All-in-one Pet Care Solution in Flutter + Laravel with ChatGPT

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Pawlly revolutionizes the world of pet care services with its comprehensive and user-friendly platform. Catering to every aspect of pet care, including veterinary services, training, walking, grooming, boarding, and daycare, Pawlly stands as the ultimate solution for pet owners seeking top-notch care for their furry companions.

The Pawlly ecosystem comprises three seamlessly integrated components: a dynamic customer mobile app, an efficient employee app, and a robust admin panel. The customer app empowers pet owners to effortlessly schedule appointments for their pets, manage multiple pets within a single booking, and select from a range of services offered. The employee app enables service providers to access their schedules, manage events and blogs, and engage with clients professionally.

At the heart of Pawlly lies the advanced admin panel, built using the Laravel 9 and Vue.js 3 technologies. The admin panel serves as the nerve center, allowing administrators to oversee employees, customers, and their pets. It facilitates efficient management of employee earnings, customer reviews, events, blogs, and notification settings. With an emphasis on data security, roles and permissions can be finely tuned to ensure optimal access control. The admin panel further offers comprehensive booking management tools, empowering administrators to orchestrate seamless service delivery.

The financial aspect is elegantly handled through a commission-based model. Admins earn a commission from each booking, while employees receive their due share. The payout process is simplified with manual disbursements initiated from the admin panel. A suite of detailed reports provides insights into earnings and payouts, ensuring transparency and accountability.

Pawlly is designed with a global audience in mind. It supports multiple languages including Arabic, French, Hindi, German, and English. The mobile apps and admin panel offer both dark and light modes to suit user preferences, enhancing usability and accessibility.

Customization is at the core of the Pawlly experience. The admin panel features the power of Hope UI, an open-source, enterprise-grade admin template. This integration empowers administrators to personalize the platform’s appearance, including colors, menu styles, and card designs, without the need for developer intervention. Business information, logos, mail settings, and time zones can be effortlessly managed through the admin panel’s intuitive settings interface.

Incorporating cutting-edge technology, such as OneSignal for push notifications and multiple payment gateways for online transactions, Pawlly ensures a seamless and secure user experience.

Discover Pawlly today and redefine the way you engage with pet care services. Experience convenience, customization, and compassion, all in one app.

Air Quality Index Prediction Using PM 2.5 value Machine Learning

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India is one of the higher air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and India-Bangalore region. Also, the data was collected through web scraping with the help of Beautiful Soup.

Data Collection

  • Air quality data was collected from the "http://en.tutiempo.net/climate". So, here I selected the India- Bangalore'sregion & collected the independent features such as Average annual temperature(AT), Annual average maximum temperature(TM), Average annual minimum temperature(Tm), Rain or snow precipitation total annual(PP), Annual average wind speed(V), Number of days with rain(RA), Number of days with snow(SN) and dependent feature as PM 2.5 values has been colected from the "dhewdhjwdhjw"

  • The dataset used can be downloaded Here from the 2013 to 2018.

Technologies Used :

  1. IDE - Pycharm
  2. Linear Regression Model
  3. Ridge and Lasso Regression
  4. Support vector regressor(SVR)
  5. Extra tree regressor
  6. Decission tree regressor
  7. Google Colab - Trained ML model
  8. Flask- Rest API

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Link 

Predict the Earlier Stages of Alzheimer’s disease Machine Learning

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The objective of this project is to develop a predictive model that can identify the early stages of Alzheimer’s disease (AD). Early diagnosis of AD can significantly improve the effectiveness of treatment and management strategies, potentially slowing the progression of the disease. This project will leverage machine learning techniques on various data sources, such as medical imaging, genetic data, and cognitive test results, to create an accurate and reliable prediction system.

Background and Motivation

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions worldwide, leading to memory loss, cognitive decline, and ultimately loss of independence. Early diagnosis is crucial but challenging due to the subtlety of initial symptoms. Current diagnostic methods rely heavily on clinical assessment and are often made at advanced stages. By predicting the onset of AD in its early stages, we can provide better intervention options, potentially improving the quality of life for patients and reducing healthcare costs.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.7, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Link

SMS Spam Detection Machine Learning Project with Source Code

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Buy Source Code ₹1501

SMS Spam Detection Machine Learning Project which predicts whether the message is a spam or not. SMS Spam Collection dataset from Kaggle was used to classify the messages into 2 classes- Ham(1) and Spam(0) using stemming, Bag of Words model and Naive Bayes Classifiers.

