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GlucoSense_Infosys_Internship_Nov2024

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Diabetes Prediction - Exploratory Data Analysis (EDA)

This project focuses on performing Exploratory Data Analysis (EDA) on a diabetes prediction dataset. The objective of this analysis is to uncover key patterns, correlations, and insights from the data, which can later help in building predictive models for diabetes classification.

Project Overview

The dataset contains various attributes related to patients' health, including both symptoms and demographic features, which are used to predict whether a person has diabetes or not.

In this EDA, we explore various aspects of the dataset such as:

• Age Distribution: Examining the spread of ages within the dataset and its relationship with diabetes.
• Correlation Analysis: Investigating relationships between features and the target variable (diabetes class).
• Symptoms Distribution: Analyzing the occurrence of various symptoms, including polyuria, polydipsia, weakness, obesity, and others, by diabetes class.
• Feature Relationships: Understanding how different features correlate with one another and how they relate to diabetes.

Dataset

• Age: Age of the individual
• Gender: Gender of the individual
• Polyuria: Frequent urination
• Polydipsia: Excessive thirst
• Weakness: Weakness or fatigue
• Obesity: Body mass index (BMI) categorization
• Sudden Weight Loss: Unexpected weight loss
• Muscle Stiffness: Presence of muscle stiffness
• Genital Thrush: Presence of fungal infection
• Class: Diabetes classification (Positive/Negative)

Key Insights

• Age Group Distribution: Diabetic cases are more prevalent among individuals aged 40-70. There is a higher occurrence of diabetes in the middle-aged and elderly groups.
• Symptoms Analysis: Certain symptoms like polyuria and polydipsia have strong correlations with diabetes, making them valuable predictors.
• Feature Correlation: Age, obesity, and certain symptoms show varying degrees of correlation with the diabetes class. Age, in particular, shows weaker correlation with the target, suggesting that other features may be more important predictors.
• Class Imbalance: A significant imbalance exists between the diabetic (Positive) and non-diabetic (Negative) classes, which may need to be addressed in future modeling steps.

Visualizations

This project uses the following visualizations to better understand the dataset:

• Histograms: To analyze the distribution of continuous features like age.
• Countplots: To examine the frequency of categorical features like class, gender, and symptoms.
• Boxplots: To visualize the spread of numerical features across different classes.
• Correlation Heatmaps: To identify relationships between numerical features and their correlation with the target variable.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

How to Run

1. Clone the repository:

   git clone https://github.com/your-username/diabetes-prediction-eda.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Jupyter notebook to view the EDA:

   jupyter notebook

Future Work

• Address class imbalance using oversampling or undersampling techniques.
• Build machine learning models (e.g., Logistic Regression, Decision Trees) to predict diabetes based on the features.
• Hyperparameter tuning for the chosen model to optimize performance.

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