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Topic : Stroke Prediction
Presented by Mansi Choudhary
Content
• Introduction and Problem Statement
• Importing libraries
• Data Understanding
• Data Cleaning
• Data Visualization
• Data Preprocessing
• Model Selection
• Model Comparison
• Testing
• Conclusion
Introduction
According to the World Health Organization (WHO) stroke is
the 2nd leading cause of death globally, responsible for
approximately 11% of total deaths.
This dataset is used to predict whether a patient is likely to get
stroke based on the input parameters like gender, age, various
diseases, and smoking status. Each row in the data provides
relevant information about the patient
Problem Statement
The goal of this exploratory analysis is to develop an effective
model for predicting the likelihood of strokes in individuals
within a healthcare dataset
Importing libraries
Data Understanding
• Displaying Dataset
• Data shape
Data Understanding
• Description of Numerical data
• Description of Categorical data
Data Cleaning
• Checking null values
After imputation null values
Checking Duplicate Values
Data Cleaning
• Dropped ‘id’ column
• Checking spelling mistake and unique values
Data Cleaning
Checking Outliers
Data Visualization
• Analysis for Categorical data
Data Visualization
• Analysis for Binary number data
Data Visualization
• Data distribution nature
Data Visualization
• Hypothesis testing for BMI , Average Glucose
level and age
Data Visualization
• Age appears to have a notable influence
Data Visualization
Aged between 20 to 60
years often exhibit
higher BMI values
Significantly heart
Diseases increases above
the age of 30
Data Visualization
Significantly hypertension
increase above the age of
20
Data Visualization
Patients above age of 40
shows higher average
glucose level
Stroke rate illustrate a
notable rise after the age
of 40
Data Visualization
Stroke patients had either
low or high glucose
levels
BMI between 40 and 60
exhibit high average glucose
levels, while those with a BMI
exceeding 60 show low
average glucose levels.
Data Visualization
Despite the absence of heart disease or hypertension,
data reveals that some patients still experienced strokes
Data Visualization
Formerly smoking individuals have a higher average
glucose level.
Data Visualization
Private sector tend to have
higher average glucose
levels
Private-sector jobs show the
highest stroke incidence
Data Visualization
Married individuals, reporting a higher average glucose level, also
experience the highest incidence of strokes
Data Visualization
Urban individuals with slightly higher average glucose levels
also experience a slightly elevated stroke incidence compared to
rural counterparts
Data Visualization
• Binning BMI column
BMI Weight Status
Below 18.5 Underweight
18.5—24.9 Healthy Weight
25.0—29.9 Overweight
30.0 and Above Obesity
Describing columns based on stroke occurrence
Data preprocessing
Used Label
Encoder
Splitting up
the data
SMOTE
Feature Scaling
Data preprocessing
• Applied Label Encoder
• Data Splitting
Data preprocessing
• Feature Scaling
• SMOTE
Data preprocessing
• SMOTE Visualization
Identifying the best feature
The feature 'age' has the greatest influence among all
features
Model Selection
Models
K Neighbors
Classifier
Decision Tree
Classifier
Gaussian
NAIVE BAYES
Random
Forest
Classifier
Support Vector
Classifier
Logistic
Regression
Model Performance
Best Model : Gaussian Naive Bayes
Testing
Conclusion
As age increases,
the likelihood of
experiencing a
stroke also
increases
Most people
experience a
stroke when they
have either a low
or high average
glucose level
Private sector
has shown a
higher number
of stroke cases
Even when patients
don't have
hypertension or
heart disease, they
still experience
strokes
01 02 03 04
Thank You!!

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Data Analysis Project: Stroke Prediction