Database project bookstore

ABSTRACT:
Online Book Store consists of a web site that stores the details of all the books in a book store and customers can login and order the books they are interested in.
There are two kinds of users here, namely Manager and Customer. A Manager will maintain the database by performing actions such as check the stock, add, delete, modify and order the books if the number of books is less than the threshold. A customer, can search the book by book title and order the books if they are interested in and ship it by ground shipping or express shipping.
The order is made using some assumptions of variables such as a auto generated customer order number, that keeps track of the order made by the customers. The quantity of the book is deducted from the database, once it is shipped to a user.

FUTURE WORK:
There may be lot of improvements made in the system, such as the security options, improved user interface and pop up windows.

Technology Used:

• Web site design tool:
Adobe Dreamweaver CS4
• Code standard:
HTML, PHP
• Server:
Apache
• Database:
MYSQL

ER Diagram:














ER mapping:

Job Applicant’s Profile Evaluator using KNN

INTRODUCTION:

K Nearest Neighbor (KNN) is the supervised data mining pattern recognition algorithm. It classifies objects based on closest training exam-nearest neighbor algorithm. It is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors. K is small positive integer and it is usually previously set.
Job applicant’s profile evaluator using KNN analyzes the status of the current job applicant based on the applicant’s details and classifies the applicant to the group of jobs that the applicant can apply.

DATASET DESCRIPTION:

Training data is the existing assignments of the jobs.

Sample Training Data:
A G 3.0 CS 2
B UG 2.5 ANY 3
C G 3.0 MPH 5

Test data is the details of the Job Applicant.

Sample Test Data:
G 3.5 CS 5

Test Data Description:
Training data has:
Class Name in 1st column
Qualification in 2nd column
GPA in 3rd column
Department in 4th column
Years of experience in 5th column

Training Data Description:
Qualification in 1st column
GPA in 2nd column
Department in 3rd column
Years of experience in 4th column

APPROACHES:

After calculating the group to which the Job Applicant belongs to, the list of jobs that the Job applicant can apply is displayed.

The algorithm of the k-nearest neighbor that we apply in our project is as follows,
1. Calculate the “distance” from the test record to the training records.
2. Find the “k - nearest” training records.
3. Check the majority class from the k – nearest training records.
4. The class label for the training record is predicted as the class with the majority votes/weight among the k – nearest training.

We are classifying the job applicants based on their details into different classes of jobs.

Group A: {Graduate Assistant, Research Assistant}
Group B: {Lab Assistant, Desk Clerk, Night Clerk}
Group C: {Shuttle driver, Receptionist}

The application has been developed using C# .NET.

FUTURE WORK:

• Convert the Windows implementation into Web Application.
• Provide direct application process to the jobs by taking the applicant’s details.