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fasilofficial / 50 Python ProgramsThis repository contains 50 absolutely simple and beginner friendly python programs. This repository gives you an idea about the basics of python language and libraries like math, random, tkinter, etc..
CarlosLugones / Lugodev Example BotEn esta serie de vídeos te enseño a crear un bot de Telegram usando Python. Vamos a desarrollar un proyecto real desde la idea hasta la puesta en producción.
mkhazaeidev / HelloPythonThis repository contains Python programming educational code. The goal is not just solving a simple example, but rather teaching idea development to nurture a programmer's mindset.
rytisss / RoadPavementSegmentationRepository for all the stuff related with road pavements defect segmentation. Main idea behind the segmentation is to create/approve/test various architectures (modification of autoencoders, such as U-Net) to find the best to do the task. Third-party library priorities: Tensorflow (with Keras frontend), OpenCV and many nifty things related to Python programming language.
ananya2001gupta / Bitcoin Price Prediction Using AI ML.Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
chengsoonong / DidbitsAn unsorted bunch of ideas on practical aspects of machine learning. Mostly in python
H-K-R / Online Judge Solves In PythonPython could learn most effectively by using practice examples. The repository includes examples of fundamental Python ideas. It is encouraged you use the examples as references and test the concepts on your own.
ashvardanian / ScalingElectionsGPU-accelerated Schulze voting method in Python, Numba, CUDA, and Mojo 🔥, using ideas from Algebraic Graph Theory
mkhazaeidev / SayingWelcomeThis repository contains Python programming educational code. The goal is not just solving a simple example, but rather teaching idea development to nurture a programmer's mindset.
binjo / Vmrun PythonA python wrapper of vmrun.exe, which is used to control Vmware. Its idea is based on Alexander Sotirov's vmrun-ruby. Currently it only support vmware 6.0 or higher version.
mbahomaid / Micro PyA collection of small, useful, and fun Python projects. Bite-sized code for big ideas.
bhaveshjaggi / PestDetectionPEST DETECTION USING IMAGE PROCESSING e The principal idea which empowered us to work on the project PEST DETECTION USING IMAGE PROCESSING is to ensure improved and better farming techniques for farmers. Our Solution: The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops and also much less use of pesticides which can ultimately lead to better crop management and production. The following softwares are required for the project: OpenCV with C++/Python : It is a library which is designed for computational efficiency with a strong focus on real time applications. Pest Detection System Following are the image processing steps which are used in the proposed system. >Color Image to Gray Image Conversion Therefore, images are converted into gray scale images so that they can be handled easily and require less storage. The following equation shows how images are converted into gray scale images. I(x,y)=0.2989*B +0.5870*G +0.1140*B > Image Filtering The PSNR value is calculated for both the average and median resulting images .The average filter provides better result as compared to the median filter. So this paper uses average filter for further processing. > Image Segmentation To detect the pests from the images, the image background is calculated using morphological operators which is most critical after this image is subtracted from the original image. So the resulting image will only have the objects with pixel values 1 and background pixel values 0. >Noise Removal Noise contains dew drops, dust and other visible parts of leaves. As only the object of interest was to be visible on the images,so the aim was to remove the noise to get better and effective results. The Erosion algorithm has been used to remove isolated noisy pixels and to smoothen object boundaries . After noise removal,the next goal was to enhance the detected pests after segmentation which was performed by using the dilation algorithm. >Feature Extraction Different properties of the images are calculated on the basis of those attributes using which image is classified. For image properties, gray level co-occurrence matrix and regional properties of the images are calculated. These properties are used to train the support vector machine to classify images. >Counting of the pests on the leaves is the main purpose, so that it can give an idea of how much pests are there on a leaf.It uses Moore neighborhood tracing algorithm and Jacob's stopping criterion Feasibility: The present framework of pest detection is quite tedious and laborious for the farmers as they have to carry out their acre-acres surveys themselves and it requires a lot of vigorous efforts to achieve the same.Image analysis provides a realistic opportunity for the automation of insect pest detection.Through this system, crop technicians can easily count the pests from the collected specimens, and right pests’ management can be applied to increase both the quantity and quality of production. Using the automated system, crop technicians can make the monitoring process easier. So in order to bring enhancements in the system,we came up with more productive and well organised system with our idea .Due to this automaton applied,lucrativeness increases and labour is reduced.
