Enhancement of SKLearn Pipeline
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Updated
May 26, 2018
Enhancement of SKLearn Pipeline
additional wrapper and classes for sklearn's pipeline API
Machine learning (ML) pipelines consist of several steps to train a model.
Demo for "Pandas In, Pandas Out" scheme based scikit-learn pipeline.
Simple Application for predicting price of the flight. It uses sklearn pipeline to perform preprocessing , feature selection and feature engineering and model building .The pipeline object is saved in a pickle file and used in the flask application for prediction
In this project, Naive Bayes and Logistic Regression models are used to develop a text classification system for Turkish news articles.
End-to-end machine learning pipeline to classify disaster messages into 36 categories and a web app to deploy the trained model.
Use Graph Analytics to Predict Relationships Between Entities
Project No.2 (Data Engineering) in the Data Scientist Nanodegree program. Build a machine learning pipeline to categorize emergency messages based on the need communicated by the sender.
To build a multi-class model using Sklearn-pipelines that’s capable of detecting different types of toxicity
Machine Learning based web application which helps users to choose an appropriate insurance premuim for subscription by predicting it based on user's details like living style, gender, smoker or not etc.
Trying out new different things
Scikit-Learn useful pre-defined Pipelines Hub
Classifying Sports Personalities
[In the top 20 percent of the Kaggle competition] To predict the survival of passengers on the Titanic, a classification model was developed with the Implementation of Sklearn-Pipeline for feature engineering and model construction using ColumnTransformer.
Natural Language Processing model to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews
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