Rpg-Random Password Generator.

Last Updated on May 3, 2021


RPG_Random Password Generator

In this project, we are going to create a Python application that will generate passwords for us. For this, we are going to use the string and random module. At first, we are going to generate the characters using the string module, then we are going to use the random module to generate a password.


  • Generate 100% secure password.

  • Random password generation.
  • We do not store passwords in our system.
  • Open Source

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Web App

Last Updated on May 3, 2021


In this project I developed a web app using JSP and Html.

I've also used various styling using CSS.

This was a part of my academic project wherein I created a web app like pinterest .

I added a login page using JSP and if the password is incorrect it directs back to login page and if its correct it will direct to the main page where I've splitted the screen into various frameset using html .

In the main frame I've added marquee of html and at the top I've added various links like home page , know about us , show us our interest.

In the home page options it always directs us to the main page if we are at some other page and click at home page. I've used response.sendRedirect of JSP for the directing options to other pages.

In show us our interest I've added various interest options Using JSP using form of JSP which takes input of interest of the visitors.

On the left side of the main frame there are various options like photography , travel , hairstyle etc.

clicking upon them will direct to the page showing various pictures of that interest.

The main page is login.html used for opening the site.

The website runs of Local host .

The server used for the deployment is APACHE-TOMCAT.

The project was done under the guidence of our JAVA professor , through this we also learned various JAVA scriptlet concepts.

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Personal Assistance

Last Updated on May 3, 2021



This project work same as siri in iphone google assistance in android.

It will take input from user voice and will manage many things like youtube, google, stackoverflow, date, time

can play music and can shut-down your pc as well.


Necessary pip installation requires are

1. pip install pyttsx3

2. pip install speechRecognition

3. pip install wikipedia

4. pip install webbrowser

5. pip install pipwin

6. pip install PyAudio

What is it capable of doing ?

     Input format :

       This piece of code tak input from the voice of user

     Output :


     1. strongly assist and open Google,Youtube,Tell date, time, open stackoverflow,gmail and play music for user.

     2. This piece of code can also shut down your pc if you ask it 

     to shutdown and give premission of 'yes'.

Module installation instruction:

    1. Please install the modules written at the beggining of the 

    code in case it throw error.(in mine pc it's working smoothly).


    2. Use pip install module_name to install packages or modules.


  1. Please run this code on jupyter notebook or any offline text edidtor ie (VS Code, Pycharm, Atom, Sublime)
  2. Please install the module in case not installed using pip install modulename.
  3. Module list are written at the begging of the code

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Cluster Ai

Last Updated on May 3, 2021


Explore a galaxy of research papers in 3D space using a state-of-the-art machine learning model.


Search engines like Google Scholar make it easy to find research papers on a specific topic. However, it can be hard to branch out from a general position to find topics for your research that need to be specified. Wouldn’t it be great to have a tool that not only recommends you research papers, but does it in a way that makes it easy to explore other related topics and solutions to your topic?

What it does

Users will input either a text query or research paper into Cluster AI. Cluster AI uses BERT (Bidirectional Encoder Representations from Transformers), a Natural Language Processing model, in order to connect users to similar papers. Cluster AI uses the CORE Research API to fetch research articles that may be relevant, then visualizes the similarity of these papers in a 3d space. Each node represents a research paper, and the distances between the nodes show the similarity between those papers. Using this, users can visualize clusters of research papers with close connections in order to quickly find resources that pertain to their topic.

Test Cluster AI here

Note: Running on CPU based server, deploying your own Django server using instructions in the Source Code is highly recommended. Demo may have delays depending on the query and number of users at any given point. 10-100 papers, but up to 20 papers requested in the query will be optimal.

Check out the Source Code!

How we built it

We used a multitude of technologies, languages, and frameworks in order to build ClusterAI.

  1. BERT (Bidirectional Encoder Representations from Transformers) and MDS (Multidimensional Scaling) with PyTorch for the Machine Learning
  2. Python and Django for the backend
  3. Javascript for the graph visualizations (ThreeJS/WebGL)
  4. Bootstrap/HTML/CSS/Javascript for the frontend

Challenges we ran into

The CORE Research API did not always provide all the necessary information that was requested. It sometimes returned papers not in English or without abstracts. We were able to solve this problem by validating the results ourselves. Getting the HTML/CSS to do exactly what we wanted gave us trouble.

Accomplishments that we're proud of

We worked with a state-of-the-art natural language processing model which successfully condensed each paper into a 3D point.

The visualization of the graph turned out great and let us see the results of the machine learning techniques we used and the similarities between large amounts of research papers.

What we learned

We learned more about HTML, CSS, JavaScript, since the frontend required new techniques and knowledge to accomplish what we wanted. We learned more about the BERT model and dimensionality reduction. The semantic analysis of each paper’s abstract the BERT model provided served as the basis for condensing each paper into 3D points.

What's next for Cluster AI

We can add filtering to the nodes so that only nodes of a given specification are shown. We can expand Cluster AI to visualize other corpora of text, such as books, movie scripts, or news articles. Some papers are in different languages; we would like to use an API to convert the different languages into a person’s native language, so anyone will be able to read the papers.

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Artificial Neural Network

Last Updated on May 3, 2021


What is ANN?

An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs. An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs.

The input values are processed through all these hidden layers to get the output value just like in human brain.

Neurons are basically building blocks of ANN as the main aim of ANN is to recreate neuron

Dendrites = receiver of signals

Axon is transmitter of signals .

Neurons communicate with one another at junctions called synapses. At a synapse, one neuron sends a message to a target neuron—another cell. Most synapses are chemical; these synapses communicate using chemical messengers. Other synapses are electrical; in these synapses, ions flow directly between cells.

Perceptron is a neural network unit that does contain computations to detect features in data basically artificial neuron.

A cost function is a single value, not a vector, because it rates how good the neural network did as a whole.

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

Problem description: Business problem on real world problem. In the dataset we have 10000 observations of approx 6 months with 14 columns about their information related to bank. RowNumber, CustomerId, Surname, CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited( dependent variable) whether the person stayed in the bank or left the bank 1 = left , 0 = stayed. So we have to understand the co relation between all the features and exited , now based on this dataset the bank wants to understand why people are preferring/ not preferring their bank as they want maximum customers in their bank . so our trained model will predict whether any new customer will leave the bank or not so that the bank can give some special offers to them so that they stay. We have to train the dataset then deploy the model on future customer by predicting the probability.


Importing the libraries

Part 1 - Data Preprocessing

Importing the dataset

Encoding categorical data

Splitting the dataset into the Training set and Test set

Feature Scaling

Part 2 - Building the ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the second hidden layer

Adding the output layer

Part 3 - Training the ANN

Compiling the ANN

Training the ANN on the Training set

Part 4 - Making the predictions and evaluating the model

Predicting the result of a single observation

Predicting the Test set results

Making the Confusion Matrix