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Overview

PyCaret is a super useful and low-code Python library for performing multiple machine learning tasks in double-quick time

Learn how to rely on PyCaret for building complex machine learning models in just a few lines of code

Introduction

My first machine learning model in Python for a hackathon was quite a cumbersome block of code. I still remember the many lines of code it took to build an ensemble model – it would have taken a wizard to untangle that mess!

When it comes to building interpretable machine learning models, especially in the industry (or when we want to explain our hackathon results to the client), writing efficient code is key to success. That’s why I strongly recommend using the PyCaret library.

I wish PyCaret was around during my rookie machine learning days! It is a super flexible and useful library that I’ve leaned on quite a bit in recent months. I firmly believe anyone with an aspiration to succeed as a data science or analytics professional will benefit a lot from using PyCaret.

We’ll see what exactly PyCaret it, how to install it on your machine, and then we’ll dive into using PyCaret for building interpretable machine learning models, including ensemble models. A lot of learning to be done so let’s dig in.

Table of Contents

What is PyCaret and Why Should you Use it?

Installing PyCaret on your Machine

Let’s Get Familiar with PyCaret

Training our Machine Learning Model using PyCaret

Building Ensemble Models using PyCaret

Let’s Analyze our Model!

Time to Make Predictions

Save and Load the Model

What is PyCaret and Why Should you Use it?

PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It is easy to use and you can do almost every data science project task with just one line of code.

I’ve found PyCaret extremely handy. Here are two primary reasons why:

PyCaret, being a low-code library, makes you more productive. You can spend less time on coding and can do more experiments

It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models

Installing PyCaret on your Machine

This is as straightforward as it gets. You can install the first stable version of PyCaret, v1.0.0, directly using pip. Just run the below command in your Jupyter Notebook to get started:

!pip3 install pycaret Let’s Get Familiar with PyCaret Problem Statement and Dataset

In this article, we are going to solve a classification problem. We have a bank dataset with features like customer age, experience, income, education, and whether he/she has a credit card or not. The bank wants to build a machine learning model that will help them identify the potential customers who have a higher probability of purchasing a personal loan.

The dataset has 5000 rows and we have kept 4000 for training our model and the remaining 1000 for testing the model. You can find the complete code and dataset used in this article here.

Let’s start by reading the dataset using the Pandas library:

The very first step before we start our machine learning project in PyCaret is to set up the environment. It’s just a two-step process:

Importing a Module: Depending upon the type of problem you are going to solve, you first need to import the module. In the first version of PyCaret, 6 different modules are available – regression, classification, clustering, natural language processing (NLP), anomaly detection, and associate mining rule. In this article, we will solve a classification problem and hence we will import the classification module

Initializing the Setup: In this step, PyCaret performs some basic preprocessing tasks, like ignoring the IDs and Date Columns, imputing the missing values, encoding the categorical variables, and splitting the dataset into the train-test split for the rest of the modeling steps. When you run the setup function, it will first confirm the data types, and then if you press enter, it will create the environment for you to go ahead

We’re all set to explore PyCaret!

Training our Machine Learning Model using PyCaret Training a Model

Training a model in PyCaret is quite simple. You just need to use the create_model function that takes just the one parameter – the model abbreviation as a string. Here, we are going to first train a decision tree model for which we have to pass “dt” and it will return a table with k-fold cross-validated scores of common evaluation metrics used for classification models.

Here’s q quick reminder of the evaluation metrics used for supervised learning:

Classification: Accuracy, AUC, Recall, Precision, F1, Kappa

Regression: MAE, MSE, RMSE, R2, RMSLE, MAPE

You can check the documentation page of PyCaret for more abbreviations.

Similarly, for training the XGBoost model, you just need to pass the string “xgboost“:

View the code on Gist.

Hyperparameter Tuning

We can tune the hyperparameters of a machine learning model by just using the tune_model function which takes one parameter – the model abbreviation string (the same as we used in the create_model function).

PyCaret provides us a lot of flexibility. For example, we can define the number of folds using the fold parameter within the tune_model function. Or we can change the number of iterations using the n_iter parameter. Increasing the n_iter parameter will obviously increase the training time but will give a much better performance.

