Predict the Earlier Stages of Alzheimer’s disease Machine Learning

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

Age and Gender Detection Using Deep Learning Python Flask

The "Age and Gender Detection Using Deep Learning" Flask project aims to build a web application that can accurately detect the age and gender of a person from an input image. The project leverages deep learning techniques to analyze facial features and make predictions. The web application will provide an intuitive user interface where users can upload images and get real-time predictions for age and gender.

Key Features:

  1. Image Upload: The web application allows users to upload images containing human faces for analysis.
  2. Age Detection: The deep learning model will predict the age of the person in the uploaded image. The model is trained on a large dataset of facial images with corresponding age labels.
  3. Gender Detection: The model will also predict the gender of the person in the uploaded image as either male or female.
  4. Real-Time Prediction: The system provides real-time predictions and displays the age and gender results immediately after image upload.
  5. User-Friendly Interface: The Flask web application offers a user-friendly interface that is easy to navigate and interact with.

Technical Details:

  1. Deep Learning Model: The age and gender detection models are built using deep learning frameworks like TensorFlow or PyTorch. The age model is usually a regression model, while the gender model is a binary classification model.
  2. Convolutional Neural Network (CNN): The models are likely based on CNN architectures to effectively learn facial features and patterns for age and gender prediction.
  3. Flask Web Framework: The web application is developed using the Flask framework, which is a lightweight and easy-to-use Python web framework.
  4. HTML/CSS and JavaScript: The front-end of the web application is built using HTML/CSS for layout and design, while JavaScript may be used for dynamic elements and handling image uploads.
  5. Deployment: The application may be deployed on a web server using platforms like Heroku, AWS, or Microsoft Azure, making it accessible online.

Limitations:

  1. Accuracy: The accuracy of age and gender prediction depends on the quality and diversity of the training data. The model may not always provide precise predictions, especially for images with challenging angles, lighting, or occlusions.
  2. Face Detection: The system assumes that the input image contains only one face, and face detection is not a part of this project.
  3. Age Range: The model's predictions might be limited to a specific age range, and its accuracy might decrease for age groups outside the training data.

Conclusion:

The "Age and Gender Detection Using Deep Learning" Flask project is an exciting application that demonstrates the capabilities of deep learning in analyzing facial features for age and gender prediction. The real-time web interface enhances user experience, making it easy for users to explore the system's predictions. However, the project also acknowledges its limitations in terms of accuracy and the need for proper data representation. With further improvements and advancements in deep learning and dataset diversity, the system's performance could be enhanced in the future.

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 3.6.10, 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 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Link 

Cat Vs Dog Image Classification CNN Project Source Code

Image classification is a fundamental problem in computer vision, and distinguishing between cats and dogs is a classic example. In this project, we aim to develop an accurate cat vs dog image classification system using Convolutional Neural Networks (CNNs). We collect a large dataset of labeled images containing cats and dogs, preprocess the data, design and train a CNN model, evaluate its performance, and deploy the model for real-world use.

Introduction :

Image classification plays a crucial role in various domains, including object recognition, medical imaging, and autonomous systems. In this project, we focus on the task of classifying images of cats and dogs. This problem presents challenges due to the high variability in appearance and poses of cats and dogs. CNNs have shown remarkable success in image classification tasks, making them a suitable choice for this project.

Dataset :- We collect a diverse dataset consisting of thousands of labeled images of cats and dogs. The dataset is split into three subsets: training, validation, and testing. The training set is used to train the CNN model, while the validation set helps tune hyperparameters and monitor the model’s performance. The testing set provides an unbiased evaluation of the final model.

Preprocessing :- Before training the CNN model, we preprocess the dataset to ensure its suitability for learning. Preprocessing steps include resizing all images to a consistent resolution, normalizing pixel values, and augmenting the training data. Data augmentation techniques such as rotation, flipping, and zooming are employed to increase the variability and robustness of the training data.

CNN Architecture We design a CNN architecture tailored for the cat vs dog image classification task. The architecture typically consists of several convolutional layers for feature extraction, followed by pooling layers to downsample the feature maps. Fully connected layers are then employed to perform classification based on the learned features. The exact configuration of the CNN, including the number of layers, filter sizes, and activation functions, is determined through experimentation and optimization.

Training The CNN model is trained using the prepared dataset. We employ a suitable optimization algorithm, such as stochastic gradient descent (SGD), and a loss function, typically categorical cross-entropy, to update the model’s parameters during training. The training process involves forward propagation, backward propagation, and gradient updates. We monitor the model’s performance on the validation set and employ techniques like early stopping to prevent overfitting.

