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1. Disease Classification
Using Machine Learning Algorithms
CLASSIFYING IF A PERSON IS DIABETIC OR NOT USING CERTAIN PARAMETERS
Mini Project

2. 1
Understanding The
Problem Statement
2
Data Collection
3
Processing Data
4
Explore Data
5
Feature Selection
6
Selecting and Training
ML Model
7
Testing Model
Flowchart For Building
Machine Learning Model
8
Deploying Model

3. Used a .csv( Comma Seprated Value)
having - Pregnancies, Glucose,
BloodPressure, SkinThickness, Insulin,
BMI, DiabetesPedigreeFunction and
Age as Feature and Outcome as label.
Data
Collection

4. Collected Data
Before Feature Selection
Pregnancies
12.5%
Glucose
12.5%
BloodPressure
12.5%
SkinThickness
12.5%
Insuline
12.5%
BMI
12.5%
DiabetesPedigreeFunction
12.5%
Age
12.5%

5. Data Types
Balanced Data Imbalanced Data

6. Raw Data
Data Collection
Data Visualization
Data Cleaning
Data integration
Make
graphs,chart,outliers
Creating Visual
Representation
Identify
Dependent/ Non
Dependent
Variables
Identify Trends
Removing NULL/0 Value
Removing Duplicate
Values
Replacing 0
Value with Mean
Value
Deleting NULL
value
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Data Processing

7. Algorithm Selection
New Project Timeline
K-Nearest
Neighbours
1
Desicion Tree
2
Random Forest
3
Support Vector
Machine (SVM)
4
Naive Bayes
5

8. Accuracy & F1 Score

9. ThankYou