Let’s train a tuned CatBoost model:

Building Ensemble Models using PyCaret

Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance.

In PyCaret, we can create bagging, boosting, blending, and stacking ensemble models with just one line of code.

If you want to learn ensemble models in-depth, I would highly recommend this article: A Comprehensive Guide to Ensemble Learning.

Let’s train a boosting ensemble model here. It will also return a table with k-fold cross-validated scores of common evaluation metrics:

Another very famous ensembling technique is blending. You just need to pass the models that you have created in a list of the blend_models function.

That’s it! You just need to write a single line of code in PyCaret to do most of the stuff.

Compare Models

This is another useful function of the PyCaret library. If you do not want to try the different models one by one, you can use the compare models function and it will train and compare common evaluation metrics for all the available models in the library of the module you have imported.

This function is only available in the pycaret.classification and pycaret.regression modules.

Let’s Analyze our Model!

Now, after training the model, the next step is to analyze the results. This especially useful from a business perspective, right? Analyzing a model in PyCaret is again very simple. Just a single line of code and you can do the following:

Plot Model Results: Analyzing model performance in PyCaret is as simple as writing plot_model. You can plot decision boundaries, precision-recall curve, validation curve, residual plots, etc.. Also, for clustering models, you can plot the elbow plot and silhouette plot. For text data, you can plot word clouds, bigram and trigram frequency plots, etc.

Interpret Results: Interpreting model results helps in debugging the model by analyzing the important features. This is a crucial step in industry-grade machine learning projects. In PyCaret, we can interpret the model by SHAP values and correlation plot with just one line of code (getting to be quite a theme this, isn’t it?)

Plot Model Results

You can plot model results by providing the model object as the parameter and the type of plot you want. Let’s plot the AUC-ROC curve and decision boundary:

Let’s plot the precision-recall curve and validation curve of the trained model:

Evaluate our Model

If you do not want to plot all these visualizations individually, then the PyCaret library has another amazing function – evaluate_model. In this function, you just need to pass the model object and PyCaret will create an interactive window for you to see and analyze the model in all the possible ways:

Pretty cool!

Interpret our Model

Interpreting complex models is very important in most machine learning projects. It helps in debugging the model by analyzing what the model thinks is important. In PyCaret, this step is as simple as writing interpret_model to get the Shapley values.

You can read about the Shapley Values here: A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values.

Let’s try to plot the correlation plot:

View the code on Gist.

Time to Make Predictions!

Finally, we will make predictions on unseen data. For this, we just need to pass the model that we will use for the predictions and the dataset. Make sure it is in the same format as we provided while setting up the environment earlier. PyCaret builds a pipeline of all the steps and will pass the unseen data into the pipeline and give us the results.

Let’s see how to predict the labels on unseen data:

View the code on Gist.

Save and Load the Model

Now, once the model is built and tested, we can save this in the pickle file using the save_model function. Pass the model to be saved and the file name and that’s it:

View the code on Gist.

We can load this model later on and predict labels on the unseen data:

View the code on Gist.

End Notes

It really is that easy to use. I’ve personally found PyCaret to be quite useful for generating quick results when I’m working with tight timelines.

Practice using it on different types of datasets – you’ll truly grasp it’s utility the more you leverage it! It even supports model deployment on cloud services like AWS and that too with just one line of code.

You're reading Running Low On Time? Use Pycaret To Build Your Machine Learning Model In Seconds

How To Use Your Fitbit To Wake Up On Time, Every Time

Fitbit alarms are a blessing for those who struggle to wake up in the morning, but how do you set them up on your Fitbit smartwatch or fitness tracker? We have an in-depth answer below, whether you own a Sense, Charge 4, or Inspire 2.

From your clock, swipe through your apps. Tap on the Alarm app.

Tap + New Alarm to add a new alarm.

Swipe up and down to set the time. Be sure to tap am or pm if you’re using 12-hour time.

When you’re happy, tap the time to set your alarm.

