Category: Python projects

Welcome to the Python Projects Section.

Find the below some projects we have developed using Python.

  1. Google Finance currency calculator API.
  2. using aprori on website to get relevent data
  3. Game like tictactoe,2048,
  4. Note/content sharing platform for college students
  5. book information parsing tool developed for opencart
  6. AI Crooswords
  7. An algorithm for steganography of multiple heterogeneous files on a single video carrier.
  8. college-administration
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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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Student Coding Assignment Evaluation Using API Project

Abstract:

Data mining in educational institutions is helping to analyze students’ details and provide an effective evaluation system in a short time. With the advancement of new technologies, the student evaluation procedure has changed from manual correction to automating the process of correction and analysis. This student coding assignment evaluation system using API is designed to evaluate students coding correction process through the automation process.

When a student submits an answer to a student’s question online faculty will evaluate coding by sending data to API and get results or error messages. By checking these messages faculty will give marks to students. This process is done through a web application that is developed in a python programming language.  

Problem statement:

Students assignment evaluation is a time taking process for faculty which required a manual process of checking each line of code and giving marks to students. 

Objective:

The coding evaluation process can be automated by using available code-checking API which can be integrated into the college assignment assigning website. Using this process evaluation is completed with just in a click and faculty can give marks based on results.

Existing system:

  • A manual process was used for checking assignments and evaluating results.
  • Data mining techniques were used for evaluation which uses previous coding datasets and predicts results that are not accurate.

Disadvantages:

  • Faculty must check each line of code to evaluate coding and give grading.
  • The time taken for the evaluation process is high.

Proposed system:

The student online coding evaluation system provides an automatic coding checking process through which faculty can assign coding assignments and get results from students and compile code in click and check results and give marks.

Advantages:

  • The entire process of assigning to evaluation is done online and coding evaluation is done in one click.
  • API is used for checking errors in code and giving grading.

System requirement: 

Programing language: python

Framework: Flask

Database: MYSQL

API: for compiling code

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Cyber Bullying Detection Using Machine Learning Project

Abstract:

Cyberbullying is the process of sending wrong messages to a person or community which causes heated debate among users. Cyberbullying is mostly seen on social networking sites where users reply to post with bullying words to threaten or insult other users. Cyberbullying is considered a misuse of technology. According to the latest survey done all over the world data day by day, cases are increasing on cyberbullying.

In order to solve this problem many natural language processing techniques are proposed by various authors which are time taking and not automatic. With the advancement of machine learning and artificial intelligence, models can be created and automatic detection can be implemented. To show this scenario live chat application is developed in python programming with multiple clients and one server and the Naive Bayes algorithm is used to train the model on a Twitter dataset using this model live detection of cyberbullying is predicted and alert messages are shown on the chat application.

Problem statement:

Social networking and online chatting applications provide a platform for any user to share knowledge and talent but few users take this platform to threaten users with cyberbullying attacks which causes issues in using these platforms.

Objective:

To provide a better platform for users to share knowledge on social networking sites there is a need for an effective detection system that can automate the process of cyberbullying detection and take decisions.

Existing system:

  • Techniques like unsupervised labeling methods which use N-gram, and TF-IDF methods to detect cyberbullying are used which use the youtube dataset to detect attacks.
  • A support vector classifier is used to train models for detection.

Disadvantages:

Techniques that are used in the existing system are not automated they need time to process requests and update responses.

Social networking and chatting sites require automated detecting and processing methods.

Proposed system:

Cyberbullying detection is designed using machine learning techniques. The Twitter data set is collected with features and labels and the mode is trained using the Naive Bayes algorithm the trained model is applied to a live chatting application that has multiple clients and a single server. For each message, cyberbullying is detected using the model and then alert messages are posted on chat boards.

Advantages:

The cyberbullying detection process is automatic and time taken for detection is less and it works in a live environment. 

The latest machine learning models are used for training models that are accurate.

Software Requirement:

Programming language: python

Front End GUI : tkinter

Dataset: Twitter cyberbullying dataset

Algorithm: Naive Bayes

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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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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
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Stock Market Analysis Python Project Report

Stock Market Analysis and prediction is a project for technical analysis, visualization, and estimation using Google Financial data. Seeing data from the market, especially some general and other software columns. Pandas used to take stock of the information, looked at different aspects of it, and finally looked at it in some way to assess the risk of a stock based on its recent performance history. Competing with the Monte Carlo method in anticipation of future prices.

OVERVIEW

Stock exchange analysis is only intended for the analysis of stock company data for various organizations. Using this method of data analysis, any organization can easily extract relevant information.

AIM OF THE PROJECT

The main goal of my project is to analyze the data of all the institutions in which form we need.

