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In this blog, we will be discussing various techniques to vectorize the texts in NLP. Before we move forward let us briefly discuss what is NLP. NLP (Natural language processing) is a branch of artificial intelligence that helps machines to understand, interpret and manipulate human language. Since the beginning of the brief history of Natural Language Processing (NLP), there has been the need to transform the text into something a machine can understand. We all know that computers don’t understand English or any language as it is. They only understand binary language that is only 1 and 0 (Bits). Thus, arises the need to transform the text into a meaningful vector (or array) of numbers (or simply to encode the text), so that the computer can better understand the text and hence the language. Machine learning algorithms most often take numeric feature vectors as input. Thus, when working with text documents, we need a way to convert each document into a numeric vector. This process is known as text vectorization. In much simpler words, the process of converting words into numbers is called Vectorization. Before actually diving into the whole process of vectorization. Let us first set a corpus to work with, we will be choosing a very common example, you might have seen it on various websites. corpus = [ 'the quick brown fox jumped over the brown dog.', 'the quick brown fox.', 'the brown brown dog.', 'the fox ate the dog.' ] Our corpus consists of four sentences, these four sentences can be thought of as four different documents. Now that we have our corpus we will get on with vectorization. Vectorization is better understood with examples. The following are the different ways of text vectorization: CountVectorizer It is a great tool provided by the sci-kit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample as shown below. For the corpus above, our matrix would be something like as follows. The first sentence or the document would be transformed into a vector [02111112]. In a similar manner, each document can be represented by a vector. Let us try to code it and see the result for ourselves. The CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words, but also to encode new documents using that vocabulary. You can use it as follows: 1. Create an instance of the CountVectorizer class. 2. Call the fit() function in order to learn a vocabulary from one or more documents. 3. Call the transform() function on one or more documents as needed to encode each as a vector. An encoded vector is returned with a length of the entire vocabulary and an integer count for the number of times each word appeared in the document. Code snippet: from sklearn.feature_extraction.text import CountVectorizer # create an object of CountVectorizer class. vectorizer = CountVectorizer() # tokenize and build vocabulary vectorizer.fit(corpus) # encode vector = vectorizer.transform(corpus) # summarize encoded vector print('Vocabulary :',vectorizer.vocabulary_) print('\nShape of the vector: ',vector.shape) print('\ntype of vector: ',type(vector)) print('\nBelow are the sentences in vector form:') print(vector.toarray()) Output: We can see that we got the same result as above. Now you must be getting what it means to vectorize text. Once the text is vectorized it is ready to be fed to machine learning models as an input. Let us try out some more methods to perform vectorization. TfIdfVectorizer It is another one of the great tools provided by the scikit-learn library. It is a very common algorithm to transform the text into a meaningful representation of numbers which is used to fit machine algorithms for prediction. Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=tfidf%20vectorizer#sklearn.feature_extraction.text.TfidfVectorizer TF-IDF is a product of two terms: TF (Term Frequency) — It is defined as the number of times a word appears in the given sentence. IDF (Inverse Document Frequency) — It is defined as the natural log of a number of the total documents divided by the documents in which the word appears. For example, we will calculate the Tf-Idf values for the first sentence in the corpus. Step 1: Create a vocabulary of unique words. In our case the vocabulary would be: ['ate', 'brown', 'dog', 'fox', 'jumped', 'over',' quick', 'the']. Step2: Create an array of zeroes for each sentence in the corpus, with a size equal to the number of unique words in the corpus. For e.g. the array for the first sentence would be [00000000]. In this way, we will get 4 arrays of length 8. Step3: We calculate Tf-Idf for each word in each sentence. We select the first sentence to illustrate this step. We know that: Total documents/sentences (N): 4 Documents in which the word appears (n): 3 Number of times the word appears in the first sentence: 1 Number of words in the first sentence: 9 Term Frequency (TF) = 1 **If smooth_idf=True (the default), the constant “1” is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1.** Inverse Document Frequency(IDF) = ln((1+N)/(1+n))+1 = ln(5/4)+1 = 0.22314355131 + 1 = 1.22314355131 TF-IDF value = 1 * 1.22314355131 = 1.22314355131 Same way Tf-Idf is calculated for each word in the vocabulary and then the values obtained are normalized. In TfIdfVectorizer the parameter 'norm' has a default value of 'l2'. In this case, the sum of squares of vector elements is 1. If the norm is set as 'l1', then sum of absolute values of vector elements is 1. We can also set value of norm parameter to be 'False' and not opt for any kind of normalization at all. The Tf-Idf values for all the words in the first sentence is: 'ate': 1.916290731874155, 'brown': 2.44628710263, 'dog': 1.2231435513142097, 'fox': 1.2231435513142097, 'jumped': 1.916290731874155, 'over': 1.916290731874155, 'quick': 1.5108256237659907, 'the': 2.0 After applying the default norm in TfIdfVectorizer i.e. the 'l2' norm. We get the following values: 'ate': 0.0, 'brown': 0.51454148, 'dog': 0.25727074, 'fox': 0.25727074, 'jumped': 0.40306433, 'over': 0.40306433, 'quick': 0.31778055, 'the': 0.42067138 Thus the vector for the first sentence would be: [0. 0.51454148 0.25727074 0.25727074 0.40306433 0.40306433 0.31778055 0.42067138] Let us try to code it and see the result for ourselves. The implementation is similar to CountVectorizer, but in this case we make an object of the TfIdfVectorizer class instead of the CountVectorizer. Code snippet: from sklearn.feature_extraction.text import TfidfVectorizer # create an object of TfidfVectorizer class. vectorizer = TfidfVectorizer() # tokenize and build vocabulary vectorizer.fit(corpus) # encode vector = vectorizer.transform(corpus) # summarize encoded vector print(vectorizer.vocabulary_) print(vectorizer.idf_) print(vector.shape) print(vector.toarray()) Output: The TfIdfVectorizer will tokenize documents, learn the vocabulary and inverse document frequency weightings, and allow you to encode new documents. Alternately, if you already have a learned CountVectorizer, you can use it with a TfidfTransformer to just calculate the inverse document frequencies and start encoding documents. HashingVectorizer This is also a method provided by scikit-learn. It also converts a collection of text documents to a matrix of token occurrences just like CountVectorizer but the process is a little different. Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html The vocabulary build while working with CountVectorizer can become very huge when the size of the documents increase. This, in turn, will require large vectors for encoding documents and impose large requirements on memory and slow down algorithms and also it would take up a lot of memory. In order to get around this HashingVectorizer is used. In HashingVectorizer we use a one way hash of words to convert them to integers. The best part is that, in this case no vocabulary is required and you can choose an arbitrary-long fixed length of the vector. A downside is that the hash is a one-way function so there is no way to convert the encoding back to a word (which may not matter for many supervised learning tasks). Here the vectorizer does not require a call to fit on the training data documents. Instead, after creating the object, it can be used directly to start encoding documents. We can set the length of vector by assigning the value to the parameter 'n_features'. In this example we will create a vector of length 10. It is acceptable since our documents are very small. In case of large documents the value of n_features should be such that it can avoid hash-collisions. code snippet: from sklearn.feature_extraction.text import HashingVectorizer # create an object of HashinVectorizer class. vectorizer = HashingVectorizer(n_features=10) # encode directly without fitting vector = vectorizer.transform(corpus) # summarize encoded vector print('\nShape of vector: ',vector.shape) print('\nBelow are the sentences in vector form:') print(vector.toarray()) Output: The values of the encoded document correspond to normalized word counts by default in the range of -1 to 1, but could be made simple integer counts by changing the default configuration. The above three methods were Bag of words approach provided by scikit learn. The next approaches would be that of neural network model. Word2Vec Word2vec is a combination of models used to represent distributed representations of words in a corpus. It is a set of neural network models that aim to represent words in the vector space. These models are highly efficient and well performing in understanding the context and relation between words. Similar words are placed close together in the vector space while dissimilar words are placed wide apart. Documentation: https://radimrehurek.com/gensim/models/word2vec.html There are two models in this class: CBOW (Continuous Bag of Words): The neural network takes a look at the surrounding words (say 3 to the left and 3 to the right or whatever may be the window size) and predicts the word that comes in between. code snippet: from gensim.models import word2vec for i, sentence in enumerate(corpus): tokenized= [] for word in sentence.split(' '): word=word.split('.')[0] word = word.lower() tokenized.append(word) corpus[i] = tokenized model1 = word2vec.Word2Vec(corpus, workers = 1, size = 2, min_count = 1, window = 3, sg = 0) vocabulary1 = model1.wv.vocab print(vocabulary1) v1 = model1.wv['fox'] print('\nShape of vector: ',v1.shape) print('\nBelow is the vector representation of the word \'fox\':') print(v1) Output: 2. Skip-grams: The neural network takes in a word and then tries to predict the surrounding words (context). The idea of skip gram model is to choose a target word and then predict the words in it’s context to some window size. It does this by maximizing the probability distribution i.e. probability of the word appearing in the context (within the specified window) given the target word. code snippet: model2 = word2vec.Word2Vec(corpus, workers = 1, size = 3 ,min_count = 1, window = 3, sg = 1) vocabulary2 = model2.wv.vocab print(vocabulary2) v2 = model2.wv['fox'] print('\nShape of vector: ',v2.shape) print('\nBelow is the vector representation of the word \'fox\':') print(v2) Output: Notice in this case I have set the size of the vector to be 3 thus the vector in this case have three elements in it. ElMo ElMo stands for Embeddings from Language Models. ElMo is a deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). These word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. They can be easily added to existing models and significantly improve the state of the art across a broad range of challenging NLP problems, including question answering, textual entailment and sentiment analysis. Unlike traditional word embeddings such as word2vec, the ElMo vector assigned to a token or word is actually a function of the entire sentence containing that word. Therefore, the same word can have different word vectors under different contexts. code snippet: import tensorflow_hub as hub import tensorflow as tf elmo = hub.load("https://tfhub.dev/google/elmo/2") # Extract ELMo features embeddings = elmo.signatures["default"](tf.constant(corpus) )["elmo"] print('\nShape of vector: ',embeddings.shape) print('\nBelow is the vector representation of the sentences:') print(embeddings) Output: The output is a 3 dimensional tensor of shape (4, 9, 1024). The first dimension of this tensor represents the number of training samples. This is 4 in our case The second dimension represents the maximum length of the longest string in the input list of strings. Which is 9 in our case. The third dimension is equal to the length of the ElMo vector Hence, every word in the input sentence has an ElMo vector of size 1024. These were few text vectorization techniques. I hope you understand the concept better now. 