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

In this article, I am going to explain the steps of how to choose an appropriate algorithm for building a machine learning model for a Data Science Project. If you are new to the Data Science world then you might know or would have encountered this situation a lot of times and you might have wished to find out a way by which you could choose a specific algorithm for a business problem and I am going to explain this process of choosing an appropriate algorithm by taking an algorithmic approach only. So, stay tuned with us till the end of the article to acquire this useful knowledge.

When we look at ML algorithms, then there is no one solution or approach that fits our problem, and choosing a machine learning algorithm you know sometimes becomes a very confusing and daunting task because it depends on a number of factors that are described below:

- How much Data you have and Is it continuous or not?
- Is it a Classification or a Regression problem statement?
- Whether the data is labelled or unlabeled?
- What is the size of the training Dataset? etc.

Image Source: **Link**

But In this article, we will be discussing the algorithmic approach to pick an appropriate algorithm for building a machine learning model. So, after reading this article you no more have to worry about which algorithm do you need to pick for a particular business problem. You just need to follow the described approach to find the appropriate algorithm.

*To follow this article properly, I assume you are familiar with the basics of Machine Learning. If not, I recommend the below popular course given by Analytics Vidhya to get started with the Machine Learning Basics:*

There are four families of algorithms on which we will be discussing in this article.

- Regression
- Classification
- Clustering
- Dimensionality Reduction

The main diagram which is the core for this article is shown below:

Image Source: **Link**

We will start from the top right corner where there is an orange start circle.

Some of the notations or representations which you keep in mind to follow the article properly while reading it are as follows:

Here note that blue circles are conditions on the basis of which you know can either select YES or NO. YES is directed by the** Green arrow** and NO is depicted by the **Red arrow**.

There are orange arrows as well which depict a particular entity as either not working or the result is not defined.

Here the green rectangular boxes are nothing but individual algorithms in a specific category of algorithms.

** **

**Now, Let’s start our discussion with the left-hand side of the start button shown in the diagram:**

If the number of sample or records or observations are less than 50, then there is a need to extract some more data either from a **Relational Database** or **Flat files **or from **NO SQL** Database. On the other hand, if the number of samples or records or observations is greater than 50, then we will check if we are predicting the categories such as,

- Whether an email is spam or not spam i.e ham.
- Whether a person should be kept in the low-income group category or medium-income group category or high-income group category.

If the answer is YES, then we will move on to check if the data is labelled or not which means whether we have a target or dependent variable present or not in our dataset. If the answer to this is YES, then it means that it’s a classification problem.

Figure Showing how Classification Algorithms work

Image Source: **Link**

Now, we need to check if the number of observations or samples, or records in a dataset is less than 100,000. If the answer is YES, then it means that we can go for linear **SVC i.e, Support Vector Classifier** **algorithm**. If somehow linear SVC doesn’t give the right results or accuracy then we will check if the data is in the text format or not.

If the dataset is in the text format, then we should go for the **Naive Bayes** **algorithm** for classification purposes since this algorithm is used for classifying sentiments of users to perform **Sentiment Analysis** and it is just once the application of sentiment analysis.

On the other hand, if the data is non-textual in nature, then we should opt for the **KNN i.e, K Nearest Neighbor** **classifier**. If somehow the KNN algorithm doesn’t work or gives the right results or accuracy, then we should try to build the model using either** SVC i.e, Support Vector Classifier** or **Ensemble Classifier**. Now coming back to the number of samples or observations. If the number of samples in the dataset is greater than 100,000 then we should pick **SGD i.e, Stochastic Gradient Descent Classifier**.

If somehow this classifier doesn’t work or gives the right results or accuracy, then we should go for the **kernel approximation classifier**. This actually pretty much covers the classification family of algorithms.

Figure Showing how Clustering Algorithms work

Image Source: **Link**

Now, coming back to the condition where we were checking if the data is labeled data or not let’s say we don’t have a labeled data i.e, no target or dependent variable available in the dataset then we will fall into the clustering family of algorithms where we will check another condition to understand if the number of categories is known to us or not.

