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Monkey Learn + Firebase Realtime Database Integrations

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and Firebase Realtime Database

About Monkey Learn

Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.

About Firebase Realtime Database

Realtime Database Stores and sync app data in milliseconds

Firebase Realtime Database Integrations
Firebase Realtime Database Alternatives

Connect the apps you use everyday and find your productivity super-powers.

  • Caspio Cloud Database Caspio Cloud Database
  • MySQL MySQL
  • RethinkDB RethinkDB
Connect Monkey Learn + Firebase Realtime Database in easier way

It's easy to connect Monkey Learn + Firebase Realtime Database without coding knowledge. Start creating your own business flow.

    Triggers
  • Edit or Updated Child Object in Firebase Realtime Database

    Triggers on updation of a child object in firebase realtime database.

  • New Child Object in a Firebase Realtime Database

    New Child Object in a Firebase Realtime Database

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create or Replace Firebase Realtime Database Record

    Creates or replaces a child object within your Firebase Realtime Database.

How Monkey Learn & Firebase Realtime Database Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and Select "Trigger" from the Triggers List.

    (30 seconds)

  2. Step 2: Authenticate Monkey Learn with Appy Pie Connect.

    (10 seconds)

  3. Step 3: Select Firebase Realtime Database as an action app.

    (30 seconds)

  4. Step 4: Pick desired action for the selected trigger.

    (10 seconds)

  5. Step 5: Authenticate Firebase Realtime Database with Appy Pie Connect.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and Firebase Realtime Database

  • Monkey Learn?
  • Monkey Learn is a machine learning platform. It allows users to create their own experiments, train their machine learning models, use pre-trained machine learning models, and integrate machine learning models in their own applications. You can create your own experiments with Monkey Learn in less than 3 minutes. Moreover, in the case of knowledge extraction, you can find out if your model extracts the right information in less than 10 minutes.

    Due to the use of machine learning techniques in the cloud, Monkey Learn is very efficient. As a result, the machines are able to learn from big datasets. The launch of Monkey Learn was really successful because of its unique features, such as the use of machine learning algorithms that are not used by other machine learning platforms, such as deep learning algorithms, which are used to allow users to create more accurate models. What's even more remarkable is that Monkey learns have been created by professional developers who have experience working for companies such as Google, Yahoo!, and more. Therefore, they bring the best of both worlds of Machine Learning and Big Data.

  • Firebase?
  • Firebase is a real-time data storage service that has many features including authentication, databases, functions, remote config, remote logging, segmentation, hosting, crash reporting, etc. You can easily integrate Firebase into your applications. Moreover, you can access Firebase from anywhere since it is cloud-based. Additionally, you can monitor the activities of users on your app without any problem.

    According to Google's official website, Firebase has more than 11K apps developed on it. Nowadays, Firebase is used by companies like Airbnb, Uber, Lyft, Pinterest, Snapchat, Nestle USA, etc.

  • Integration of Monkey Learn and Firebase Realtime Database
  • In this chapter we will show you how you can integrate Monkey Learn and Firebase Realtime Database in less than 5 minutes! Let's get started!

    First of all, let's log in to our account at www.monkey-learn.com and then click on "Create Free Account". After that you will be prompted to enter a name for your account and verify it via email:

    Now we're ready to start creating our experiment! Let's click on "New Experiment" and then we will be redirected to the experiment creation page:

    Since we want to create an experiment about sentiment analysis, we'll choose Sentiment Analysis in the "Type" dropdown menu:

    Next we'll need to add our training and testing datasets in CSV format (the format in which Monkey Learn reads. You can upload them via a web browser or you can use a direct link (e.g. https://dl.dropboxusercontent.com/u/9762732/mll_sentiment_analysis_testset.csv. . In this tutorial we'll use a CSV file stored on Dropbox so we'll need to select "Dropbox" from the "Dataset CSV Upload" dropdown menu:

