Library Management System Project

Last Updated on May 3, 2021


A library management system also known as an automated library system is software that has been developed to handle basic housekeeping function of a library. It is well organized software solution for a library. It help to provide information on any book present in library to the user as well as staff member. It keeps a track of book issued, returned and added to library.

The technologies are used in this project are PHP and MySQL. The Web server which is used in the library Management System is Apache 3.0 and the Java version is Wamp 1.7.1. Backend Database is MySQL. IDE is used to create the project is notepad++ 7.6.

The library management system has been computed successfully and was also tested successfully by taking "test cases". It is user friendly and has required option, Which can be utilized by the user to perform the desired operation. The software is developed using PHP as front end and MySQL as backend in windows environment.

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Ai Based Attendance System Using Neural Net

Last Updated on May 3, 2021


This repositoy contains two folders : Problem Statement Part 1 (For Hackathon Model) and Problem Statement Part 2 (For report on FCN in Autonomous Vehicle)

The model for Hackathon is trained using the idea that each folder in Trainset is a department for the comapny with image of each employee (both passport and selfie). This was necessary for now as training the model using each employee's picture was not possible due to availability of less documents.

The above code for task 1 of Deep Learning CV Hackathon is trained on the data set in the link

Hackathon.ipynb is the trained model that was trained in GOOGLE COLAB, so I would recommend you to run the hackathon.ipynb on Google Colab, in case you don't want to use !mkdir or !unzip in your code, you can run the file in jupyter even, after commenting these statements. is the required python file that takes in two arguments : the selfies image and passport image along with the extension and directory ( if not present in the same folder as the model). The file should be run on python IDE , not on Command Prompt as this has been programmed accordingly.

haarcasccade_frontalface_default.xml file has been used for Face Region of Interest detection. This needs to be in the same folder as Hackathon.ipynb and

The model is saved by the name of saved_model .

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Wholesale Management And Online Shopping Website

Last Updated on May 3, 2021


 Web Application for Wholesale Management System provides businesses with a simplified and strategic way of generating and receiving invoices, tracking orders, monitoring sales, purchase order and inventory maintenance.  


•Manage brand, category and subcategory details of products.

•Manage product details and minimum product stock details

•View product review given by online customers

•Generate inventory, product review reports.

•Manage raw material and their minimum stock details

Sales order:

•Manage sales order of Products and generate invoice for offline customers.

•Generate weekly, monthly, yearly sales summary report.

Purchase order:

•Manage purchase order for raw material and generate invoice order.

•Generate purchase summary report for purchased raw material.


Online order:

•Manage Online Orders

•Update Order Status of purchased product such as shipped, order place, delivered etc. 

•Generate Invoice for placed order.



•Manage Supplier Details


•Manage Employee Details


•Manage Expense Details like rent, maintenance, light bill, Food bill etc.

•Generate weekly,monthly ,yearly Expense report.


•System provides search facility on customer name, Order Placed, date of order, date of order dispatch, date of transaction, transaction amount, etc.

•System maintains details about placing order/dispatch order i.e., Order placed.

•Customers browse the Product catalog and able to search result.

•Customer is able to give Product review.



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Retail Analysis Of Walmart Data

Last 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.


Dataset Description

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


Holiday Events

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


Analysis Tasks

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


Statistical Model

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.

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Last Updated on May 3, 2021


The Objective of this problem is to predict whether a person is ‘Defaulted’ or ‘Not Defaulted’ on the basis of the given 8 predictor variables.

The data consists of 8 Independent Variables and 1 dependent variable. The Independent Variables are I. Age: It is a continuous variable. This feature depicts the age of the person. II. Ed: It is a categorical variable. This feature has the education category of the person converted to numerical form. III. Employ: It is a categorical variable. This feature contains information about the geographic location of the person. This column has also been converted to numeric values. IV. Income: It is a continuous variable. This feature contains the gross income of each person. V. DebtInc: It is a continuous variable. This feature tells us an individual’s debt to his or her gross income. VI. Creddebt: It is a continuous variable. This feature tells us about the debt-to-credit ratio. It is a measurement of how much a person owes their creditors as a percentage of its available credit. VII. Othdebt: It is a continuous variable. It tells about any other debt a person owes. VIII. Default: It is a categorical variable. It tells whether a person is a Default (1) or Not-Default (0).

After performing extensive exploratory data analysis the data is given to multiple models like Logistic Regression, Decision Tree classifier, Random Forest classifier, KNN, Gradient Boosting classifier with and without hyperparameter tuning, the final results are obtained and compared on metrics like precision score, recall score, AUC-ROC score.

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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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