Stroke Data 30 Insights Visualized with ML Model

Overview:

Welcome to the Stroke Data & Predictions project! This repository features a comprehensive analysis of a stroke prediction dataset, conducted as part of my NTI Data Analysis Training. The project includes detailed visualizations and a predictive model using Random Forest.

Project Highlights:

30 Key Insights: In-depth analysis with 30 visualizations revealing significant patterns related to stroke occurrences.

Predictive Modeling: Development of a Random Forest model to predict stroke likelihood based on various factors.

Key Findings: Insights into variables such as age, hypertension, glucose levels, and more.

Dataset

The dataset used in this project includes the following columns:

id

gender

age

hypertension

heart_disease

ever_married

work_type

Residence_type

avg_glucose_level

bmi

smoking_status

stroke

Analysis and Modeling

The notebook covers:

Data Preprocessing: Handling missing values, encoding categorical variables, and feature scaling.

Exploratory Data Analysis (EDA): Visualizing key insights and trends related to stroke occurrences.

Predictive Modeling: Implementing and evaluating a Random Forest model to predict stroke likelihood.