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To get started with the deployment process of a Machine Learning Model over Amazon's SageMaker, first one needs to get familiar with the basic terminologies involved in the subject matter. Some of these are mentioned below: Amazon Web services are a set of over simplified, serialisable, scalable on-demand, cloud services offered by Amazon through its subsidiary Amazon Web services Inc. Some of these services include Sagemaker, EC2, S3, RDS, Augmented AI, Container services etc. To stay in context with the agenda in hand we will go through only few relevant ones : Amazon EC2 (Amazon Elastic Compute Cloud) These are onn demand virtual servers, Allows you to launch your own virtual server in cloud with the operating system of your choice with just couple of mouse clicks. S3 (Simplified Storage System) Is an object storage service which allows you to upload your files, documents movies, music, videos etc at very low cost. (Ex. Dropbox actually uses s3 to store all of the user uploaded files). S3 even offers static website hosting, i.e if you have a school/college website developed in plain html (no server side coding) you can just deploy it in S3. RDS(Relational Database Service) This service allows people to host their database schema and data without worrying to manage underlying database server. All other things like patching, updating servers and maintaining will be taken care by Amazon itself. You just have to create your schema and connect to it from your application and then start using it. AWS Sagemaker SageMaker has been a great deal for most data scientists who would want to accomplish a truly end-to-end ML solution. This is because of the fact that it takes care of abstracting a ton of software development skills necessary to accomplish the task while still being highly effective and flexible. These abstraction otherwise are too hefty to be built locally. Some of these abstractions include: Estimators -Encapsulate training on SageMaker. Models -Encapsulate built ML models. Predictors -Provide real-time inference and transformation using Python data-types against a SageMaker endpoint. Session -Provides a collection of methods for working with SageMaker resources. Transformers -Encapsulate batch transform jobs for inference on SageMaker Processors -Encapsulate running processing jobs for data processing on SageMaker. Docker is an open platform for developing, shipping, and running applications. . A Docker image is a recipe for running a containerised process. In order to deploy a model in Amazons sagemaker, we need to load the image of that particular algorithm into our notebook and then create an endpoint which in turn can be served as an API to an application. Container is a standard unit of software that stores up code and all its dependencies so the application runs fast and reliably from one computing environment to different ones. These are available for both Linux and Windows-based applications, containerised software will always run the same, regardless of the infrastructure. Containers isolate software from its environment and ensure that it works uniformly despite differences. Docker Image is a read-only template that contains a set of instructions for creating a container that can run on the Docker platform. It provides a convenient way to package up applications and pre-configured server environments, which you can use for your own private use or share publicly with other Docker users Machine learning is a very broad topic, mostly considered as the back bone of Artificial Intelligence. So in simpler terms it can be defined as a set of algorithms used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition. Setups involved in Deployment process: Environment Setup Dependencies Creation of an Amazon S3 Bucket Creation of an Amazon SageMaker Notebook Instance Transform the Training Data Train a Model Deploy the Model to Amazon SageMaker Validate the Model Clean Up Environment Setup: In this blog, I would like to keep the main focus on the the deployment workflow as a real world scenario, although the documentation is just as important, so keeping that i mind, before we can use Amazon SageMaker we must sign up for an AWS account, create an IAM admin user, and get onboard to Amazon SageMaker notebook instances. After you complete these tasks, try out the Get Started guides. The guides walk you through training your first model using SageMaker notebook or the SageMaker console and the SageMaker API. To get to Get Started guides click here. Once your are done with the account setup part, you will see the dashboard, and all of the AWS services are listed in different categories whether it be Database related or ML. A snippet of this is shown below: Now we are inside the main framework of AWS, to get to our SageMaker simply click on SageMaker, and then we create a notebook instance. Which looks like the snippet below: In this snippet we can see the specifications of the instance I have already created, the instance type signifies the computational capacity , and other feature are quiet straight forward as signified by there names. Dependencies Now that all this is setup, we can get to the packages we need for a smoother workflow of our Python SDK all through importing data to deployment process. Some of these dependencies include: import sagemaker import boto3 from sagemaker.amazon.amazon_estimator import get_image_uri from sagemaker.session import s3_input, Session One of most important package here is the boto3, it is the name of the Python SDK for AWS. It allows you to directly create, update, and delete AWS resources from our Python scripts. We use this package to set up the s3 bucket to make the pipleline till model deployment easier. Two important variables that need to be setup are the region and the name of bucket. The snippet below shows how we create bucket into s3 and set the output folder to save our model and other resources into. s3 = boto3.resource('s3') try: if my_region == 'us-east-\1': s3.create_bucket(Bucket=bucket_name) except Exception as e: print('S3 error: ',e) prefix = 'xgboost-as-a-built-in-algo' output_path ='s3://{}/{}/output'.format(bucket_name, prefix) I have kept the prefix as xgboost as we are going to deploy a pre-built algorithm in the cloud. boto3 is the Amazon Web Services (AWS) SDK for Python. It enables Python developers to create, configure, and manage AWS services, such as EC2 and S3. Boto provides an easy to use, object-oriented API, as well as low-level access to AWS services. Creation of an Amazon S3 Bucket Now it is time to load data into an s3 bucket. Amazon Simple Storage Service is storage for the Internet. It is designed to make web-scale computing easier for developers. Amazon S3 has a simple web services interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web. Keeping this in mind we load data from a url , into a pandas dataframe and then from there using boto3 we can load it in to a s3 bucket. try: urllib.request.urlretrieve ("https://data.csv") print('Success') except Exception as e: print('Data load error: ',e) try: model_data =pd.read_csv('./data.csv',index_col=0) print('Success: Data loaded into dataframe.') except Exception as e: print('Data load error: ',e) boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix,train/train.csv')).upload_file('train.csv') s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train'. format(bucket_name, prefix), content_type='csv') boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix,'test/test.csv')).upload_file('test.csv') s3_input_test = sagemaker.s3_input(s3_data='s3://{}/{}/test' .format(bucket_name, prefix),content_type='csv') boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix,'test/test.csv')).upload_file('test.csv') s3_input_test = sagemaker.s3_input(s3_data='s3://{}/{}/test'. format(bucket_name, prefix), content_type='csv') In this snippet we can see how one can download the data from a given url pre-process it and then save it into an s3 bucket. We can also manually open s3 and load data into it. The bucket looks something like the image below: These are all of the buckets, in our s3 database but for the sake of this blog , we will limit the usage to boosting, as you can see there are different named buckets with the region, accesype and data. Using the code above we have loaded the data into boostin bucket as we are trying to implement an xgboost in sagemaker. All the output will be stored in this bucket only, such as our model endpoints etc. and we can access this data from anywhere , since s3 have multiple data centres globally. There fore causing no pressure on local machine. Creation of an Amazon SageMaker Notebook Instance An Amazon SageMaker notebook instance provides a Jupyter notebook app through a fully managed machine learning (ML) Amazon EC2 instance. Amazon SageMaker Jupyter notebooks are used to perform advanced data exploration, create training jobs, deploy models to Amazon SageMaker hosting, and test or validation of different models. A preview of these instances in given below: For a basic data set which is say 5 gb in size,if we try to load this dataset into memory on the notebook instance for exploration/pre-processing, the primary bottleneck here would be ensuring the instance has enough memory for your dataset. This would require at least the 16gb memory, for complexities like this aws has provided a large variety of highly scalable processors, memory units etc , situated at multiple nodes across the world. A complete list of ML instance types is available here. Some of these are listed below: ml.t3.medium ml.t3.large ml.t3.xlarge ml.t3.2xlarge ml.m5.large ml.m5.xlarge ml.m5.2xlarge ml.m5.4xlarge ml.m5.8xlarge ml.m5.12xlarge ml.m5.16xlarge ml.m5.24xlarge Using the Amazon SageMaker SDK, the training data is loaded and distributed to the training cluster, allowing the training job to be completely separate from the instance which hosted notebook is running on. Figuring out the ideal instance type for training will depend on whether our algorithm of choice/training job is memory, CPU, or IO bound. Transform the Training Data Data transformation is a technique used to convert the raw data into a suitable format as needed by the model and put it in to the output path to our s3 which we defined earlier. This could involve a large range of strategies from feature engineering to vectorization, some of these are listed below: Data Smoothing. Data Aggregation. Discretization. Generalisation. Attribute construction. Normalization. Next stage to this pipline is splitting the data into Training and test sets. And we save it back to our bucket in order to be used by our Estimator. import numpy as np from sagemaker.predictor import csv_serializer import os #train_test_split train_data, test_data = np.split(model_data.sample(frac=1, random_state=1729), [int(0.7 * len(model_data))]) #Raw Data Into a Suitable Format pd.concat([train_data['Target_1'], train_data.drop(['Target_', 'y_yes'],axis=1)] ,axis=1). to_csv('train.csv', index=False, header=False) #Train Data Transfer To s3 Bucket boto3.Session().resource('s3').Bucket(bucket_name). Object(os.path. . join(prefix, 'train/train.csv')). upload_file('train.csv') s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train'. format(bucket_name, prefix), content_type='csv') Train a Model Now that our data is ready in our database in order to train our model is choose a model set its hyper-parameters and start the training process. AWS comes with a list of inbuilt algorithm already built into docker images. So all we have to do is pull that image from the docker cloud, load it into a container and the model is ready to be trained. Alternatively we can also design our own model and then train it as well. In this blog we will stick to the agenda and deploy a builtin xgboost model on our data set. # this line automatically looks for the XGBoost image URI and builds an XGBoost container. container = get_image_uri(boto3.Session().region_name, 'xgboost', repo_version='1.0-1') In a general manner of speaking XGboost is a widely used algorithm in machine learning, whether the problem is a classification or a regression problem. It is known for its good performance as compared to many other machine learning algorithms. Now that our model is ready its hyper-meters needs to be set in order to achieve the optimal performance from the model. Below is a dictionary containing hyper-parameters for the model to be trained on. # initialize hyperparameters hyperparameters = {"max_depth":"5","eta":"0.2","gamma":"4", 'min_child_weight":"6","subsample":"0.7", "objective":"binary:logistic","num_round":50} SageMaker has a built in class which allows us to pass the model container, hyper-parameters , and other parameters in order to initialise a model object. Once this is done then we directly fit the Estimator to our data which resides in s3 bucket. estimator.fit({'train': s3_input_train,'validation': s3_input_test}) Ones we execute this command the model starts the training and we can see the error rate decreasing each epoch. After the training is complete the sagemaker uploads the model into the output path which we had defined earlier. It also outputs other details like training time, billable time etc. Deploy the Model to Amazon SageMaker The final step in the whole process is deploying the finalized model and creating an endpoint that will be accessed by external interfaces. The machine allocated in the endpoint will be in a running state and the cost will be deduced accordingly. So, when other external applications are not using it, then the endpoint should be removed using the delete option. The code below shows how to deploy your model as an endpoint. Estimator.deploy(initial_instance_count=1,instance_type='ml.m4.xlarge') Now that our model is out there as an API and could be used by different interfaces with appropriate privilege access to it. Validate the Mode The reason for doing cross validation is to avoid over-fitting to data. For example, you, while using the training data, you may manage to learn everything so perfectly, that you may have zero training error, but the classifier may not be generalized to give you a good performance on unseen data. Cross-validation helps us evaluate this performance on unseen data. In the next section we will test our model over the test set I had created earlier. This test set does not contain any label so the model does not know the labels and are totally foreign to the model. predictions = xgb_predictor.predict(test_data_array).decode('utf-8') There are many techniques to check how good a model has performed, one of the most prominent such technique is use of confusion matrix. The snippet below shows an classification report of our analysis. Clean Up Clean up is done in order to remove any extra charges an organisation may incur from. For this purpose we remove all the endpoints we have created as well as the buckets and other sources. A simple way to do this is shown below: sagemaker.Session().delete_endpoint(xgb_predictor.endpoint) bucket_to_delete = boto3.resource('s3').Bucket(bucket_name) bucket_to_delete.objects.all().delete() Related links: Build and Deploy your Machine Learning Application with Dockers. Get Started Now! Codersarts offers programming assignment help, programming expert help, database assignment help, web programming help, Android app development. We are a group of top coders and developers, providing the best service with expertise in the specific domain of technology. instant help support.

