Road Accident Severity Prediction Using Machine Learning Project

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 algorithmslogistic regression, decision tree, and random forest classifierto 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

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Liver Cirrhosis Stage Prediction Machine Learning Python Web App

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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_DaysCL 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_CStatus_CLStatus_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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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: streamlitpandas 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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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

 

Fruits Freshness Classification using Deep Learning Python Project

Fruits Freshness Classification using Deep Learning Python Project  is a web application, implemented with Python (Flask framework), which uses a convolutional neural network on the back-end to perform fruit classification. The system is able to distinguish 6 classes of fruits: fresh/rotten apples, fresh/rotten oranges and fresh/rotten bananas. The user is able to interact with the app by uploading images or by showing the fruits to the web-camera. The app uses Web Speech API to make the experience more interactive and fun.

Warning! As there's no fallback class like "a non-fruit object", please don't take it personally when the model classifies the photo of you as a rotten banana 😅 (this also applies to any object, that doesn't belong to the mentioned classes).

Dependencies

For this project, the following tools were used:

  1. Tensorflow 2 for building and training the model;
  2. Numpy for working with arrays;
  3. Matplotlib for visualizing the data;
  4. Flask for implementing the server side;
  5. HTML5, CSS3, JavaScript (with Web Speech API and particles.js) on the front-end.

Dataset for training

The dataset used for training and evaluating the model: Fruits fresh and rotten for classification by Sriram Reddy Kalluri. The obtained model has achieved 99% accuracy on the test set.

Network implementation

The network itself was implemented using transfer learning. The MobileNet V2 model developed at Google was used as a base model for feature extraction from our data. A custom classification layer was added on top and trained separately.

Installation

To install and run locally in a production mode:


cmd-1 - pip install -r requirements.txt --user
cmd-2 - python app.py

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  1. One Time Free Installation Support.
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  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.

Computer Parts Classification using CNN Web App Project

In today's digital era, the demand for automated systems capable of recognizing and categorizing objects in images has surged dramatically. One area where such systems can prove invaluable is in the classification of computer parts. From CPUs and GPUs to RAM modules and motherboards, accurately identifying these components is crucial for various applications, including inventory management, e-commerce, and technical support.

This project aims to leverage the power of Convolutional Neural Networks (CNNs), a type of deep learning algorithm well-suited for image classification tasks, to develop a system capable of accurately classifying computer parts. Additionally, the system will be deployed within a web application using the Flask framework, allowing users to easily interact with it through a user-friendly interface.

By combining advanced computer vision techniques with web development technologies, this project not only addresses the challenge of computer parts classification but also demonstrates the practical application of artificial intelligence in real-world scenarios. This project report will detail the methodology employed, the results obtained, and conclusions drawn from the development and deployment of the

CNN-based computer parts classification system in Flask.

We Have use image scraper with chrome driver to scrap the images for training, to use image scraper-

  1. Run the chrome driver in same directry 2.Then open img_scrape.ipynb file
  2. Mension the no of images you required 4.Change the output directry according to your preference
  3. Done , It will automaticly save the images for you

CNN Model building for computer part classification

 

I have used 6 classes of computer parts like- CPU,Monitor,Mouse,Keybord,SSD,Webcam

I used 100 epochs,To increase the accuracy you can increase the epochs

Technology Overview:

