Tag: Anaconda

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Analysis Of Energy Consumption In India Python Project

Energy is one of the most important resources available to man and it is necessary to keep a check on the growing need for energy day by day.

The Issue of the availability of Energy is getting prominent these days. So to analyze the consumption of energy and production of Energy via available Energy Resources is important.

The project describes the consumption of energy resources of all states of India in the last few years with respect to the population of India state-wise and predicts the future energy requirements for every state.

INTRODUCTION

India is a growing economic superpower. At this point in time, we are sitting at the tip of our economic explosion. The vast reserves of resources in all factors of production have earned us the title of The Land of Potential. But this comes at a cost, with this growth potential comes the need to satisfy the potential through the generation of energy.

To meet this challenge of growing energy is very important for India and it is even more important to predict the future requirements of energy in our country.

If we are able to predict the energy required in the future it will boost the potential of the country and increase the overall growth in every field

Background and Basics:

The programming language Python is very useful for the analysis of data in every field.
Python has been used to show the analysis of data in a diagrammatical format like a Pie Chart, Bar Chart, and Multiple Bar Chart.
It also shows a map of India with respect to the intensity of Energy Consumption as well as the Population of India state-wise. By using Machine Learning.
We have predicted the requirement of the amount of energy for every state using The Linear Regression Machine Learning Algorithm. It uses two parameters on the outcome and one on which the outcome depends
The population has been used as a parameter on which energy depends

Future Use

This program gives a clear idea about the energy requirement in the Future.

Software and Hardware Requirements

Details of software

Python
Anaconda (Spyder) IDE
Required Python Libraries:
Numpy
Pandas
Matplotlib
Tkinter
PIL
Mpl_toolkits.basemapDetails of hardware

Details of Hardware

Working PC

Methodology

The SUBMIT button on the GUI checks the availability of state i.e.it checks the correct state.

The PIE Chart on the GUI plots the energy resource required percentage-wise.
The BAR Chart on the GUI plots the energy resource required percentage-wise.
Flow of Project
Our project takes a dataset of the population from the year 2013 to 2017 and energy requirements in India per state from the year 2013 to 2016.

The data from every set from the years 2013-16 has been used in order to train the machine using linear Regression and data from 2017 for the population has been used in order to test the model to predict the future requirement of energy.

The energy requirements predicted as well as actually have been represented using the map of India i.e. greater the intensity on the map higher the energy required for that state, bar chart, and Pie chart.

Results and Discussion

Pie Chart of Energy Resources of Maharashtra Year 2015
Resource-wise Production of energy

Map of India according to energy consumption

Conclusion

We have used python to show the analysis of data in a diagrammatical format like a Pie Chart, Bar Chart, and Multiple Bar Chart.
It also shows a map of India with respect to the intensity of Energy Consumption as well as the Population of India state-wise.
By using Machine Learning, we have predicted the requirement of the amount of energy in the specified year for each state in India.
Technologies used in the project are Python, Machine learning, and Data Analysis.
This program gives a clear idea about the energy requirement in the Future.

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Detecting Impersonators in Examination Centres using AI

 

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency. In order to solve this problem, an effective method is required with less manpower.

With the advancement of machine learning and AI technology, it is easy to solve this problem. In this project we are developing an AI system where images of students are collected with names and hall ticket numbers are pre-trained using the KDTree algorithm and the model is saved. Whenever a student enters the classroom, the student should look at the camera and enter class, after the given time or class is filled the student’s information will store in a  video file with the student’s name and hall ticket no. The video will have a user with a hall ticket no and name on each face. If the admin finds any unknown user tag on the face admin can recheck and trace impersonators. 

Problem statement:

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency.

Existing system:

Information given in the hall ticket is used as verification to check if the student is the impersonator or not.  Manual security checks performed are not perfect and sometimes students can even change images from the hall ticket.    

Advantages:

Manual verification methods are used for checking personally for each student which is not possible to check each student personally.

Chances of changing images from hall tickets are possible which doesn’t have a verification method.

