Android Sudoku Game App Project with source code

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Sudoku  is an engaging and user-friendly Android application designed to provide a seamless and enjoyable experience for Sudoku enthusiasts. The app offers a classic Sudoku gameplay experience with a sleek and intuitive interface, catering to both beginners and seasoned players.

Key Features:

  1. Intuitive User Interface:
    • The app boasts a clean and user-friendly interface, ensuring easy navigation for players of all skill levels.
    • Intuitive controls make it effortless for users to input numbers and navigate through the game.
  2. Multiple Difficulty Levels:
    • Sudoku Master offers a range of difficulty levels, including easy, medium, hard, and expert, allowing players to choose a challenge that suits their skill level.
  3. Offline Play:
    • The app supports offline play, ensuring that users can enjoy Sudoku anytime, anywhere, without the need for an internet connection.
  4. Responsive Design:
    • The app is designed to be responsive across a variety of Android devices, providing a consistent and enjoyable experience on both smartphones and tablets.

Sudoku Master aims to be the go-to Sudoku app for Android users, combining a classic game with modern features and a visually appealing design. Whether you're a Sudoku beginner or an advanced player, this app promises to deliver a challenging and entertaining experience.

Technology in Used :

  1. Android XML : Page layout has been designed in Android XML
  2. Android : This project has been developed over the Android Platform
  3. Java : All the coding has been written in Java
  4. SQLite : inbuilt database
  5. Android Studio : We have used Android Studio for developing the project

Download Apk File 

Download Source code

Android Text to Speech App Project with Source code

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In our project, we developed an Android application that leverages speech recognition and text-to-speech (TTS) technologies to create a seamless and interactive user experience. The app will empower users to speak into their devices, convert spoken words into text, process the text, and then output the information through synthesized speech.

Key Features:

  1. Speech Recognition:
    1. Utilize the Android SpeechRecognizer class for capturing spoken input.
    2. Implement asynchronous processing to avoid blocking the main thread.
    3. Provide real-time feedback on recognized speech.
  2. Text Processing:
    1. Process the recognized text, enabling features like language translation or sentiment analysis.
    2. Display the processed information in the app's user interface.
  3. Text-to-Speech (TTS):
    1. Integrate the Android TextToSpeech class for converting processed text into synthesized speech.
    2. Allow users to control TTS playback, including options for adjusting speed and pitch.
  4. User Interface (UI):
    1. Design an intuitive and responsive UI with buttons or voice-triggered controls.
    2. Enable users to initiate speech recognition and TTS functionalities easily.
  5. Error Handling and Optimization:
    1. Implement robust error handling for scenarios like failed speech recognition.
    2. Optimize the app for performance, considering resource usage and responsiveness.
  6. Additional Features:
    1. Support multiple languages for both speech recognition and TTS.
    2. Explore cloud integration for enhanced speech recognition capabilities.
    3. Allow customization of settings such as speech rate and pitch.

Technology in Used :

  1. Android XML : Page layout has been designed in Android XML
  2. Android : This project has been developed over the Android Platform
  3. Java : All the coding has been written in Java
  4. SQLite : inbuilt database
  5. Android Studio : We have used Android Studio for developing the project

Download Apk File

Download Source code

 

Android Bouncing ball Project Source Code

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Got some time to kill? Try out my bouncing balls! A casual game where you make and watch bouncy balls bounce around your screen.

  1. Tap the screen to create a ball.
  2. Tap the ball to remove it.
  3. Drag your finger to make balls of any size.
  4. Rotate your device and watch them bounce!