PROBLEMS FOR THE ANALYSIS

Share financial data with quandl for the following companies:

  • Apple
  • Amazon
  • Microsoft
  • Google


Perform basic data analysis

  • Get last year’s data
  • Check Apple values
  • Indicate the final price
  • The stock market has seen a rate hike
  • Gather all the company data together for the final price


Make daily return analyzes and show the relationship between the different stocks

  • Percentage change plan for Apple product
  • Find a shared website for Apple and Google
  • Use PairPlot to show the relationship between everything

Perform risk analysis


CONCLUSION AND FUTURE SCOPE


We evaluated two basic measurements of the analysis and found no conclusive evidence about their estimated value.
These predictions are also very long-lasting and will see a year in the future. Suggestions on this scale are not the main project time. Instead, we will focus on predicting daily market trends. Because of these problems, we avoided basic analysis.

Performing risk analysis Results:

Download the attached  Stock Market Analysis python Project Report

 

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Python Academic Projects for Students

These are the Academic Python Projects for Final Year Students. Download the complete project code, report ppt

  1. University Exam Center Management System Project:

The main aim of developing this application is to provide a complete online examination system for the university

Modules & Users of the System:

  • Administration
  • Head Of the Department
  • Vice Chancellor
  • Department

The Functionalities involved in the project are below:

  • Exam Center Allotment  Information Page
  • Exam Center  Information Page
  • Institute  Information Page
  • Panel  Information Page
  • Papersetter Appointment  Information Page
  • Papersetter Bank  Information Page
  • Papersetter  Information Page
  • Paper Print Order  Information Page
  • Payment  Information Page
  • Program  Information Page
  • Subject  Information Page
  • Time Table  Information Page
  • User  Information Page

Download the complete project on University Examination Center Management System Project.

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Village Development System Python Project

ABSTRACT

A social forum for villagers to be held so that they can spread the problem, improve it and anyone in the world can see and answer. 3D images of wells, data visualization, data analysis. Previously, If any problem occurs in the village, people have to go and ask higher authorities, and also there is no interaction between people and higher officials. So they can only solve their own problems because of no communication between Rural and Urban people. Everything is digitized, People can easily share their problems all over the place, So here in this paper we created a platform that anyone (village people, officers, common people)can log in to the system and do the operations, This project also having a special feature called Prediction, Farmers can easily predict the agriculture fluctuations based on the previous data, This feature helps to people when they will do the agriculture, this project also involves farms, wells, houses.  Anyone can interact with any person and post their problems. This application is implemented using Python, Django, and databases like DB SQLite.

INDEX TERMS: Village management, Farmers, Problems.

Existing system

Previously, If any problem occurs in the village, people have to go and ask higher authorities, and also there is no interaction between people and higher officials. So they can only solve their own problems because of no communication between Rural and Urban people.

Modules:

Admin module: Such administrative help you change FirstSearch to serve the needs of the user. This module provides information that acts as the backbone of the remainder of the system. The security issue is dealt with through the module that discussed the rights of users.

Volunteer module:  Volunteer modules allow you to help people in the village who provide services such as medical care, roads, transport, etc. Model volunteer work is crucial to understanding criminal needs and providing good support.

Reporter module: The reporter module allows for the unattended processing of alarm signals and the reporters are employed to report the news.

Farmers Module: The farmer module can add their Problems to add in the Website

Problems Module: If any Problems occurred in the village entered into this Application.

Software Requirements:

OS: Windows

Python IDE: Python 2.7.x and above

Pycharm IDE

Setup tools and pip to be installed for 3.6.x and above

Hardware Requirements

 RAM:  4GB and Higher

Processor:  Intel i3 and above

Hard Disk: 500GB Minimum

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Article Rewrite or Plagiarism Remover Project in Python

Abstract:

Maintaining data uniqueness is one of the important features for many areas like in colleges and universities Plagiarism check and data uniqueness is one of the main criteria for preparing a paper of paper publishing. In order to maintain plagiarism free content, there is a need for effective methods wherein the existing method used should rewrite entire content manually if there are many pages of content to be written then it takes a lot of time which is time taking process.

With the advancement of machine learning and artificial intelligence, we can develop an application that can automate process of content rewriting. In this project, we are developing an application in python named article rewriter or plagiarism remover in python which will rewrite entire given content in a short time. In this project, Natural language processing is used in which text summarizer libraries are used. In this project, we also compare the output of different text summarizer algorithms.

Existing System:

In the existing system in order to remove plagiarism for the content manual process was involved in which the user should understand each meaning of the sentence and rewrite the entire content with its own words. Which is time taking process.

Proposed System:

In this project, NLP is used for understanding input text or data from URL and then summarize text and rewrite entire content in a short time by using a text summarizer algorithm.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

 Operating system: Windows 7 or 10.
 Tool :Anaconda ( Jupiter )

SOFTWARE REQUIREMENTS:

 Software :Python 3.5
 Dependencies : numpy
 Libraries: pandas, keras, scipy, sklearn,NLP