Why Choose NoCodeMLApp?

See Use Cases & Video Demos 

01. Text Classifier for Sentiment, Emotion or Product Review Analysis Using File-Based Approach

You can use the NoCodeMachineLearningApp or in short NoCodeMLApp for Sentiment Analysis or Emotion Analysis or any type of text analytics that requires supervised learning. There are two MLTextAnalysis options in the NoCodeMLApp. One uses training data stored in a .csv or .json file.The other uses Directory-based approach. In this section, we look at the .csv or .json file-based option. The Directory-based option is discussed below in Section 14. Please click on the video below to see a demo of the Machine Learning Text Classifier for classifying sentences into positive or negative sentiments. This Text Classifier can also classify text that requires multiple classes such as anger, joy, happiness, depression, etc for emotion analysis.

Note: The Data Set used in the Text Classifier Video is from the "From Group to Individual Labels using Deep Features", Kotzias et al., KDD 2015 and hosted at the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml).

02. Image Classification Directory-Based 

You can perform image analysis and train your image classifier without writing a single line of code. All you need to do is to place images in Directories or Folders. For example, if you want to classify various types of fruits and train your Machine Learning Model all you have to do is place unlabeled images of various fruits say banana, dates, apricot, etc. in named folders. You place bunch of unlabeled banana images in 'Banana' folder then bunch of dates images in 'Dates' folder and a bunch of apricot images in 'Apricot' folder.

You place all of these named folders under a common folder called 'Fruits'. Then you choose the MLImageClassification option and point to the top level "Fruits" folder which now contains the subfolders for 'Banana', 'Dates' and "Apricot". The MLImageClassifier will learn to classify images based on Folder names. Instead of reading about it, just click on the video below for a demo.

Note: Image Classifier Data Set was obtained from the following authors and Institution:

Citation:

Horea Muresan, Mihai Oltean (https://mihaioltean.github.io), Fruit recognition from images using deep learning, Technical Report, Babes-Bolyai University, 2017

03. Automatic ML Regressor

You can use the Automatic ML Regressor option if you want the best ML Regressor Algorithm to be chosen automatically based on the Training Data Set. This option, based on the characteristics of the Training Data, will select the Regressor Algorithm that creates the best prediction Machine Learning Model. Watch the demo Video below to see how NYC taxi fare can be predicted using NYC Taxi Fare Data Set with over a million rows of data.​

Note: NYC Taxi Fare data was used in this demo video. These data are originally from NYC TLC Taxi Trip Data Set.

04. Automatic ML Classifier

This option of the NoCodeMachineLearningApp automatically selects the best Machine Learning Classifier Model based on the Training Data Set.Watch the demo video below so see how Iris Flowers can be classified using Sepal Length, Sepal Width, Petal Length and Petal Width. 

Note: The Automatic Machine Learning Classifier demo Video uses Iris Flowers Data Set.

Citation:

Dua, D. and Graff, C (2019). UCI Machine Learning Repository (http://archive.ics.uci.edu/ml), Irvine, CA: University of California, School of Information and Computer Science.

05. Boosted Tree Regressor

You can perform predictive analytics using Boosted Tree Regressor Algorithm included as part of the NodeCodeMachineLearningApp. In this demo video, we use NYC Taxi Fare Data for both Training and Prediction. If you want to customize hyper-parameters of your ML Algorithm such as Booted Tree Regressor, now you can do that without any code. Watch the video below to see how NYC Taxi Fare can be predicted using Boosted Tree.

Note: NYC Taxi Fare data was used in this demo video. These data are originally from NYC TLC Taxi Trip Data Set.

06. Decision Tree Classifier

You can use the Decision Tree Classifier to train a Machine Learning Model to classify Real Estate on Mars. You can also classify desirable Real Estate property. See the demo video below where you also have the option to customize Decision Tree Classifier hyper-parameters to choose the best Machine Learning Model.

Note: Credit for making the  Marshabitat Data Set available for training and research belongs to Apple and is available at Apple Developer Website.

07. Support Vector Machine Classifier

Support Vector Machine (SVM) Classifier Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

08. Boosted Tree Classifier

Boosted Tree Classifier Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

9. Logistics Regression Classifier

Logistics Regression Classifier Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

10. Random Forest Classifier

Random Forest Classifier Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

11. Linear Regressor

Linear Regressor Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

12.  Decision Tree Regressor 

Decision Tree Regressor Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

13. Random Forest Regressor

Random Forest Regressor Demo Video will be available very soon!!

Video Coming Soon!!!

Note: Credit for the Data...

14. Text Classifier for Sentiment, Emotion or Product Review Analysis Using Directory-based Approach

In this use case, you will see how to use MLTextAnalysis for Sentiment Analysis but instead of using trained data file in .csv or .json format you will use Directory-based approach. What is Directory-based approach? In the Directory-based approach you label each directory with the name of the category. For example, you create Directory (or Folder) called "positive_sentiment" and another Directory called "negative_sentiment". Then in the directory called "positive_sentiment" you place sample text files containing "positive" sentiment text in .csv or .json format. You do NOT need to label individual text files. Similarly you place sample text files containing "negative" sentiment text in .csv or .json format. Again you do NOT have to label individual files. Once you point the upper level Directory containing both the "positive_sentiment" and "negative_sentiment" sub-directories to the NoCodeMLApp MLTextAnalysis - Directory-based option, the algorithm will train itself based the training data. Full demonstration video is coming soon!!!

Video Coming Soon!!!

Note: Credit for the Data...

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