Keatext
Chattermill
RapidMiner
Farrago
monkeylearn
Tibco Data Science
Stratifyd
Customer Thermometer
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
Exploratory
htm.java
Keatext is a CX analytics solution that provides AI-based predictive recommendations to improve customer satisfaction.
Get recommendations from your customer responses like reviews, open ended surveys, and contact center tickets to support data-driven decisions and be a more customer-centric organization.
Bring forward decisions with impact: Zero in on opportunities to increase ROI and identify strengths and weaknesses from your customer data
Monitor every stage of the customer journey: Build a complete view of what drives customer satisfaction and discontent
Automate how you generate customer intelligence: Uncover customer insights you can act on without any heavy lifting
Key features
The application is cloud-based and requires no advanced setup or training to start getting insights.
Keatext is trusted by companies in over 10 industries including Lenovo, Intuit, and Intelcom. By using the platform, clients not only improve their customer experience KPIs but help their organization become more customer-centric.
Keatext Scikit-learnNo features have been listed yet.
Based on our record, Scikit-learn seems to be more popular. It has been mentiond 27 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 11 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: 12 months ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: 12 months ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
Chattermill - Extract actionable insights from customer feedback using deep learning
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
RapidMiner - RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
OpenCV - OpenCV is the world's biggest computer vision library
Farrago - Intuitive soundboard app to quickly play sound bites, audio effects, and music clips.
NumPy - NumPy is the fundamental package for scientific computing with Python