Proposed system:

  • In the proposed system initially, images of each student are collected and each dataset consists of 50 images of each student. These images are trained using kdtree algorithm using the image processing technique and the model is saved in the system this model can be used for automatic prediction of students in exam halls from live video or images. 

Advantages:

  • The student verification process is fast and accurate with the least effort. Reduces impersonator’s issue with live verification.
  • The time taken for prediction and processing is less and prediction is done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting students at exam centers and display them in the video.  

SOFTWARE REQUIREMENT: 

  •  Operating system:           Windows XP/7/10

  • Coding Language:           python

  • Development Kit             anaconda

  • Library:     Keras, OpenCV

  • Dataset:   any student’s dataset

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Movie Character Recognition From Video And Images Project

Live tracking of characters from movies is important for automating the process of classification for user-friendly information management systems like online platforms where characters in a movie can be seen before watching the movie. At present manual method is used which can be automated using this movie character classification method. The objective of this work is to collect a dataset of any movie characters and train a model which captures the facial features of all characters and the model is saved for prediction. 

For testing purposes, a real-time live video can be used to track characters. This application also works for images where users can give input as images of trained movie characters and get results with character names on the image as output. In this project for training dataset KDTree, the algorithm is used which takes images from a given folder and trains each image and saves the model into a dump file in the system. In the second stage using this trained model input image or input video is predicted with the model and the result is shown as a video or image.

Problem statement:

Classification of characters for each movie manually is a time taking process and the database should be managed.

Objective:

The objective of this project is to develop an automatic classification of characters after training from the dataset. If the one-time model is created it can be used for prediction at any time from images or video

Existing system:

In the existing system movie characters are managed in the database and which are used for displaying when required in this process database is the important to the time taken for processing is more.

Disadvantages:

  • The time taken for processing is more and the database should be managed and integrated with the required system whenever required.
  • This method includes the manual process of data collection and updating and deleting data. 

Proposed system:

In the proposed system initially, a dataset of respected move characters is collected and each dataset consists of 50 images. These images are trained using the KDDTree algorithm using the image processing technique and the model is saved in the system this model can be used for the automatic prediction of characters from live video or images.

Advantages:

  • The time taken for prediction and processing is less and prediction is done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting characters and displays on screens.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:  Python
  • Development Kit: Anaconda
  • Library:   TensorFlow, Keras, OpenCV
  • Dataset:  Any movie dataset

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Canteen Automation System using NLTK and Machine Learning

The canteen automation system project is designed to select the food items from a web application with cost, time of cooking, and give rating for products. This application is designed to help students to order food items without giving orders to waiters or going to the counter and giving orders. Most of the colleges don’t have order-taking system students should directly reach the counter and give an order which is time taking process in order to solve this problem this online order-booking system is designed.

As there will be many students who will be giving orders from different departments as a web application is designed with multiple admins, each department will have one admin who will take request and process request. Another problem is best food from today’s canteen menu can be known by checking ratings given by other users based on that students can give orders. Students can also give reviews for each food item along with ratings. NLT is used to calculate the sentiment of each review by taking the yelp dataset and applying machine learning and NLTK to calculate sentiment and store it in the database.

Proposed system:

  • In the proposed system food ordering is done online and each department has its own admin who handles requests on daily basis, users can give a rating of food items which will help other students to select the food item from the list. Sentiment analysis using Yelp data set and NLTK and Machine learning are used to store the sentiment of each review given by the student.

Advantages:

  • Helps students to give orders from any location inside the campus and save time by reaching the canteen based on the given cooking time from the application.
  • Sentiment analysis is done for reviews using NLTK and Machine Learning. Sentiment and Rating are useful for students to select food items.

