Trending March 2024 # 5 Best Desktops For Machine Learning & Deep Learning # Suggested April 2024 # Top 7 Popular

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5 best desktops for machine learning & deep learning

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Machine learning and deep learning require powerful computers. Amazon has a fair amount of choices for people and budgets of all kinds.

This article will help you decide what to buy by showing you a list of different computers with their pros and cons.

Looking for more computer-related articles? Check out our detailed Computers Hub for more buying guides and information.

Visit our thorough Buying Guides Section if you want more help regarding your choice of gadgets and devices.

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Deep & machine learning are tools that try to replicate the brain’s neural network in machines. They introduce self-learning techniques that teach AI to behave under certain conditions. Usually, the AI has to do a certain task and learn from its own mistakes in order to fulfill it.

If you want to get into machine learning and deep learning you might need to take a look at your current computer. In other words, even if your desktop can perform everyday tasks with ease, that doesn’t mean it will have the computing power to run machine learning & deep learning programs.

GPU and CPU are crucial. You need a graphics card with high memory and your processor must have many cores. In addition, your RAM memory needs to be high as well, somewhere around 8 gigs or more.

Because these processes run for long periods of time, the computer you’re looking for needs to be able to run them as long as possible without problems. In conclusion, a powerful cooler is required to stop your components from overheating and causing thermal throttling.

What are the best desktops for machine & deep learning?

RTX 2080 Super has over 8GB of dedicated memory

The Intel Core i9-9900K has an ideal 8 cores and can be turbo boosted

Over 32GB of HyperX DDR4 3000mhz RAM memory

Liquid cooling keeps your temps as low as possible even during intensive use

The product is expensive, its price starting from 2000$

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HP Obelisk Omen is the most powerful item on our list. Geared with the latest hardware such as the 9th generation Intel Core i9-9900K Processor and the hyper-realistic NVIDIA GeForce RTX 2080 Super,

It is perfect for machine learning and deep learning.

If you want speed, power, customization, and the best quality products out there, this is the choice for you.

The GTX 1660 TI offers 6GB of dedicated memory

The i5-9400f has 6 cores

16Gb of DDR4 memory

The processor does not support overclocking

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Our last item on this list is another midrange choice, the Hp Pavilion. It is close in performance to the Skytech Shiva while also being a bit cheaper.

The GeForce GTX 1660 TI is just about 10% weaker than the aforementioned RTX 2060, but it is less expensive. In addition, the i5-9400f is still capable of deep learning & machine learning processes.

The Ryzen 5 2600 offers 6 cores

The Video Card has 6GB of DDR6 memory

3x RGB RING Fans for Maximum Air Flow, powered by 80 Plus Certified

500 Watt Power Supply

It only has 8GB of RAM

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Expert tip:

Equipped with a Ryzen 5 2600 processor and a GTX 1660 TI graphics card, it is capable enough of running parsing data algorithms. Furthermore, both the GPU and the CPU can be overclocked.

Intel Core i7-9700k offers has an ideal 8 cores

The NVIDIA RTX 2070 Super comes with 8GB of dedicated memory

Liquid cooling keeps the temperatures low

16GB of DDR4 RAM is enough for deep learning & machine learning

You need to update your firmware if you want to get the proper CPU speed boosts working

Check price

CyberpowerPC Gamer Supreme is our next recommendation on the list. Coming close to the HP Omen mentioned above, this desktop trades a bit of power for a lower price.

The Intel Core i7-9700k and GeForce RTX 2070 Super still offer cutting-edge performance for more affordable prices. Moreover, this desktop can also be overclocked with no problem.

The Ryzen 5 2600 processor has 6 cores

It has 16GB of DDR4 RAM

The Graphics Card has 6GB of dedicated memory

Equipped with 3x RGB Ring Fans ensuring good airflow

Lacks a USB Type-C port

Check price

Skytech Shiva is our first budget-oriented choice. Significantly cheaper than the other two desktops from above, yet still holding strong in terms of performance, this computer is perfect for those who want some balance between price and power.

This product is geared with an AMD processor, precisely the Ryzen 5 2600, and an RTX 2060 non-Super version. The CPU is about 20% slower than an i7-9900k, but it is so much cheaper. Moreover, both the CPU and GPU can be easily overclocked.

This list covers all you need to buy a brand new desktop for deep learning & machine learning.

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Top 10 Machine Learning Algorithms For Beginners

It’s never too late to start off with machine learning basics

There’s no denying that the area of machine learning or artificial intelligence has grown in prominence in recent years. Machine learning is very effective for making predictions or calculating suggestions based on vast quantities of data, which is the trendiest topic in the tech sector right now. In this article, we will discuss the top 10 ML algorithms for newbies.  

What are machine learning algorithms?

Any other algorithm in computer programming can be connected to a machine learning method. An ML algorithm is a data-driven process for developing a production-ready ML model. If you consider ML as a train that will get you to your destination, then ML algorithms are the engines that will get you there. The sort of ML algorithm that works best is determined by the business challenge at hand, the dataset’s structure, and the available resources.  

Types of Machine Learning Algorithms

Supervised ML Algorithms

Unsupervised ML Algorithms

Reinforcement ML Algorithms

Best ML Algorithms Decision Tree

The decision tree is a decision-making aid that employs a tree-like graph or model of options, as well as their potential consequences, such as chance-event results, resource costs, and execution. Decision trees are the top pick for categorizing both categorical and continuous dependent variables since they are supervised learning algorithms. The population is divided into two or more homogenous datasets using the most significant features or independent variables in this technique.  