Help of play core library provided by google. Check for update availability. Start an update Get a callback for update status Handle a flexible update Install a flexible update Handle an immediate update First add Library firebase remote config we create in-app default values that control the behavior and appearance of our app This is Available for version 4.1 of android if you are using another version of android studio check the newer version of android studio. implementation 'com.google.firebase:firebase-config:20.0.2' config update code : textCiewCurrentVersion = findViewById(R.id.version); textCiewCurrentVersion.setText(" Current Version Code: " +getVersionCode()); HashMap defaultsRate = new HashMap<>(); defaultsRate.put("new_version_code", String.valueOf(getVersionCode())); mFirebaseRemoteConfig = FirebaseRemoteConfig.getInstance(); FirebaseRemoteConfigSettings configSettings = new FirebaseRemoteConfigSettings.Builder() .setMinimumFetchIntervalInSeconds(10) .build(); mFirebaseRemoteConfig.setConfigSettingsAsync(configSettings); mFirebaseRemoteConfig.setDefaultsAsync(defaultsRate); mFirebaseRemoteConfig.fetchAndActivate().addOnCompleteListener(this, new OnCompleteListener() { @Override public void onComplete(@NonNull Task task) { if (task.isSuccessful()) { final String new_version_code = mFirebaseRemoteConfig.getString("new_version_code"); if(Integer.parseInt(new_version_code) > getVersionCode()) showTheDialog("com.facebook.lite", new_version_code ); } else Log.e("MYLOG", "mFirebaseRemoteConfig.fetchAndActivate() NOT Successful"); } }); } private PackageInfo pInfo; public int getVersionCode() { pInfo = null; try { pInfo = getPackageManager().getPackageInfo(getPackageName(), 0); } catch (PackageManager.NameNotFoundException e) { Log.i("MYLOG", "NameNotFoundException: "+e.getMessage()); } return pInfo.versionCode; } private void showTheDialog(final String appPackageName, String versionFromRemoteConfig){ final AlertDialog dialog = new AlertDialog.Builder(this) .setTitle("Update Available") .setMessage("This version is Old, please update to version: "+versionFromRemoteConfig) .setPositiveButton("UPDATE", null) .show(); dialog.setCancelable(false); Button positiveButton = dialog.getButton(AlertDialog.BUTTON_POSITIVE); positiveButton.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { try { startActivity(new Intent(Intent.ACTION_VIEW, Uri.parse("market://details?id=" + appPackageName))); } catch (android.content.ActivityNotFoundException anfe) { startActivity(new Intent(Intent.ACTION_VIEW, Uri.parse("https://play.google.com/store/apps/details?id=" + appPackageName))); } } }); Now we need go https://firebase.google.com/ and open app and go remote of firebase In Add para meter add key Ex : new_version_code From Config Update Code. Hire an android developer to get quick help for all your android app development needs. with the hands-on android assignment help and android project help by Codersarts android expert. You can contact the android programming help expert any time; we will help you overcome all the issues and find the right solution. Want to get help right now Or Want to know price quote CONTACT US NOW

What is AWS? Amazon web (AWS) service is a platform that offers flexible, reliable, scalable, easy-to-use and cost-effective cloud computing solutions. It is a comprehensive, easy to use computing platform offered Amazon. The platform is developed with a combination of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS) offerings. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centres globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWS was first established in 2002 because the company wanted to sell its unused infrastructure as a service or as an offering to customers. In 2006, Amazon Web Services (AWS) was re-launched and began offering IT infrastructure services to businesses in the form of web services -- now commonly known as cloud computing. Below is a list of companies utilising AWS: Instagram Zoopla Smugmug Pinterest Netflix Dropbox Etsy Talkbox Playfish Ftopia Advantages of AWS Following are the pros of using AWS services: AWS allows organizations to use the already familiar programming models, operating systems, databases, and architectures. You only need to pay for the service you avail, without any up-front or long-term commitments. You will not require to spend money on running and maintaining data centres. Offers fast deployments You can easily add or remove capacity. You are allowed cloud access quickly with limitless capacity. Total Cost of Ownership is very low compared to any private/dedicated servers. Offers Centralized Billing and management Offers Hybrid Capabilities Allows you to deploy your application in multiple regions around the world with just a few clicks Disadvantages of AWS Following are the cons of using AWS services: If you need more immediate or intensive assistance, you'll have to opt for paid support packages. Amazon Web Services may have some common cloud computing issues when you move to a cloud. For example, downtime, limited control, and backup protection. AWS sets default limits on resources which differ from region to region. These resources consist of images, volumes and snapshots. Hardware-level changes happen to your application which may not offer the best performance and usage of your applications. AWS for machine learning Machine learning is a field in computational science that analyses patterns and structures in data to help with learning, reasoning, and decision-making—all without human interaction. Data is the lifeblood of business, and machine learning helps identify signals among the data noise. AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. Named a leader in Gartner's Cloud AI Developer services' Magic Quadrant, AWS is helping tens of thousands of customers accelerate their machine learning journey. Now that we have ample information about AWS we will move one step forward to discuss its services available for machine learning. 1. Amazon Sagemaker Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models at scale. It removes the complexity from each step of the ML workflow so you can more easily deploy your ML use cases, anything from predictive maintenance to computer vision to predicting customer behaviors. The SageMaker comprises of the following 12 features: Amazon SageMaker Studio It is the first fully integrated development environment designed specifically for ML that brings everything you need for ML under one unified, visual user interface. You can use Amazon SageMaker’s integrated capabilities for ML development, in order to eliminate months of writing custom integration code, and ultimately reduce cost. Amazon SageMaker Autopilot It automatically builds, trains, and tunes the best machine learning models based on your data, while allowing you to maintain full control and visibility. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality. Amazon SageMaker Ground Truth It is a fully managed data labelling service that makes it easy to build highly accurate training datasets for machine learning. Get started with labelling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labelling workflows. These workflows support a variety of use cases including 3D point clouds, video, images, and text. As part of the workflows, labellers have access to assistive labelling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required in labelling datasets. In addition, Ground Truth offers automatic data labelling which uses a machine learning model to label your data. Amazon SageMaker JumpStart It helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing, object detection, and image classification models. Amazon SageMaker Data Wrangler It reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Amazon SageMaker Feature Store It is a fully managed, purpose-built repository to store, update, retrieve, and share machine learning (ML) features. It is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. It keeps track of the metadata of stored features (e.g. feature name or version number) so that you can query the features for the right attributes in batches or in real time using Amazon Athena, an interactive query service. It also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference. Amazon SageMaker Clarify It provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. It detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset or in your trained model and receive a detailed report that quantifies different types of possible bias. It also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct. Amazon SageMaker Debugger It makes it easy to optimize machine learning (ML) models by capturing training metrics in real-time such as data loss during regression and sending alerts when anomalies are detected. This helps you immediately rectify inaccurate model predictions such as an incorrect identification of an image. SageMaker Debugger automatically stops the training process when the desired accuracy is achieved, reducing the time and cost of training ML models. Amazon SageMaker Model Monitor It helps you maintain high quality machine learning (ML) models by automatically detecting and alerting on inaccurate predictions from models deployed in production. It helps you maintain high quality ML models by detecting model and concept drift in real-time, and sending you alerts so you can take immediate action. Model and concept drift are detected by monitoring the quality of the model based on independent and dependent variables. Further, SageMaker Model Monitor constantly monitors model performance characteristics such as accuracy which measures the number of correct predictions compared to the total number of predictions so you can take action to address anomalies. Amazon SageMaker distributed training It offers the fastest and easiest methods for training large deep learning models and datasets. Using partitioning algorithms, SageMaker distributed training automatically splits large deep learning models and training datasets across AWS GPU instances in a fraction of the time it takes to do manually. SageMaker achieves these efficiencies through two techniques: data parallelism and model parallelism. With only a few lines of additional code, you can add either data parallelism or model parallelism to your PyTorch and TensorFlow training scripts and Amazon SageMaker will apply your selected method for you. It will determine the best approach to split your model by using graph partitioning algorithms to balance the computation of each GPU while minimizing the communication between GPU instances. SageMaker also optimizes your distributed training jobs through algorithms that are designed to fully utilize AWS compute and network infrastructure in order to achieve near-linear scaling efficiency, which allows you to complete training faster than manual implementations. Amazon SageMaker Pipelines It is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. Since it is purpose-built for machine learning, SageMaker Pipelines helps you automate different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment. With it, you can build dozens of ML models a week, manage massive volumes of data, thousands of training experiments, and hundreds of different model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization. Amazon SageMaker Edge Manager It allows you to optimize, secure, monitor, and maintain ML models on fleets of smart cameras, robots, personal computers, and mobile devices. Amazon SageMaker Edge Manager provides a software agent that runs on edge devices. The agent comes with a ML model optimized with SageMaker Neo automatically so you don’t need to have Neo runtime installed on your devices in order to take advantage of the model optimizations. The agent also collects prediction data and sends a sample of the data to the cloud for monitoring, labelling, and retraining so you can keep models accurate over time. All data can be viewed in the SageMaker Edge Manager dashboard which reports on the operation of deployed models. And, because it enables you to manage models separately from the rest of the application, you can update the model and the application independently reducing costly downtime and service disruptions. It also cryptographically signs your models so you can verify that it was not tampered with as it moves from the cloud to edge devices. 2. Amazon Polly Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. It was launched in November 2016 and now includes 60 voices across 29 languages. 3. Amazon Lex Amazon Lex is a service for building conversational interfaces into any application using voice and text. It powers the Amazon Alexa virtual assistant. You can design, build, and deploy chatbots with it. 4. Amazon Rekognition Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. It was launched in 2016. 5. Amazon Comprehend Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required. 6. Amazon Transcribe Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. 7. Amazon Fraud Detector Amazon Fraud Detector is a fully managed service that uses machine learning (ML) and more than 20 years of fraud detection expertise from Amazon, to identify potentially fraudulent activity so customers can catch more online fraud faster. Amazon Fraud Detector automates the time-consuming and expensive steps to build, train, and deploy an ML model for fraud detection, making it easier for customers to leverage the technology. Amazon Fraud Detector customizes each model it creates to a customer’s own dataset, making the accuracy of models higher than current one-size-fits all ML solutions. 8. Amazon Forecast Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. Once you provide your data, Amazon Forecast will automatically examine it, identify what is meaningful, and produce a forecasting model capable of making predictions that are up to 50% more accurate than looking at time series data alone. These were few of the important services offered by Amazon in relation to ML but there are many more services offered by them in a variety of field. Do check them out. Related links: Machine Learning Model Deployment in Cloud using SageMaker. Different Applications of Machine Learning. Get started. CONTACT US NOW

