Posted on

Students Marks Prediction Using Linear Regression

Abstract:

Analyzing and prediction of academic performance is important for any education institutions. Predicting student performance can help teachers to take steps in developing strategy for improving performance at early stages. With the advancement of machine learning supervised and unsupervised techniques developing these kinds of applications are helping teachers to analyze students in better way compare to existing methods. In this student marks prediction using Linear regression project students’ academic performance is prediction considering input as previous students marks and predict next subject marks and accuracy of the model is calculated.

Problem statement:

Analyzing and prediction of marks for students was done based on guess and students’ personal marks details are not considered for academic evaluation.

Objective:

Machine learning based data mining techniques are used to automate process of student performance prediction using linear regression technique.

Existing system:

  • Researches has done work on Grading systems which final examination marks are used for giving grades for students and evaluation of each student is done.
  • Association rule mining and apriori algorithms are used for classifying students based on their marks

Disadvantages:

  • Most of these methods work on data mining techniques which are based on after completing data.
  • Early stage evaluation is not possible in these methods.

Proposed system:

  • Students marks of other subjects are taken as input for evaluation students’ performance. Data set is pre-processed and features and labels are extracted from dataset then dataset is split in to test and train sets then linear regression is applied to dataset for prediction.

Advantages:

  • Before final marks of all subjects are evaluated prediction can be performed.
  • Using machine learning process automation of marks prediction can be done.

SOFTWARE REQUIREMENTS:

  • Operating system                  : Windows XP/7/10
  • Coding Language            :            python 
  • Development environment : anaconda, Jupiter
  • Dataset                         : students marks dataset
  • IDE :           Jupiter notebook
Posted on

COVID-19 Data Analysis And Cases Prediction Using CNN

ABSTRACT:

Corona virus ( COVID-19 ) is creating panic all over the world with fast growing cases. There are various datasets available which provides information of world-wide effected information. Covid has affected all counties with large number of cases with variation of numbers under death, survived, effected. In this project we are using data set which has county wise details of cases with various combined features and labels. Covid data analysis and case prediction project provide solution for data analysis of various counties on various time and data factors and creating model for survival and death cases and prediction cases in future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for next few months are done using this project.  

PROBLEM STATEMENT:

      With the increase of COVID 19 cases all over the world daily predictions and analysis is required for effective control of pandemic all over the world

OBJECTIVE:

    By collecting data from Kaggle and new York dataset data preprocessing is performed and data analysis is performed on dataset and machine learning model is generated for future prediction of cases.

EXISTING SYSTEM:

  • Prediction was performed on COVID 19 cases based on different machine learning techniques which are based on x ray data set collected from COVID 19 patients.
  • Disease prediction from x ray images is done using deep learning techniques.

Disadvantages:

  • Data set used for predicting disease is different compare to one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

Using data set pre-processing is performed on the collected data set and various steps for deep learning model is performed and prediction of cases is done then data analysis is done on various factors.

Advantages:

  • Data analysis and prediction is performed on textual data
  • Deep learning models are generated for predicting future cases.
  • Data analysis is performed for various factors.

SOFTWARE REQUIREMENTS

  • Operating system                   :           Windows XP/7/10
  • Coding Language              :           python
  • Development Kit                  :        anaconda 
  • Programming language: Python
  • IDE                                               :           Anaconda prompt
Posted on

File Security Using Elliptic Curve Cryptography (ECC) in Cloud

Abstract:

Data security in cloud computing is a mostly researched topic which has various solutions like applying encryption to data and using multi cloud environment. But still there are many issues related to data security. In this project we are using ECC digital signature method to sign signature of user data while uploading to cloud and use same digital signature to download when required.

The Elliptic Curve Cryptography (ECC) is modern family of public-key cryptosystems, you can use an Elliptic Curve algorithm for public/private key cryptography. To be able to use ECC; cryptographic signatures, hash functions and others that help secure the messages or files are to be studied at a deeper level.

It implements all major capabilities of the asymmetric cryptosystems: Encryption, Signatures and Key Exchange The main advantage is that keys are a lot smaller. With RSA you need key servers to distribute public keys. With Elliptic Curves, you can provide your own public key.

In python, the above described method can be implemented using the   ECDSA Algorithm. 

Objective:

  • Using public key cryptosystems with both public and private key can give security for data compare to single key encryption. In this project ECC algorithm is used for security data to cloud and uploading data to cloud.

Existing system:

  • AES, DES are mostly used crypto graphic algorithms for securing data. These methods are used in most of the applications which use single key for encryption and decryption.

Disadvantages:

  • These methods are old methods which are used in most of the applications.
  • They use single key for encryption and decryption.

Proposed system:

  • In cloud environment data security is very important as data is stored in third party servers there is need to effective multi key encryption techniques like ECC algorithms. In this project we are using ECC algorithm in python language and using cloud to store encrypted data.