    Note. You can't upload more than 10 files at a time so you should split your datasets into multiple CSV files if necessary. However, you can upload a single CSV file with a single cpumn which contains multiple records separated by new lines. If this is the case then just leave the number of rows blank for each file. You can find a sample dataset here. https://dl.dropboxusercontent.com/u/9762732/mll_sentiment_analysis_testset.csv . This dataset contains 1st party reviews from The New York Times and Glassdoor regarding technpogy products purchased by users from Best Buy stores in the US. We've verified that this dataset works well with MonkeyLearn. And you can find out more about the dataset here. http://techcrunch.com/2014/10/14/how-to-get-60000-customers-in-6-weeks-with-dropbox-best-buy-and-12-million-in-funding/ . We've included this sample dataset as it may be useful as a starting point for some tutorials you would like to develop using MonkeyLearn :. . In order to see some tutorials about this dataset please go to our website www.monkeylearn.com and have a look at those tutorials :. To be clear about this dataset lets explain some details about it. firstly it contains a header row where the labels are listed as cpumns and secondly it contains a single cpumn which represents different reviews from first parties (users who have made purchases from Best Buy. Each review is represented as a single line where each element on that line represents a feature that describes that review (e.g. product category=TVs. In particular this dataset contains two cpumns. product_category and sentiment . In this tutorial we'll only use the product category cpumn but feel free to play with the sentiment cpumn ;. Also note that the sentiment cpumn contains positive reviews only (i.e., 1st party reviews where users liked a product. and will be used as a test set while the product_category cpumn will be used as a training set (i.e., training set+testing set. After adding our CSVs we need to provide some description about our experiment. Here you'll need to provide some description about your experiment such as title , description , tags , etc.. Here we need to specify some information about our model such as name , description , etc.. Here we choose some parameters for our model such as type of classifier , algorithm used , etc.. As explained previously we will use Naive Bayes Algorithm with J48 Tree and we will also use the output of multiclass classification as input for our regression model. Here we specify some parameters for our model such as confidence intervals for regression and probabilities for classification. As shown above we're building a regression model where we'll predict whether an item has positive sentiment or not by analyzing its product category . How do we know that? Well remember that our training dataset contains two cpumns. product_category and sentiment . Since we want to build a regression model based on product category we must select product_category as input for our regression model (also known as input feature. But what about the label (i.e., either +1 or -1)? That will be determined by building another model (we'll call it classifier. which will assign labels based on product category (or product_category . and then we'll use those labels instead of +1 and -1 respectively as the prediction value for our regression model. So we'll build two models in total one for regression (+1 or -1. and another one for classification (+1 or -1. which are connected together through product_category . We could have built just one model for both regression and classification but then there would be no connection between those two tasks so both models would have learned completely separate things. That's why it's better to have two independent models connected together through common input features so they work together toward the same goal which is predicting whether an item has positive or negative sentiment . Note that there are other layers under Model Configuration where you could configure other things such as number of classes per group , maximum number of iterations , etc.. Note that if you're using your own data then you should change the default settings described above otherwise MonkeyLearn won't be able to process your data properly! Finally, let's click on "Save & Continue" button then click on "I'm done!" button then click on "Continue". Now it's time to train our models! Our first model will be sentiment analysis based on product categories . Now it's time to train our second model which will predict whether an item has positive sentiment or not by analyzing its product category . After clicking on "Train Models!" button you'll see something like this below. Now let's save our model! Let's click on "Create Experiment" button then click on "Saved Experiment" button then click on "Experiment Settings" button then click on "Experiment Settings" button again then click on "Save Model" button then click on "Save Model" button again then click on "SAVE" button finally. *Note that if you want to improve your model or build another one you should keep your original experiment because otherwise you'll lose all information about your original model! Ok now let's open Firebase Realtime Database via firebase conspe! When opening Firebase Realtime Database you'll see something like this below. Now let's create a database called 'mlcdb' (you can name your database anything you wish. then let

    The process to integrate 403 Forbidden and 403 Forbidden may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.