  1. Convolutional Neural Networks (CNNs):
    • CNNs are a type of deep learning algorithm specifically designed for image recognition and classification tasks.
    • They consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers, allowing them to automatically learn features from input images.
    • CNNs have shown remarkable performance in various computer vision tasks and are widely used in image classification, object detection, and image segmentation.
  2. Flask Framework:
    • Flask is a lightweight and extensible web framework for Python, ideal for building web applications and APIs.
    • It provides tools and libraries for routing requests, handling HTTP responses, and managing application states.
    • Flask follows the WSGI (Web Server Gateway Interface) specification, making it compatible with various web servers and deployment environments.
  3. TensorFlow:
    • TensorFlow is an open-source machine learning framework developed by Google for building and training neural network models.
    • It offers high-level APIs for building and training models quickly, as well as low-level APIs for advanced customization and optimization.
    • TensorFlow includes tools for distributed training, model serving, and deployment across different platforms and devices.
  4. Data Preprocessing Techniques:
    • Data preprocessing plays a crucial role in preparing the input data for training neural network models.
    • Techniques such as resizing images to a uniform size, normalizing pixel values, and augmenting data with transformations like rotation and flipping help improve model performance and generalization.
  5. HTML/CSS/JavaScript:
    • Front-end technologies like HTML, CSS, and JavaScript are used to create the user interface of the web application.
    • HTML provides the structure of the web page, CSS styles the elements, and JavaScript adds interactivity and dynamic behavior to the application.
  6. Model Deployment:
    • Once the CNN model is trained and evaluated, it needs to be deployed in a production environment for real-world usage.
    • Flask provides a convenient way to deploy machine learning models by integrating them into web applications as RESTful APIs.
    • The trained model can be loaded and executed within the Flask application, allowing users to interact with it through HTTP requests.

Installation

Use the package manager pip to install the requirements.txt file package.

  • cmd-1 - pip install -r requirements.txt --user
  • cmd -3  python app.app

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  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://products.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.

Ai generated Fake Face and Real Face detection using Deepfake web app project

This is a Ai generated Fake Face and Real Face detection using Deepfake Machine Learning project  built with convolutional neural networks. The classifier was trained on a data set comprised of 1400 images (700 of each class) and tested on 600 images (300 per class). The classifier achieved an accuracy of **83.2%**. You can find more performance metrics and information about this project in
the repository. To use this web application just drag and drop a face image to be classified by the model. While you think about that, have a 🍪 and refresh the page once or twice to classify a few built-in faces embedded into the app. The classifier will return the result with the associated probability that a specific face image belongs to either the ```Real``` or ```Fake``` class. The model's architecture summary is also presented below

In recent years, advancements in artificial intelligence (AI) have led to the emergence of sophisticated techniques for generating fake images and videos, commonly known as deepfakes. These manipulations, facilitated by deep learning algorithms, have raised significant concerns regarding their potential to spread misinformation, manipulate public opinion, and infringe upon individuals' privacy and security.

Detecting deepfake content has become a crucial challenge in combating the proliferation of misleading information and protecting digital integrity. This project focuses on the development of a deep learning-based system for the detection of AI-generated fake faces, with the ultimate goal of distinguishing them from real faces.

The proliferation of deepfake technology has profound implications across various domains, including journalism, politics, entertainment, and cybersecurity. Misuse of deepfake content can lead to reputational damage, identity theft, and even exacerbate societal tensions. Therefore, developing robust techniques to identify deepfakes is imperative to mitigate these risks and safeguard the integrity of digital content.

This project aims to contribute to the ongoing efforts in deepfake detection by leveraging machine learning algorithms and computer vision techniques. By analyzing subtle discrepancies between real and fake faces, the proposed system seeks to provide a reliable means of identifying manipulated content and enhancing trust in digital media.

Technologies Used:

