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Diabetes Data Analysis

This repository contains a Jupyter Notebook for analyzing diabetes data. The analysis is performed using Python with libraries such as Pandas, NumPy, Matplotlib, and Seaborn.

Getting Started

Prerequisites

  • Python 3.12
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn

Installation

  1. Clone the repository:

    git clone https://github.com/AKSHITHA-CHILUKA/Akshitha-GlucoSense-Infy-Nov24.git
cd Akshitha-GlucoSense-Infy-Nov24

    1.Install the required packages:

    pip install pandas numpy matplotlib seaborn

Usage

1.Open the Jupyter Notebook:

jupyter notebook Diabities_data_analysis.ipynb

2.Run the cells to perform the data analysis. The notebook includes the following steps:

Importing libraries Loading the dataset Displaying the first few rows of the dataset Checking data types and missing values Basic data analysis and visualization

Dataset

The dataset used in this analysis is diabetes_data.csv, which contains the following columns:

  • age
  • gender
  • polyuria
  • polydipsia
  • sudden_weight_loss
  • weakness
  • polyphagia
  • genital_thrush
  • visual_blurring
  • itching
  • irritability
  • delayed_healing
  • partial_paresis
  • muscle_stiffness
  • alopecia
  • obesity
  • class

Analysis

The notebook provides an initial exploration of the dataset, including:

-Displaying the first few rows of the dataset -Checking the data types and missing values -Basic statistical analysis and visualizations

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.

License

This project is licensed under the MIT License.

Acknowledgements

Google Colab for providing an interactive environment for running Jupyter Notebooks.