You can also turn on Smart Wake, which will attempt to wake you during light sleep up to 30 minutes before your alarm. Take this into consideration when setting your alarm, too.

Choose the days you want the alarm to sound by ticking the checkboxes.

Finally, swipe right to save your alarm and view your alarms.

Fitbit Versa 2, Versa, Versa Lite

From your clock, swipe through your apps. Tap on the Alarm app.

Tap + New Alarm to add a new alarm.

Tap the time, then swipe up or down on the minutes and hours to set the time. Tap am or pm if this setting is enabled.

Here, you can turn on Smart Wake by tapping the checkmark.

Choose the days you want the alarm to sound by ticking the checkboxes.

Finally, press the button to save your alarm and view all alarms.

Importantly, the Sense and Versa lines won’t sound alarms if they have less than 8% battery. Be sure to charge these devices before going to bed.

Fitbit Charge 5 and Luxe

From your clock, swipe left through your apps. Tap on the Alarm app.

Tap New Alarm to add a new alarm.

Swipe up and down to adjust the time, and select am or pm if required.

Enable Smart Wake if need be, and choose the days you wish the alarm to sound in the Repeat section.

Finally, swipe right to save your alarm and view all alarms.

Fitbit Charge 4

From your clock, swipe left through your apps. Tap on the Alarm app.

Tap + to add a new alarm.

Swipe up and down to adjust the time, and select am or pm if required.

Finally, tap the sides of the Charge 4 to set the alarm and view other alarms.

See also: The best Fitbit smartwatches and trackers you can buy

Use the Fitbit app to set alarms

You have to go through the Fitbit app to manage alarms if you own an older or simpler Fitbit device. This includes the Inspire 2 and Inspire HR.

Open the Fitbit app and tap your profile image in the top-left.

Tap your device name.

Tap Silent Alarms, then select Set a New Alarm.

Finally, select the time, am or pm, and the days you want the alarm to sound. Save the alarm.

How to dismiss alarms on your Fitbit device

To dismiss an alarm, you can tap the button displayed on the screen for most Fitbit smartwatches. You’ll need to swipe up from the bottom of the screen to open the dismiss dialog if you own a Fitbit Charge 5 or a Luxe. For Inspire 2 and Ace 3 users, tap both buttons on the device simultaneously to dismiss an alarm. Oh, and one more tip: turn down your Fitbit’s brightness so you can avoid burning your eyes when you wake up to the alarm!

Hyperparameters In Machine Learning Explained

To improve the learning model of machine learning, there are various concepts given in machine learning. Hyperparameters are one of such important concepts that are used to improve the learning model. They are generally classified as model hyperparameters that are not included while setting or fitting the machine to the training set because they refer to the model selection task. In deep learning and machine learning, hyperparameters are the variables that you need to apply or set before the application of a learning algorithm to a dataset.

What are Hyperparameters?

Hyperparameters are those parameters that are specifically defined by the user to improve the learning model and control the process of training the machine. They are explicitly used in machine learning so that their values are set before applying the learning process of the model. This simply means that the values cannot be changed during the training of machine learning. Hyperparameters make it easy for the learning process to control the overfitting of the training set. Hyperparameters provide the best or optimal way to control the learning process.

Hyperparameters are externally applied to the training process and their values cannot be changed during the process. Most of the time, people get confused between parameters and hyperparameters used in the learning process. But parameters and hyperparameters are different in various aspects. Let us have a brief look over the differences between parameters and hyperparameters in the below section.

Parameters Vs Hyperparameters

These are generally misunderstood terms by users. But hyperparameters and parameters are very different from each other. You will get to know these differences as below −

Model parameters are the variables that are learned from the training data by the model itself. On the other hand, hyperparameters are set by the user before training the model.

The values of model parameters are learned during the process whereas, the values of hyperparameters cannot be learned or changed during the learning process.

Model parameters, as the name suggests, have a fixed number of parameters, and hyperparameters are not part of the trained model so the values of hyperparameters are not saved.

