Last Updated on May 3, 2021


virtual assistant is an independent contractor who provides administrative services to clients while operating outside of the client's office. A virtual assistant typically operates from a home office but can access the necessary planning documents, such as shared calendars, remotely.

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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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Research Paper On Face Detection Using Haar Cascade Classifier

Last Updated on May 3, 2021



In the last several years, face detection has been listed as one of the most engaging fields in research. Face detection algorithms are used for the detection of frontal human faces. Face detection finds use in many applications such as face tracking, face analysis, and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study, we would be focusing on some of the face detection technology in use.


In this study, we covered and studied in detail about face detection technique using haar cascades classifier and OpenCV to get the desired output. Using the OpenCV library, the haar cascade classifier was able to perform successful face detection with high accuracy and efficiency. We also used the OpenCV package to extract some of the features of the face to compare them. Also, we discussed some popular face detection methods. Further, we discussed the scope of face detection in the future and some of its applications. At last, we conclude that the future of facial detection technology is bright Security and surveillance is the major segments that will be deeply influenced. Other areas that are now welcoming it are private industries, public buildings, and schools


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Predicting Credit Card Approvals

Last Updated on May 3, 2021


Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do! I have used the Credit Card Approval dataset from the UCI Machine Learning Repository.

The structure of this notebook is as follows: First, we will start off by loading and viewing the dataset. We will see that the dataset has a mixture of both numerical and non-numerical features, that it contains values from different ranges, plus that it contains a number of missing entries. We will have to preprocess the dataset to ensure the machine learning model we choose can make good predictions. After our data is in good shape, we will do some exploratory data analysis to build our intuitions. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted.

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Tic-Tac-Toe Game

Last Updated on May 3, 2021


This project Tic Tac Toe game against a simple artificial intelligence. An artificial intelligence (or AI) is a computer program that can intelligently respond to the player’s moves. This game doesn’t introduce any complicated new concepts. The artificial intelligence that plays Tic Tac Toe is really just a few lines of code.

Two people play Tic Tac Toe with paper and pencil. One player is X and the other player is O. Players take turns placing their X or O. If a player gets three of their marks on the board in a row, column or one of the two diagonals, they win. When the board fills up with neither player winning, the game ends in a draw.

This chapter doesn’t introduce many new programming concepts. It makes use of our existing programming knowledge to make an intelligent Tic Tac Toe player. The player makes their move by entering the number of the space they want to go. These numbers are in the same places as the number keys on your keyboard's keypad

First, you must figure out how to represent the board as data in a variable. On paper, the Tic Tac Toe board is drawn as a pair of horizontal lines and a pair of vertical lines, with either an X, O, or empty space in each of the nine spaces.

In the program, the Tic Tac Toe board is represented as a list of strings. Each string will represent one of the nine spaces on the board. To make it easier to remember which index in the list is for which space, they will mirror the numbers on a keyboard’s number keypad.

The strings will either be 'X' for the X player, 'O' for the O player, or a single space ' ' for a blank space.

So if a list with ten strings was stored in a variable named board, then board[7] would be the top-left space on the board. board[5] would be the center. board[4] would be the left side space, and so on. The program will ignore the string at index 0 in the list. The player will enter a number from 1 to 9 to tell the game which space they want to move on.

Creating a program that can play a game comes down to carefully considering all the possible situations the AI can be in and how it should respond in each of those situations. The Tic Tac Toe AI is simple because there are not many possible moves in Tic Tac Toe compared to a game like chess or checkers.

Our AI checks if any possible move can allow itself to win. Otherwise, it checks if it must block the player’s move. Then the AI simply chooses any available corner space, then the center space, then the side spaces. This is a simple algorithm for the computer to follow.

The key to implementing our AI is by making copies of the board data and simulating moves on the copy. That way, the AI code can see if a move results in a win or loss. Then the AI can make that move on the real board. This type of simulation is effective at predicting what is a good move or not.

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''Human Identification And Detection Of Diseases By Extracting Sclera Veins''

Last Updated on May 3, 2021


Biometric includes the physiological and knowledge based method in that Sclera vein recognition is the one of the methods which is utilized for the most accurate method to identify the person and also detect the diseases.

Many researchers have developed more methods for ID. In this project, firstly have to efficiently partition the eye images into clusters depends on their region of interest(ROI) for that we have apply the segmentation.

Here the k-means clustering method is used to cluster the image and to separate the sclera part from image of the eye. This divides the three cluster sclera, IRIS and around the eye to take the sclera part for the person identification.

Sclera vessel pattern's images are saturated and the organization of vessel patterns is multi layered and also it is quite complex the features of vein from sclera.

In order that enhancement is necessary because the vessel patterns are not prominent in the sclera, so here Gabor filter is used to filter out the unwanted part or noise to extract the feature for the further use here local binary patter is used and classifications can be done to ID person and discover the diseases.

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