Ryujin-K / Cloudscraper RsEarly-stage Cloudflare challenge solver bringing Python's Cloudscraper ideas to Rust
nealholt / Python Programming CurriculaThis repo contains 3 years (2 semesters each year) of high school level Python programming curricula. It incorporates content from many sources, not all of which are my own. I hope it will be of use to new and veteran educators alike. Note that it is based on a mostly-self-paced points system in which each project completed contributes toward a student's total points for the semester. Check out the spreadsheets at the bottom of each of the three main sub-folders to get an idea of how I implemented my point system.
bozhu / IDEA PythonA Python implementation of the block cipher IDEA
augusto539 / Reserva Automatica De Turno Para Pasaporte Consulado De Italia En MendozaEste escript de python esta echo con la idea de reservar de manera automatica un turno para sacar el pasaporte italiano en la paguina web del consulado de Mendoza Argentina
robert-krasinski / JiraAndPythonForManagersIdeas on how to use Jira with Python
yatengLG / Leetcode Pythonpython刷leetcode记录。项目含有详细的代码注释和解题思路，并配有对应的leetcode中英文题目。The project contains detailed code comments and solution ideas(chinese), and has corresponding leetcode question content(Chinese and English).
priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image ClassificationUsing-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.
uttkarshparmar50 / LIBRARY MANAGEMENT SYSTEM L I B R A 1-Project Title “Library Management System (L_I_B_R_A)” The “Library Management System” has been developed to override the problems prevailing in the practicing manual system. This software is supported to eliminate and in some cases reduce the hardship faced by this existing system. Moreover this system is designed for the particular need of the institution to carry out operating in a smooth and effective manner. 2-Domain Library management institutional management non-profitable organization. 3-Problem of Statement In our existing system all the transaction of books are done manually, so taking more time for a transaction like borrowing a book or returning a book and also for searching of member and books. Another major disadvantage is that to preparing the list of book borrowed and the available book in the library will take more time, currently it is doing as a day process for verifying all records. So after conducting he feasibility study we decided to make the manual library management system to be computerised. Proposed system is an automated library management system. Through our software user can add member, edit information, borrow and return books in quick time. Some of the problems being faced in faced in manual system are as follows: • Fast report generation is not possible. • Tracing a book is difficult. • Information about issue/return of the books is not properly maintained. • No central database can be created as information is not available in database. All the manual difficulty in managing the Library have been rectified by implementing computerization. This application is reduced as much as possible to avoid errors while entering the data. It also provides error message while entering invalid data. No, formal knowledge is needed for the user to use this system. Thus by this all it is user-friendly. Library Management System as described above can lead error free, secure, reliable and fast management system. It can assist the user to concentrate on other activities rather to concentrate on the record keeping. Thus it will help organisation in better utilization of resources. So that’s why I can choose this topic to make it simple. is a sub-discipline of issues faced by libraries and library management professionals. Library management encompasses normal managerial tasks, as well as intellectual that focuses on specific freedom and fundraising responsibilities. Issues faced in library management frequently overlap with those faced in managing Title: L_I_B_R_A Page 5 of 16 TMU-CCSIT Version 1. 4-Project Description The software to be produced is on Library Management System. A library card will also be provided to the customers who visit daily. A person can also borrow the book for particular days. All the information will be entered in the system. If the person doesn’t return the due book. Admin has the authority to add, delete or modify the details of the book available to/from the system. He also has the authority to provide username and password for the receptionist. He can also add the details of the book purchased from shops along with the shop name. Project plan Requirement Design Process description implementation STATE DIAGRAM : LIBRARIAN OBJECT Title: L_I_B_R_A Page 6 of 16 TMU-CCSIT Version 1. 