Classification of Hyperparameters

Hyperparameters are broadly classified into two categories. They are explained below −

Hyperparameter for Optimization

The hyperparameters that are used for the enhancement of the learning model are known as hyperparameters for optimization. The most important optimization hyperparameters are given below −

Learning Rate − The learning rate hyperparameter decides how it overrides the previously available data in the dataset. If the learning rate hyperparameter has a high value of optimization, then the learning model will be unable to optimize properly and this will lead to the possibility that the hyperparameter will skip over minima. Alternatively, if the learning rate hyperparameter has a very low value of optimization, then the convergence will also be very slow which may raise problems in determining the cross-checking of the learning model.

Batch Size − The optimization of a learning model depends upon different hyperparameters. Batch size is one of those hyperparameters. The speed of the learning process can be enhanced using the batch method. This method involves speeding up the learning process of the dataset by dividing the hyperparameters into different batches. To adjust the values of all the hyperparameters, the batch method is acquired. In this method, the training model follows the procedure of making small batches, training them, and evaluating to adjust the different values of all the hyperparameters. Batch size affects many factors like memory, time, etc. If you increase the size of the batch, then more learning time will be needed and more memory will also be required to process the calculation. In the same manner, the smaller size of the batch will lower the performance of hyperparameters and it will lead to more noise in the error calculation.

Number of Epochs − An epoch in machine learning is a type of hyperparameter that specifies one complete cycle of training data. The epoch number is a major hyperparameter for the training of the data. An epoch number is always an integer value that is represented after every cycle. An epoch plays a major role in the learning process where repetition of trial and error procedure is required. Validation errors can be controlled by increasing the number of epochs. Epoch is also named as an early stopping hyperparameter.

Hyperparameter for Specific Models

Number of Hidden Units − There are various neural networks hidden in deep learning models. These neural networks must be defined to know the learning capacity of the model. The hyperparameter used to find the number of these neural networks is known as the number of hidden units. The number of hidden units is defined for critical functions and it should not overfit the learning model.

Number of Layers − Hyperparameters that use more layers can give better performance than that of less number of layers. It helps in performance enhancement as it makes the training model more reliable and error-free.

Conclusion

Hyperparameters are those parameters that are externally defined by machine learning engineers to improve the learning model.

Hyperparameters control the process of training the machine.

Parameters and hyperparameters are terms that sound similar but they differ in nature and performance completely.

Parameters are the variables that can be changed during the learning process but hyperparameters are externally applied to the training process and their values cannot be changed during the process.

There are various methods categorized in different types of hyperparameters that enhance the performance of the learning model and also make error-free learning models.

Dealing With Sparse Datasets In Machine Learning

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

Introduction

Missing data in machine learning is a type of data that contains null values, whereas Sparse data is a type of data that does not contain the actual values of sing data.

Sparse datasets with high zero values can cause problems like over-fitting in the machine learning models and several other problems. That is why dealin arse data is one of the most hectic processes in machine learning.

Most of the time, sparsity in the dataset is not a good fit for the machine learning problems in it should be handled properly. Still, sparsity in the dataset is good in some cases as it reduces the memory footprint of regular networks to fit mobile devices and shortens training time for ever-growing networks in deep learning.

In the above Image, we can see the dataset with a high amount of zeros, meaning that the dataset is sparse. Most of the time, while working with a one-hot encoder, this type of sparsity is observed due to the working principle of the one-hot encoder.

The Need For Sparse Data

Handling

Several problems with the sparse datasets cause problems while training machine learning models. Due to the problem associated with sparse data, it should be handled properly.

A common problem with sparse data is:

1. Over-fitting:

if there are too many features included in the training data, then while training a model, the model with tend to follow every step of the training data, results in higher accuracy in training data and lower performance in the testing dataset.

In the above image, we can see that the model is over-fitted on the training data and tries to follow or mimic every trend of the training data. This will result in lower performance of the model on testing or unknown data.

2. Avoiding Important Data:

Some machine-learning algorithms avoid the importance of sparse data and only tend to train and fit on the dense dataset. They do not tend to fit on sparse datasets.

