Eliza Type Chatbot

Last Updated on May 3, 2021


ELIZA: the first ever chatbot.

i have made a prototype of ELIZA using Data Science Techniques and Python Libraries.

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Last Updated on May 3, 2021


- Implement E-Commerce Web App which had started from 13 November to 12 December 2020.

- In this Web App user can able to purchase the various products which is available in Database and virtually placing the orders.

- Applied Python , DJANGO , Bootstrap and JavaScript ,especially focus on backend to explore the skill and knowledge of backend.

- This Web App consist of proper Database functionality which help to implement different function and operations.

- User can able to ask any query regarding products and processes , also there is special search functionality in which user can able to filter their required products by simply search on there.

- There are pop-down Cart which shows the product available in the Cart which is select by the user with two buttons in the bottom, one is Checkout and another is Clear Cart.

- On clicking the Checkout button it render the user to the place order page in which user should give all their details by filling the blanks input and finally place the Order.

- All the orders detail of user, orders and query will be stored in the Databases with their Username and Date.

- On clicking the Clear Cart button , it clear all the product which is select by the user for purchase.

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Multi Label Question Classification For Agricultural Domain

Last Updated on May 3, 2021



Basically, Multi-Label means, the questions asked by a client are of what type? 

Here our ML model should be able to segregate the questions into descriptive type question or one-word type question or both and then answer it effectively.

We have classified the data into three major types:

1) Definition Type Question 2) Descriptive Type Questions 3) Factoid Type Questions 

KEYWORD IDENTIFIER Keywords are used to identify the question type.

The input query is scanned and the primary keywords such as who, when etc. are identified.

These keywords help in finding out the expected answer type. But words like how and which do not give a clear idea about the question type.

To get a clear idea about the question type and obtain the relevant answer, additional keywords are required.

These are known as secondary keywords. The secondary keywords provide additional information about the question type which further helps in extracting the answer from the document.

 For example

a)Which sector is the backbone of the Indian economy? Which is the best season for a particular crop?

 Here the “which” keyword acts as the primary key. This helps us understand the question type as which and the expected answer for such type of question is a factoid type of answer which precisely answers the type of “sector”


Data preprocessing techniques such as data redundancy, stop words, data cleaning, puntuactions, etc are done first.

Using the Naive Bayes Classifier &Python's Scikit-learn package we were able to upload the Model database containing 50 questions with their answers.

Naive Bayes and SVM is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. 

Training and testing of data are done and then the model predicts the appropriate answer.

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Smart Health Monitoring App

Last Updated on May 3, 2021


The proposed solution will be an online mobile based application. This app will contain information regarding pre and post maternal session. The app will help a pregnant lady to know about pregnancy milestone and when to worry and when to not. According to this app, user needs to register by entering name, age, mobile number and preferred language. The app will be user friendly making it multi-lingual and audio-video guide to help people who have impaired hearing or sight keeping in mind women who reside in rural areas and one deprived of primary education. The app will encompass two sections pre-natal and post- natal.

           In case of emergency i.e. when the water breaks (indication) there will be a provision to send emergency message (notification) that will be sent to FCM (Firebase Cloud Messaging), it then at first tries to access the GPS settings in cell, in case the GPS isn’t on, Geolocation API will be used. Using Wi-Fi nodes that mobile device can detect, Internet, Google’s datasets, nearby towers, a precise location is generated and sent via Geocoding to FCM, that in turn generates push notifications, and the tokens will be sent to registered user’s, hospitals, nearby doctors, etc. and necessary actions will be implemented, so that timely            help will be provided

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Hotel Management System Using Python

Last Updated on May 3, 2021


This Project is done using python 3.x which depicts a front end interface of the hotel management system which is done using GUI interface and has the menu, where the user has a list of choices to select the food he wants and this interface has the food rating section where the user has to give the rating in which the food he took, and this interface is done using basic components of GUI. The GUI I used here is Tkinter, and by using List boxes, buttons, the text box is deployed in this interface, which is user-friendly. This interface is done because the situation of covid is increasing tremendously, to reduce the people frequently going outside for food, this interface has been developed. In this interface, we can also set background color and

font color. Here we can also set the background dimension and in this application, we can also change font sizes and also with rows and columns. This interface asks the user to enter his name, mobile number, email id and also asks whether a user prefers a choice of veg or nonveg and also gives a chance to give the food specification whether he needs the food spicy, salty, and some other and the user can choose whether he needs to pay cash, or online payment either which he can also give food rating and he can also select coupons and apply in this interface.


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Dynamic Timetable

Last Updated on May 3, 2021



The "Dynamic Timetable" is my final year project which is PHP-based assigned by my college. Is useful to the scheduling process in colleges, and schools by generating timetable dynamically which could minimize the human work and maximize efficiency, and the timetable was stored in a database which could be easy to access. 

The problem was generating a timetable manually is more complex and difficult to assign all the labs, classrooms, classes, and staff without getting overlapped. Also, manage the timetable manually is more time-consuming and handy. So for solving this problem we choose to develop a user-friendly web application, Which helps to generate timetable automatically and saves a lot of time and also avoids overlapping. 

The main purpose of this project is to make user-friendly software, avoid manual problems/overlapping, Human errors, and efforts are reduced, Manages Lectures and practical.It also shows timetables for individual staff, for the classroom, lab, and for class(departments). While generating a timetable, avoids the overlapping between teachers, classes, labs over their scheduled time. If the staff/classroom/lab is already assigned to the same time slot and we are trying to assign them to a different class, this overlapping is solved in this project. By automating this process with Dynamic Timetable can save a lot of precious time of administrators who are involved in creating and managing timetables.


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