Evaluation After training, we evaluate the performance of the CNN model using the testing set. We measure various metrics, including accuracy, precision, recall, and F1 score, to assess the model’s ability to correctly classify cat and dog images. We also analyze the model’s confusion matrix to identify specific areas where the model may struggle.

Deployment Once the model achieves satisfactory performance, we deploy it for real-world use. This can be done through various means, such as building a web application or creating an API. Users can then upload images of cats or dogs, and the deployed model will classify them accordingly. We consider scalability, performance, and user experience during the deployment process.

Conclusion In conclusion, we have successfully developed a cat vs dog image classification system using CNNs. Through careful dataset collection, preprocessing, and model training, we achieved a high level of accuracy in distinguishing between cats and dogs. The deployed system provides a practical solution for image classification tasks involving cats and dogs, and it can be further improved by considering additional datasets, advanced CNN architectures, or transfer learning techniques.

Hardware and Software Requirements:

  1. Hardware Requirements:
    1. CPU: A multi-core processor (e.g., Intel Core i5 or higher) is recommended for faster training and inference.
    2. GPU (Optional): A dedicated graphics card, such as NVIDIA GeForce or AMD Radeon, with CUDA support can significantly accelerate the training process.
    3. RAM: Sufficient RAM (at least 8GB or higher) to handle the dataset and model computations efficiently.
    4. Storage: Adequate storage space to store the dataset, trained models, and any additional resources.
  2. Software Requirements:
    1. Operating System: Most popular operating systems, including Windows, macOS, or Linux distributions, can be used.
    2. Python: Install Python programming language (version 3.6 or higher) as a prerequisite for running deep learning frameworks and libraries.
    3. Deep Learning Framework: Install TensorFlow, Keras, or PyTorch, depending on your preference, to build and train CNN models. These frameworks can be installed using Python package managers like pip or Anaconda.
    4. Image Processing Libraries: Install libraries like OpenCV or PIL (Python Imaging Library) for image loading, preprocessing, and augmentation.
    5. Development Environment: Choose a preferred Integrated Development Environment (IDE) such as Jupyter Notebook, PyCharm, or Visual Studio Code to write and run Python code efficiently.
  3. Dataset:
    1. Collect or acquire a dataset of labeled cat and dog images. The dataset should be organized into separate folders for training, validation, and testing.
    2. Ensure that the dataset has a sufficient number of images for each class and covers a wide range of variations in cat and dog appearances.
  4. GPU Acceleration (Optional):
    1. If GPU acceleration is desired for faster training, install the appropriate GPU drivers and CUDA Toolkit provided by the GPU manufacturer (e.g., NVIDIA) according to the specific hardware and software compatibility.
  5. Additional Libraries:
    1. Depending on the specific requirements of the project, additional Python libraries may be needed, such as pandas for data manipulation, scikit-learn for evaluation metrics, and matplotlib or seaborn for data visualization.

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 3.6.10, 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 3.6.8
  2. command 1 – python -m pip install –user -r requirements.txt
  3. command 2 – python app.py

Download Link

Plant Disease Prediction using CNN Flask Web App

Buy Now Source Code ₹1501

Buy Now Project Report ₹1001

The plant disease prediction Flask project is a web application that utilizes machine learning algorithms to predict whether a plant is healthy or diseased based on an image of the plant. The project involves building a machine learning model that can classify plant images as healthy or diseased and integrating this model into a Flask web application.

The project generally consists of the following steps:

  1. Data collection: Collect images of healthy plants and plants with different types of diseases.
  2. Data preprocessing: Clean and prepare the image data for use in the machine learning model.
  3. Model training: Train a machine learning model using the preprocessed image data.
  4. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately classify plant images.
  5. Flask app development: Develop a Flask web application that allows users to upload images of plants and get a prediction of whether the plant is healthy or diseased.
  6. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the plant disease prediction Flask project is an innovative solution to the problem of identifying plant diseases and can be a valuable tool for farmers and researchers.

Overview of the CNN algorithm:

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that are well-suited for image classification tasks. The key idea behind CNNs is to learn a set of filters that can be used to extract meaningful features from the input image. These filters are learned automatically during the training process.