Advantages:

  • Time taken for encryption process is less
  • Multiple keys are used for encryption and decryption process.

Architecture:

Software Requirement: 

  • Operating system           :           Windows XP/7/10
  • Coding Language           :           Html, JavaScript,  
  • Development Kit             :        Flask Framework
  • Database                             :           SQLite
  • IDE                                          :           Anaconda prompt
Posted on

Crop Yield Prediction using KNN classification

ABSTRACT:

Agriculture is considered as import field all over the world where there are many challenges in solving problems in the process of estimating crops based on the conditions. This has become a challenge for developing countries.  Using latest technologies many companies are using IOT based services and Mechanical technology to reduce manual work. These methods are mostly useful in the case on reducing manual work but not in prediction process. In this project crop yield prediction using Machine learning latest ML technology and KNN classification algorithm is used for prediction crop yield based on soil and temperature factors.  Dataset is prepared with various soil conditions as features and labels for predicting type of each label is related to certain crop. In prediction process user can give input as soil features and result will be type of crop suitable for specific conditions and application also helps in suggesting best crops with yield for hector.

PROBLEM STATEMENT:

  • In our country large amount of population are depending on agriculture though government is taking financial steps to help farmers still they are facing problems due to lack of data analysis and prediction on crops.

OBJECTIVE:

  • Our objective is to develop an application using machine learning for predicting which crop to be used based on soil condition using k nearest neighbor classification.

Existing system:

          Image based analysis was one of the methods which was previously used for detection land type and then analysis was done.

Disadvantages:

         Process is based on image analysis results are not accurate as in this method soil conditions are not considered.

       Image processing is a time taking process.

Proposed system:

        Machine learning is the latest technology which python programming language gives advantage in using various algorithms for crop yield prediction based on the input data set. In this process KNN classification algorithm is used for prediction. In this project testing training is performed on given text dataset which includes soil and temperature conditions as features and type of crop as labels.

Advantages:

        Crop yield prediction is performed based on textual dataset and any user can check type of crop best suits for conditions and get crop suggestions. 

 

System Requirement:

  • Operating system         :           Windows XP/7/10
  • Coding Language :           Html, JavaScript, 
  • Development Kit :        Flask Framework
  • Programming language: Python
  • IDE :           Anaconda prompt
Posted on

Deal Tracker System Groovy, XML, CSS, HTML Project Report

The Deal Tracker system automates the creation, monitoring, and management of investment opportunities using common tools for all types of assets. Deal Tracker is an effective tool for tracking activities and tracking key factors that affect activities. This allows managers to record transaction information on a regular basis. Note that managers can revise contracts and change the status of existing contracts. Contacts are sent via email and pop-ups to customers and managers. Debts and expenses can be estimated by clicking the mouse before the transaction is completed. Relationships between different exchanges in the form of evidence disagree on different dates.

Deal Tracker is up and workflow. Workflow is a series of related steps. It emphasizes the concept of flow and looks like a collection of real work. Each step is done quickly and completed before the next step begins. They also work as a real concept for real work. It serves and promotes human-machine relationships. Workflow concepts are closely related to other concepts that are used to define the organizational structure, such as functions, structures, policies, and leadership.

PROJECT DESCRIPTION

With data monitoring, the Request Manager can register new data information directly. Note that managers can revise contracts and change the status of existing contracts. Customers and managers are notified of communications, popups, and email services. It allows you to simplify the effectiveness of business activities ahead of different areas without wasting much time. Workflow is a series of related steps. It emphasizes the concept of flow and looks like a collection of real work. Each step is done quickly and quickly and completed before the next step begins. It also got a real concept for a real job. It works to attract and promote relationships between humans and machines. Workflow concepts are closely related to other concepts that are used to define the organizational structure, such as functions, structures, policies, and leadership.

OBJECTIVE

The main purpose of this tool is to provide an automated way to handle many complex business tasks in a short time. Deal Tracker aims to be a powerful and useful tool that records business activities in seconds. The purpose is also to reuse the system for the same type of data.

SCOPE

Deal Tracker allows us to effectively manage the storage and processing of important information. This business provides the best solution for many activities performed by users every day. This system saves business time and money, facilitates access to data, and provides access to important information in real-time instead of writing and sharing data in an Excel spreadsheet.

PROBLEM STATEMENT

Before there was a Deal Tracker, it was difficult to manage corporate funds from different regions. Managers should keep all contract information in an Excel file and process it. Excel is a spreadsheet program used for calculations, map tools, pivot tables, and more to create new spreadsheets for each activity and save a lot of data for future use. Searching for data becomes a problem. Some operations create difficult situations for data administrators. Some suggestions are missing due to lengthy responses and other comments. As a result of this problem, a new system/device was developed – Deal Tracker.

Download Deal Tracker System Groovy, XML, CSS, HTML Project Report Deal Tracker System Groovy, XML, CSS, HTML Project Report