4.1-Scope of the Work This project is helpful to track all the book and library information and to rate the maximum number of books, the students wished to allot books. The software will be able to handle all the necessary information related to the library. From a librarian perspective, the Library Management System Project enhanced searchable database for the search books, managing library members, issuing and receiving books . • Search Books, Managing Library Members, Issuing and Receiving Books: An enhanced atomized system is developed to maintain Books, Authors, Issuing and Receiving Books and maintain the history of transaction. • To utilize resources in an efficient manner by increasing their productivity through automation. • It satisfies the user requirement. Title: L_I_B_R_A Page 7 of 16 TMU-CCSIT Version 1. 4.2-Project Modules • Books: This module consist the details of the books available in library and their categories. Title: L_I_B_R_A Page 8 of 16 TMU-CCSIT Version 1. • Member Account details To issue an book from the library, one should have a account in the library. The registration contains all the details about the member like registration number, name, address, contact number etc.. • Book Request: This module is used by the member to request a book from the library. The search can be performed by using name of the book, author name, and subject name. Title: L_I_B_R_A Page 9 of 16 TMU-CCSIT Version 1. • Issue of books: This module is used by the librarian to issue a book based on the request made by the members. • Returning Books: In this module the librarian maintains the details of the books returned by the member, which also includes the fine details, damage book details, lost book details. • History: In this module the member can view the details about the previous issued books, requested books and returned books etc. • Reports: This module includes the details about the issued books, returned books, member reports, fine reports, or any damage to the book or details of the book which are not returned. Title: L_I_B_R_A Page 10 of 16 TMU-CCSIT Version 1. 5-Implementation Methodology In this I am trying to give an Idea of “How I can implement the library management system” . FUNCTIONAL DECOMPOSITION OF LIBRARY MANAGEMENT SYSTEM CLASS DIAGRAM OF LIBRARY MANAGEMENT SYSTEM Title: L_I_B_R_A Page 11 of 16 TMU-CCSIT Version 1. DFD OF LIBRARY MANAGEMENT SYSTEM ER DIAGRAM OF LIBRARY MANAGEMENT SYSYTEM Title: L_I_B_R_A Page 12 of 16 TMU-CCSIT Version 1. DATABASE OF LIBRARY MANAGEMENT SYSTEM Title: L_I_B_R_A Page 13 of 16 TMU-CCSIT Version 1. 6-Technologies to be used 6.1 -Software Platform a) Front-end ----python (3.8) ----tk-inter (GUI) b) Back-end -----sqlite (database) 6.2 -Hardware Platform RAM — 8 GB Hard Disk — not used OS — Mac OS (Mojave-10.14.6) Editor — idle (Available with python package) Processor — 1.8 GHz intel core i5 6.3 -Tools No tool used. 7-Advantages of this Project Our proposed system has the following advantages. User friendly interface Fast access to database Less error More storage capacity Search facility Look and feel environment Quick transaction 8-Future Scope and further enhancement of the Project o In future we can make this application online so that members will be able to search the book from any places as well as can send book request. o Book reading facility can be provided through on-line. o In the area of data security and system security. o Provide more online tips and help. o Implementation of ISBN BAR code reader Title: L_I_B_R_A Page 14 of 16 TMU-CCSIT Version 1. 9-Project Repository Location S# Project Artifacts (softcopy) Location Verified by Project Guide Verified by Lab In-Charge 1. Project Synopsis Report (Final Version) https://s.docworkspace.com/d/AEsSC- 7eqLpR6Z6S_OSdFA Tushar mehrotra Name and Signature 2. Project Progress updates Name and Signature Name and Signature 3. Project Requirement specifications Name and Signature Name and Signature 4. Project Report (Final Version) Name and Signature Name and Signature 5. Test Repository Name and Signature Name and Signature 6. Any other document, give details Name and Signature Name and Signature 10-Team Details Project Name Course Name Student ID Student Name Role Signature LIBRARY MANAGEMENT SYSTEM INDUSTRIAL TRAINING(PYTHON) (ECS 509) TCA1809026 UTTKARSH PARMAR Developer 11-Conclusion “Library Management System” allows the user to store the book details and the customer details. This software package allows storing the details of all the data related to library. The system is strong enough to withstand regressive yearly operations under conditions where the Title: L_I_B_R_A Page 15 of 16 TMU-CCSIT Version 1. database is maintained and cleared over a certain time of span. The implementation of the system in the organization will considerably reduce data entry, time and also provide readily calculated reports. 12-References • Website http://www.wikipedia.com http://www.sololearn.com And also my mentor from Ducat (Noida) • Book Python-basic-handbook ( writer- vivek Krishnamoorthy, jay Parmar, mario pisa pena)