Technology in Used :

  1. Android XML : Page layout has been designed in Android XML
  2. Android : This project has been developed over the Android Platform
  3. Java : All the coding has been written in Java
  4. SQLite : inbuilt database
  5. Android Studio : We have used Android Studio for developing the project

Download Source code

Weapon Detection System Using CNN FLask Web app

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Buy Now ₹1501

ML powered system for detecting weapons within images

Business Problem

  1. Mass shootings have become increasingly prevalent at public gatherings
  • Creating an algorithm that that be integrated into traditional surveillance systems can be used to detect threats faster and more efficiently than those monitored by people
  • In modern surveillance systems, there is a person or group of people, in charge of watching monitors which can span across multiple floors of a given area
  1. Violence on social media platforms such as Youtube, Facebook, and TikTok
  • An algorithm that integrate itself into traditional upload systems can detect violent videos before they are spread on a given website
  • Considering the graphs below, the United States ranks among the top 5 countries in terms of firearm deaths

Solution

  1. Create a neural network that can be integrated into traditional surveillance systems
  2. This neural network will be able to detect whether a firearm is present in a frame, and if so, it will notify authorities/managers of its detection

Requirements

  1. keras (PlaidML backend --> GPU: RX 580 8GB)
  2. numpy
  3. pandas
  4. opencv (opencv-contrib-python)
  5. matplotlib
  6. beautifulsoup

Datasets

Predicting Student Performance Using Machine Learning

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In today's educational landscape, understanding the factors that contribute to a student's academic performance is crucial for educators, parents, and policymakers. This project leverages machine learning techniques to predict a student's performance in mathematics based on various factors. By providing accurate predictions, this tool can help identify students who may need additional support and tailor educational strategies accordingly.

Note: This Project is for Educational Purposes Only

The Student Exam Performance Predictor project is developed for educational purposes to showcase the application of machine learning techniques in predicting student performance. The results obtained from this project are based on a specific dataset and machine learning model, and should not be considered as definitive or accurate predictions for real-world scenarios. The primary goal of this project is to demonstrate the end-to-end process of developing a machine learning model and provide insights into the factors influencing student performance.

This project aims to predict student performance based on various factors such as gender, ethnicity, parental level of education, lunch type, test preparation course, and exam scores. The machine learning model trained on a dataset of student information can provide insights into predicting a student's performance in mathematics.

Features

  1. Predicts student performance in mathematics based on multiple factors.
  2. Provides insights into the influence of gender, ethnicity, parental level of education, lunch type, and test preparation course on student performance.
  3. User-friendly interface for inputting student information and obtaining predictions.

Dataset

The dataset used for training the machine learning model is sourced from Kaggle - Students Performance in Exams. It contains information about students' demographics, parental education, lunch type, test preparation course, and their corresponding math scores.

Model Training

The machine learning model is trained using a supervised learning algorithm, such as a decision tree or random forest, to predict the math score based on the input features. The dataset is split into training and testing sets to evaluate the model's performance.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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Employee Attrition Prediction using machine learning

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Attrition is the silent killer that can switly disable even the most successful and stable of the organizations in a shockingly spare amount of time. Hiring new employees are extremely complex task that requires capital, time and skills.Also new employee costs a lot more than that Persons salary.

  • The cost of hiring an employee goes far beyond just paying for their salary to encompass recruiting, training, benefits, and more.
  • Small companies spent, on average, more than $1,500 on training, per employee, in 2019.
  • Integrating a new employee into the organization can also require time and expenditures.
  • It can take up to six months or more for a company to break even on its investment in a new hire.

The Cost of Hiring a New Employee - Investopedia

In this project, I have developed a Machine Learning Model to predict the Employee Attrition by implementing various Machine Learning Algorithms. Conducted exploratory data analysis using various data visualization techniques.

Achieved good accuracy on the 'IBM HR Analytics Employee Attrition & Performance' dataset from Kaggle,using Logistic Regression.

