Object Recognizer

Last Updated on May 3, 2021


This is a simple Deep Learning project. developed with the help of transfer learning.

in this project you just need to upload an image and you will get the predicted name of the object present in image

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Ai Real Time Car And Pedestrian Tracking App

Last Updated on May 3, 2021



A real-time app using python as the programming language with importing open cv.

Learning from this:

  1. Haar features and algorithms
  2. how the haar cascade algorithm works in real-time upon grayscaled images
  3. why it works better on grayscaled images than taking colored frames instead.
  4. simple lines of code can do magic just putting the right things at right places

The result from this:

  1. we can detect images of person and vehicle and identify them in real-time webcam support to get the real time frame or taking the video as the import
  2. multiple real-time images can be detected and also with regular changing of dimensions
  3. this can lead to avoidance of the accident as also suggested by the tesla in their dashcam video

Challenges faced

  1. the most important challenge is to train the data and it's time-consuming so to build a simple prototype taking OpenCV trained data is beneficial as it saves lots of time.
  2. haar algorithm how it works is again one of the most important challenges as it has to be quite accurate to detect the face in real-time
  3. importing OpenCV required installation of multiple packages and different versions of python have different versions of that library.
  4. detecting person with nonliving vehicles is itself a challenge to make the training data in its work for both using two different cascade classifiers

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Loan Prediction

Last Updated on May 3, 2021


A Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers. So in this project our main objective is to predict whether a individual is eligible for loan or not based on given dataset.

For simplicity i divided my projects into small parts-

  1. Data Collection :- I collected data from 'Anylitical Vidhya' as a CSV file. We have two CSV file one is train data which is used for training the data and other is test data which is used for prediction based on training of model.
  2. Import Libraries:- I import differnt Sklearn package for algorithm and different tasks.
  3. Reading data:- i read the data using pandas 'read csv()' function.
  4. Data Preprocessing -: In this part i first found missing values then i remove a column or imputed some value (mean, mode, median) According to the amount of data missing for a particular column.

I checked the unique value in each column. Then i did label encoding to convert all string types data to integer value. I used dummie function to convert each unique value to different columns . I find out correlation matrix which shows the correlation between columns to each other.

Then i split the data. I did analysis on each column and row of dataset.

Here i selected a classifier algorithm because it is a classification problem i.e. in this problem target value is of categorial datatype.

Then i create a model . I trained that model using Logistic regression Algorithm , which is a classification algorithm. I feed training dataset to model using Logistic regression algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained model which predict the values of this test dataset. And then i found accuracy of this model using actual target value which is given in training dataset. and predict target value which we predict from test dataset.

After this i used another algorithm which is random forest classifier. i did traied the model using random forest classifier and then calculate the accuracy.

I compared the accuracy of both algorithm and i preffered algorithm which had better accuracy.

In this project i got 78.03% accuracy when i create model using random forest classifier and got 80.06% when i create model using logistic regression.

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Convolutional Neural Network Application To Classify Identical Twins(December 2019-March 2020) (Python,Ml)

Last Updated on May 3, 2021


-Transfer learning is employed to classify the images of identical twins.

-Transfer learning basically means using the knowledge acquired during a previous problem solving for finding a solution to a new problem in hand.

-Weights and biases values are used with some fine tuning.

-Pre-trained Convolutional Neural Network models which are developed for image recognition tasks on ImageNet data set, provided by Keras API is used as feature extractor preprocessor.

-All these models are proven to be very efficient in image recognition task.They have showcased very high accuracy on ImageNet dataset.The labels they have given for images were highly accurate with very less error percentage.

-Only convolutional base layers are used here,that is fully connected layers are not included here.

-Fully connected layer is not included and a new fully connected layer is addee at the end for the required categorisation of the data.

-During dataset building-collected images of the identical twins.Two categoried were defined in this way.

-VGG19 is used as a standalone program to extract features from the dataset.

-Feature vectors for training dataset is obtained and mean feature vector for both categories were calculated.

-Testing is done by comparing the feature vectors of testing data with mean feature vectors of each category using cosine similarity.

-Obtained a fair accuracy while testing. 

More Details: Convolutional Neural Network Application to classify Identical Twins(December 2019-March 2020) (Python,ML)

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Evolution Of Sars-Cov-2 And The Antibody Immune Response In Humans During Infection

Last Updated on May 3, 2021


Aim: to understand the evolution of the SARS-CoV-2 viral population in hosts during illness, in relation with the infectivity of the virus and the establishment of the humoral (antibody-based) immune response. This knowledge is important to identify trends in disease progression and help improve the treatment and post-treatment follow-up given to patients. Detailed data on the evolution of the SARS-CoV-2 population in relation with disease progression, antiviral treatment and viral shedding sites will also be useful in evaluating links with disease severity and the potential failure of antiviral treatments.

The aim is to elucidate the kinetics of SARS-CoV-2 shedding in patients with Covid-19, depending on the severity of the disease, age and existing comorbidities (shedding in upper respiratory tracts – from the nose to the larynx – and lower respiratory tracts – from the windpipe to the alveoli, or at non-respiratory sites), and also to analyze the kinetics of the viral load at respiratory and non-respiratory sites in confirmed cases according to severity, age group and existing comorbidities. The project also intends to determine the correlation between the development of the viral load and infectivity, and to understand the kinetics of introducing a neutralizing humoral response (the body's production of antibodies to neutralize the virus).

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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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