Principal Component Analysis

When data contains several dimensions, dimension reduction methods are among the most essential algorithms in ML. Consider a dataset with “n” dimensions, such as a data profession list that works with financial data with features such as credit score, personal information, staff compensation, and so on. He or she may now utilize the dimensionality reduction approach to identify the relevant labels for creating the needed model, and PCA is the ideal algorithm for decreasing dimensions.  

Deep Learning Algorithms

Deep learning algorithms are based on the neurological system of a person and are usually built on neural networks that have a lot of computing power. To execute certain tasks, all of these algorithms employ various forms of neural networks.  

Naive Bayes Classifier

The occurrence of a selected feature in a class is unrelated to the appearance of any other feature, according to a Naive Bayes classifier. Even though one attribute is connected to the others, it considers all of them independent when computing the likelihood of a specific result. There are two sorts of probability in the model:

Probability of each class

Conditional Probability

Both probabilities may be computed directly from training data, and the probability model can then be used to forecast fresh data using the Bayes Theorem.  

Ordinary Least Square Regression

The least-square is the technique for doing linear regression in statistics. The traditional least-squares approach is to draw a clear line between an independent variable and a dependent variable, then compute the vertical distance between the spot and the line for each data set, and add them up.  

Linear Regression

Linear regression describes the effect on the dependent variable when the independent variable is changed; as a result, the independent variable is referred to as the explained variable, and the dependent variable is referred to as the factor of interest. It depicts the relationship between an independent and a dependent variable, as well as predictions and estimates in continuous values. It may be used, for example, in the insurance industry to assess risk and determine the number of applications for users of various ages.  

Logistic Regression

A useful statistical method for modeling a binomial output including one or more explanatory factors is logistic regression. It calculates the relationship between the categorical dependent variable and one or even more independent variables by using a logistic function to measure probabilities. The logistic regression algorithm works with discrete data and is ideally suited to classification models, where an event is categorized as 1 if it happens successfully and 0 if it does not. As a result, the likelihood of a given event occurring is calculated using the specified predictor factors.  

Support Vector Machines

In SVM, a hyperplane is used to correctly divide the data points throughout the input variable space by their corresponding class, which is either 0 or 1. Essentially, the SVM method calculates the coefficients that result in a reasonable separation of the different classes through the hyperplane, with the margin referring to the distance between the hyperplane and the nearest data points. The line with the biggest margin, on the other hand, is the best hyperplane for separating the two classes.  

Clustering Algorithms

Clustering is a data analysis method of finding meaningful data patterns, such as groupings of consumers based on their behavior or geography, because it is an unsupervised learning issue. Clustering algorithms relate to the job of grouping an assemblage of items in such a manner that each entity in the same group is more similar to each other than those in different groups.  

Gradient Boosting & AdaBoost

When dealing with large amounts of data, boosting algorithms are employed to make very accurate predictions. It is an ensemble learning method that mixes the different susceptible and mediocre predictors to produce strong predictors or estimators by combining the predictive power of varied base estimators in order to improve resilience.  

Conclusion

Machine Learning For Kids: Baby Steps Into Ml For Babies

Introducing machine learning to kids is not that challenging as you think.

Kids can make wonders; many surveys and stats prove that the age from 5-15 is the best time for anyone in their entire lifetime to store as much data into their brain as possible. But the very idea of introducing them to such complex topics like AI and ML can be extremely terrifying. But worry not, we are here to help you take your kids into the world of ML. Machine learning is definitely not as easy as teaching rhymes but we can make it fun. “Rome wasn’t built in a day” and hence, make sure you don’t dig deep into the topics like robots or automation. Start from the least bottom level, the scratch, start by taking an example of a toy car. Start by explaining how elements like the steering, gearbox, and clutch work in the car with certain mechanisms and now, introduce the car they see every day as a ‘machine’. Putting in simpler terms, the definition of a machine would be: A device that does a physical task with certain orders. Some machines make communications easier like a phone. Other machines carry people from place to place like a car. Some others print documents like the computer. Other machines even talk with us and do the tasks assigned like a robot. All these are nothing but machines. Let’s make baby steps at the beginning instead of rushing. Now begin with the basic definition of machine learning.  

Machine Learning

Machine learning is where these simple machines like a car, robot, etc are given access to user information called ‘data’ which is used by the machines to learn how to function all by themselves. In other words, it is simply getting a computer to perform a certain task.  

What Does It Do?

After learning about machines and machine learning with live examples, the child will now be able to understand the fundamentals of ML. Now, this is the right time, start off with what it actually does. Start with the basics like —ML is used for learning under supervision with a proper feedback/response. Playing games is supervised learning. As you keep in mind the rules to play, then execute and later, check your performance. The feedback you give yourself allows you to perform better the next time to play. Explain to them that ML just does this. It takes inputs and tries to improve with proper feedback.  

Cite Some Examples

Show them live examples now, let them interpret the uses and applications of ML.  

Smart Cars:

Smart cars detect the environment around them.  If they detect an object or a wall or some obstacle, they automatically take a step backward. Now, it happened so, because the car was taught to react like that. It was given commands and this itself sums up machine learning.  

YouTube Recommendations

If your kid watches Five Minutes Crafts videos on YouTube, the following recommendations would all be related to art and crafts and hacks. Explain to them that this filtration process also happens through the very mechanism of machine learning as to what to view next and autoplay next.  

Machine Learning as a Career

After giving such wonderful, lively yet crisp, and easy demonstrations, anyone kid would find the concept of ML quite interesting and if your baby child is somewhere around 12-15 years then try introducing them to the world of ML. Tell them the opportunities and how in dire need in the world of such amazing geniuses into ML.

