Human Computer Interaction Using Iris,Head And Eye DetectionLast Updated on May 3, 2021
HCI stands for the human computer interaction which means the interaction between the humans and the computer.
We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.
We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.
It is our moral responsibility to make it accessible to disabled people.
So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.
It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.
It also uses the formula theta equals tan inverse of b/a to calculate the deviation.
Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.
Here we actually build the neural network and train it to predict
Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.
As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.
Retail Analysis Of Walmart DataLast Updated on May 3, 2021
One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An
ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.
Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.
This is the historical data which covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:
- Store - the store number
- Date - the week of sales
- Weekly_Sales - sales for the given store
- Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week
- Temperature - Temperature on the day of sale
- Fuel_Price - Cost of fuel in the region
- CPI – Prevailing consumer price index
- Unemployment - Prevailing unemployment rate
Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13
Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13
Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13
Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13
Basic Statistics tasks
- Which store has maximum sales
- Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation
- Which store/s has good quarterly growth rate in Q3’2012
- Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together
- Provide a monthly and semester view of sales in units and give insights
For Store 1 – Build prediction models to forecast demand
- Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.
- Change dates into days by creating new variable.
Select the model which gives best accuracy.
Real Estate Price PredictionLast Updated on May 3, 2021
People looking to buy a new home tend to be more conservative with their budgets and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. The goal of this project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this project involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.
Housing prices are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. In this project. house prices will be predicted given explanatory variables that cover many aspects of residential houses. Thus, there is a need to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. This project uses random forest algorithm to predict prices by analyzing current house prices, thereby forecasting the future prices according to the user’s requirements. The goal of this project is to create a regression model that are able to accurately estimate the price of the house given the features.
Worklet Allocation SystemLast Updated on May 3, 2021
Innovation and Technology have granted numerous opportunities for people around the world who are in need of employment. It has created new marketplaces that offer stable economic benefits which were never thought of before. However, in this modern society, with a plethora of media & mass communication approaches, people offering domestic services still struggle to find jobs on their own and most of them end up joining agencies which take away a significant portion of their income. Services such as home repairs, beauty, and cleaning can be provided at much cheaper rates if the workers are approached directly without any inefficient middlemen.
A web-based home services marketplace is a more convenient and efficient way for people to locate, hire, and provide feedback about nearby domestic employees who are willing to provide their services as per the customer’s requirement. Our proposed system aims to hire skilled workers and connect them to the right clients based on locational proximity. India has a huge demand for these kinds of services and a platform such as this can be used to cater to them.
The aim of this project: to provide a worklet-servicing application, capable of managing its workers employed in a variety of fields as well as its clientele who enlist the services on a day-to-day basis. Our Algorithm aims to match the client to the best service professionals as per their need that is closest to their location in a shorter time period with the help of effective allocation algorithms such as the Shortest Job First and the Banker’s Algorithm.
Project is built in NodeJS,MongoDB as well as presently algorithm runs in python which needs to called as an API in future enhancements
In this application there are three interfaces Admin,Customer,Client
Client is one who needs the services on a daily basis, Customer is one who needs the services for some period of time in a day.
Admin has the access to the workers data and live tracking of their location where they are working.
To decide for how many hours the service is required we made a questionaire through which a rough estimation of time can be done to allocate the workers.
Future enhancements of the project are -
- We intend to add the feature where a worker can give their attendance for the day right from within the mobile application and possibly add a chat feature in order to let them communicate with the consumer.
Age And Gender DetectionLast 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 detect.py --image image_name
- Detecting Gender and Age of face through webcam Use Command :