  1. Deep Learning Frameworks:
    • TensorFlow or PyTorch: Widely-used frameworks for building and training deep learning models, including convolutional neural networks (CNNs) for image classification and detection tasks.
  2. Computer Vision Libraries:
    • OpenCV: A popular library for computer vision tasks such as image preprocessing, feature extraction, and object detection.
    • scikit-image: Provides a collection of algorithms for image processing and manipulation, which can be useful for data preprocessing and augmentation.
  3. Machine Learning Tools:
    • scikit-learn: Offers a range of machine learning algorithms and tools for data preprocessing, model evaluation, and metrics calculation.
    • XGBoost or LightGBM: Gradient boosting libraries that can be used for classification tasks, especially if ensemble methods are desired.
  4. Streamlit and Web Development:
    • Streamlit: The primary framework for building interactive web applications with Python, allowing for the seamless integration of machine learning models with user-friendly interfaces.
    • Flask or FastAPI: Lightweight web frameworks that can be used for building backend APIs to support the Streamlit application.
  5. Image Manipulation and Visualization:
    • Matplotlib or Seaborn: Libraries for creating visualizations and plots to display model outputs, evaluation metrics, and detection results.
    • Pillow: Python Imaging Library for opening, manipulating, and saving many different image file formats.
  6. Other Utilities:
    • NumPy and Pandas: Fundamental libraries for numerical computing and data manipulation, which are essential for handling image data and preprocessing.
    • tqdm: Provides a progress bar for tracking the progress of data loading, model training, and inference tasks.

 

Installation

Use the package manager pip to install the requirements.txt file package.

  • cmd-1 - pip install -r requirements.txt --user
  • cmd-2   cd app
  • cmd -3  python -m streamlit run.app

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Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://products.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.

Campus Recruitment Prediction with Source Code Python

This project aims to predict the salary of students in campus recruitment using a dataset named train.csv. The dataset contains the following columns: sl_no, gender, ssc_p, ssc_b, hsc_p, hsc_b, degree_p, degree_t, workex, etest_p, specialisation, mba_p, status, and salary.

Table of Contents

  1. Introduction
  2. Project Structure
  3. Data Processing and Modeling
  4. Flask Web Application

Introduction

In this project, we analyze the provided dataset and build a predictive model for campus recruitment. We first perform data processing and exploratory data analysis (EDA) using a Jupyter Notebook (notebook.ipynb). Next, we implement a Flask web application (app.py) to deploy the trained predictive model and allow users to make predictions based on the provided input.

Project Structure

  1. train.csv: Dataset containing recruitment-related information.
  2. notebook.ipynb: Jupyter Notebook containing data preprocessing, EDA, and model selection.
  3. app.py: Flask web application for model deployment.
  4. templates/: Directory containing HTML templates for the web application.
    1. index.html: Homepage of the web application.
    2. prediction.html: Page displaying predictions.
  5. requirements.txt: File listing all the necessary libraries for running the web app.
  6. model.pkl: Pickled file containing the trained predictive model (Ridge model).
  7. scaler.pkl: Pickled file containing the scaler used for standardization.

Data Processing and Modeling

In the Jupyter Notebook (notebook.ipynb), we perform the following steps:

  1. Import necessary libraries.
  2. Load the dataset (train.csv).
  3. Preprocess the data by dropping unnecessary columns and handling missing values.
  4. Visualize data through various plots and charts.
  5. Perform one-hot encoding for categorical variables.
  6. Split the dataset into training and testing sets.
  7. Standardize the data using StandardScaler.
  8. Explore and select the best scoring model using GridSearchCV and ShuffleSplit.
  9. Save the best-fitted model and scaler using pickle (model.pkl and scaler.pkl).

Flask Web Application

The Flask web application (app.py) is created to deploy the trained predictive model. It allows users to input their information and receive predictions regarding their placement status and expected salary. The web application consists of two main HTML templates:

  • index.html: The homepage where users input their details.
  • prediction.html: The page displaying the predicted placement status and salary.

To run the web application, use the libraries specified in requirements.txt.

Python runtime : 3.11

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Ai Mental Health Chatbot project python with source code

This is an AI-powered bot designed to provide emotional support and assistance to individuals struggling with mental health issues. It can help individuals access mental health resources, offer guidance and support. With the integration of Language translation, this chatbot will be very efficient as it will be able to break the language barriers.

The creation of a chatbot capable of language translation, holds transformative potential, acting as a catalyst in overcoming language barriers for effective communication and information exchange. Its impact spans diverse sectors, including: healthcare, commerce, and governance etc. offering a versatile solution to bridge linguistic gaps.

https://codeaxe.co.ke/multilingobot/

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 3.8, 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
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py