The avoided sparse data can also have some training power and useful information, which the algorithm neglects. So it is not always a better approach to deal with sparse datasets.

3. Space Complexity

If the dataset has a sparse feature, it will take more space to store than dense data; hence, the space complexity will increase. Due to this, higher computational power will be needed to work with this type of data.

4. Time Complexity

If the dataset is sparse, then training the model will take more time to train compared to the dense dataset on the data as the size of the dataset is also higher than the dense dataset.

5. Change in Behavior of the algorithms

Some of the algorithms might perform badly or low on sparse datasets. Some algorithms tend to perform badly while training them on sparse datasets. Logistic Regression is one of the algorithms which shows flawed behavior in the best fit line while training it on a space dataset.

Ways to Deal with Sparse Datasets

As discussed above, sparse datasets can be proven bad for training a machine learning model and should be handled properly. There are several ways to deal with sparse datasets.

1. Convert the feature to dense from sparse

It is always good to have dense features in the dataset while training a machine learning model. If the dataset has sparse data, it would be a better approach to convert it to dense features.

There are several ways to make the features dense:

1. Use Principle Component Analysis:

PCA is a dimensionality reduction method used to reduce the dimension of the dataset and select important features only in the output.

Example:

Implementing PCA on the dataset

from sklearn.decomposition import PCA pca = PCA(n_components=2) principalComponents = pca.fit_transform(df) pca_df = pd.DataFrame(data = principalComponents , columns = ['principal component 1', 'principal component 2']) df = pd.concat([pca_df, df[['label']]], axis = 1)

2. Use Feature Hashing:

Feature hashing is a technique used on sparse datasets in which the dataset can be binned into the desired number of outputs.

from sklearn.feature_extraction import FeatureHasher h = FeatureHasher(n_features=10) p = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}] f = h.transform(p) f.toarray()

Output:

array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.], [ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])

3. Perform Feature Selection and Feature Extraction

4. Use t-Distributed Stochastic Neighbor Embedding (t-SNE)

5. Use low variance filter

2. Remove the features from the model

It is one of the easiest and quick methods for handling sparse datasets. This method includes removing some of the features from the dataset which are not so important for the model training.

However, it should be noted that sometimes sparse datasets can also have some useful and important information that should not be removed from the dataset for better model training, which can cause lower performance or accuracy.

Dropping a whole column having sparse data:

import pandas as pd df = pd.drop(['SparseColumnName'],axis=1)

Dropping a column having sparse datatype:

import pandas as pd import numpy as np df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])}) df.sparse.to_dense() print(df)

3. Use methods that are not affected by sparse datasets

Some of the machine learning models are robust to the sparse dataset, and the behavior of the models is not affected by the sparse datasets. This approach can be used if there is no restriction to using these algorithms.

For example, Normal K means the algorithm is affected by sparse datasets and performs badly, resulting in lower accuracy. Still, the entropy-weighted k means algorithm is not affected by the sparse data, giving reliable results. So it can be used while dealing with sparse datasets.

Conclusion

Sparse data in machine learning is a widespread problem, especially when working with one hot encoding. Due to the problem caused by sparse data (like over-fitting, lower performance of the models, etc.), handling these types of data is more recommended for better model building and higher performance of the machine-learning models.

Some Key Insights from this blog are:

1. Sparse data is completely different from missing data. It is a form of data that contains a high amount of zero values.

2. The sparse data should be handled properly to avoid problems like time and space complexity, lower performance of the models, over-fitting, etc.

3. Dimensionality reduction, converting the sparse features into dense features and using algorithms like entropy-weighted k means, which are robust to sparsity, can be the solution while dealing with sparse datasets.

