushsiloiya619
• Created: 2024-04-05T04:02:39.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-09T12:06:48.000Z (9 months ago)
• Last Synced: 2024-04-10T07:26:21.417Z (9 months ago)
• Topics: data-analysis, matplotlib-pyplot, python3, seaborn, seaborn-python
• Language: Jupyter Notebook
• Homepage:
• Size: 396 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Brain Stroke Analysis

## Overview

This project focuses on analyzing brain stroke occurrences using Python and data analysis techniques. It involves thorough data preprocessing, exploratory analysis, statistical tests, and visualization to gain valuable insights into this critical healthcare domain.

## Project Highlights

- **Null Value Handling:** Identified and addressed null values meticulously, ensuring impeccable data integrity and accuracy for further analysis.

- **In-depth Analysis:** Conducted thorough analysis to decipher patterns and trends related to brain stroke occurrences, providing valuable insights for healthcare professionals.

- **Data Manipulation Expertise:** Employed advanced techniques to fill missing values and optimize data quality, enhancing the reliability and usefulness of the dataset.

- **Python Libraries Mastery:** Leveraged powerful Python libraries including Pandas and NumPy for seamless data preprocessing, cleaning, and analysis, maximizing efficiency.

- **Visualization Proficiency:** Utilized state-of-the-art visualization tools such as Matplotlib and Seaborn to craft visually engaging charts and graphs, effectively communicating complex insights to stakeholders.

## Tools Used

- Python

- Pandas

- NumPy

- Matplotlib

- Seaborn

- Jupyter Notebooks

## Workflow

1. Data Collection and Preprocessing

   - Gathered and cleaned data to ensure data integrity.

2. Exploratory Data Analysis (EDA)

   - Analyzed data to identify patterns and correlations.

3. Statistical Analysis

   - Performed statistical tests to validate findings.

4. Documentation and Presentation

   - Documented code, analysis steps, and findings for clear communication.