Kids can make wonders; many surveys and stats prove that the age from 5-15 is the best time for anyone in their entire lifetime to store as much data into their brain as possible. But the very idea of introducing them to such complex topics like AI and ML can be extremely terrifying. But worry not, we are here to help you take your kids into the world of ML. Machine learning is definitely not as easy as teaching rhymes but we can make it fun. “Rome wasn’t built in a day” and hence, make sure you don’t dig deep into the topics like robots or automation. Start from the least bottom level, the scratch, start by taking an example of a toy car. Start by explaining how elements like the steering, gearbox, and clutch work in the car with certain mechanisms and now, introduce the car they see every day as a ‘machine’. Putting in simpler terms, the definition of a machine would be: A device that does a physical task with certain orders. Some machines make communications easier like a phone. Other machines carry people from place to place like a car. Some others print documents like the computer. Other machines even talk with us and do the tasks assigned like a robot. All these are nothing but machines. Let’s make baby steps at the beginning instead of rushing. Now begin with the basic definition of machine learning.Machine learning is where these simple machines like a car, robot, etc are given access to user information called ‘data’ which is used by the machines to learn how to function all by themselves. In other words, it is simply getting a computer to perform a certain task.After learning about machines and machine learning with live examples, the child will now be able to understand the fundamentals of ML. Now, this is the right time, start off with what it actually does. Start with the basics like —ML is used for learning under supervision with a proper feedback/response. Playing games is supervised learning. As you keep in mind the rules to play, then execute and later, check your performance. The feedback you give yourself allows you to perform better the next time to play. Explain to them that ML just does this. It takes inputs and tries to improve with proper chúng tôi them live examples now, let them interpret the uses and applications of ML.Smart cars detect the environment around them. If they detect an object or a wall or some obstacle, they automatically take a step backward. Now, it happened so, because the car was taught to react like that. It was given commands and this itself sums up machine chúng tôi your kid watches Five Minutes Crafts videos on YouTube, the following recommendations would all be related to art and crafts and hacks. Explain to them that this filtration process also happens through the very mechanism of machine learning as to what to view next and autoplay next.After giving such wonderful, lively yet crisp, and easy demonstrations, anyone kid would find the concept of ML quite interesting and if your baby child is somewhere around 12-15 years then try introducing them to the world of ML. Tell them the opportunities and how in dire need in the world of such amazing geniuses into ML. We have so many platforms like YouTube which has tonnes of videos on ML. Online course platforms like Udemy, Coursera also offer courses on ML for kids. By fostering these ideas, introducing your kids to the world of ML, and making them excel into it is all made easy.

Paragraph Segmentation Using Machine Learning

Introduction

Natural language processing (NLP) relies heavily on paragraph segmentation, which has various practical applications such as text summarization, sentiment analysis, and topic modeling. Text summarizing algorithms, for example, frequently rely on paragraph segmentation to find the most important areas of a document that must be summarized. Similarly, paragraph segmentation may be required for sentiment analysis algorithms in order to grasp the context and tone of each paragraph independently.

Paragraph Segmentation

The technique of splitting a given text into different paragraphs based on structural and linguistic criteria is known as paragraph segmentation. Paragraph segmentation is used to improve the readability and organization of huge documents such as articles, novels, or reports. Readers can traverse the text more simply, get the information they need more quickly, and absorb the content more effectively using paragraph segmentation.

Depending on the individual properties of the text and the purposes of the segmentation, there are numerous ways to divide it into paragraphs.

1. Text indentation

This book discusses the issue of indentation in written text. Indentation refers to the space at the beginning of a line of text that is commonly used to signify the start of a new paragraph in numerous writing styles. Readers can benefit from indentation to visually differentiate where one paragraph finishes and another begins. Text indentation may also be used as a characteristic for automated paragraph segmentation, which is a natural language processing approach for automatically identifying and separating paragraphs in a body of text. The computer may be trained to recognize where paragraphs begin and end by analyzing indentation patterns, which is valuable in a variety of text analysis applications.

2. Punctuation marks

This book discusses the role of punctuation indicators which include periods, question marks, and exclamation points. These symbols are widely used to indicate the conclusion of a paragraph and the start of a new one. They can also be used to signal the conclusion of one paragraph and the start of another. Punctuation marks in written communication should be utilized appropriately since they help to clarify material and make the text easier to read and understand.

3. Text length

A paragraph seems to be a writing style composed of a sequence of connected phrases that address a particular topic or issue. The text’s length can be utilized to split it into paragraphs. A huge block of content, for example, can be divided into smaller paragraphs depending on sentence length. This means that if multiple sentences in a sequence discuss the same topic, they can be concatenated to form a paragraph. Similarly, if the topic or notion changes, a new paragraph may be added to alert the reader. Ultimately, the objective of paragraphs is to arrange and structure written content in an easy-to-read and understandable manner.

4. Text coherence

Paragraphs are an important part of writing since they assist to organize ideas and thoughts in a clear and logical way. A cohesive paragraph has sentences that are all connected to and contribute to a major concept or thinking. The coherence of the text refers to the flow of ideas and the logical links between phrases that allow the reader to discern between the beginning and finish of a paragraph. When reading a text, look for a shift in the topic or the introduction of a new concept to identify the beginning of a new paragraph. Similarly, a concluding phrase or a transition to a new concept might signal the conclusion of a paragraph. Ultimately, text coherence is an important aspect in distinguishing paragraph borders and interpreting the writer’s intended meaning.

Paragraph segmentation using machine learning

Machine learning algorithms have been employed to automate the job of paragraph segmentation in recent years, attaining remarkable levels of accuracy and speed. Machine learning algorithms are trained on a vast corpus of manually annotated text data with paragraph boundaries. This training data is used to understand the patterns and characteristics that differentiate various paragraphs.