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

How Machine Learning Is Transforming Healthcare In India

The integration of machine learning in the healthcare industry of India is set to transform conventional methods

Healthcare has become one of the biggest sectors in India’s economy. According to a report from

Solving the problem: too much raw data, too few real insights

Healthcare settings are flooded with unprecedented volumes of complex data from clinicians’ notes, medical devices, labs, and more. Remote patient wearables are increasingly adding to the onslaught. Electronic health records are helping digitize the information, but their job is not to ease the administrative workload on the front end or provide at-a-glance decision support. All the data coming in is only as valuable as the insights that can be quickly gleaned from it and appropriately actioned to improve healthcare delivery. Machine learning can make that possible, especially for digitized data sets with clear patterns. Machine learning not only collects but also unifies, data from disparate sources. It can perform the complex calculations required for doctors, nurses, and other members of the healthcare team to make quick sense of raw physiological, behavioral, and imaging information.

Automation of manual tasks

Machine learning reduces the workload of physicians, radiologists, pathologists, and other providers by employing algorithms to garner insights. Automated workflows designed around how healthcare teams work in the real world are often used in tandem for easy information sharing and collaboration. Typical applications include:

Imaging analysis leveraging widely available data sets

Precise patient monitoring in the ICU or OR

Real-time remote patient monitoring through wearables that track heart rate, activity level, and more

Streamlining tedious administrative tasks like clinical documentation

Powerful predictive capabilities

Precise predictive analysis of what a given patient will likely need next has historically been stopped by two barriers: the burden of collecting data and the difficulty of calculation. With machine learning, data collection speed and calculation complexity no longer depend on what humans can do by hand. Using these powerful algorithms, one can imagine treatment decisions tailored to each patient’s specific situation and better outcomes as a result.

Digital transformation: what to expect next

India is poised for an exciting digital transformation in healthcare. The penetration of machine learning and other innovative technologies, including automation and other AI techniques like natural language processing, is surging—with 5G coming soon. A vibrant ecosystem of startup and established health-tech companies is now in-country, with a rising population to fill new roles. Healthcare providers have gained a greater awareness of tech-enabled ways to do more with less manual effort. The government has stepped up with increased spending on evolving healthcare delivery, and the general public is in support.

Government’s mission is to transform the healthcare infrastructure

Since 2023 due to the Covid-19 pandemic, there has been a huge focus by the government on investing in India’s healthcare infrastructure. This has also enabled technology firms to dive into the healthcare segment and innovate to contribute to the improvement of healthcare facilities in the country. Under the Digital India Initiative, the government has recently announced the launch of the Ayushman Bharat Health Mission which aims at creating India’s digital health ecosystem. The initiative focuses on creating digital health records for the citizens and their families to access and share digitally. Under this mission, the citizens will receive a randomly generated 14-digit number used for the purposes of uniquely identifying persons, authenticating them, and threading their health records only with their informed consent across multiple systems and stakeholders. Moreover, inclusion is one of the key principles of ABDM. The digital health ecosystem created by ABDM supports continuity of care across primary, secondary, and tertiary healthcare in a seamless manner. It aids the availability of health care services, particularly in remote and rural areas through various technology interventions like telemedicine etc. Digital health start-ups in India provide a vast backdrop for solutions with the government’s push to strengthen the digital healthcare infrastructure. The start-up landscape within the Indian healthcare ecosystem goes well beyond a specific disease, therapeutic area, geography, type of product, and service or business model. In a country where access to affordable healthcare is still a looming issue, the public stands to gain immensely from the development of the Digital Health industry. The ABDM is a one-of-a-kind strategy to unify the healthcare system in India and promote innovation in the industry. With the public interest in the minds of both the Government as well as the innovators, it remains to be seen how Digital Health will be perceived in law. While there is a long way to go, the use of AI and ML has gained a strong foothold in India over the past year and we foresee a promising future for the industry.

