How to build your own chatbot using Data Science?
Are you keen on developing chatbots on your own? You can develop innovative and helpful chatbots using powerful frameworks and bot development platforms. These tools enable development of intelligent chatbots and implementation of advanced customer solutions.

Every business has gone dynamic in recent times. Most importantly, the online businesses have gone wild since the inception of data science and digital marketing. Chatbots are playing a key role in the rapid growth of businesses. Lives of stressed customer care employees have been simplified now. Chatbots have impacted the customer experience immensely.

In this blog, we will discuss how to develop a basic and simple chatbot using the data science field called deep learning. Here, you can learn the steps to develop a chatbot from scratch using Keras.

Understanding Key Concept
Before diving deep into the coding part, first, we need to clear our basic concepts. Since chatbot development is based on deep learning models, we need appropriate data for model training.

For a very simple chatbot, no large data sets are required. One can simply create their own dataset for model training. Such data sets are based on the intention of the developer. In other words, we must understand the intent of the model. Every user interacting with chatbot must get appropriate responses for their queries. Hence, clarifying the intention of creating a chatbot is preliminary. Depending upon the intention, the chatbot solutions can vary. Therefore, it is critical to understand the accurate intentions of the chatbot and its relevance in your work field.

One important point that you must remember here is to understand the need of defining these intentions. For a chatbot to become perfect, it must regularly answer questions from a particular domain. The regular conversations with the chatbot helps it to understand what the user wishes to know from it. This leads to clarification of intentions between the user and the chatbot.

Now you might wonder, how can you make the intentions of a chatbot clear? How can it serve the purpose of responding accurately to its users? The answer to this is defining different intentions and training models for different data sets.

Getting started
First of all, read about the python packages required for chatbot development. You can go for these packages- tensorflow. Scikit, flask, numpy. Colorama, and more.
The Intent
The next step is to define the simple intents and responses to those intents. Also, mapping the responses as per each category is necessary. You can create a JSON file with any name (xyz) that would include this data (intent and responses).
Some examples include:
{“intents”: [ { “tag”: “greet”, “pattern”: [“Hello”, “Hi”, “Hey”, “Heyy”, “Is anyone here?”, “Hey there”], “responses”: [“Hello”, “Hello How can I help?”,”Hi”] },
{“intents”:……….} and so on.
Data preparation
After clarifying the intent,the next step is to prepare data. But before that you need to import all the required packages. Such as importing json, numpy, tensorflow, keras, models, layers, sklearn, and more.
Some examples include:
import json
import numpy
import tensorflow
from tensorflow import keras
…..and so on
Creating JSON file
Now load the JSON file and extract the data. Assign variable (sentences) for holding the training data and variable (tra_labels) for target labels that corresponds to each training data.
Some examples include:
With open(‘xyz.json’) as file:
data=json.load(file)
sentences=[ ] tra_labels= [ ] labels= [ ] responses= [ ] And so on…..

At this point, you must convert the target labels into simple and understandable form. Employ the “LabelEncoder()” function present in scikit-learn for this purpose.

Now it’s time to vectorize textual data using the “Tokenizer” class. This allows you to shorten the size of vocabulary upto a certain limit. Upon usage of this class, all the punctuations are removed during the text pre-processing task. The regular punctuated sentences are converted to space-separated wordy sequences. These sentences are further reduced into lists of tokens. These tokens are then indexed. On adding “oov_token” at inference time, one can easily deal with “out of vocabulary” (tokens) or words.
Training Model
Now comes the most important step of developing a chatbot. By using the “sequential” model class of Keras, we can define the neural network architecture for the model.

All you need to do now is train the model. Call the “fit” method with training labels and training data.

Soon after training, you must save the required files in the correct order of usage at the inference time. Now you can save the trained model, fitted tokenizer object, and fit label encoder object.

Inference
After performing all these steps, you can check how the model performs. You can implement a chat function to see how it works with a real user. Whenever a new message is received from the user end, the chatbot will calculate the received message with the training data. Based on the confidence score for each category, the message of the user is categorised. The highest confidence score is given preference over the other categories.

You’ll be surprised to see how perfectly your chatbot works. Only thing that you must ensure is the correct coding and integration of relevant data for training. You can also integrate this program with chat applications.
Chat applications
A trained chatbot model can be easily integrated with chatting applications. Now you can effectively deal with the customers of the real world using the chatbot in a chat application.
Conclusion
So now we have learnt how to develop a chatbot from scratch and integrate it with the chatting application. We Also witnessed how deep learning algorithms can be used to engage with real-world customers. By employing these simple steps, anyone can develop their own chatbot for their company or work domain.

Here are a few tips and tricks that will help in improving the performance of chatbot and its features.

Using appropriate and huge amounts of data: Try to add more and more training data sets. Adding a larger dataset with better intents can turn a Chatbot into a powerful industrial tool. However, you must ensure to use only accurate data for better solutions. Do not try to include everything that could ruin the overall program function.

Employing different NLP solutions: you can experiment and employ different NLP solutions to make your chat bot unique. You can make it like named entity recognition for adding more features into the chatbot. The integration of the NER model with chatbot can resolve your user-based conversations. It easily identifies any entity that appears in the client chat messages. This feature enables further conversations. Along with this solution, you can add a sentiment analysis model. A very powerful technique to identify mood tones of the messenger. It allows the chatbot to give additional information that could help the client.

Use different neural network architecture: you can develop innovative and Super intelligent chatbots by using different neural network architecture. By adjusting the different hyperparameters, the developer can come up with powerful interactive chatbots.

Adding interactive emojis: many developers ignore the emojis during chatbot development. But, it is one of the most powerful and useful features to be included while building the models.

Go for creativity and uniqueness: it is highly recommended to do something different and non-generic. If you have creativity skills and imagination powers, go for something rare and hyperactive. Try to develop models that resolve every client’s issues and problems. You can add as many data, tags, intent, and files as much you wish.
Do not over complicate anything nor make it too simple or basic.

The codes used in this blog can be found on the Github repository. You can visit the website and read the different codes available for chatbot development.

Hope you enjoyed reading this blog. Stay tuned for more interesting write ups on data science and its applications.

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