Paragraph segmentation may be accomplished using supervised learning methods. Supervised learning algorithms are machine learning algorithms that learn on labeled data, which has already been labeled with correct answers. The labeled data for paragraph segmentation would consist of text that has been split into paragraphs and each paragraph has been labeled with a unique ID.

Two supervised learning approaches for paragraph segmentation are support vector machines (SVMs) and decision trees. These algorithms employ labeled data to learn patterns and rules that may be used to predict the boundaries of paragraphs in new texts. When given new, unlabeled text, the algorithms may utilize their previously acquired patterns and rules to forecast where one paragraph ends and another begins. This method is especially effective for evaluating vast amounts of text where manual paragraph segmentation would be impossible or time-consuming. Overall, supervised learning algorithms provide a reliable and efficient way for automating paragraph segmentation in a wide range of applications.

For paragraph segmentation, unsupervised learning methods can be employed. Unlike supervised learning algorithms, which require labeled training data, unsupervised learning algorithms may separate paragraphs without any prior knowledge of how the text should be split. Unsupervised learning algorithms use statistical analysis and clustering techniques to detect similar patterns in text. Clustering algorithms, for example, can group together phrases with similar qualities, such as lexicon or grammar, and identify them as belonging to the same paragraph. Topic modeling is another unsupervised learning approach that may be used to discover clusters of linked phrases that may constitute a paragraph. These algorithms do not rely on predetermined rules or patterns, but rather on statistical approaches to find significant patterns and groups in text. Unsupervised learning methods are very beneficial for text segmentation when the structure or formatting of the text is uneven or uncertain. Overall, unsupervised learning algorithms provide a versatile and powerful way for automating paragraph segmentation in a range of applications.

The text file below contains the paragraph above that is starting with ‘For paragraph segmentation, ……’

Python Program import nltk from chúng tôi import SVC from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.pipeline import make_pipeline # Load the data with open('/content/data.txt', 'r') as file: data = file.read() # Tokenize the data into sentences sentences = nltk.sent_tokenize(data) # Label the sentences as belonging to the same or a new paragraph # Create a feature matrix using TF-IDF vectorization vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(sentences) # Create a support vector machine classifier clf = make_pipeline(SVC(kernel='linear')) # Train the classifier on the labeled data clf.fit(X, labels) # Use the classifier to predict the paragraph boundaries in new text new_sentences = nltk.sent_tokenize(new_text) new_X = vectorizer.transform(new_sentences) new_labels = clf.predict(new_X) # Print the predicted paragraph boundaries for i in range(len(new_sentences)): if new_labels[i] == 1: print(new_sentences[i]) Output This is a new paragraph. It is separate from the previous one. This is the second sentence of the second paragraph. Conclusion

Finally, paragraph segmentation is an important job in natural language processing that may enhance the readability and structure of enormous texts greatly. In this domain, machine learning algorithms have made great progress, enabling precise and efficient segmentation based on structural data and statistical analysis. However, further study is needed to enhance these models’ performance on more complicated and diverse texts, as well as to investigate novel ways to paragraph segmentation based on deep learning and other sophisticated techniques.

What To Know About Machine Learning

Machine learning is a discipline of computer science which investigates the analysis and structure of algorithms that can learn from information, according to which they may make predictions. It’s used in several programs like self-driven automobiles, effective Internet search, speech recognition, etc.. Classic programming techniques assume that you know the issue clearly and, with that knowledge, will compose a collection of clear directions to resolve this specific issue or to perform a specific job. Examples for these forms of problems/tasks are numerous; in reality, the majority of the apps are composed with very clear expectations of input, output signal along with a fantastic algorithm for the procedure — for example, sorting of amounts, eliminating a specific series in a text file, copying a document, etc..

But, there’s a specific class of problems that conventional problem-solving or programming methods won’t be of much use. By way of instance, let us assume you have about 50,000 files which need to be categorized into particular categories like sports, business and entertainment — without going through every one of these. Or consider another example of looking for a specific thing in tens of thousands of pictures. In the latter scenario, the thing could be photographed in another orientation, or under different lighting conditions. How do you tell which pictures include the thing? Another very helpful case in point is of constructing an internet payment gateway and needing to stop fraudulent transactions. 1 method is to identify indications of possibly fraudulent transactions and triggering alarms before the trade is complete. Just how would you call this correctly without creating unnecessary alerts?

Since you can readily imagine, it is impossible to write quite exact calculations for all those issues. What we can do is to create systems which work like an individual specialist. A physician can tell what disorder a specific patient has by taking a look at the evaluation reports. As soon as it is not feasible to allow him to make a precise identification 100 percent of their moment, he’ll be right most times. Nobody programmed the physician, but he learned those things by analyzing and by expertise.

Machine learning and mathematics

While the illustration of this physician might be somewhat different from real machine learning, the core concept of machine learning is that machines can learn from big data collections and they improve as they gain expertise. As information comprises randomness and doubt, we are going to have to employ concepts from probability and statistics. In reality, machine learning algorithms are so determined by concepts from data that a lot of men and women refer to system learning as statistical understanding. Aside from figures, another important branch of math that is very much in usage is linear algebra. Concepts of matrices, options for systems of equations and optimisation calculations — all play significant roles in machine learning.

Machine studying and Big Data

There are essentially two kinds of machine learning – supervised learning and unsupervised learning. Supervised learning identifies information with tags, examples of which are displayed below:

Let us presume that we must forecast whether it rains in the day, using wind and temperature information. Whether it rains or not will be saved in the column in the day, which becomes the tag.