Firebase Cloud Messaging, formerly known as Google Cloud Messaging, is a cross-platform cloud solution for messages and notifications for Android, iOS, and web applications, which currently can be used at no cost. Open new Project in android studio click on Tools In firebase go to Connect Firebase, Click Add FCM in Android to Add dependencies After that open https://console.firebase.google.com/ And go to engage Click on Cloud Messaging Then click on Send your first message Enter Information and Click Next Click next and Target App and Click next then Schedule Now and Click next then click review then Publish Notification Send Then Finally Notification send to your phone Hire an android developer to get quick help for all your android app development needs. with the hands-on android assignment help and android project help by Codersarts android expert. You can contact the android programming help expert any time; we will help you overcome all the issues and find the right solution. Want to get help right now Or Want to know price quote CONTACT US NOW

To do this, the first thing you will have to do is to have the NetBeans 11 development environment installed In your computer and proceed to use Hibernate. Create a "Java with Maven" project to perform the task and use the following versions of software/libraries and settings: JAVA JDK 1.8 NetBeans 11.1 MySQL 8 (user "root", password "root") Mysql jdbc connector according to the installed MySQ HIbernate v5 Make a Java project in NetBeans that performs the following steps: 1. Install the sakila database, whose creation files are attached. 2. Create the Hibernate configuration file, with access to the sakila database 3. Use the Hibernate reverse engineering file wizard to access the film, category and language tables in the database. You will also have to include those tables that relate to them 4. implements a JFrame form that allows you to search for films by; Title. Use a text field. The title of the film must contain the text entered by the user. Category. Use a drop-down list with the list of existing categories in the sakila database.. Rating. Use a drop-down list of the different recommended age (rating) that the films have in the database.El programa debe permitir combinar las tres opciones de búsqueda. 5. The project must allow you to combine the three search options. 6. The results should be displayed or showed in a table ordered alphabetically and showing the headings: title, year, duration, rating and category list (concatenated with comma). Are you looking for Java Programming experts to solve your Assignment ,homework, coursework, coding , and projects? Codersarts java web developer experts offer the best quality web programming, coding or web programming experts. Get Web Assignment Help at an affordable price from the best professional experts Assignment Help. Order now at get 15% off. Contact us for this java assignment Solutions by Codersarts Specialist who can help you mentor and guide for java assignments.

Answer each of the following five questions. All of the questions are based on one dataset, which is an extract from the 2018 European Social Survey. The dataframe ess2018 will have the following variables: Variable: satisfaction_life level: 0-10 Descriptiion: How satisfied are you with your life? Variable: satisfaction_economy level: 0-10 Descriptiion: How satisfied are you with the present state of the economy? Variable: satisfaction_government level: 0-10 Descriptiion: Thinking about the [country] government, how satisfied are you with the way it is doing its job? Variable: satisfaction_democracy level: 0-10 Descriptiion: How satisfied are you with the way democracy works in [country]? Variable: satisfaction_education level: 0-10 Descriptiion: Please say what you think overall about the state of education in [country] Variable: satisfaction_health_services level: 0-10 Descriptiion: Please say what you think overall about the state of health services in [country] nowadays? Variable: immigration_same_ethnicity level: 0-4 Descriptiion: To what extent do you think [country] should allow people of the same race or ethnic group as most [country]’s people to come and live here? Variable: immigration_diff_ethnicity level: 0-4 Descriptiion: To what extent do you think [country] should allow people of a different race or ethnic group as most [country]’s people to come and live here? Variable: immigration_world_poor level: 0-4 Descriptiion: To what extent do you think [country] should allow people from the poorer countries outside Europe to come and live here? Variable: country level: Nominal Descriptiion: Country Name Variable: gender level: Nominal Descriptiion: Male or Female Variable: age level: 15-90 Descriptiion: Age in Years Variable: degree level: Nominal Descriptiion: TRUE = holds university degree Variable: weight level: Interval Descriptiion: ESS survey weight The 0-10 scales for the first four satisfaction variables run from 0 (“Very dissatisfied”) to 10 (“Very Satisfied”) and the scales for the last two from “Very bad” to “Very good”. No labels were provided to survey respondents for the intermediate numerical values, only for 0 and 10. The 1-4 scales for the immigration questions run (1) “Allow many to come and live here”, (2) “Allow some”, (3) “Allow a few”, (4) “Allow none”. You may find it useful to extract the satisfaction_ questions into a separate data frame using this command: satisfaction_questions <- ess2018[,grep("satisfaction",names(ess2018))] Many of the questions below have more than one right answer. If I ask for you to identify/describe “one” or “two” of something, that does not mean that there are only that many good answers to the question. Question 1 Consider the following four measures, constructed for each respondent ii from their responses to the three immigration_ questions: A. the mean of the individual’s responses to immigration_same_ethnicity, immigration_diff_ethnicity, and immigration_world_poor B. the difference between the individual’s responses: immigration_same_ethnicity - immigration_diff_ethnicity C. the difference between the individual’s responses: immigration_diff_ethnicity - immigration_world_poor D. the minimum value of the individual’s responses to immigration_same_ethnicity, immigration_diff_ethnicity, and immigration_world_poor Note for the last of these that minimum means the numerical minimum value, see the above statement for how the numerical levels relate to the survey responses. State the following for each of the four measures: What is the range of the measure? What are the units of the measure? What assumptions are made in the construction of the measure? What concept does the measure come closest to measuring? Question 2 A: Construct a set of histograms for the satisfaction_ variables and calculate the means of all the variables as well. Comment on what we learn from the means of the different variables. B: Construct an equal weight index of the six satisfaction_ variables. C: If you had to put a label on the concept measured by this equal weight index, what would it be? Please provide a short justification. D: Identify an alternative concept that you might have measured with a subset of these indicators. What is the concept, and which indicators would you use to measure that concept? Question 3 A: Fit a linear regression (lm()) for the equal weight index with dummy variables for countries and include a weight=ess2018$weight argument so that you are using the survey weights. Describe general patterns in which countries’ citizens have higher and lower values of the satisfaction index. B: Now, extend the analysis in part A to separately analyse the equal weight index for for survey respondents with and without degrees, in each country. There are many ways you could do this, but whichever analysis you do, state clearly what you have done, and describe what we learn about the relationship between educational background and the index, and how it varies across European countries. You will likely find it helpful to present theresults of your analysis in a table and/or figure. Question 4 A: Use cor() to examine the pairwise correlations between the satisfaction_ variables. Describe any major patterns that you see. B: Use prcomp() to do principle components analysis on the satisfaction_ variables. Examine the coefficients and give an interpretation for the first principle component C: Examine the coefficients and give an interpretation for the second principle components. D: Create the screeplot for this principle components analysis. What do we learn from this? E: Explain how the application of factor analysis to these data would be similar/different than using PCA as you have done in the preceding items. You do not need to run the factor analysis, describe the similarities/differences at a conceptual level. Question 5 Use the following commands to do a k-means clustering on the satisfaction_ variables where k=4k=4: set.seed(42) # this will ensure that you get the same clusters as everyone else kmeans_4 <- kmeans(satisfaction_questions,centers=4) What do the four clusters correspond to? Do whatever analysis that you need to do in order to establish what distinguishes the four clusters, and to explain how they relate to the underlying indicators and to the previous analysis that we did with principle components analysis. Write 2-3 paragraphs answering these questions, with supporting tables and figures as required. You can send your requirement/project/assignment files, directly at contact@codersarts.com, and get our instant assistance or CONTACT on below details