SOFTWARE REQUIREMENTS:

 Operating system:  Windows XP/7/10

  • Coding Language:  Html, JavaScript,  
  • Development Kit:  Flask Framework
  • Database:  MySQL
  • Dataset:  YELP
  • IDE:  Anaconda prompt

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Stress Detection from Sensor Data using Machine Learning

Stress is commonly defined as a feeling of strain and pressure which occurs from any event or thought that makes you feel frustrated, angry, or nervous. In the present situation, many people have succumbed to stress especially the adolescent and the working people. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. The current technology, using Galvanic skin response (GSR), Heart rate variability (HRV), and Skin temperature are being used individually to detect stress.

In this project data set is created using five features age, gender, body temperature, heartbeat, and blood pressure, and four stages of labels are used for detecting the level of stress.  A decision tree algorithm is used to train the data set and create a model and use the Flask framework to take input data and predict the stress level of the user. 

EXISTING SYSTEM:

 Existing systems were designed to detect stress by taking tweets as input from the Twitter or Facebook data set and machine learning algorithms are applied to detect stress from tweets.

Disadvantages:

  • Most of the existing system works were on social networking stress data not on body-based sensor data.
  • Stress level is calculated based on tweets posted by users.

PROPOSED SYSTEM:

The proposed system is designed by collecting data from sensors and preparing data set on three features (temperature, heartbeat, age, male or female). Using this data set machine learning Decision tree algorithm is applied using and the model is saved. Front end web application is designed to collect new user features and passed them to the model to predict stress stages which are divided into 4 stages.

Advantages: 

  • Data is collected from real-time sensors and a data set is created for different ages and male and female users.
  • Data is trained using machine learning which helps automate the process of stress detection.
  • The web applications can help users to easily check their stress state based on their features.

Data collection:

  • In this state data is collected from real-time sensors and stored in an excel sheet with five features age, gender, temperature, heartbeat, and this data is applied for machine learning, and a model is created.

Data preprocessing:

  • Features are extracted from the data set and stored in the variable as train variable and labels are stored in y train variable. Data is preprocessing by standard scalar function and new features and labels are generated. 

Testing training:

  • In this stage, data is sent to the testing and training function and divided into four parts x test train, and y test train. Train variables are used for passing to the algorithm whereas tests are used for calculating the accuracy of the algorithm. 

Initializing Decision tree Algorithm:

  • In this stage, the decision tree algorithm is initialized and train values are given to the algorithm by this information algorithm will know what are features and label. Then data is modeled and stored as a pickle file in the system which can be used for prediction. 

Predict data:

  • In this stage, new data is taken as input and trained models are loaded using pickle and then values are preprocessed and passed to predict function to find out a result which is shown on the web application.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:           Html, JavaScript,  
  • Development Kit:        Flask Framework
  • IDE:           Anaconda prompt
  • Dataset:          Stress dataset

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Securing Data Using DES, RSA, AES And LSB Steganography

ABSTRACT:

Data security is the main concern in different types of applications from data storing in clouds to sending messages using chat. In order to provide security for data in the cloud, there are many types of techniques which are already been proposed like AES, DES, and RSA but in existing methods, most of the time only a single type of encryption was used either AES, OR DES, OR RSA based on user requirement but in this system main problem is each encryption is done using encryption keys if these keys are exposed in any case entire data is lost so we need an effective method which can provide more security so in this project hybrid cryptography is used where existing encryption methods are used but three methods will be used.

When the user uploads data will split into three parts the first part will be encrypted using AES, the second part will be encrypted using DES, the third part will be encrypted using RSA  and these three encrypted files will be stored in the cloud and keys used for AES, DES, and RSA are stored in the image using LSB steganography when users want to download total data from cloud-first keys should be retrieved from the image and these keys are used for decrypting data again by using AES, DES and RSA and final data is combined and stored in the file. This method provides more security for data.

OBJECTIVE:

Data security is the main issue in cloud data management there is a chance of developing effective methods like hybrid cryptography for improving security. In this project, AES, DES, and RSA are used along with LSB.

INTRODUCTION:

The cloud is playing important role in data management and is another type of service that provides a secure way of data handling and remote data accessing where users from anywhere can use the cloud for data access. As the cloud is a third-party application where data uploaded by users must provide security features to reduce risks from data attacks in order to do that encryption techniques here are used like AES, DES, and RSA.