The algorithms which learn from these data are known as supervised learning algorithms. Though some information can be extracted/generated mechanically, such as in a system log, frequently it might need to be tagged manually, which might raise the price of information acquisition.

We could even classify machine learning algorithms utilizing another logic — regression calculations and classification algorithms. Regression calculations are machine learning algorithms which in fact forecast amount’ — such as the subsequent day’s temperature, the stock market’s closing indicator, etc.. Classification algorithms are the ones which could classify an input signal, such as if it is going to rain or notify the stock exchange will shut negative or positive; if it’s disease x, illness y, or disorder, etc.

Also read:

Top 10 Best Artificial Intelligence Software

It is important to comprehend and love that machine learning algorithms are essentially mathematical calculations and we can execute them in almost any language we enjoy. However, the one I like and use a great deal is the R language. There are lots of popular machine learning modules or modules in various languages. Weka is strong machine learning applications written in Java and is extremely common. Scikit-learn is a favorite among Python programmers. An individual may also choose the Orange machine learning toolbox accessible in Python. While Weka is so strong, it’s some permit issues for industrial usage. Though growth seems to have ceased, it’s an adequate library worth attempting and with sufficient documentation/tutorials to begin easily. I’d recommend people with innovative should learn more about the profound learning algorithms.

Mental Health Prediction Using Machine Learning

Text(0.5, 0, ‘Age’)

Inference: The above plot shows the Age column with respect to density. We can see that density is higher from Age 10 to 20 years in our dataset.

j = sns.FacetGrid(train_df, col='treatment', size=5) j = j.map(sns.distplot, "Age")

Inference: Treatment 0 means treatment is not necessary 1 means it is. First Barplot shows that from age 0 to 10-year treatment is not necessary and is needed after 15 years.

plt.figure(figsize=(12,8)) labels = labelDict['label_Gender'] j = sns.countplot(x="treatment", data=train_df) j.set_xticklabels(labels) plt.title('Total Distribution by treated or not')

Text(0.5, 1.0, ‘Total Distribution by treated or not’)

Inference: Here we can see that more males are treated as compared to females in the dataset.

o = labelDict['label_age_range'] j = sns.factorplot(x="age_range", y="treatment", hue="Gender", data=train_df, kind="bar", ci=None, size=5, aspect=2, legend_out = True) j.set_xticklabels(o) plt.title('Probability of mental health condition') plt.ylabel('Probability x 100') plt.xlabel('Age') new_labels = labelDict['label_Gender'] for t, l in zip(j._legend.texts, new_labels): t.set_text(l) j.fig.subplots_adjust(top=0.9,right=0.8) plt.show()

Inference: This barplot shows the mental health of females, males, and transgender according to different age groups. we can analyze that from the age group of 66 to 100, mental health is very high in females as compared to another gender. And from age 21 to 64, mental health is very high in transgender as compared to males.

o = labelDict['label_family_history'] j = sns.factorplot(x="family_history", y="treatment", hue="Gender", data=train_df, kind="bar", ci=None, size=5, aspect=2, legend_out = True) j.set_xticklabels(o) plt.title('Probability of mental health condition') plt.ylabel('Probability x 100') plt.xlabel('Family History') new_labels = labelDict['label_Gender'] for t, l in zip(g._legend.texts, new_labels): t.set_text(l) j.fig.subplots_adjust(top=0.9,right=0.8) plt.show() o = labelDict['label_care_options'] j = sns.factorplot(x="care_options", y="treatment", hue="Gender", data=train_df, kind="bar", ci=None, size=5, aspect=2, legend_out = True) j.set_xticklabels(o) plt.title('Probability of mental health condition') plt.ylabel('Probability x 100') plt.xlabel('Care options') new_labels = labelDict['label_Gender'] for t, l in zip(g._legend.texts, new_labels): t.set_text(l) j.fig.subplots_adjust(top=0.9,right=0.8) plt.show()

Inference: In the dataset, for those who are having a family history of mental health problems, the Probability of mental health will be high. So here we can see that probability of mental health conditions for transgender is almost 90% as they have a family history of medical health conditions.

Inference: This barplot shows health status with respect to care options. In the dataset, for Those who are not having care options, the Probability of mental health situation will be high. So here we can see that the mental health of transgender is very high who have not care options and low for those who are having care options.

o = labelDict['label_benefits'] j = sns.factorplot(x="care_options", y="treatment", hue="Gender", data=train_df, kind="bar", ci=None, size=5, aspect=2, legend_out = True) j.set_xticklabels(o) plt.title('Probability of mental health condition') plt.ylabel('Probability x 100') plt.xlabel('Benefits') new_labels = labelDict['label_Gender'] for t, l in zip(j._legend.texts, new_labels): t.set_text(l) j.fig.subplots_adjust(top=0.9,right=0.8) plt.show()

Inference: This barplot shows the probability of health conditions with respect to Benefits. In the dataset, for those who are not having any benefits, the Probability of mental health conditions will be high. So here we can see that probability of mental health conditions for transgender is very high who have not getting any benefits. and probability is low for those who are having benefits options.

o = labelDict['label_work_interfere'] j = sns.factorplot(x="work_interfere", y="treatment", hue="Gender", data=train_df, kind="bar", ci=None, size=5, aspect=2, legend_out = True) j.set_xticklabels(o) plt.title('Probability of mental health condition') plt.ylabel('Probability x 100') plt.xlabel('Work interfere') new_labels = labelDict['label_Gender'] for t, l in zip(g._legend.texts, new_labels): t.set_text(l) j.fig.subplots_adjust(top=0.9,right=0.8) plt.show()

Inference: This barplot shows the probability of health conditions with respect to work interference. For those who are not having any work interference, the Probability of mental health conditions will be very less. and probability is high for those who are having work interference rarely.