Note:The dataset is an unbalanced dataset and therefore, for this situation the role of Precision becomes quite important.Precision is more focused in the positive class than in the negative class, it actually measures the probability of correct detection of positive values,

Consider the following case scenario -'suppose if the message is not a spam and if it's been predicted by the model as a spam, the consumer is going to miss that message.' So, for this type of unbalanced dataset, precision defined as {TP/(TP+FP)} plays an important role along with accuracy_score. My objective was to reduce the FP(False Positive) value as much as possible for this case and in order to overcome this issue, Naive Bayes classifiers namely, MultinomiallNB and BernoulliNB were implemented to get best accuracy_score and precision_score from the dataset.

The code is written in Python 3.6.10. If you don't have Python installed, you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after downloading the code:

About Datasets :

The original dataset can be found here. The creators would like to note that in case you find the dataset useful, please make a reference to previous paper and the web page: http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/ in your papers, research, etc.

We offer a comprehensive study of this corpus in the following paper. This work presents a number of statistics, studies and baseline results for several machine learning methods.

Almeida, T.A., Gómez Hidalgo, J.M., Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt --user
  • Run flask web app
    python app.py

Road Accident Severity Prediction Using Machine Learning Project

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The majority of fatalities and serious injuries occur as a result of incidents involving motor vehicles. If the traffic  management system is going to do its job of reducing the frequency and severity of traffic accidents, it needs a model for doing so. In this paper, we combine the results of three machine learning algorithms—logistic regression, decision tree, and random forest classifier—to build a predictive model. In order to forecast the severity of accidents in different regions, we used ML algorithms on a dataset of accidents from the United States. In addition, we examine vast quantities of traffic data, extracting  helpful accident patterns in order to pinpoint the factors that have a direct bearing on road accidents and make actionable suggestions for improvement. When compared to two other ML algorithms, random forest performed best on accuracy. The severity rating in this paper is not meant to reflect the severity of injuries sustained, but rather how the accident affects traffic flow. Accident severity, decision trees, random forests, and logistic regression are all terms that are often used to describe this area of study
Road accidents have become a major concern globally, causing a significant number of fatalities and injuries every year. The aim of this project is to predict road accidents severity using machine learning
techniques, in order to reduce their occurrence and mitigate the associated risks. The project uses data collected from various sources such as accident reports, weather conditions, and road infrastructure to train and evaluate various supervised learning algorithms and predict the accident severity. Four algorithms were compared, including Decision Tree, Naive Bayes, Random forest . Most probably occurring road accident locations are identified and that particular region is indicated as black pot. The proposed method can be used to provide real-time risk information to road users, helping them to make informed decisions and avoid potential accidents. The project highlights the importance of using machine learning techniques in road safety analysis, providing a foundation for further research in this field.

About Dataset

Dataset Link - https://www.kaggle.com/datasets/s3programmer/road-accident-severity-in-india

The data set has been prepared from manual records of road traffic accidents for the years 2017–22. All the sensitive information has been excluded during data encoding, and finally, it has 32 features and 12316 instances of the accident. Then it is preprocessed for the identification of major causes of the accident by analyzing it using different machine learning classification algorithms. Road.csv is the preprocessed dataset.

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt --user
  • Run flask web app
    python app.py

Download Link

Liver Cirrhosis Stage Prediction Machine Learning Python Web App

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Buy Source Code ₹1501

Liver cirrhosis is a widespread problem especially in North America due to high intake of alcohol. In this project, we will predict liver cirrhosis in a patient based on certain lifestyle and health conditions of a patient.

Cirrhosis is a late stage of scarring (fibrosis) of the liver caused by many forms of liver diseases and conditions, such as hepatitis and chronic alcoholism. The following data contains the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. A description of the clinical background for the trial and the covariates recorded here is in Chapter 0, especially Section 0.2 of Fleming and Harrington, Counting Processes and Survival Analysis, Wiley, 1991. A more extended discussion can be found in Dickson, et al., Hepatology 10:1-7 (1989) and in Markus, et al., N Eng J of Med 320:1709-13 (1989).

A total of 424 PBC patients, referred to Mayo Clinic during that ten-year interval, met eligibility criteria for the randomized placebo-controlled trial of the drug D-penicillamine. The first 312 cases in the dataset participated in the randomized trial and contain largely complete data. The additional 112 cases did not participate in the clinical trial but consented to have basic measurements recorded and to be followed for survival. Six of those cases were lost to follow-up shortly after diagnosis, so the data here are on an additional 106 cases as well as the 312 randomized participants.

Dataset Description

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Cirrhosis Patient Survival Prediction dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.