Author:

Healthcare has become one of the biggest sectors in India’s economy. According to a report from NITI Ayog , the sector has grown at a compound annual growth rate (CAGR) of 22% since 2023. Millions of jobs have been created, with millions more to come. How can a country short on trained clinical resources with vast inequities in care distribution grow at this pace? Machine learning is one way to help close the gaps.Healthcare settings are flooded with unprecedented volumes of complex data from clinicians’ notes, medical devices, labs, and more. Remote patient wearables are increasingly adding to the onslaught. Electronic health records are helping digitize the information, but their job is not to ease the administrative workload on the front end or provide at-a-glance decision support. All the data coming in is only as valuable as the insights that can be quickly gleaned from it and appropriately actioned to improve healthcare delivery. Machine learning can make that possible, especially for digitized data sets with clear patterns. Machine learning not only collects but also unifies, data from disparate sources. It can perform the complex calculations required for doctors, nurses, and other members of the healthcare team to make quick sense of raw physiological, behavioral, and imaging information.Machine learning reduces the workload of physicians, radiologists, pathologists, and other providers by employing algorithms to garner insights. Automated workflows designed around how healthcare teams work in the real world are often used in tandem for easy information sharing and collaboration. Typical applications include:Precise predictive analysis of what a given patient will likely need next has historically been stopped by two barriers: the burden of collecting data and the difficulty of calculation. With machine learning, data collection speed and calculation complexity no longer depend on what humans can do by hand. Using these powerful algorithms, one can imagine treatment decisions tailored to each patient’s specific situation and better outcomes as a result.India is poised for an exciting digital transformation in healthcare. The penetration of machine learning and other innovative technologies, including automation and other AI techniques like natural language processing, is surging—with 5G coming soon. A vibrant ecosystem of startup and established health-tech companies is now in-country, with a rising population to fill new roles. Healthcare providers have gained a greater awareness of tech-enabled ways to do more with less manual effort. The government has stepped up with increased spending on evolving healthcare delivery, and the general public is in support.Since 2023 due to the Covid-19 pandemic, there has been a huge focus by the government on investing in India’s healthcare infrastructure. This has also enabled technology firms to dive into the healthcare segment and innovate to contribute to the improvement of healthcare facilities in the country. Under the Digital India Initiative, the government has recently announced the launch of the Ayushman Bharat Health Mission which aims at creating India’s digital health ecosystem. The initiative focuses on creating digital health records for the citizens and their families to access and share digitally. Under this mission, the citizens will receive a randomly generated 14-digit number used for the purposes of uniquely identifying persons, authenticating them, and threading their health records only with their informed consent across multiple systems and stakeholders. Moreover, inclusion is one of the key principles of ABDM. The digital health ecosystem created by ABDM supports continuity of care across primary, secondary, and tertiary healthcare in a seamless manner. It aids the availability of health care services, particularly in remote and rural areas through various technology interventions like telemedicine etc. Digital health start-ups in India provide a vast backdrop for solutions with the government’s push to strengthen the digital healthcare infrastructure. The start-up landscape within the Indian healthcare ecosystem goes well beyond a specific disease, therapeutic area, geography, type of product, and service or business model. In a country where access to affordable healthcare is still a looming issue, the public stands to gain immensely from the development of the Digital Health industry. The ABDM is a one-of-a-kind strategy to unify the healthcare system in India and promote innovation in the industry. With the public interest in the minds of both the Government as well as the innovators, it remains to be seen how Digital Health will be perceived in law. While there is a long way to go, the use of AI and ML has gained a strong foothold in India over the past year and we foresee a promising future for the industry.Punit Soni, Founder & CEO, Suki

What Role Does Machine Learning Play In Biotechnology?

ML is changing biological research. This has led to new discoveries in biotechnology and healthcare.

Machine Learning and Artificial Intelligence are changing the way that people live and work. These fields have been praised and criticized. AI and ML, or as they are commonly known, have many applications and benefits across a wide variety of industries. They are changing biological research and resulting in new discoveries in biotechnology and healthcare.

What are the Applications of Machine Learning in Biotechnology?

Here are some use cases of ML in biotech:

Identifying Gene Coding Regions

Next-generation sequencing is a fast and efficient way to study genomics. The machine-learning approach to discovering gene coding regions in a genome is now being used. These machine-learning-based gene prediction techniques are more sensitive than traditional sequence analysis based on homology.

Structure Prediction

PPI has been mentioned in the context of proteomics before. However, ML has improved structure prediction accuracy by more than 70% to over 80%. Text mining has great potential. Training sets can be used to identify new or unusual pharmacological targets using many journals articles and secondary databases.