7 Steps to Complete a Machine Learning Project

Machine learning is a very interesting field of study for people from computer science backgrounds. Even after having all the required knowledge of machine learning, making an ML project could feel like a tedious task. While ML projects vary in size and complexity, necessitating diverse data science teams, the basic framework remains the same. Some Machine Learning Project Ideas for Beginners are here.

 

In this article, we have tried to simplify the task by listing out a systematic approach that can help you complete a machine learning project from scratch, without being intimidated.

 

 

Steps to Complete a Machine Learning Project

 

1. Identify the objective and frame the problem:

 

The objective of the problem must be defined first. It's crucial to know how the machine learning system's answer will be employed in the end. Comparable situations and current solutions for a specific problem, as well as assumptions being considered, are reviewed at this stage, and the degree of necessity for human knowledge is evaluated. 

 

Other important technical considerations in this stage include choosing the sort of machine learning issue that applies (supervised, unsupervised, etc.) and selecting an acceptable performance indicator.

 

2. Gather the required data:

 

The method for acquiring data depends on the sort of project we want to create. For example, if we want to create an ML project that uses real-time data, we may create an IoT system that uses data from various sensors. 

 

The data set can come from a variety of places, including a file, a database, a sensor, and many other places, but it cannot be utilized immediately for analysis since there may be a lot of missing data, excessively big values, disorganized text data, or noisy data. As a result, Data Preparation is completed to address this issue. (here)

 

Types of data:

 

There are 6 types of data available, as listed by Analytics Vidhya:

 

• Structured data:

 

Structured data is presented in a tabular fashion (rows and columns). It comprises a variety of data kinds, including numerical, category, and time-series data.

 

• Unstructured data:

 

Images, video, audio, and natural language writing) have no fixed structure.

 

• Nominal data:

 

Nominal data is data that is mutually exclusive and contains just one object. Color, for example, is a category for automotive scales. A blue automobile is not the same as a white car. It makes no difference what order you put things in.

 

• Ordinal data:

 

Data with some order, but no way of knowing how far apart the numbers are.

 

• Numerical data:

 

Any continuous value with a significant variation between them. For example, $107,850 is more than $56,400 when selling a home.

 

• Time-series data:

 

This form of data is data that has been collected over a period of time. For instance, have a look at the historical sale prices of bulldozers from 2012 to 2018.

 

 

3. Explore and prepare the data:

 

This section of the checklist is similar to what is known as exploratory data analysis (EDA). Prior to modelling, the idea is to try to get insights from the data. One of the most crucial processes in machine learning is data pre-processing. It is the most crucial stage in improving the accuracy of machine learning models.

 

What is data pre-processing?

 

Data pre-processing is the process of cleaning raw data, which is data that has been obtained in the real world and turned into a clean data collection. 

 

In other words, anytime data is received from many sources, it is collected in a raw format, which makes analysis impossible. As a result, specific processes are taken to transform the data into a smaller, clean data set, which is referred to as data pre-processing.

                                    

Towards data science has written more about data preprocessing in detail. 

 

Some data pre-processing techniques to convert raw data:

 

• Because Machine Learning models can only deal with numeric features, category and ordinal data must be turned into numeric features in some way.

• We can eliminate the row or column of data if there is missing data in the data collection, depending on our needs. This approach is known to be efficient, however, it should not be used if the dataset has a large number of missing values.

• We may manually fill in missing data in the data set wherever we see it, most typically using the mean, median, or greatest frequency value.

• If any data is missing, we may use the remaining data to forecast what data will be available at the vacant spot.

• There may be some incorrect data in our data collection that differs significantly from other observations in the data set.

 

4. Research the best performing model:

 

We must select an ML model that is most suitable for the project in order to train the best performing model feasible utilising the pre-processed data.

 

In supervised learning, an AI system is supplied with data that has been labelled, meaning that each piece of information has been assigned to the appropriate label. "Classification" and "Regression" are two more categories in which supervised learning is classified.

 

Some of the most often used categorization ML algorithms are listed below.

 

• K-Nearest Neighbor

• Naive Bayes

• Decision Trees/Random Forest

• Support Vector Machine

• Logistic Regression

 

When the target variable is continuous, it is referred to as a regression issue. These are some of the most often used regression methods.

 

• Linear Regression

• Support Vector Regression

• Decision Tress/Random Forest

• Gaussian Progresses Regression

• Ensemble Methods

 

An AI system is supplied with unlabeled, uncategorized data in unsupervised learning, and the system's algorithms operate on the data without any prior training. Unsupervised learning is further divided into two categories: "Clustering" and "Association."

 

Some methods used for clustering are:

 

• Gaussian mixtures

• K-Means Clustering

• Boosting

• Hierarchical Clustering

• Spectral Clustering

 

Also Read | 6 Types of Clustering Algorithms in Machine Learning

 

5. Fine-tune the chosen model:

 

The hyperparameters of the selected models should now be fine-tuned, and ensemble approaches should be studied at this point. 

 

If dataset samples were utilised in the previous modelling phase, whole datasets should be used in this stage; no fine-tuned model should be chosen as the "winner" without being exposed to all training data or compared to other models that have also been exposed to all training data.

 

To begin training a model, we divide it into three sections: 'Training data,' 'Validation data,' and 'Testing data.'

 

• Training set:

 

The training set is the material that the computer uses to learn how to analyse data. The training component of machine learning is done through algorithms. A set of data is used for learning or fitting the classifier's parameters.

 

• Validation set:

 

Cross-validation is most commonly used in applied machine learning to evaluate a machine learning model's competence on unknown data. To fine-tune the parameters of a classifier, unseen data is extracted from the training data.

 

• Test set:

 

Unseen data is only used to evaluate the performance of a fully described classifier. We may begin the training procedure after the data has been separated into three pieces.
 

 

6. Evaluate the model:

 

Model evaluation is an important step in the creation of a model. It aids in the selection of the best model to represent our data and the prediction of how well the chosen model will perform in the future.

 

Cross-validation is one of the more efficient approaches for model evaluation and adjustment. The most popular tuning approach is cross-validation. It includes dividing a training dataset into 10 sections of equal size (folds). 

 

A model is trained on nine folds before being tested on the tenth (the one previously left out). Training will continue until all folds have been set aside and tested. An expert creates a cross-validated score for each set of hyperparameters as a consequence of the model performance measure.

 

A data scientist develops models using various hyperparameters in order to determine which model has the best prediction accuracy. The average model performance across ten hold-out folds is represented by the cross-validated score.

 

 

7. Deploy the ML model:

 

A data engineer is responsible for developing, testing, and maintaining infrastructure components that ensure correct data collection, storage, and accessibility. A data engineer participates in model deployment in addition to dealing with big data and constructing and managing a data warehouse.

 

An expert does this by converting the final model from high-level programming languages (such as Python and R) to low-level languages like C/C++ and Java. A data engineer can use A/B testing to evaluate the performance of a model after it has been translated into the proper language. Testing can reveal how a large number of consumers interacted with a personalised recommendation model.

 

The blog ends here. Finally, prepare the machine learning system for use in production; it will need to be integrated into a larger production plan or system. As a software solution, it will be subjected to unit testing before going live, and it will need to be closely monitored once it is up and running.

 

Although implementing an ML model is more complicated, following the given steps can make it easier. 

 

Next read | What are Different Types of Learning in Machine Learning?