Files

  • train.csv - the training dataset; Status is the categorical target; C (censored) indicates the patient was alive at N_Days, CL indicates the patient was alive at N_Days due to liver a transplant, and D indicates the patient was deceased at N_Days.
  • test.csv - the test dataset; your objective is to predict the probability of each of the three Status values, e.g., Status_C, Status_CL, Status_D.
  • sample_submission.csv - a sample submission file in the correct format

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt --user
  • Run flask web app
    python app.py

 

Alternative Medicine Recommendation System Machine Learning Project

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Buy Source Code ₹1501

The medicine recommendation system is intended to suggest alternative medicines based on the cosine similarity between a patient's symptoms and the effects of various medications. The system makes use of a database of medications and their indications, as well as a list of symptoms that a patient may exhibit. It vectorizes the data, applies filters, and makes suggestions. Medicines with a higher cosine similarity are considered more relevant and recommended to patients. In the scenario of a medical emergency, when physicians or prescribed medications are unavailable, this recommender serves as a valuable resource. The proposed medicine recommendation system has the potential to help healthcare professionals and patients make educated decisions about alternative medications. The system can reduce the risk of adverse drug reactions and improve patient outcomes by suggesting alternative medicines that are more effective and have fewer side effects. Overall, the proposed medicine recommendation system
has the potential to significantly improve patient care by making effective recommendations for alternative medications. It can also reduce healthcare professionals’ workload by automating the process of
identifying.

Kaggle Dataset

Link :

https://www.kaggle.com/code/mpwolke/medicine-recommendation/input

The remarkable technological advancements in the health care industry have improved recently for the betterment of patients’ life and providing better clinical decisions. Applications of machine learning and data mining can change the available data to valuable information that can be used for recommending appropriate drugs by analyzing symptoms of the disease.

A machine learning approach for multi-disease with drug recommendation can be proposed to provide accurate drug recommendations for the patients suffering from various diseases. This approach generates appropriate recommendations for the patients suffering from cardiac, common cold, fever, obesity, optical, and ortho. Supervised machine learning approaches such as Support Vector Machine (SVM), Random Forest, Decision Tree, and K-nearest neighbors can be used for generating recommendations for patients.

Steps to Open Localhost for application:

  1. Download Pycharm IDE and Open this application folder in it.
  2. Open Termial.
  3. Import Libraries: streamlit, pandas and pickle.
  4. Type- streamlit run app.py
  5. if the application does not start then type python -m streamlit run app.py

Note Special Instruction if terminal throws an error "streamlit is not recognized as an internal or external command" still after importing all libraries.

Image-Based Bird Species Identification Using Machine Learning

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Buy Source Code ₹1501

This project contains code and resources for building a web application that utilizes Convolutional Neural Networks (CNN) to predict bird images. The application allows users to upload an image of a bird, and the trained CNN model will predict the species of the bird.

Introduction

With the advancement of deep learning techniques, building image classifiers has become more accessible than ever. This project demonstrates how to leverage CNNs to create a web application for predicting bird species from images.

Features

  1. Upload bird images to get predictions on their species.
  2. User-friendly interface for easy interaction.
  3. Utilizes a CNN model trained on bird image datasets for accurate predictions.

Model Training

The CNN model used for predicting bird species is trained on a bird image dataset. If you wish to retrain the model or use a different dataset, you can modify the training script (train.py) and replace the dataset accordingly.

To train the model:

python train.py

Dataset

This version of the dataset adds 10 new species to the previous version. In addition using a dataset analysis tool I was able to clean the dataset so that there are no duplicate or near-duplicate images in the dataset. This ensures no leakage between the train, test and validation datasets. Also so defective low information images were also removed. So now you are using a clean dataset.
Data set of 525 bird species. 84635 training images, 2625 test images(5 images per species) and 2625 validation images(5 images per species. This is a very high quality dataset where there is only one bird in each image and the bird typically takes up at least 50% of the pixels in the image. As a result even a moderately complex model will achieve training and test accuracies in the mid 90% range. Note: all images are original and not created by augmentation
All images are 224 X 224 X 3 color images in jpg format. Data set includes a train set, test set and validation set. Each set contains 525 sub directories, one for each bird species. The data structure is convenient if you use the Keras ImageDataGenerator.flow_from_directory to create the train, test and valid data generators. The data set also include a file birds.csv. This cvs file contains 5 columns. The filepaths column contains the relative file path to an image file. The labels column contains the bird species class name associated with the image file. The scientific label column contains the latin scientific name for the image. The data set column denotes which dataset (train, test or valid) the filepath resides in. The class_id column contains the class index value associated with the image file's class.
NOTE: The test and validation images in the data set were hand selected to be the "best" images so your model will probably get the highest accuracy score using those data sets versus creating your own test and validation sets. However the latter case is more accurate in terms of model performance on unseen images.

Link :- https://www.kaggle.com/datasets/gpiosenka/100-bird-species