Resume Up-Loader

Last Updated on May 3, 2021



Ever you apply to an organisation with cv through mail but it might happen that specific organisation don't know that actually candidate need like job preference or type of job, so it get easier when we use this app called resume up-loader.

working model:-

It is my first self project using Django (python

framework) called Resume Up-loader .

where you put every detail about yourself ,job location photos,signature,CV,after submitting the information load on the server and next page you can look all your information and download the Resume also ,i am continuously working on it and upgrading that it list all the company on that preference job location for your current qualification and skill it help the candidate to know in which company is he/she is suitable for and it also company to know their candidate batter

Under a projects section

To make this single page website I have use the python web framework called Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

I have also use HTML to define the structure of front-end and use style tag to make this beautiful

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Go Alexa Go

Last Updated on May 3, 2021


GO ALEXA GO was created with millennials in mind. Why drive and text when you can just text and have Alexa drive?


We were inspired by Millennials' dangerous texting and driving habits, so we developed a driving system to allow them to text and still drive at the same time.

What it does

Our HTC Vive virtual reality experience allows the user to issue commands to our taxi driver, Alexa, and explore Sponsorville.

How we built it

We built our HTC Vive VR experience in Unity using C# and our Amazon backend with node.js and the Alexa skillset. The Amazon Alexa is able to take a user's directional input voice command through Amazon's unique browser-based web services built with node.js, and notifies Unity of the user's input with a web API hosted on Microsoft Azure.

Challenges we ran into

The first challenge we ran to was division of work. Charlie became our Unity/C#/HTC-Vive programmer, Randy became our impromptu Scrum Master/Front-End Designer/3D-modeler, and Caleb and Colin worked on node.js/Azure-IoT/Amazon Web Services. After we had a better sense of everyone's skill-set and strengths, we were able to snowball each other consistently throughout the course of the hackathon. Regarding Unity and C#, we ran into rigidbody and trigger debugging issues early on. With Alexa, we had troubles getting the browser based web service to work with node.js/Azure but by the middle of the second day, we were able to create a working prototype.

Accomplishments that we're proud of

Getting an Amazon Alexa to take voice commands and convert them to directional output in a Unity VR environment.

What we learned

Make sure you go into a hackathon with your division of work ready between your teammates. Additionally, make sure you teammates actually have a solid background in coding the work that is handed to them. Get together with your teammates every few hours, AGILE style, and see what progress has been made and if anyone needs help. Make sure everyone on your team can at some point handle paperwork because there will be a good amount of it throughout the course of the hackathon from the gathering of your teammates, to the final 12 hours before showtime. There needs to be a HUGE sense of trust between you and your teammates. Without some form of solid workflow (we used 2-hour scrums), you can run into problems like people just going off and coding who knows what for 3-4 hours of your hackathon before you realize you have issues.

What's next for Go Alexa Go

We plan on buying our own private islands and moving there with our solid-gold rocket ships from the amount of sponsorship money we've made from our amazing SponsorVille sponsors at Spartahack 2017.

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Web Base Application Heart Failure Prediction System

Last Updated on May 3, 2021


In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.

In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.

In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.

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Password Checker

Last Updated on May 3, 2021


This can be the most secure way for you to check if your password has ever been hacked. This is a password checker which checks whether this password has been used before or not. and if it has been used then the number of times it has been found. It makes it easy for you to understand that your password is strong enough to keep or is it too light. Its working is pretty simple, in my terminal i write the python file with my code followed by the password to check if its ever been hacked , its gonna check as many passwords as we list in the terminal. I have used passwords API (pawned password) and SHAH1 (algorithm) to hash the given password into some complex output which is hard to hack also only the first five characters of hash version of password has been used for super privacy so that the real one is safe. The concept of k-anonymity is used  it provides privacy protection by guaranteeing that each record relates to at least k individuals even if the released records are directly linked (or matched) to external information. I have added this on my Github repository.

password-checker/ at main · THC1111/password-checker (



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Portfolio Website

Last Updated on May 3, 2021



  1. HOME PAGE : this is the home page and it greets the viewer and tells what this is about. and on the top of this page we can see the clicks for other pages which are discussed below.