In this blog we will be exploring visualisation of data using matplotlib and seaborn. Before we start let us discuss about Matplotlib and Seaborn. Matplotlib was introduced by John Hunter in 2002. It is the main visualisation library in Python, all other libraries are built on top of matplotlib. The library itself is huge, with approximately 70,000 total lines of code and is still developing. Typically it is used together with the numerical mathematics extension: NumPy. It contains an interface "pyplot" which is designed to to resemble that of MATLAB. We can plot anything with matplotlib but plotting non-basic can be very complex to implement. Thus, it is advised to use some other higher-level tools when creating complex graphics. Coming to Seaborn: It is a library for creating statistical graphics in Python. It is built on top of matplotlib and integrates closely with pandas data structures. It is considered as a superset of the Matplotlib library and thus is inherently better than matplotlib. Its plots are naturally prettier and easy to customise with colour palettes. The aim of Seaborn is to provide high-level commands to create a variety of plot types that are useful for statistical data exploration, and even some statistical model fitting. It has many built-in complex plots. First we will see how we can plot the same graphs using Matplotlib and Seaborn. This would help us to make a comparison between the two. We will use datasets available in the Seaborn library to plot the graphs. some useful links: choosing colormaps in matplotlib: https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html list of named colors in matplotlib: https://matplotlib.org/3.1.0/gallery/color/named_colors.html color demo: https://matplotlib.org/3.2.1/gallery/color/color_demo.html markers in matplotlib: https://matplotlib.org/3.3.3/api/markers_api.html choosing color palettes in seaborn: https://seaborn.pydata.org/tutorial/color_palettes.html Scatterplot For this kind of plot we will use the Penguin dataset which is already available in seaborn. The dataset contains details about three species of penguins namely, Adelie, Chinstrap and Gentoo. Matplotlib code: plt.figure(figsize=(14,7)) plt.scatter('bill_length_mm', 'bill_depth_mm', data=df,c='species',cmap='Set2') plt.xlabel('Bill length', fontsize='large') plt.ylabel('Bill depth', fontsize='large'); We have plotted the bill length against the bill depth. Bill refers to the beak of penguins. They are of various shapes and sizes and vary from species to species. Clearly in the above graph we can't make out which data belongs to which species. This is due to Matplotlib being unable to produce a legend when a plot is made in this manner. Let us now plot the same graph along with the legend. Matplotlib code: plt.rcParams['figure.figsize'] = [15, 10] fontdict={'fontsize': 18, 'weight' : 'bold', 'horizontalalignment': 'center'} fontdictx={'fontsize': 18, 'weight' : 'bold', 'horizontalalignment': 'center'} fontdicty={'fontsize': 16, 'weight' : 'bold', 'verticalalignment': 'baseline', 'horizontalalignment': 'center'} Adelie = plt.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==1], marker='o', color='skyblue') Chinstrap = plt.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==2], marker='o', color='yellowgreen') Gentoo = plt.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==3], marker='o', color='darkgray') plt.legend(handles=(Adelie,Chinstrap,Gentoo), labels=('Adelie','Chinstrap','Gentoo'), title="Species", title_fontsize=16, scatterpoints=1, bbox_to_anchor=(1, 0.7), loc=2, borderaxespad=1., ncol=1, fontsize=14) plt.title('Penguins', fontdict=fontdict, color="black") plt.xlabel("Bill length (mm)", fontdict=fontdictx) plt.ylabel("Bill depth (mm)", fontdict=fontdicty); Let's discuss a few points in the above code: plt.rcParams['figure.figsize'] = [15, 10] allows to control the size of the entire plot. This corresponds to a 15∗10 (length∗width) plot. fontdict is a dictionary that can be passed in as arguments for labeling axes. fontdict for the title, fontdictx for the x-axis and fontdicty for the y-axis. There are now 4 plt.scatter() function calls corresponding to one of the four seasons. This is seen again in the data argument in which it has been subsetted to correspond to a single season. marker and color arguments correspond to using a 'o' to visually represent a data point and the respective color of that marker. We will now do the same thing using Seaborn. Seaborn code: plt.figure(figsize=(14,7)) fontdict={'fontsize': 18, 'weight' : 'bold', 'horizontalalignment': 'center'} sns.set_context('talk', font_scale=0.9) sns.set_style('ticks') sns.scatterplot(x='bill_length_mm', y='bill_depth_mm', hue='species', data=df, style='species',palette="rocket", legend='full') plt.legend(scatterpoints=1,bbox_to_anchor=(1, 0.7), loc=2, borderaxespad=1., ncol=1,fontsize=14) plt.xlabel('Bill Length (mm)', fontsize=16, fontweight='bold') plt.ylabel('Bill Depth (mm)', fontsize=16, fontweight='bold') plt.title('Penguins', fontdict=fontdict, color="black", position=(0.5,1)); A few points to discuss: sns.set_style() must be one of : 'white', 'dark', 'whitegrid', 'darkgrid', 'ticks'. This controls the plot area. Such as the color, grid and presence of ticks. sns.set_context() must be one of: 'paper', 'notebook', 'talk', 'poster'. This controls the layout of the plot in terms of how it is to be read. Such as if it was on a 'poster' where we will see enlarged images and text. 'Talk' will create a plot with a more bold font. We can see that with Seaborn we needed less lines of code to produce a beautiful graph with legend. We will now try our hand at making subplots to represent each species using a different graph in the same plot. Matplotlib code: fig = plt.figure() plt.rcParams['figure.figsize'] = [15,10] plt.rcParams["font.weight"] = "bold" fontdict={'fontsize': 25, 'weight' : 'bold'} fontdicty={'fontsize': 18, 'weight' : 'bold', 'verticalalignment': 'baseline', 'horizontalalignment': 'center'} fontdictx={'fontsize': 18, 'weight' : 'bold', 'horizontalalignment': 'center'} plt.subplots_adjust(wspace=0.2, hspace=0.5) fig.suptitle('Penguins', fontsize=25,fontweight="bold", color="black", position=(0.5,1.01)) #subplot 1 ax1 = fig.add_subplot(221) ax1.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==1], c="skyblue") ax1.set_title('Adelie', fontdict=fontdict, color="skyblue") ax1.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(0,0.5)) ax1.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); ax2 = fig.add_subplot(222) ax2.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==2], c="yellowgreen") ax2.set_title('Chinstrap', fontdict=fontdict, color="yellowgreen") ax2.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); ax3 = fig.add_subplot(223) ax3.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==3], c="darkgray") ax3.set_title('Gentoo', fontdict=fontdict, color="darkgray") ax3.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(0,0.5)) ax3.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); Here we have created subplots representing each species. But the graphs don’t help us to make a comparison at first glance. That is because each graph has a varying x-axis. Let’s make it uniform. Matplotlib code: fig = plt.figure() plt.rcParams['figure.figsize'] = [12,12] plt.rcParams["font.weight"] = "bold" plt.subplots_adjust(hspace=0.60) fontdicty={'fontsize': 20, 'weight' : 'bold', 'verticalalignment': 'baseline', 'horizontalalignment': 'center'} fontdictx={'fontsize': 20, 'weight' : 'bold', 'horizontalalignment': 'center'} fig.suptitle('Penguins', fontsize=25,fontweight="bold", color="black", position=(0.5,1.0)) #ax2 is defined first because the other plots are sharing its x-axis ax2 = fig.add_subplot(412, sharex=ax2) ax2.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==2], c="skyblue") ax2.set_title('Adelie', fontdict=fontdict, color="skyblue") ax2.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(-0.3,0.3)) ax1 = fig.add_subplot(411, sharex=ax2) ax1.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==1], c="yellowgreen") ax1.set_title('Chinstrap', fontdict=fontdict, color="yellowgreen") ax3 = fig.add_subplot(413, sharex=ax2) ax3.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==3], c="darkgray") ax3.set_title('Gentoo', fontdict=fontdict, color="darkgray") ax3.set_xlabel("Bill Length (mm)", fontdict=fontdictx); Let’s change the shape of the markers in the above graph to make it look more customised. Matplotlib code: fig = plt.figure() plt.rcParams['figure.figsize'] = [15,10] plt.rcParams["font.weight"] = "bold" fontdict={'fontsize': 25, 'weight' : 'bold'} fontdicty={'fontsize': 18, 'weight' : 'bold', 'verticalalignment': 'baseline', 'horizontalalignment': 'center'} fontdictx={'fontsize': 18, 'weight' : 'bold', 'horizontalalignment': 'center'} plt.subplots_adjust(wspace=0.2, hspace=0.5) fig.suptitle('Penguins', fontsize=25,fontweight="bold", color="black", position=(0.5,1.01)) #subplot 1 ax1 = fig.add_subplot(221) ax1.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==1], c="skyblue",marker='x') ax1.set_title('Adelie', fontdict=fontdict, color="skyblue") ax1.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(0,0.5)) ax1.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); ax2 = fig.add_subplot(222) ax2.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==2], c="yellowgreen",marker='^') ax2.set_title('Chinstrap', fontdict=fontdict, color="yellowgreen") ax2.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); ax3 = fig.add_subplot(223) ax3.scatter('bill_length_mm', 'bill_depth_mm', data=df[df['species']==3], c="darkgray",marker='*') ax3.set_title('Gentoo', fontdict=fontdict, color="darkgray") ax3.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(0,0.5)) ax3.set_xlabel("Bill Length (mm)", fontdict=fontdictx, position=(0.5,0)); We will create the same plot using Seaborn as well. Seaborn code: sns.set(rc={'figure.figsize':(20,20)}) sns.set_context('talk', font_scale=1) sns.set_style('ticks') g = sns.relplot(x='bill_length_mm', y='bill_depth_mm', hue='sex', data=df,palette="rocket", legend='full',col='species', col_wrap=2, height=4, aspect=1.6, sizes=(800,800)) g.fig.suptitle('Penguins',position=(0.5,1.05), fontweight='bold', size=20) g.set_xlabels("Bill Length (mm)",fontweight='bold', size=15) g.set_ylabels("Bill Depth (mm)",fontweight='bold', size=15); Notice that here the subplots representing the species are further divided into two classes i.e. Male and Female. Again we can notice how Seaborn stands out to be superior by producing a better graph with a few lines of code. We can also add different markers for each species in the above graph. Let’s do that. Seaborn code: sns.set(rc={'figure.figsize':(20,20)}) sns.set_context('talk', font_scale=1) sns.set_style('ticks') g = sns.relplot(x='bill_length_mm', y='bill_depth_mm', hue='species', data=df,palette="rocket", col='species', col_wrap=4, legend='full', height=6, aspect=0.5, style='species', sizes=(800,1000)) g.fig.suptitle('Penguins' ,position=(0.4,1.05), fontweight='bold', size=20) g.set_xlabels("Bill Length (mm)",fontweight='bold', size=15) g.set_ylabels("Bill Depth (mm)",fontweight='bold', size=15); In a similar fashion as shown above, we can make the subplots share the same y-axis instead of sharing the same x-axis. The following plots represent the same. Matplotlib code: fig = plt.figure() plt.rcParams['figure.figsize'] = [12,12] plt.rcParams["font.weight"] = "bold" plt.subplots_adjust(hspace=0.60) fontdicty={'fontsize': 20, 'weight' : 'bold', 'verticalalignment': 'baseline', 'horizontalalignment': 'center'} fontdictx={'fontsize': 20, 'weight' : 'bold', 'horizontalalignment': 'center'} fig.suptitle('Penguins', fontsize=25,fontweight="bold", color="black", position=(0.5,1.0)) #ax2 is defined first because the other plots are sharing its x-axis ax2 = fig.add_subplot(141, sharex=ax2) ax2.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==2], c="skyblue") ax2.set_title('Adelie', fontdict=fontdict, color="skyblue") ax2.set_ylabel("Bill depth (mm)", fontdict=fontdicty, position=(-0.3,0.5)) ax1 = fig.add_subplot(142, sharex=ax2) ax1.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==1], c="yellowgreen") ax1.set_title('Chinstrap', fontdict=fontdict, color="yellowgreen") ax3 = fig.add_subplot(143, sharex=ax2) ax3.scatter('bill_length_mm', 'bill_depth_mm', data=df.loc[df['species']==3], c="darkgray") ax3.set_title('Gentoo', fontdict=fontdict, color="darkgray") ax3.set_xlabel("Bill Length (mm)", fontdict=fontdictx,position=(-0.7,0)); Seaborn code: sns.set(rc={'figure.figsize':(20,20)}) sns.set_context('talk', font_scale=1) sns.set_style('ticks') g = sns.relplot(x='bill_length_mm', y='bill_depth_mm', hue='species', data=df,palette="rocket", col='species', col_wrap=4, legend='full', height=6, aspect=0.5, style='species', sizes=(800,1000)) g.fig.suptitle('Penguins' ,position=(0.4,1.05), fontweight='bold', size=20) g.set_xlabels("Bill Length (mm)",fontweight='bold', size=15) g.set_ylabels("Bill Depth (mm)",fontweight='bold', size=15); So, this is how you can create subplots. It can be done with any other kind of graphs as well such as line graphs, histograms etc. Let us try our hand at different kinds of graphs for visualization. Line plot For plotting this kind of graph we will create some random data using numpy and random libraries. Code for creating data: import numpy as np from random import * rng = np.random.RandomState(0) x = np.linspace(0, 10, 8) y = np.cumsum(rng.randn(8, 8), 0) Matplotlib code: plt.figure(figsize=(14,7)) plt.plot(x, y) plt.legend('ABCDEF', ncol=2, loc='upper left'); The same matplotlib code with seaborn overwriting matplotlib’s default parameters to generate a more pleasing graph. Seaborn code: sns.set(rc={'figure.figsize':(14,7)}) sns.set_context('talk', font_scale=0.9) sns.set_style('darkgrid') plt.plot(x, y) plt.legend('ABCDEF', ncol=2, loc='upper left'); To enhance the graph we could include markers this way: Code: