One fact is clear and indisputable: consumers around the world accept chatbots and frequently interact with businesses in this way. According to Forbes, France, the UK, and Australia report the highest levels of chatbot usage, at 70% user penetration, while the US and Germany are lagging behind. Artificial intelligence is integrating smoothly in an array of customer journey workflows. As AI advances and integrations increase, the opportunities and expectations for chatbots grow ever higher. Tech-savvy customers welcome chatbots evolving and dealing with more complex requests
Hospitals and medical institutions can benefit from weaving chatbots into operations. Chatbots can play a pivotal role at every step, from registration to diagnosis, health tracking, notifications, and beyond. And all of this can be fulfilled using text, video, and voice recognition. Meticulous Research reports that the global medical chatbots market is expected to reach $703.2 million by 2025. Over the course of the current coronavirus pandemic, chatbots have played a larger and larger role in updating medical providers and the public on virus news, the latest statistics, and protective measures (information from World Economic Forum).
Our article focuses on chatbot anatomy; more specifically, creating a chatbot on the basis of sklearn Python. Let’s go through the main terminology and key elements.
Python Scikit: What is That?
Scikit-learn (previously known as scikits.learn and now sklearn, for short) is an open source, free-of-charge ML library used for the Python programming language.
Scikit-learn library is the most common choice for solving classic machine learning problems. It provides a wide variety of both supervised and unsupervised learning algorithms. Supervised learning assumes the presence of a labeled dataset in which the value of the target feature is known. While unsupervised learning does not imply the presence of markup in the dataset, you still need to learn how to extract useful information from arbitrary data. One of the main advantages of the library is that it works on the basis of several common math libraries and easily integrates them with one other. Other benefits are broad community support and detailed documentation.
Scikit-learn is widely used for research, for industrial systems that apply classical machine learning algorithms, as well as for beginners in the field of machine learning.
Scikit-learn uses the following popular libraries:
- NumPy: mathematical and tensor operations
- SciPy: scientific and technical computing
- Matplotlib: data visualization
- IPython: an interactive console for Python
- SymPy: symbolic mathematics
- Pandas: data processing, manipulation, and analysis
“Some photo-hosting services, such as Google Photos, are good examples of this. Once you upload all your family photos to the service, it automatically recognizes that the same person A shows up in photos 1, 5, and 11, while another person B shows up in photos 2, 5, and 7. This is the unsupervised part of the algorithm (clustering). Now all the system needs is for you to tell it who these people are. Just one label per person, and it is able to name everyone in every photo, which is useful for searching photos.”
— Aurélien Géron, Author, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Here’s a detailed step-by-step instruction on installing scikit-learn. Now, we will move on to chatbots.
Why Create a Chatbot in Python?
The Scikit-learn library does not cover loading, processing, data manipulation, and visualization. The Pandas and NumPy libraries already do an excellent job with these tasks. Scikit-learn specializes in machine learning algorithms for supervised learning problems: classifications (predicting a feature, the set of valid values of which is limited) and regression (predicting a feature with real values), as well as for unsupervised learning tasks: clustering (dividing data into classes that the model will determine itself), dimension reduction (presentation of data in a space of lower dimension with minimal loss of useful information), and anomaly detection.
Scikit learn model selection relies on specific tasks. The library uses the following key methods:
- Linear: models, the task of which is to build a separating (for classification) or approximating (for regression) hyperplane.
- Metric: Models that calculate the distance using one of the metrics between the objects in the sample and make decisions based on this distance (K nearest neighbors).
- Decision trees: Training models based on a set of conditions that are optimally selected to solve a problem.
- Ensemble methods: Methods based on decision trees that combine the power of many trees, and thus improve their performance, and also allow selection of features (boosting, bagging, random forest, majority voting).
- Neural Networks: A comprehensive nonlinear method for regression and classification problems.
- SVM: A non-linear method that learns to define the boundaries of decision-making.
- Naive Bayes: Forward probabilistic modeling for classification problems.
- PCA: Linear dimensionality reduction and feature selection.
- t-SNE: A non-linear dimensionality reduction method.
- K-Means: The most common method for clustering, requiring as input the number of clusters over which the data should be distributed.
- Cross-validation: A method in which the entire dataset is used for training (as opposed to splitting into train/test samples). However, training occurs repeatedly, and different parts of the dataset act as a validation sample at each step. The final result is an averaging of the results obtained.
- Grid Search: A method for finding the optimal hyperparameters of a model by building a grid from the values of the hyperparameters and sequentially training the models with all possible combinations of hyperparameters from the grid.
This is just a basic list. In addition, Scikit-learn contains functions for calculating metric values, selecting models, data preprocessing, and other tasks.
“At Hugging Face we’re using NLP and probabilistic models to generate conversational Artificial intelligences that are fun to chat with. Despite using deep neural nets for a few of our NLP tasks, scikit-learn is still the bread-and-butter of our daily machine learning routine. The ease of use and predictability of the interface, as well as the straightforward mathematical explanations that are here when you need them, is the killer feature. We use a variety of scikit-learn models in production and they are also operationally very pleasant to work with.”
— Julien Chaumond, Co-founder and Chief Technology Officer, Hugging Face
Decision Trees: Sklearn and Making a Choice
Decision trees in an efficient ML model provide high accuracy in solving many problems while maintaining a high level of interpretation. The clarity of presentation makes decision trees special among other machine learning models. The data mastered by the decision tree is directly formed into a hierarchical structure that stores and presents information in a form that is understandable, even for newbies.
Decision tree models are built in two stages: induction and pruning. In induction, we create the tree setting all of the boundaries of the hierarchical solution based on our data. Because of their nature, trainable decision trees can be subject to significant overfitting. In pruning, we remove the unnecessary structure from the decision tree, effectively making it easier to understand and avoiding overfitting.
Anaconda Install: Sklearn Style
“By installing Anaconda, you get about 400 of various packages. It may seem redundant, but in fact, it turns out to be very convenient—almost everything you might need is at your fingertips. It is very convenient to work in such an environment. It helps us to be ahead of the curve and push the boundaries with our solutions. We help healthcare companies innovate and exceed the expectations of their clients.”
— Vlad Medvedovsky, Founder and Chief Executive Officer at Proxet, custom software development solutions company.
To install the Anaconda distribution, you first need to download it from the official website. Before clicking the "download" button, check the parameters of your platform. While the installation is in progress, you can examine available packages and documentation. At the end of the day, you’ll have a vast variety of tools.
Ultimately, building a chatbot in Python is a common practice for successful businesses around the world. Check it out.
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