Logistic Regression Analysis And Coefficient Of Correlation Using Python And Jupyter Notebook

The problem is about to create the machine learning Model to predict the like hood of the person to have diabetes or not based on the number of factors such as Glucose Level, Blood-Pressure, Skin-Thickness, Insulin, BMI, Age. Finally the Model will find how far these factors influence diabetes as a general and one by one

The act of looking at the X features as I mentioned above whether those features are related to the Y variable, that is called regression where it can be positive or negative Regression, and the act of looking to what extent the independent variables that are X how far influences the dependent variable that is Y is called the coefficient of correlation.

Finally, we will check to what extent each X feature has contributed to Y.
The main Objective of this video tutorial is to To develop a mathematical relationship (model) between variables. using Python Machine Learning Libraries

FULL PROJECT LIVE DEMO

FULL PROJECT ON GITHUB

The specific Objectives are-:
1. Problem statement
2. Data collection
3. Exploratory Data Analysis
4. Data cleaning
5. Model selection & Training
6. Model Evaluation & Accuracy Testing
7. Features selection
8. Hyperparameter tuning
9. Prediction
10. Model Deployment by Streamlit or Django

TIMELINE:

0:00 Introduction to Logistic Regression
0:00:49 Specific Objectives
0:01:35 Problem Statement
0:02:25 Download Source Code
0:03:00 installation of python dependencies
0:05:19 uploading Excel file to Jupyter Notebook
0:06:35 import python libraries
0:07:30 Loading Dataset Excel File
0:09:08 Exploratory Data Analysis

Source Code:

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