Abalone Age Prediction Python Data Science Case Study Abalone Dataset Live Machine Learning Model Using Linear Regression

The Age of the Abalone is :

Abalone Length:

(any float value from 0 to 1)

Abalone Height:

(any float value from 0 to 1)

Abalone Shucked Weight:

(any float value from 0 to 1.5)

Abalone Shell Weight:

(any float value from 0 to 1)

Abalone Gender:

Male Female Infant

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How This Linear Regression Model Works?

Why Are We Using Machine Learning Linear Regression Model To Predict Abalone Age?
Linear Regression Is A Simplest Statistical Regression Method Used For Predictive Analysis In Machine Learning.
Linear Regression Shows The Linear Relationship Between The Independent(predictor) Variable i.e. x-axis And The Dependent(output) Variable i.e. y-axis, Called Linear Regression.
If There Is A Single Input Variable X(independent Variable), Such Linear Regression Is Called Simple Linear Regression.

In The Given Problem Statement More Than 1 Independent Variables Are Given. So How To Approach The Problem Statement?
Multiple Linear Regression Is A Technique To Understand The Relationship Between A Single Dependent Variable And Multiple Independent Variables.
The Formulation For Multiple Linear Regression Is Also Similar To Simple Linear Regression With The Small Change That Instead Of Having One Beta(b) Variable, You Will Now Have Betas For All The Variables Used.

Now Let Us See The Steps In Building This Machine Learning Model :
1) First Load The Required Dataset.
2) Check If There Are Any Missing Values Inside The Dataset. The Dataset has no missing values.
3) Get A Brief Description About The Dataset.
4) Select The Dependent And Independent Features Of The Dataset.
We Are Performing Assumptions Of Multi-collinearity Using VIF ( Variance Inflation Factor ).
We Gonna Discuss The Variance Inflation Factor (VIF) But Before That Let’s Have A Quick Discussion On Multicollinearity.
1) Multicollinearity Means Independent Variables In A Model Are Correlated.
2) Multicollinearity Among Independent Variables Can Reduce The Performance Of The Model.
3) Multicollinearity Can Be A Problem In Multiple Regression Because The Input Variables Are All Influencing Each Other.therefore, They Are Not Actually Independent, And It Is Difficult To Test How Much The Combination Of The Independent Variables Affects The Dependent Variable Or Outcome.
Hence We Need Variance Inflation Factor (VIF) As It Is A Tool To Help Measure The Degree Of Multicollinearity.

What Is The Variance Inflation Factor (VIF)?
The Variance Inflation Factor (VIF) Measures The Severity Of Multicollinearity In Regression Analysis.
It Is A Statistical Concept That Indicates The Increase In The Variance Of A Regression Coefficient As A Result Of Collinearity.
The Formula is Given by:
Where Ri2 represents the unadjusted coefficient of determination for regressing the ith independent variable on the remaining ones.
After Performing Assumptions Of Multi-collinearity We Get VIF Factor As Given Below:
After Removing Variables With High VIF. We Get The VIF As Given Below:
Build The Model With The Remaining Variables.
Linear Regression:
We Have Also Applied Regularised Linear Regression Techniques Such As Ridge, Lasso And Elastic Net.

Ridge Regression:
What Is Ridge Regression?
Ridge Regression Is A Model Tuning Method That Is Used To Analyse Any Data That Suffers From Multicollinearity. This Method Performs L2 Regularization.
When The Issue Of Multicollinearity Occurs, Least-squares Are Unbiased, And Variances Are Large, This Results In Predicted Values Being Far Away From The Actual Values.

What Does Ridge Regression Do?
1) It Shrinks The Parameters, Therefore It Is Mostly Used To Prevent Multicollinearity.
2) It Reduces The Model Complexity By Coefficient Shrinkage.
3) It Uses L2 Regularization Technique.

Lasso Regression:
What Is Lasso Regression?
Lasso Regression Is A Type Of Linear Regression That Uses Shrinkage. Shrinkage Is Where Data Values Are Shrunk Towards A Central Point, Like The Mean.
The Lasso Procedure Encourages Simple, Sparse Models (i.e. Models With Fewer Parameters). This Particular Type Of Regression Is Well-suited For Models
Showing High Levels Of Muticollinearity Or When You Want To Automate Certain Parts Of Model Selection, Like Variable Selection/parameter Elimination.

What Does Lasso Regression Do?
1) It Uses L1 Regularization Technique.
2) It Is Generally Used When We Have More Number Of Features, Because It Automatically Does Feature Selection.

Elastic Net Regression:
What Is Elastic Net Regression?
Elastic Net Linear Regression Uses The Penalties From Both The Lasso And Ridge Techniques To Regularize Regression Models.
The Technique Combines Both The Lasso And Ridge Regression Methods By Learning From Their Shortcomings To Improve The Regularization Of Statistical Models.

What Does Elastic Net Regression Do?
1) The Elastic Net Method Performs Variable Selection And Regularization Simultaneously.
2) The Elastic Net Technique Is Most Appropriate Where The Dimensional Data Is Greater Than The Number Of Samples Used.
3) Groupings And Variables Selection Are The Key Roles Of The Elastic Net Technique.

Conclusions:
We have Evalulated Our Models Based On Two Performance Metrics

a. Mean Squared Error.
b. Mean Absolute Error.

Mean Squared Error:
Mean Absolute Error:
1) It Can Be Said That Lasso And Elastic Net Have Higher Model Performance As Compared To Other Models.
2) This Can Be Said Because Of Lesser Mean Squared Error And Mean Absolute Error Are Obtained.

EDA Of Abalone Dataset

Get A Brief Description About Dataset.
1) The Dataset Has Given Features With The Given Datatypes.
Sex Object
Length Float64
Diameter Float64
Height Float64
Whole Weight Float64
Shucked Weight Float64
Viscera Weight Float64
Shell Weight Float64
Rings Int64

2) Create A Correlation Matrix Of All Features.
3) To Get A Distribution About Continuous Features, Do The Boxplot.
Boxplot Of Length
Boxplot Of Diameter
Boxplot Of Whole Weight
Similarly, Boxplot Of Remaining Numerical Features Can Be Plotted Like This.

Perform Countplot On Gender Feature
Bi Variate EDA :
After Plotting Correlation Matrix, It Can Be Found Out That Some Variables Are Highly Correlated With Each Other.
Use The Scatterplot To See The Relationships Between Two Variables.
Scatterplot Of Length vs Height
Scatterplot Of Diameter vs Viscera Weight
Scatterplot Of Whole Weight vs Diameter
Similarly, Scatter Plot Of Remaining Numerical Features Can Be Plotted Like This.
References:
1. Google Search.
2. Wikipedia.
3. Kaggle.

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