Scaling and Fitting # Scaling Age scaler = MinMaxScaler() train_df['Age'] = scaler.fit_transform(train_df[['Age']]) train_df.head() # define X and y feature_cols1 = ['Age', 'Gender', 'family_history', 'benefits', 'care_options', 'anonymity', 'leave', 'work_interfere'] X = train_df[feature_cols1] y = train_df.treatment X_train1, X_test1, y_train1, y_test1 = train_test_split(X, y, test_size=0.30, Random_state1=0) # Create dictionaries for final graph # Use: methodDict['Stacking'] = accuracy_score methodDict = {} rmseDict = () forest = ExtraTreesClassifier(n_estimators=250, Random_state1=0) forest.fit(X, y) importances = forest.feature_importances_ std = np.std([tree1.feature_importances_ for tree in forest.estimators_], axis=0) indices = np.argsort(importances)[::-1] labels = [] for f in Range(x.shape[1]): labels.append(feature_cols1[f]) plt.figure(figsize=(12,8)) plt.title("Feature importances") plt.bar(range(X.shape[1]), importances[indices], color="r", yerr=std[indices],) plt.Xticks(range(X.shape[1]), labels, rotation='vertical') plt.xlim([-1, X.shape[1]]) plt.show() Tuning def evalClassModel(model, y_test1, y_pred_class, plot=False): #Classification accuracy: percentage of correct predictions # calculate accuracy print('Accuracy:', metrics.accuracy_score(y_test1, y_pred_class)) print('Null accuracy:n', y_test1.value_counts()) # calculate the percentage of ones print('Percentage of ones:', y_test1.mean()) # calculate the percentage of zeros print('Percentage of zeros:',1 - y_test1.mean()) print('True:', y_test1.values[0:25]) print('Pred:', y_pred_class[0:25]) #Confusion matrix confusion = metrics.confusion_matrix(y_test1, y_pred_class) #[row, column] TP = confusion[1, 1] TN = confusion[0, 0] FP = confusion[0, 1] FN = confusion[1, 0] # visualize Confusion Matrix sns.heatmap(confusion,annot=True,fmt="d") plt.title('Confusion Matrix') plt.xlabel('Predicted') plt.ylabel('Actual') plt.show() accuracy = metrics.accuracy_score(y_test1, y_pred_class) print('Classification Accuracy:', accuracy) print('Classification Error:', 1 - metrics.accuracy_score(y_test1, y_pred_class)) fp_rate = FP / float(TN + FP) print('False Positive Rate:', fp_rate) print('Precision:', metrics.precision_score(y_test1, y_pred_class)) print('AUC Score:', metrics.roc_auc_score(y_test1, y_pred_class)) # calculate cross-validated AUC print('Crossvalidated AUC values:', cross_val_score1(model, X, y, cv=10, scoring='roc_auc').mean()) print('First 10 predicted responses:n', model.predict(X_test1)[0:10]) print('First 10 predicted probabilities of class members:n', model.predict_proba(X_test1)[0:10]) model.predict_proba(X_test1)[0:10, 1] y_pred_prob = model.predict_proba(X_test1)[:, 1] if plot == True: # histogram of predicted probabilities plt.rcParams['font.size'] = 12 plt.hist(y_pred_prob, bins=8) plt.xlim(0,1) plt.title('Histogram of predicted probabilities') plt.xlabel('Predicted probability of treatment') plt.ylabel('Frequency') y_pred_prob = y_pred_prob.reshape(-1,1) y_pred_class = binarize(y_pred_prob, 0.3)[0] print('First 10 predicted probabilities:n', y_pred_prob[0:10]) roc_auc = metrics.roc_auc_score(y_test1, y_pred_prob) fpr, tpr, thresholds = metrics.roc_curve(y_test1, y_pred_prob) if plot == True: plt.figure() plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.0]) plt.rcParams['font.size'] = 12 plt.title('ROC curve for treatment classifier') plt.xlabel('False Positive Rate (1 - Specificity)') plt.ylabel('True Positive Rate (Sensitivity)') plt.legend(loc="lower right") plt.show() def evaluate_threshold(threshold): confusion = metrics.confusion_matrix(y_test1, predict_mine) print(confusion) return accuracy

Tuning with cross-validation score

def tuningCV(knn): k_Range = list(Range(1, 31)) k_scores = [] for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) scores = cross_val_score1(knn, X, y, cv=10, scoring='accuracy') k_scores.append(scores.mean()) print(k_scores) plt.plot(k_Range, k_scores) plt.xlabel('Value of K for KNN') plt.ylabel('Cross-Validated Accuracy') plt.show()

Tuning with GridSearchCV

def tuningGridSerach(knn): k_Range = list(range(1, 31)) print(k_Range) param_grid = dict(n_neighbors=k_range) print(param_grid) grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy') grid.fit(X, y) grid.grid_scores1_ print(grid.grid_scores_[0].parameters) print(grid.grid_scores_[0].cv_validation_scores) print(grid.grid_scores_[0].mean_validation_score) grid_mean_scores1 = [result.mean_validation_score for result in grid.grid_scores_] print(grid_mean_scores1) # plot the results plt.plot(k_Range, grid_mean_scores1) plt.xlabel('Value of K for KNN') plt.ylabel('Cross-Validated Accuracy') plt.show() # examine the best model print('GridSearch best score', grid.best_score_) print('GridSearch best params', grid.best_params_) print('GridSearch best estimator', grid.best_estimator_)