sns.set(rc={'figure.figsize':(14,7)}) sns.set_context('talk', font_scale=0.9) sns.set_style('darkgrid') plt.plot(x, y, marker='o') plt.legend('ABCDEF', ncol=2, loc='upper left'); To make each line distinct we can add different makers along different lines the following way. Code: sns.set(rc={'figure.figsize':(14,7)}) sns.set_context('talk', font_scale=0.9) sns.set_style('darkgrid') L=[] for j in range(len(y)): l=[] for i in y: l.append(i[j]) L.append(l) plt.plot(x, L[0], marker='o',label='A') plt.plot(x, L[1], marker='^',label='B') plt.plot(x, L[2], marker='s',label='C') plt.plot(x, L[3], marker='D',label='D') plt.plot(x, L[4], marker='*',label='E') plt.plot(x, L[5], marker='+',label='F') plt.legend(ncol=2,loc='lower left'); Notice that we have now altered the position of the legend in the graph. Bar graphs For playing around with such graphs we will be using 'titanic' dataset available in Seaborn library. The dataset contains details like age, sex, class, fare,embark_town, survived or not etc of people aboard the titanic. Let's begin. We will be plotting the graph showing the count of 'survival' or 'no survival' for different classes of people. We will convert the dataset into a pandas dataframe. Code for dataset: df2 = sns.load_dataset("titanic") df2.head() First= df2[df2['class']=='First']['survived'].value_counts() Second= df2[df2['class']=='Second']['survived'].value_counts() Third=df2[df2['class']=='Third']['survived'].value_counts() df3 = pd.DataFrame([First,Second,Third]) df3.index=['First','Second','Third'] Matplotlib code: df3.plot(kind='bar',figsize=(14,7),title='Titanic survial on basis of class',cmap='Set2') plt.show() Note that here we have two separate colored bars to represent the 'survived' column of the data. A value of '0' represents 'not survived' and a value of '1' represents 'survived'. We can clearly make the observation that the most people who did not survive were from the 'Third' class. Coming back to the graph. We can make it look more attractive by changing the default parameters. We can change the orientation of the xticks and y ticks of the graph and we can add annotation to it as well to make the graph easily understandable. Code: ax= df3.plot(kind='bar',figsize=(14,7), title='Titanic survial on basis of class',cmap='Set2') plt.xticks(rotation=20) plt.yticks(rotation=20) for i in ax.patches: # get_x pulls left or right; get_height pushes up or down ax.text(i.get_x()+0.07, i.get_height()+5, \ str(round((i.get_height()), 2)), fontsize=11, color='steelblue') plt.show() To add more to it we can also include some design in the bars to make them look more stylish. This is done by using the parameter 'hatch'. Also we will be tilting the annotation to add in one more difference to the graph. Code: ax= df3.plot(kind='bar',figsize=(14,7), title='Titanic survial on basis of class',cmap='Set2',hatch='|',edgecolor='aliceblue') plt.xticks(rotation=20) for i in ax.patches: # get_x pulls left or right; get_height pushes up or down ax.text(i.get_x()+0.07, i.get_height()+5, \ str(round((i.get_height()), 2)), fontsize=11, color='steelblue', rotation=45) plt.show() Moreover, we can also go further and can change the hatch design for the two different bars as shown below. Code: ax= df3.plot(kind='bar',figsize=(14,7), title='Titanic survial on basis of class',cmap='Set2',hatch='O',edgecolor='aliceblue') plt.xticks(rotation=20) plt.yticks(rotation=20) bars = ax.patches patterns = ['/', '.'] # set hatch patterns in the correct order hatches = [] # list for hatches in the order of the bars for h in patterns: # loop over patterns to create bar-ordered hatches for i in range(int(len(bars) / len(patterns))): hatches.append(h) for bar, hatch in zip(bars, hatches): # loop over bars and hatches to set hatches in correct order bar.set_hatch(hatch) # generate legend. this is important to set explicitly, otherwise no hatches will be shown! for i in ax.patches: # get_x pulls left or right; get_height pushes up or down ax.text(i.get_x()+0.07, i.get_height()+5, \ str(round((i.get_height()), 2)), fontsize=11, color='steelblue') ax.legend() plt.show() You can also give each bar its unique hatch design by adding as many hatches as bars in the 'patterns' list. Go ahead and give it a try. In addition, we can also have this bar graph in a horizontal orientation. The only change is that we will be using the parameter 'kind' equal to 'barh' instead of 'bar' while plotting. Code: ax= df3.plot(kind='barh',figsize=(14,7), title='Titanic survial on basis of class',cmap='Set2',hatch='O',edgecolor='aliceblue') plt.xticks(rotation=20) plt.yticks(rotation=20) bars = ax.patches patterns = ['/', '.'] # set hatch patterns in the correct order hatches = [] # list for hatches in the order of the bars for h in patterns: # loop over patterns to create bar-ordered hatches for i in range(int(len(bars) / len(patterns))): hatches.append(h) for bar, hatch in zip(bars, hatches): # loop over bars and hatches to set hatches in correct order bar.set_hatch(hatch) # generate legend. this is important to set explicitly, otherwise no hatches will be shown! ax.legend() plt.show() We can also transform the graph into a stacked graph as shown below. Code: ax= df3.plot(kind='bar',figsize=(14,7), title='Titanic survial on basis of class',cmap='Set2',hatch='O',edgecolor='aliceblue',stacked=True) plt.xticks(rotation=20) plt.yticks(rotation=20) bars = ax.patches patterns = ['/', '.'] # set hatch patterns in the correct order hatches = [] # list for hatches in the order of the bars for h in patterns: # loop over patterns to create bar-ordered hatches for i in range(int(len(bars) / len(patterns))): hatches.append(h) for bar, hatch in zip(bars, hatches): # loop over bars and hatches to set hatches in correct order bar.set_hatch(hatch) ax.legend() plt.show() Creating the same bar plot using Seaborn. Here we will be using countplot method of Seaborn as it serves our purpose well and tremendously reduces our lines of code. You will notice that here we don't need to create another dataframe using the original dataframe to create this plot. Seaborn code: sns.set(palette='dark') bar=sns.countplot(x='class', hue= 'survived', data=df2) plt.xticks(rotation=20) plt.yticks(rotation=20) for p in bar.patches: bar.annotate(format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') # Define some hatches hatches = ['-', '-', '-', '\\', '\\', '\\'] # Loop over the bars for i,thisbar in enumerate(bar.patches): # Set a different hatch for each bar thisbar.set_hatch(hatches[i]) plt.show() To generate a horizontal graph: Seaborn code: sns.set(palette='dark') bar=sns.countplot(y='class', hue= 'survived', data=df2) plt.xticks(rotation=20) plt.yticks(rotation=20) # Define some hatches hatches = ['-', '-', '-', '\\', '\\', '\\'] # Loop over the bars for i,thisbar in enumerate(bar.patches): # Set a different hatch for each bar thisbar.set_hatch(hatches[i]) plt.show() Unfortunately, seaborn is not so fond of stacked bar graphs and thus plotting stacked bar graph isn't as smooth, though there are ways to get around it which we won't b e discussing here. Try it out yourself. Histograms We will continue using the same dataset as above. First we will make a histogram using matplotlib and then using seaborn. Matplotlib code: df2=df2.dropna() plt.figure(figsize=(14,7)) plt.hist(df2['fare']) plt.xlabel('Fare',fontsize=20) plt.ylabel('values', fontsize=20) plt.show() We can also make multiple histograms in the same graph as follows. code: df2[['fare','age','pclass']].plot.hist(stacked=True, bins=30, figsize=(14,7)) plt.xlabel('Fare',fontsize=20) plt.ylabel('values', fontsize=20) plt.show() Now, with seaborn we can create the same graphs but much better. Seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.distplot(df2['fare'],kde=False) plt.xlabel('Fare',fontsize=20) plt.ylabel('values', fontsize=20) plt.show() We can also add a line outline the histogram. It is called a kernel density plot and can be displayed by setting the option 'kde' to True. code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.distplot(df2['fare'],kde=True) plt.xlabel('Fare',fontsize=20) plt.ylabel('values', fontsize=20) plt.show() Multiple hisotgrams: Seaborn code: sns.set(style='dark') fig, ax = plt.subplots(figsize=(14,7)) for a in ['fare','age','pclass']: sns.distplot(df2[a], bins=range(1, 110, 10), ax=ax, kde=False,label=a) plt.xlabel('Fare',fontsize=20) plt.ylabel('values', fontsize=20) plt.legend() plt.show() The bars are slightly transparent which lets us compare them easily. Boxplots We will continue with the same dataset with slight modifications. A basic boxplot looks like something as shown below. code: Female= df2[df2['sex']=='female']['age'] Male= df2[df2['sex']=='male']['age'] df3 = pd.DataFrame([Female, Male]) df3.index=['Female','Male'] df3=df3.T Matplotlib code: plt.figure(figsize=(14,7)) plt.boxplot([df2['age'],df2['fare']],labels=['age','fare']) plt.show() We can add a notch to the boxplot by setting the parameter with the same name as True. An example is shown below. Matplotlib code: plt.figure(figsize=(14,7)) plt.boxplot([df2['age'],df2['fare']],labels=['age','fare'],notch=True) plt.show() See the difference, to enhance it even further we can also change the shape and color of the outliers. Matplotlib code: plt.figure(figsize=(14,7)) green_diamond = dict(markerfacecolor='g', marker='D') plt.boxplot([df2['age'],df2['fare']],labels=['age','fare'],notch=True ,flierprops=green_diamond) plt.show() Moving on with seaborn. code: sns.set(style='dark') df3=df2[['age','fare']] plt.figure(figsize=(14,7)) sns.boxplot(data=df3) plt.show() There is no way to change the outlier design but it still looks cool. But there are other benefits to using seaborn for example we can add easily add hue to the graphs and can get more insight from the data. Let's try it. Seaborn code: plt.figure(figsize=(14,7)) sns.boxplot(x='sex', y='age', data=df2, hue="survived") plt.show() Similarly we can create violin plots, strip plots and swarm plots. Out of these only violin plots can be created using matplotlib rest are the features specific to seaborn. Hence, to be quick we will create these plots using seaborn only. Violinplots A basic violinplot. seaborn code: sns.set(style='dark') df3=df2[['age','fare']] plt.figure(figsize=(14,7)) sns.violinplot(data=df3,palette='rocket') plt.show() A plot with hue. seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.violinplot(x='sex', y='age', data=df2, hue='survived',palette='rocket') plt.show() A variation of the above plot. seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.violinplot(x='sex', y='age', data=df2, hue='survived',palette='rocket', split=True) plt.show() This change is introduced by adding the parameter 'split' in the code. Stripplot A basic stripplot. Seaborn code: sns.set(style='dark') plt.figure(figsize=(12,6)) sns.stripplot(x='sex', y='age', data=df2, palette='rocket',jitter=False) plt.show() The above plot isn't that much comprehensible, the dsitribution of the data remains ambiguous. To get some more insight we can set the jitter option to be set True as shown below. Seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.stripplot(x='sex', y='age', data=df2, palette='rocket',jitter=True) plt.show() To enhance the plot further we can add hue to it as well. Seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.stripplot(x='sex', y='age', data=df2, jitter=True, hue='survived',palette='rocket') plt.show() Swarmplot A basic swarm plot. Seaborn code: df4 = sns.load_dataset("titanic") df4.head() sns.set(style='dark') plt.figure(figsize=(14,7)) sns.swarmplot(x='sex', y='age', data=df4,palette='rocket',dodge=False) plt.show() A swarm plot withe hue. Seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.swarmplot(x='sex', y='age', data=df4, hue='survived',dodge=False, palette='rocket') plt.show() We can also split swarm plots as we did ealier with the boxplot and the vioilin plot Seaborn code: sns.set(style='dark') plt.figure(figsize=(14,7)) sns.swarmplot(x='sex', y='age', data=df4, hue='survived', dodge=True, palette='rocket') plt.show() Pie chart For this chart we will changing our dataset. We will be using a supermarket dataset named 'SampleSuperstore.csv'. A very basic pie chart is as follows. Matplotlib code: plt.figure(figsize=(16,10)) raw_data['Category'].value_counts().plot.pie() plt.show() To make it look better we can add some shadow to each pie slice. Also we can explode the chart as well. Let us see what I meant by this. Matplotlib code: plt.figure(figsize=(16,10)) raw_data['Category'].value_counts().plot.pie(shadow=True,autopct="%1.1f%%",explode = (0, 0.1, 0)) plt.legend() plt.show() Here we have exploded only one slice. We can do this for all the slices by changing the values in the tuple assigned to the parameter explode. We would have made Pie charts using Seaborn but this feature isn't available in it. Donut plot This will be the last plot of this blog. We will generate this graph by creating our own data. Code: plt.figure(figsize=(16,10)) names='Tim', 'Robbie', 'Mark', 'Harry', number=[160,12,75,200] my_circle=plt.Circle( (0,0), 0.7, color='white') plt.pie(number, labels=names, colors=['red','green','blue','skyblue']) p=plt.gcf() p.gca().add_artist(my_circle) plt.show() There are endless possibilities to explore in terms of data visualisation. This blog will stop here but you can keep trying to get better results by experimenting. Good luck with exploring.

