Stock Market Prediction

Last Updated on May 3, 2021


About the project

Stock market analysis is mainly driven by its Technical Analysis, which includes reading the charts and using statistical figures to identify the trends in the stock market. This will predict the growth of stock market for next 50 days using ML.

Technology/tools Used

Juypter notebook, python3

More Details: Stock Market Prediction

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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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Social Distance Monitoring System(Python, Deep Learning And Opencv)(Research Paper)

Last Updated on May 3, 2021


Social distancing is one of the community mitigation measures that may be recommended during Covid-19 pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as ATM,Airport Or market place .

Covid-19 pandemics have demonstrated that we cannot expect to contain geographically the next influenza pandemic in the location it emerges, nor can we expect to prevent international spread of infection for more than a short period. Vaccines are not expected to be available during the early stage of the next pandemic (1), a Therefore, we came up with this system to limit the spread of COVID via ensuring social distancing among people. It will use cctv camera feed to identify social distancing violations

We are first going to apply object detection using a YOLOv3 model trained on a coco dataset that has 80 classes. YOLO uses darknet frameworks to process incoming feed frame by frame. It returns the detections with their IDs, centroids, corner coordinates and the confidences in the form of multidimensional ndarrays. We receive that information and remove the IDs that are not a “person”. We will draw bounding boxes to highlight the detections in frames. Then we use centroids to calculate the euclidean distance between people in pixels. Then we will check if the distance between two centroids is less than the configured value then the system will throw an alert with a beeping sound and will turn the bounding boxes of violators to red.

Research paper link:

More Details: Social Distance Monitoring System(Python, Deep Learning And OpenCV)(Research paper)

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Python Project: Pillow, Tesseract, And Opencv

Last Updated on May 3, 2021


The Project

Take a ZIP file) of images and process them, using a library built into python that you need to learn how to use. A ZIP file takes several different files and compresses them, thus saving space, into one single file. The files in the ZIP file we provide are newspaper images (like you saw in week 3). Your task is to write python code which allows one to search through the images looking for the occurrences of keywords and faces. E.g. if you search for "pizza" it will return a contact sheet of all of the faces which were located on the newspaper page which mentions "pizza". This will test your ability to learn a new (library), your ability to use OpenCV to detect faces, your ability to use tesseract to do optical character recognition, and your ability to use PIL to composite images together into contact sheets.

Each page of the newspapers is saved as a single PNG image in a file called These newspapers are in english, and contain a variety of stories, advertisements and images. Note: This file is fairly large (~200 MB) and may take some time to work with, I would encourage you to use for testing.

More Details: Python Project: pillow, tesseract, and opencv

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Age And Gender Detection

Last Updated on May 3, 2021


objective :To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.

Description : In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by Tal hassner and Gil levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.

For this python project, I had used the Adience dataset; the dataset is available in the public domain. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.

Working : Open your Command Prompt or Terminal and change directory to the folder where all the files are present.

  • Detecting Gender and Age of face in Image Use Command :

python --image image_name

  • Detecting Gender and Age of face through webcam Use Command :


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Last Updated on May 3, 2021


A Chrome extension that fights online harassment by filtering out comments with strong language.


YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.

What it does

HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.

How we built it

HackTube uses JavaScript to parse through every YouTube comment almost instantly, comparing its content to large arrays that we made which are full of words that are commonly used in hate speech. We chose our lists of words carefully to ensure that the extension would focus on injurious comments rather than helpful criticism. We used standard HTML and CSS to style the popup for the extension and the format of the censored comments.

Challenges we ran into

We are trying to use cookies to create settings for the user which would be remembered even after the user closes the browser. That way anyone who uses HackTube will be able to choose exactly which types of comments they don't want to see and then have those preferences remembered by the extension. Unfortunately, Chrome blocks the use of cookies unless you use a special API, and we didn't have enough time to complete our implementation of that API at this hackathon.

Accomplishments that we're proud of

We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.

What we learned

We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.

What's next for HackTube

Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.

More Details: HackTube

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