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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Intuition Behind Correlation – Definition and It’s Types

Chirag Goyal
Chirag Goyal Last Updated : 13 Dec, 2023
6 min read

This article was published as a part of the Data Science Blogathon.

What is Correlation?

Correlation is used to find the relationship between two variables which is important in real life because we can predict the value of one variable with the help of other variables, who is being correlated with it. It is a type of  Bivariate statistics since two variables are involved here. 

It is a statistical technique that helps us to analyze the relationship between two or more variables. 

Some of the Statisticians defines “Correlation” in the following way:

1. “Correlation is an analysis of the co-variation between two or more variables”—(A.M Tuttle)

2. “Correlation analysis attempts to determine the degree of relationship between variables”—(Ya Lun Chou)

3. “Correlation analysis deals with the association between two or more variables”— (Simpson and Kafka)

We can now conclude that the association of any two variables is known as Correlation. It is the numerical measurement showing the degree of relation between two variables.

Correlation and Causation 

Correlation: It is a numerical measure of the direction and magnitude of the mutual relationship between the variables(X and Y).

Causation: X is the cause of change in Y i.e, the change of Y is the effect of change in X.

NOTE:

–  If X and Y are correlated then X and Y may or may not have a casual relationship.

– If X and Y have a causal relationship then X and Y must be correlated.

Reasons Behind Correlation

It may happen because of several reasons like:

1. Mutual dependence Between the variables: Both the variables may be mutually influencing each other so that neither can be designated as the cause and the other the effect.

When two variables(X and Y) affect each other mutually, we cannot say X is the cause or Y is the cause.

For Example, The price of a commodity is affected by demand and supply.

2. Due to pure chance: In a small sample, X and Y are highly correlated but in the universe X and Y are not correlated.

For Example, Correlation between income and weight of a person. This may be due to:

– Sampling fluctuations

– Bias of investigator in selecting the sample.

Such a relation is called a non-sense or spurious relation.

3. Correlation due to any third common factor: Both the correlated variables may be influenced by one or other variables.

– X and Y don’t have a direct correlation.

For Example, It is between the production of tea and rice per hectare. Here they are not directly correlated instead the cause is the good rainfall well in time.

Utility of Correlation

1. It is very useful for Economists to study the relationships between variables.

2. It helps in measuring the degree of relationship between the variables.

3. We can also test the significance of the relationship.

4. Sampling error can also be calculated by knowing the correlation.

5. It is the basis for the study of regression.

6. Estimate the value of one variable based on the other variable.

7. It is used to determine the relationship between datasets in business.

Types of Correlation

types of correlation

Based on the degree of correlation:

1. Positive correlation: It is said to be positive when the values of the two variables move in the same direction so that an increase in one variable is followed by an increase in the other variable or a decrease in one variable is followed by a decrease in the other variable.

  • Two variables X and Y are going in the same direction.
  • If X rises, Y also rises, and vice-versa.
  • Examples of positive correlation are (a) Age and Income, (b) Amount of rainfall, and the yield of the crop.

2. Negative correlation: It is said to be negative when the values of the two variables move in the opposite direction so that an increase in one variable is followed by a decrease in the other variable.

  • Two variables X and Y are going in the opposite direction.
  • If X rises, Y falls, and vice versa.
  • Examples of negative correlation are (a) Height above sea level and temperature, (b) Sales of woolen clothes and temperature.
Positive Correlation, Negative Correlation, No Correlation

Based on the change in proportion:

1. Linear: If the value of the amount of change in one variable tends to preserve a constant ratio to the amount of change in other variables, then the correlation is said to be linear. For Example, Whenever the price rises by 10%, then supply rises by 20%.

Linear Variable

2. Non-linear: If the value of the amount of change in one variable does not preserve a constant ratio to the amount of change in the other variables, then the it is said to be a Non-linear correlation. It is also known as the Curvilinear correlation. For Example, Whenever price rises by 10%, but supply rises sometimes by 20%, sometimes by 10%, and sometimes by 40%.

Non - Linear Variable

Based on the number of variables studied:

1. Simple Correlation:  When we consider only two variables(Bivariate analysis) and check the correlation between only those variables, it is said to be a Simple Correlation. For example, Price and demand, Height and Weight, Income and consumption, etc.

2. Multiple Correlation:  When we consider more than three or three variables for correlation simultaneously, it is termed Multiple Correlation. For example, When we study the relationship between the yield of rice per hectare and both the amount of rainfall along with the number of fertilizers are used to find the relationship with rice production.

3. Partial Correlation:  When one or more variables are kept constant and the relationship is studied between the remaining variables, then it is termed Partial Corr. Study the relationship between 2 variables and assuming other variables are constant. For example, Relationship between rainfall and rice yields under constant temperature.

How to Calculate Corr Coefficient Using Python?

Step-1: Import necessary Dependencies.

correlation

Step-2: Calculate Pearson’s core coefficient using Numpy.

correlation

CONCLUSION

The output of the Numpy corrcoef() function is the correlation matrix, in which the diagonal entries give the correlation between a variable with itself and non-diagonal entries represent the corr between variables. This matrix is symmetric.

Thanks for reading!

Frequently Asked Questions

Q1.How is correlation calculated in machine learning?

The most common method is Pearson’s correlation coefficient, which ranges from -1 to 1. Positive values indicate a positive correlation, negative values a negative correlation, and 0 means no correlation.

Q2. Are there limitations to using correlation in machine learning?

Correlation may not capture nonlinear relationships, and outliers can strongly influence results. Complementing correlation analysis with other methods is essential for a comprehensive understanding.

Q3.How is correlation used in feature selection in machine learning?

Correlation analysis helps identify redundant features. Features highly correlated with each other may provide similar information, and removing one can improve model efficiency.

If you liked this and want to know more, go visit my other articles on Data Science and Machine Learning by clicking on the Link

Please feel free to contact me on Linkedin, Email.

Something not mentioned or want to share your thoughts? Feel free to comment below And I’ll get back to you.

Till then Stay Home, Stay Safe to prevent the spread of COVID-19, and Keep Learning!

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

Chirag Goyal
Chirag Goyal

I am a B.Tech. student (Computer Science major) currently in the pre-final year of my undergrad. My interest lies in the field of Data Science and Machine Learning. I have been pursuing this interest and am eager to work more in these directions. I feel proud to share that I am one of the best students in my class who has a desire to learn many new things in my field.

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