What is big data? Big data is a field that: treats ways to analyze, systematically extract information from, or otherwise deal with data sets That is too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Later another two concepts were added to define it which are: value and veracity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. The thing with Big Data is that it has to have enough volume so that the amount of bad data or missing data becomes statistically insignificant. When the errors in the data are common enough to cancel each other out, when the missing data is proportionally small enough to be negligible and when the data access requirements and algorithms are functional even with incomplete and inaccurate data, then we have "Big Data". Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and at a very large scale. It is said that Big Data implies a large amount of information (terabytes and petabytes or zettabytes) which is true to some extent but Big Data is not really about the volume, it is about the characteristics of the data. What is Big Data Analytics? Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. It is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Analysis of big data allows analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data. History and evolution The concept of big data is not recent, in fact it has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spread sheet that were manually examined) to uncover insights and trends. The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before. Why Big Data Analytics? Big Data analytics is fuelling everything we do online—in every industry. Take the music streaming platform Spotify for example. The company has nearly 96 million users that generate a tremendous amount of data every day. Through this information, the cloud-based platform automatically generates suggested songs—through a smart recommendation engine—based on likes, shares, search history, and more. What enables this are the techniques, tools, and frameworks that are a result of Big Data analytics. If you are a Spotify user, then you must have come across the top recommendation section, which is based on your likes, past history, and other things. It is done by utilizing a recommendation engine that leverages data filtering tools which collects data and then filters it using algorithms. This is what Spotify does. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things. Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways: 1. Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business. 2. Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyse new sources of data, businesses are able to analyse information immediately – and make decisions based on what they’ve learned. 3. New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs. Tools and Techniques The following are the techniques used in big data analytics: 1. Association rule learning Are people who purchase tea more or less likely to purchase carbonated drinks? Association rule learning is a method for discovering interesting correlations between variables in large databases. It was first used by major supermarket chains to discover interesting relations between products, using data from supermarket point-of-sale (POS) systems. Association rule learning is being used to help: place products in better proximity to each other in order to increase sales extract information about visitors to websites from web server logs analyse biological data to uncover new relationships monitor system logs to detect intruders and malicious activity identify if people who buy milk and butter are more likely to buy diapers 2. Classification tree analysis Which categories does this document belong to? Statistical classification is a method of identifying categories that a new observation belongs to. It requires a training set of correctly identified observations – historical data in other words. The output is in the tree format with nodes and there is an association in the nodes. And this can be read to form if-then rules. It builds classification in the form of trees structure. As shown in figure below, it divides the data nodes into small subgroup. These methods can be used when the data mining task has predictions or classification of outcomes. Statistical classification is being used to: automatically assign documents to categories categorize organisms into groupings develop profiles of students who take online courses 3. Genetic algorithms Which TV programs should we broadcast, and in what time slot, to maximize our ratings? Genetic algorithms are inspired by the way evolution works – that is, through mechanisms such as inheritance, mutation and natural selection. These mechanisms are used to “evolve” useful solutions to problems that require optimization. Genetic algorithms are being used to: schedule doctors for hospital emergency rooms return combinations of the optimal materials and engineering practices required to develop fuel-efficient cars generate “artificially creative” content such as puns and jokes 4. Machine Learning Which movies from our catalogue would this customer most likely want to watch next, based on their viewing history? Machine learning includes software that can learn from data. It gives computers the ability to learn without being explicitly programmed, and is focused on making predictions based on known properties learned from sets of “training data.” Machine learning is being used to help: distinguish between spam and non-spam email messages learn user preferences and make recommendations based on this information determine the best content for engaging prospective customers determine the probability of winning a case, and setting legal billing rates 5. Regression Analysis How does your age affect the kind of car you buy? At a basic level, regression analysis involves manipulating some independent variable (i.e. background music) to see how it influences a dependent variable (i.e. time spent in store). It describes how the value of a dependent variable changes when the independent variable is varied. It works best with continuous quantitative data like weight, speed or age. Regression analysis is being used to determine how: levels of customer satisfaction affect customer loyalty the number of supports calls received may be influenced by the weather forecast given the previous day neighborhood and size affect the listing price of houses to find the love of your life via online dating sites 6. Sentiment Analysis How well the new return policy is received? Sentiment analysis helps researchers determine the sentiments of speakers or writers with respect to a topic. Sentiment analysis is being used to help: improve service at a hotel chain by analyzing guest comments customize incentives and services to address what customers are really asking for determine what consumers really think based on opinions from social media 7. Social Network Analysis How many degrees of separation are you from Kevin Bacon? Social network analysis is a technique that was first used in the telecommunications industry, and then quickly adopted by sociologists to study interpersonal relationships. It is now being applied to analyze the relationships between people in many fields and commercial activities. Nodes represent individuals within a network, while ties represent the relationships between the individuals. Social network analysis is being used to: see how people from different populations form ties with outsiders find the importance or influence of a particular individual within a group find the minimum number of direct ties required to connect two individuals understand the social structure of a customer base The following are the tools used in big data analytics: 1. Hadoop An open-source framework, Hadoop offers massive storage for all kinds of data. With its amazing processing power and capability to handle innumerable tasks, Hadoop never allows you to ponder over hardware failure. Though you need to know Java to work with Hadoop, it’s worth every effort. Apache Hadoop is the one of the technology designed to process Big Data, which is unification of structured and unstructured data of huge volume. Apache Hadoop is an open source platform and processing framework that exclusively provides batch processing. Hadoop was firstly influenced by Google's Map Reduce. In Map Reduce software framework the whole program is divided into a number of parts which are small in size. These small parts are also called as fragments. These fragments can be executed on any system in the cluster. Components of Hadoop: There are a lot of components which are used in composition of Hadoop. These all worked together to execute batch data. Main components are as: HDFS: The Hadoop Distributed File System (HDFS) is the main component of the Hadoop software framework. It is the file system of Hadoop. HDFS is configured to save large volume of data. It is a fault –tolerant storage system that stores large size files from TB to PB. There are two types of nodes in HDFS Name node and Data node. Name Node, works as the master node. It contains all the information related to the entire data node. It has the information of free space, addresses of nodes, and all the data that they store, active node, passive node. It also keeps the information of task tracker and job tracker. Data node is also known as slave node. Data node in Hadoop is used to store the data. And it is the duty of TaskTracker to keep the track of on-going job which resides on the data node and it also take care of the jobs coming from name node. MapReduce: It is a framework that helps developers to jot down programs to method massive volume of unstructured knowledge parallel over a distributed design. MapReduce consists of many elements like JobTracker, TaskTracker and JobHistorySever etc. It is additionally referred to as the Hadoop's native instruction execution engine. It was introduced to process the huge amount of data and to store these huge data on commodity hardware. For processing the large volume data it uses clusters to store records. Map function and Reduce function are two functions that are the base of the Map Reduce programming model. In master node the Map function works. And it accepts the input. And after then divide that accepted input into sub modules and then distribute it into slave nodes. YARN (Yet another Resource Negotiator): It is the core Hadoop services that supports two major Services: World resource management (ResourceManager) and per application management (ApplicationMaster). It is the cluster coordinating element of the Hadoop stack. YARN makes it attainable to execute. It is the MapReduce engine that is responsible for practicality of Hadoop. MapReduce is a framework that run on hardware that are less costly. It doesn't conceive to save anything in memory. MapReduce has unimaginable measurability potential. It has been employed in creation of thousands of nodes. Different additions to the Hadoop scheme will scale back the effect of this to variable degrees; however it will always be an element in quickly implementation of an inspiration on a cluster of Hadoop. Working of Hadoop: In the architecture of Hadoop there is only one master node, works as master server known as JobTracker. There are several slave node servers known as TaskTracker's. Keeping the track of the slave nodes is the central job of JobTracker. It established an interface infrastructure for various job. Users input the MR (MapReduce) jobs to the JobTracker, where the pending jobs are reside in queue. The order of access is FIFO. It is the responsibility of JobTracker to coordinate the mapper’s execution and reducer’s execution. When the Map Task is completed, the JobTracker starts its functionality by initiating the reduce task. Now it is the duty of JobTracker to give proper instruction to TaskTracker. After then TaskTracker starts the downloading files and mainly concatenate the various files into a single unit (entity). 