EXISTING SYSTEM:

In the existing system, the cloud is used to use any one of the encryption techniques and key verification is done using the identity of the user. Based on application requirements different encryption techniques are used.

DISADVANTAGES:

Only single encryption techniques are used and if keys are not managed effectively there are chances of leakage of keys. 

PROPOSED SYSTEM:

In order to improve security for cloud data compared to existing techniques where keys are shared security between users new hybrid cryptography technique is proposed where three types of encryption are used AES, DES, and RSA, and the LSB steganography technique is used for secure key sharing.

ADVANTAGES:

Data is split into three parts and each part is encrypted using one encryption technique and keys are shared securely by embedding in the image.

SOFTWARE REQUIREMENTS: 

  • Operating system: Windows 7.
  • Coding Language: python
  • Tool: anaconda, visual studio code
  • Database: SQL lite

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Students Marks Prediction Using Linear Regression

Abstract:

Education institutions use new technologies to improve the quality of education but most of the applications which are used in colleges are related to service and development there are web applications that are helping students to take online training and tests. There are very few methods that can help teachers to know about student’s performance. Considering this problem machine learning techniques are used to predict students’ marks based on previous marks and predict results. Linear regression models are used to predict student performance and predict the next subject’s marks.

Problem statement:

Education institutions use web applications for training students and checking performance based on marks but there are no specific steps followed for predicting students’ performance and taking measures to improve performance.

Objective:

Design a machine learning model for the prediction of students’ marks and take measures to improve student performance. The linear regression algorithm is used to train the model and prediction.

Existing system:

Researchers had done work on the automation of grading techniques in which previous marks were used to give grades to students.

Algorithms like association rule mining and apriori algorithms are used for classifying students’ marks.

Disadvantages:

Existing methods mostly work based on marks obtained from exams.

Algorithms are used for classifying students based on marks. 

Proposed system:

The dataset of other subject marks is taken as input and the data set is processed with labels and features then test split is performed on the dataset and then the machine learning model is applied to the dataset then the prediction is performed.

Advantages:

Before the final marks of all subjects are evaluated prediction can be performed.

Using a 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 mark the dataset
  • IDE: Jupiter notebook
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Students Marks Prediction Using Linear Regression Project

Abstract:

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

Problem statement:

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

Objective:

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

Existing system:

  • Researchers have done work on Grading systems in which final examination marks are used for giving grades to 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 that are based on complete data.
  • Early-stage evaluation is not possible in these methods.

Proposed system:

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

Advantages:

  • Before the final marks of all subjects are evaluated prediction can be performed.
  • Using a 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 mark the dataset
  • IDE :           Jupiter notebook

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COVID-19 Data Analysis And Cases Prediction Using CNN Project

ABSTRACT:

Coronavirus ( COVID-19 ) is creating panic all over the world with fast-growing cases. There are various datasets available that provide information on the world affected information. Covid has affected all counties with a large number of cases with a variety of numbers under death, survived, and affected. In this project, we are using a data set that has county-wise details of cases with various combined features and labels.

Covid data analysis and case prediction project provide solutions for data analysis of various counties on various time and data factors and creating models for survival and death cases and prediction cases in the future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for the next few months done using this project.  

PROBLEM STATEMENT:

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

OBJECTIVE:

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

EXISTING SYSTEM:

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

Disadvantages:

  • The data set used for predicting disease is different compared to the one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

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

Advantages:

  • Data analysis and prediction are 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

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File Security Using Elliptic Curve Cryptography (ECC) in Cloud

Abstract:

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

Elliptic Curve Cryptography (ECC) is a 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 keys can give security for data compared to single key encryption. In this project, the ECC algorithm is used for securing data to the cloud and uploading data to the cloud.

Existing system:

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

Disadvantages:

  • These methods are old methods that are used in most applications.
  • They use a single key for encryption and decryption.

Proposed system:

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

Advantages:

  • The time taken for the encryption process is less
  • Multiple keys are used for the 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