Tuning with RandomizedSearchCV

def tuningRandomizedSearchCV(model, param_dist): rand1 = RandomizedSearchCV(model, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state1=5) rand1.fit(X, y) rand1.cv_results_ print('Rand1. Best Score: ', rand.best_score_) print('Rand1. Best Params: ', rand.best_params_) best_scores = [] for _ in Range(20): rand1 = RandomizedSearchCV(model, param_dist, cv=10, scoring='accuracy', n_iter=10) rand1.fit(X, y) best_scores.append(round(rand.best_score_, 3)) print(best_scores)

Tuning by searching multiple parameters simultaneously

def tuningMultParam(knn): k_Range = list(Range(1, 31)) weight_options = ['uniform', 'distance'] param_grid = dict(N_neighbors=k_range, weights=weight_options) print(param_grid) grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy') grid.fit(X, y) print(grid.grid_scores_) print('Multiparam. Best Score: ', grid.best_score_) print('Multiparam. Best Params: ', grid.best_params_) Evaluating Models

Logistic Regression

def logisticRegression(): logreg = LogisticRegression() logreg.fit(X_train, y_train) y_pred_class = logreg.predict(X_test1) accuracy_score = evalClassModel(logreg, y_test1, y_pred_class, True) #Data for final graph methodDict['Log. Regression'] = accuracy_score * 100 logisticRegression()

True value: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0] Predicted value: [1 0 0 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 0]

[[0.09193053 0.90806947] [0.95991564 0.04008436] [0.96547467 0.03452533] [0.78757121 0.21242879] [0.38959922 0.61040078] [0.05264207 0.94735793] [0.75035574 0.24964426] [0.19065116 0.80934884] [0.61612081 0.38387919] [0.47699963 0.52300037]] [[0.90806947] [0.04008436] [0.03452533] [0.21242879] [0.61040078] [0.94735793] [0.24964426] [0.80934884] [0.38387919] [0.52300037]]

[[142 49] [ 28 159]]

KNeighbors Classifier

def Knn(): # Calculating the best parameters knn = KNeighborsClassifier(n_neighbors=5) k_Range = list(Range(1, 31)) weight_options = ['uniform', 'distance'] param_dist = dict(N_neighbors=k_range, weights=weight_options) tuningRandomizedSearchCV(knn, param_dist) knn = KNeighborsClassifier(n_neighbors=27, weights='uniform') knn.fit(X_train1, y_train1) y_pred_class = knn.predict(X_test1) accuracy_score = evalClassModel(knn, y_test1, y_pred_class, True) #Data for final graph methodDict['K-Neighbors'] = accuracy_score * 100 Knn()

[0.816, 0.812, 0.821, 0.823, 0.823, 0.818, 0.821, 0.821, 0.815, 0.812, 0.819, 0.811, 0.819, 0.818, 0.82, 0.815, 0.803, 0.821, 0.823, 0.815] True val: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0] Pred val: [1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 0]

[[0.33333333 0.66666667] [1. 0. ] [1. 0. ] [0.66666667 0.33333333] [0.37037037 0.62962963] [0.03703704 0.96296296] [0.59259259 0.40740741] [0.37037037 0.62962963] [0.33333333 0.66666667] [0.33333333 0.66666667]] [[0.66666667] [0. ] [0. ] [0.33333333] [0.62962963] [0.96296296] [0.40740741] [0.62962963] [0.66666667] [0.66666667]]

[[135 56] [ 18 169]]

Decision Tree 

def treeClassifier(): # Calculating the best parameters tree1 = DecisionTreeClassifier() featuresSize = feature_cols1.__len__() param_dist = {"max_depth": [3, None], "max_features": randint(1, featuresSize), "min_samples_split": randint(2, 9), "min_samples_leaf": randint(1, 9), "criterion": ["gini", "entropy"]} tuningRandomizedSearchCV(tree1, param_dist) tree1 = DecisionTreeClassifier(max_depth=3, min_samples_split=8, max_features=6, criterion='entropy', min_samples_leaf=7) tree.fit(X_train1, y_train1) y_pred_class = tree1.predict(X_test1) accuracy_score = evalClassModel(tree1, y_test1, y_pred_class, True) #Data for final graph methodDict['Decision Tree Classifier'] = accuracy_score * 100 treeClassifier()

[0.83, 0.827, 0.831, 0.829, 0.831, 0.83, 0.783, 0.831, 0.821, 0.831, 0.831, 0.831, 0.8, 0.79, 0.831, 0.831, 0.831, 0.829, 0.831, 0.831] True val: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0] Pred val: [1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 0]

[[0.18 0.82 ] [0.96534653 0.03465347] [0.96534653 0.03465347] [0.89473684 0.10526316] [0.36097561 0.63902439] [0.18 0.82 ] [0.89473684 0.10526316] [0.11320755 0.88679245] [0.36097561 0.63902439] [0.36097561 0.63902439]] [[0.82 ] [0.03465347] [0.03465347] [0.10526316] [0.63902439] [0.82 ] [0.10526316] [0.88679245] [0.63902439] [0.63902439]]

[[130 61] [ 12 175]]

Random Forests

def randomForest(): # Calculating the best parameters forest1 = RandomForestClassifier(n_estimators = 20) featuresSize = feature_cols1.__len__() param_dist = {"max_depth": [3, None], "max_features": randint(1, featuresSize), "min_samples_split": randint(2, 9), "min_samples_leaf": randint(1, 9), "criterion": ["gini", "entropy"]} tuningRandomizedSearchCV(forest1, param_dist) forest1 = RandomForestClassifier(max_depth = None, min_samples_leaf=8, min_samples_split=2, n_estimators = 20, random_state = 1) my_forest = forest.fit(X_train1, y_train1) y_pred_class = my_forest.predict(X_test1) accuracy_score = evalClassModel(my_forest, y_test1, y_pred_class, True) #Data for final graph methodDict['Random Forest'] = accuracy_score * 100 randomForest()