2. Spark Apache Spark is a great open source option for people who need big data analysis on a budget. This data analytics engine's speed and scalability have made it very popular among data scientists. One of the great things about Spark is how compatible it is with almost everything. It can also be used for a variety of different things, like cleansing and transforming data, building models for evaluation and scoring, and determining data science pipelines for production. The lazy execution is really nice. This feature allows you to set a series of complex transformations and have them represent as a single object. This allows you to inspect its structure and end result without executing individual steps along the way. Spark even checks for errors in the execution plan before submitting it, which prevents bad code from taking over the process. 3. MongoDB MongoDB is a contemporary alternative to databases. MongoDB is a database that is based on JSON documents. It is written in C++ and launched in 2009, and is still expanding. It’s the best for working on data sets that vary or change frequently or the ones that are semi or unstructured. . MongoDB database basically holds the set of data that has no defined schema. There is no predefined format like tables, and can stores data in the form of BSON documents. BSON are binary encoded JSON like objects. User can go for MongoDB rather than MySQL if the requirement is knowledge intensive because it stores information and queries. MongoDB is especially engineered for storage of information and retrieval of stored information. It can do processing and measurability. It supported C++ and belongs to the NoSQL family. Some of the best uses of MongoDB include storage of data from mobile apps, content management systems, product catalogues and more. Like Hadoop, you can’t get started with MongoDB instantly. You need to learn the tool from scratch and be aware of working on queries. 4. Tableau Tableau is extremely powerful. The fact that it is one of the most mature and powerful options available shows as soon as you see the available features. It’s a bit steeper to learn this platform, but once you do it is well worth it. Tableau has been around since the early days of big data analytics, and it continues to mature and grow with the industry. It is extremely intuitive and offers comprehensive features. Tableau can handle any amount of data, no matter the size. It offers customizable dashboards and real time data in visual methods for exploration and analysis. It can blend data in powerful ways because of how flexible the settings are. It has tons of smart features and works at lightning speed. Best of all, it is interactive and can even work on mobile devices and share data through shared dashboards. 5. Elastisearch This open-sourced enterprise search engine is developed on Java and released under the license of Apache. It works across multiple platforms and can distribute data easily. It even has a Lucerne based search engine. This is one of the most popular enterprise search engines on the market today. One of its best functionalities lies in supporting data discovery apps with its super-fast search capabilities. Elastisearch is included as an integrated solution with Logstash and Kibana. Logstash can collect data and parse logs and Kibana is a great platform for the visualization of data and analysis. The three products work together in what is known as Elastic Stack. A lot of people don’t like open source software because it can be difficult to follow when there’s no one to call for tech support. Happily, Elastic has a very active community and their documentation is incredibly easy to understand. It makes it super easy to use the NO-SQL search engine and storage. Elastic also has APIs for just about anything you will ever need. 6. Cassandra Used by industry players like Cisco, Netflix, Twitter and more, it was first developed by the social media giant Facebook as a NoSQL solution. It’s a distributed database that is high-performing and deployed to handle mass chunks of data on commodity servers. Apache’s Cassandra offers no space for failure anywhere in the awesome set of features, which include simple ring architecture, automated replication, and easy log structured storage and is one of the most reliable Big Data tools. Although troubleshooting and maintenance can take a little more effort than other tools, the free price makes it worth it. Mix that with the fact that it has such a rapid response time, and doesn’t take too much system resources. 7. Drill It’s an open-source framework that allows experts to work on interactive analyses of large scale datasets. Developed by Apache, Drill was designed to scale 10,000+ servers and processes in seconds data of size of petabytes and millions of records. It supports tons of file systems and databases such as MongoDB, HDFS, Amazon S3, Google Cloud Storage and more. 8. Oozie One of the best workflow processing systems, Oozie allows you to define a diverse range of jobs written or programmed across multiple languages. Moreover, the tool also links them to each other and conveniently allows users to mention dependencies. 9. Apache Storm Storm supports real-time processing of unstructured data sets. It is reliable, fault-proof and is compatible with any programming language. Hailing from the Apache family of tools, Twitter now owns Storm as an open-sourced real-time distributed computing framework. 10. Kafka Apache Kafka is an open-source stream-processing software platform developed by the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. It is a distributed streaming platform that is used for fault-tolerant storage. 11. HCatalog HCatalog allows users to view data stored across all Hadoop clusters and even allows them to use tools like Hive and Pig for data processing, without having to know where the datasets are physically present. A metadata management tool, HCatalog also functions as a sharing service for Apache Hadoop. Codersarts is a top rated website for students which is looking for online Programming Assignment Help, Homework help, Coursework Help in deep learning to students at all levels whether it is school, college and university level Coursework Help or Real time project. Hire us and Get your projects done by computer science expert. CONTACT US NOW

PAGE 1: “HOME PAGE” [INSERT MY PHOTO] Tamia Guzman, 21 years old, is originally from the San Francisco Bay Area. She is attending Cal State University- Channel Islands with a major emphasis in Entrepreneurial Business. She is a first generation college student and will be graduating with a bachelors in 2021. Her long term goals are (1) to establish a property investment enterprise which specializes in the development of commercial and residential properties and (2) to build up profitable business ventures and sell them. Tamia is currently a Marketing Developer for a real estate group which eventually led her to a partnership with real estate professionals. She has just received her credential as a real estate agent and plans to utilize her current experience towards starting her journey with real estate investment and development. Learning Objectives of MIS 310: 1. Build process design skills 2. For effective SEO 3. To embed, track, and understand analytics PAGE 2: “RESUME PAGE” One Camarillo Drive, CSU Channel Islands, Camarillo, CA 93012 Email: tamiaguzman.guzman262@myci.csuci.edu Education B.S 12/21 CSU Channel Islands GED 5/17 Foothill High School Work Experience 02/20- Present Marketing Developer Christie's Realty Group 08/17-12/19 Content Creation RG Marketing 05/16-08/17 Manager Q Fashion PAGE 3: “RESOURCE PAGE” My favorite webpages 1. OANN : The network contains news headlines that are fact-based and not narrative based. 2. SpaceX : I enjoy staying up to date with current space events. 3. Crunchbase : I enjoy seeing how businesses receive capital funding/investments. Links relevant to this course 1. Inc.Com 2. Functions of MIS 3. Great explanation PAGE 4: “SURVEY PAGE” What is your age? What is your education level? Where do you live? What is your profession? What is your household size? Contact us for this HTML, CSS, JavaScript assignment Solutions by Codersarts Specialist who can help you mentor and guide for such HTML, CSS, JavaScript assignments.If you have project or assignment files, CONTACT US NOW

Problem Statement: Recall the code you wrote for Project 1, where you had a class called “HouseList”, which encapsulated an ArrayList of “House” objects that were created by reading in a “houses.txt” file. We are now going to write a program that reads the criteria to search for a house through a GUI, and then the list of houses available for sale will be searched to find a set of matching houses, and then randomly select one of these available houses and present it to the user for viewing. In other words, you have to design a GUI screen that looks as follows (you will be working on the same lines as you did for Lab Exercise 7): You should work of all the code you wrote for Project 1, but the additional specifications for this project are as follows: Initially, when the blank screen comes up, the first (left) button, with the label “Find my dream house!” is clickable (live), and the second (right) button, with the label “Not my dream – find me another!” is NOT clickable (not live). In other words, nothing should happen if the user clicks it. When the user enters the values for the various criteria variables into the screen above, and then clicks the “Find my dream house!” button, the text field with the label “Chosen Home” should be populated with the address of one of the homes that match the entered criteria, chosen randomly (see the “Helpful Hints” section below) from the matching set of available houses. At this point, the second (right) button, the one with the label “Not my dream – find me another!” should become clickable (live), and the button with the label “Find my dream house!” should not be clickable. PLEASE NOTE: If the user leaves any of the criteria text fields BLANK and clicks the button with the label “Find my dream house!”, then the program should still function correctly (not crash!). The program should assume that the value entered into the field is 0 (zero) if the field left blank was one of the “minimum fields” (price, area, beds) and it should assume that the value entered into the field is Integer.MAX_VALUE if the field left blank was one of the “maximum fields” (again, for price, area or number of bedrooms). Based on these assumptions, the program should find a list of matching available houses, and then randomly select one house’s address to show. Once the second (right) button with the label “Not my dream – find me another!” becomes live and clickable, and the user clicks that button, the program should go through the list of available houses matching the entered criteria and select another house from this list randomly. It should then show the address of this house in the “Chosen Home” text field. Care must be taken to ensure that the newly shown address is NOT the same as any address that has already been shown to the user. In other words, you must keep track of a house the user has already seen, and be sure to show another. If there are no unseen available houses left in the list matching the entered criteria, then show the text “No more available houses” in the “Chosen Home” text field. When the user clicks the “Reset” button, the program essentially starts all over again. In other words, all the text fields on the GUI screen above are cleared. Also, any records your program may have kept – such as the ArrayList of matching houses, and any record of already seen houses (as described in point (4) above) – need to be cleared, and restored to the state that these variables were in when the program first started up (NOTE: the list of available houses read in from the “houses.txt” file, of course, remains unchanged). Also, at this point, the first (left) button with the label “Find my dream house!” should be live and clickable, and the second (right) button with the label “Not my dream – find me another!” should not be live and clickable – i.e., again, things should be just as they were when the program first started up. At this point, the user can enter new criteria, click the first (left) button and “go” again as described above. Contact us for this JavaFX assignment Solutions by Codersarts Specialist who can help you mentor and guide for such JavaFX assignments.If you have project or assignment files CONTACT US NOW