Python Machine Learning Web App For Multiple Linear Regression using Streamlit Python, CSS and MySQ

in this video, we are going to create a machine learning web project driven by a probabilistic model that is multiple linear regression.

There are three variables the purpose is to check how far the linear relationship is between these variables, where X has two features as independent variables and Y has one feature as dependent variable. This is a classification problem using multiple regression analysis for the data in MySQL. finally visualizing the measure of variations and lines of the best fit

1. load data from MySQL using python
2. basic data exploration
3. data modeling & feature selection
4. X-features to be standard normal distribution (z score)
5. finding the coefficient of correlation (r)
6. finding the coefficient of determination (R2)
7. the regression equation and line of best fit
8. mean squared error, mean absolute error
9. finding the adjusted coefficient of correlation

Live Demo
https://business-force.streamlit.app/

Source Code
https://github.com/shamiraty/MULTIPLE-REGRESSION-STREAMLIT-AND-MYSQL

FULL PROJECT LIVE DEMO
https://open-projects.streamlit.app/

TIMELINE:

0:00 introduction to this Analytics dashboard
0:06:49 download project source code
0:07:35 python dependencies installation
0:08:41 creating streamlit project
0:10:33 importing python Libraries
0:11:58 streamlit page setup
0:12:00 Running Streamlit project
0:15:19 Creating MySQL database
0:15:48 importing Excel file to MySQL
0:17:55 database connection using API python file
0:11:44 Applying material Design CSS to streamlit project
0:30:52 streamlit sidebar with multi-selector
0:33:50 Streamlit Download CSV from MySQL
0:36:42 Exploratory Data analysis using RegPlot
0:43:26 Box Plot and Whisker for variable outliers
0:45:02 Variable Distribution Exploration by Histogram
0:50:08 Default Correlation by Scatter Plot
0:52:21 Python Exceptional Handling Concept
0:54:10 Features Selection and Model Training
0:55:59 Standard Uniform distribution Z SCORE
0:58:06 Fit the model to Linear Regression
0:59:50 Model Performance Metrics and Prediction
1:03:45 Regression Measure of variations Metrics
1:07:29 Regression Line Of Best Fit
1:10:00 Standard Normal distribution Curve

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reference code
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Video tutorial
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