Algorithm :

  1. *Logistic Regression* is used for development of model.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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Chronic kidney disease prediction machine learning web app

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Buy Now ₹1501

Buy Now Project Report ₹1001

This webapp was developed using Flask Web Framework. The models used to predict the diseases were trained on large Datasets. All the links for datasets and the python notebooks used for model creation are mentioned below in this readme. The webapp can predict following Disease. Our kidneys perform an important function to help filter blood and pass waste as urine. Chronic kidney disease, also called chronic kidney failure, describes the gradual loss of this function. At advanced stages, dangerous levels of fluid, electrolytes and wastes can build up in the body. Once this happens, patients must go through dialysis or consider a transplant. Our goal in this project is to see if we can predict if a patient will have chronic kidney disease or not using 24 predictors. If we are able to find variables with a strong influence on kidney failure, we may be able to detect and help patients at risk to prevent it.

 Algorithm :

  1. *Random Forest Classifier* is used for development of model.
  2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
    1. Accuracy of the model using Logistic Regression is 95%.
    2. Accuracy of the model using Random Forest Classifier is 99%.
    3. Accuracy of the model using XGBoost Classifier is 99%.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Brain Stroke Prediction Machine Learning Source Code

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Buy Now ₹1501

Brain Stroke Prediction Machine Learning. Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests.

Datasets 

This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.

Link - https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

Attribute Information

1) id: unique identifier
2) gender: "Male", "Female" or "Other"
3) age: age of the patient
4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease
6) ever_married: "No" or "Yes"
7) work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"
8) Residence_type: "Rural" or "Urban"
9) avg_glucose_level: average glucose level in blood
10) bmi: body mass index
11) smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"*
12) stroke: 1 if the patient had a stroke or 0 if not
*Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Technology used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. python 3.7
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Multi vendor Event Ticket Booking Management App Full Solution

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sounds like a comprehensive app that helps with event management, organization, and planning. As a virtual assistant, Provide some general information about event management apps and their features.

Overall, an event management app like MagicMate can be incredibly useful for anyone who regularly plans events, from small gatherings to large conferences. The convenience of having all the necessary information and tools in one place can save time and reduce stress, making the planning process more enjoyable and successful.

Features :

  1. A complete fully working system.
  2. Customer App & Organizer App Flutter code.
  3. An Admin Panel & Organizer Panel Dashboard(PHP Based)
  4. Database(MySQL)
  5. Constant development with regular updates.
  6. Clean, well-structured and maintainable code
  7. Active customer support to help.
  8. Value for money system.
  9. Flexible prices, no surprises!

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Flutter Online Earning App with Admin Panel

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flutter earning app is a Flutter earning application with an Admin Panel Back-end system that comes with App Source code for Free. There are many options available for earning cash. Users can earn cash via Play Games, Scratch card, complete task challenges & Offerwalls. Also earn with Watch Video, Complete offer, Spin wheel, Website visit, App Installs, Refer Task and Daily Bonus rewards. Cash Rocket flutter earning app application is built with Android Studio and we used clean and quality code for best performance with Unique UI Design that attracts Most users and is already integrated with Ad Networks.

APPLICATION FEATURES :

  1. Splash Screen
  2. Intro Screens
  3. Spin to Earn
  4. Refer to Earn
  5. SDK Offerwalls
  6. Redeem Coin
  7. Daily Earn
  8. Watch and Earn
  9. Transaction History
  10. Rewards History
  11. Signup
  12. sign in
  13. User Block/Unblock
  14. Contact Us
  15. Dashboard
  16. Slider Banner
  17. Share App
  18. Rate App
  19. Applovin Max bidding
  20. FB Ads
  21. Admob Ad
  22. Tendy UI Design
  23. Clean code
  24. well documentation
  25. 24/7 online Support

ADMIN PANEL FEATURES :

  1. Dashboard With Chart & Full Reports
  2. Manage Users
  3. User Full Reports
  4. Manage Quiz Categories
  5. Manage Quizzes
  6. Manage Questions
  7. Manage Refer Points
  8. Manage Withdraw Methods
  9. Manage Withdraw Requests
  10. Adnetworks Settings
  11. Reward Settings
  12. System Settings
  13. User Referal System etc.

WHAT YOU GET :

  1. Flutter App Source Code ( Android & iOS
  2. Admin Panel Source Code
  3. SQL Database File
  4. Documentation

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included