[0.831, 0.831, 0.831, 0.831, 0.831, 0.831, 0.831, 0.832, 0.831, 0.831, 0.831, 0.831, 0.837, 0.834, 0.831, 0.832, 0.831, 0.831, 0.831, 0.831] True val: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0] Pred val: [1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 0]

[[0.2555794 0.7444206 ] [0.95069083 0.04930917] [0.93851009 0.06148991] [0.87096597 0.12903403] [0.40653554 0.59346446] [0.17282958 0.82717042] [0.89450448 0.10549552] [0.4065912 0.5934088 ] [0.20540631 0.79459369] [0.19337644 0.80662356]] [[0.7444206 ] [0.04930917] [0.06148991] [0.12903403] [0.59346446] [0.82717042] [0.10549552] [0.5934088 ] [0.79459369] [0.80662356]]

Boosting

def boosting(): # Building and fitting clf = DecisionTreeClassifier(criterion='entropy', max_depth=1) boost = AdaBoostClassifier(base_estimator=clf, n_estimators=500) boost.fit(X_train1, y_train1) y_pred_class = boost.predict(X_test1) accuracy_score = evalClassModel(boost, y_test1, y_pred_class, True) #Data for final graph methodDict['Boosting'] = accuracy_score * 100 boosting()

True val: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0] Pred val: [1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 0]

[[0.49924555 0.50075445] [0.50285507 0.49714493] [0.50291786 0.49708214] [0.50127788 0.49872212] [0.50013552 0.49986448] [0.49796157 0.50203843] [0.50046371 0.49953629] [0.49939483 0.50060517] [0.49921757 0.50078243] [0.49897133 0.50102867]] [[0.50075445] [0.49714493] [0.49708214] [0.49872212] [0.49986448] [0.50203843] [0.49953629] [0.50060517] [0.50078243] [0.50102867]]

Predicting with Neural Network

Create input function

%tensorflow_version 1.x import tensorflow as tf import argparse

TensorFlow 1.x selected.

batch_size = 100 train_steps = 1000 X_train1, X_test1, y_train1, y_test1 = train_test1_split(X, y, test_size=0.30, random_state=0) def train_input_fn(features, labels, batch_size): dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) return dataset.shuffle(1000).repeat().batch(batch_size) def eval_input_fn(features, labels, batch_size): features=dict(features) if labels is None: # No labels, use only features. inputs = features else: inputs = (features, labels) dataset = tf.data.Dataset.from_tensor_slices(inputs) dataset = dataset.batch(batch_size) # Return the dataset. return dataset

Define the feature columns

# Define Tensorflow feature columns age = tf.feature_column.numeric_column("Age") gender = tf.feature_column.numeric_column("Gender") family_history = tf.feature_column.numeric_column("family_history") benefits = tf.feature_column.numeric_column("benefits") care_options = tf.feature_column.numeric_column("care_options") anonymity = tf.feature_column.numeric_column("anonymity") leave = tf.feature_column.numeric_column("leave") work_interfere = tf.feature_column.numeric_column("work_interfere") feature_column = [age, gender, family_history, benefits, care_options, anonymity, leave, work_interfere]

Instantiate an Estimator

model = tf.estimator.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 10], optimizer=tf.train.ProximalAdagradOptimizer( learning_rate=0.1, l1_regularization_strength=0.001 ))

Train the model

model.train(input_fn=lambda:train_input_fn(X_train1, y_train1, batch_size), steps=train_steps)

Evaluate the model

# Evaluate the model. eval_result = model.evaluate( input_fn=lambda:eval_input_fn(X_test1, y_test1, batch_size)) print('nTest set accuracy: {accuracy:0.2f}n'.format(**eval_result)) #Data for final graph accuracy = eval_result['accuracy'] * 100 methodDict['Neural Network'] = accuracy

The test set accuracy: 0.80

Making predictions (inferring) from the trained model

predictions = list(model.predict(input_fn=lambda:eval_input_fn(X_train1, y_train1, batch_size=batch_size))) # Generate predictions from the model template = ('nIndex: "{}", Prediction is "{}" ({:.1f}%), expected "{}"') # Dictionary for predictions col1 = [] col2 = [] col3 = [] for idx, input, p in zip(X_train1.index, y_train1, predictions): v = p["class_ids"][0] class_id = p['class_ids'][0] probability = p['probabilities'][class_id] # Probability # Adding to dataframe col1.append(idx) # Index col2.append(v) # Prediction col3.append(input) # Expecter #print(template.format(idx, v, 100 * probability, input)) results = pd.DataFrame({'index':col1, 'prediction':col2, 'expected':col3}) results.head() Creating Predictions on the Test Set

# Generate predictions with the best methodology

clf = AdaBoostClassifier() clf.fit(X, y) dfTestPredictions = clf.predict(X_test1) # Write predictions to csv file results = pd.DataFrame({'Index': X_test1.index, 'Treatment': dfTestPredictions}) # Save to file results.to_csv('results.csv', index=False) results.head() Submission results = pd.DataFrame({'Index': X_test1.index, 'Treatment': dfTestPredictions}) results

The final prediction consists of 0 and 1. 0 means the person is not needed any mental health treatment and 1 means the person is needed mental health treatment.

Conclusion

After using all these Employee records, we are able to build various machine learning models. From all the models, ADA–Boost achieved 81.75% accuracy with an AUC of 0.8185 along with that we were able to draw some insights from the data via data analysis and visualization.

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