If the number of categories is known, then we will check if the number of samples or observations is less than 10,000 in a dataset. If the answer to this is YES, then we should go for the **K-means algorithm**, and if somehow if the k-means algorithm gives a bad accuracy or results, then we should either go for **Spectral Clustering **or **GMM i.e, Gaussian Mixture Model clustering**.

Now, coming back to the number of observations or samples. If the number of samples in a dataset is not less than 10,000 then we should go for **mini-batch k-means clustering** for our business problem.

Now, let’s go one step back to check if the number of categories is known. If in this case, the answer is NO i.e, we don’t know the number of defined categories for our business problem, then we will check if the number of samples or observations in a given dataset. If the number of observations is less than 10,000, then we should go for algorithms like **Mean Shift** or **VBGMM i.e, Variational Bayesian Gaussian Mixture model**. On the other hand, if the number of observations is not less than 10,000, then it’s pretty difficult to define a model for a business problem statement.

Figure showing how Linear Regression Algorithm work (Regression Family)

Image Source: **Link**

Now, let’s step back a bit further where we checking if we were predicting a category or not so here if in case we are not predicting a category, then we will move to check if there is a requirement to predict a quantity or not.

If we are predicting a quantity then we will fall into a regression family of algorithms. Here firstly we will try to find out if a dataset has less than 100,000 samples or observations.

If the number of samples in a dataset is greater than 100,000, then we will choose** SGD i.e, Stochastic Gradient Descent Regressor**. Otherwise, we will check one more condition to decide if few features should be important from a prediction point of view.

If that is true, then we would choose algorithms like** Lasso or ElasticNet regression** otherwise we would pick algorithms like **Ridge regression or SVR i.e, Support Vector Regressor** with kernel defined as linear i.e, the graph will be a straight line in this case(described below).

Image Source: **Link**

So, moving on let’s say in neither **Ridge regression nor SVR** with the linear kernel is working or not giving the right accuracy, then we can go for again SVR but this time, the kernel would be **RBF i.e, Radial Basis Function** which is also a default kernel i.e, the graph will be curvilinear in nature as you can see in left of the above picture. Here we can also try for fitting a model on an **ensemble regressor** or an **ensemble regression algorithm**.

Figure showing how LDA and PCA works(Dimensionality Reduction Family)

Image Source: **Link**

Now, step back one step back once again to a condition where we were checking a condition of whether we have a requirement of predicting a quantity or not. So, If your requirement was not to predict a quantity rather you just wanted to take a look at features or independent variables such that you wanted to shortlist the independent variables having maximum variability maybe thousands of columns of your dataset, so that means we are falling into dimensionality reduction family of algorithms.

Now, we know as we know that it’s not possible to build a model with thousands of columns or variables in a dataset, so we try to find the limited number of independent variables or columns or features to generate predictions and hence we use algorithms in this category to solve this purpose. This means we are trying to perform some kind of dimensionality reduction i.e, reducing the number of variables that can actually provide the still gives you comparable accuracy when it comes to building prediction models.

Now, we pick **randomized PCA i.e, Principal Component Analysis** here. If this algorithm is not giving the right results, then you can move forward to check the number of samples is less than 10,000 or not. If not, then we can pick or choose a **kernel approximation algorithm** otherwise we can either go for** Isomap or Spectral Embedding**. If neither of these algorithms works then we can pick **LLE i.e, Local linear embedding algorithm**.

So, now let’s step back to the condition where we were checking the condition of just taking a look at features. If the answer to this is NO here, then we can assume that we have a hard time or tough luck for choosing an algorithm for our business problem statement.

So, Guys in this article, I clearly explained how to choose an appropriate algorithm efficiently for the given business problem statement so that you don’t have to look around here and there.

You can also check my previous blog posts.

**Previous Data Science Blog posts.**

Here is **my Linkedin profile** in case you want to connect with me. I’ll be happy to be connected with you.

For any queries, you can mail me on **Gmail**.

*Thanks for reading!*

I hope that you have enjoyed the article. If you like it, share it with your friends also.* *Something not mentioned or want to share your thoughts? Feel free to comment below And I’ll get back to you. 😉

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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 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

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

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

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