Here are the main steps involved in a CNN algorithm:

  1. Convolution: The input image is convolved with a set of learnable filters. The filters are applied to small patches of the image and slide across the entire image to produce a set of feature maps.
  2. ReLU Activation: The feature maps are passed through a Rectified Linear Unit (ReLU) activation function, which applies a non-linear transformation to the output of each convolutional layer.
  3. Pooling: The feature maps are downsampled using a pooling operation, which reduces the spatial dimensionality of the feature maps while retaining the most important features.
  4. Fully Connected Layers: The output of the convolutional and pooling layers is flattened and passed through one or more fully connected layers, which compute the final classification scores.
  5. Softmax Activation: The final layer uses a softmax activation function to produce a probability distribution over the possible classes.
  6. Training: During training, the CNN is fed a set of labeled images and adjusts the weights of its filters to minimize the difference between the predicted output and the actual output.
  7. Evaluation: After training, the CNN is evaluated on a separate set of images to measure its performance. This involves computing metrics such as accuracy, precision, recall, and F1 score.

Overall, CNNs have achieved state-of-the-art performance on a wide range of image classification tasks, including plant disease prediction.

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 3.6.10, 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 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

 

Fire Detection Using Surveillence Camera web app Project with Source Code

Buy Now ₹1501

Introduction:

The objective of this project is to develop a web application that uses surveillance cameras to detect fire and alert users in real-time. The application uses computer vision algorithms and machine learning techniques to analyze video footage from the cameras and detect the presence of fire. The project aims to improve fire safety by detecting potential fire hazards early and allowing users to take appropriate action.

Methods:

The project involved several steps, including collecting and labeling a dataset of video footage that contained both fire and non-fire events, preprocessing the video footage to extract individual frames, and training a machine learning model using the preprocessed dataset. The machine learning model was a convolutional neural network (CNN) that was trained to detect the presence of fire in an image.

Once the machine learning model was trained, a web application was developed that allowed users to upload video footage from their surveillance cameras. The uploaded footage was analyzed frame by frame using the trained machine learning model to detect the presence of fire. If fire was detected, the application triggered an alert and notified the user via email or SMS. The application also provided a live video feed from the surveillance camera and highlighted the region where the fire was detected.

Results:

The developed web application was able to accurately detect the presence of fire in video footage from surveillance cameras. The machine learning model achieved an accuracy of over 95% on the test dataset, indicating that it was able to accurately distinguish between fire and non-fire events. The web application was also able to provide real-time alerts and notifications to users when fire was detected, allowing them to take appropriate action.

Discussion:

The developed web application has several potential applications in improving fire safety in buildings. For example, it can be used in warehouses, factories, and other industrial settings where fire hazards are common. The application can also be used in homes and other residential settings, alerting residents to potential fire hazards in real-time.

The project has several limitations that should be considered. One limitation is the need for high-quality video footage from surveillance cameras. The accuracy of the machine learning model is highly dependent on the quality of the video footage. Another limitation is the need for periodic retraining of the machine learning model to ensure that it continues to accurately detect fire over time.

Conclusion:

The project has demonstrated the feasibility of using surveillance cameras and machine learning algorithms to develop a web application for fire detection. The application has the potential to improve fire safety in various settings, including industrial and residential settings. Further research is needed to optimize the accuracy of the machine learning model and to develop additional features that can enhance the functionality of the application.

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 3.6.10, 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 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Anomaly Detection in Time Series Data using Autoencoder Project Proposal

Project Title: Anomaly Detection in Time Series Data using Autoencoder

Project Description: The objective of this project is to detect anomalies in time series data using Autoencoder, a type of deep neural network that can learn to encode and decode input data. Anomaly detection in time series data is important in various fields, such as finance, manufacturing, and healthcare, as it can help identify unusual patterns or events that may require further investigation.

As a student, you can start by understanding the concept of time series data and anomalies. You can then collect a dataset of time series data, such as sensor readings, stock prices, or healthcare data. The data should have both normal and abnormal instances.

You can preprocess the data, split it into training and testing sets, and use Autoencoder to build a model that can learn the normal behavior of the data. Once the model is trained, you can use it to predict the output of the testing set. Any instance that deviates significantly from the predicted output can be considered an anomaly.

You can evaluate the performance of the model using metrics such as precision, recall, and F1 score. You can also visualize the anomalies to understand their patterns and characteristics.

The final deliverable can be a report detailing the methodology, findings, and recommendations for the field of application.

Expected Deliverables:

  1. A detailed analysis of time series data and anomalies
  2. A deep learning model using Autoencoder to detect anomalies
  3. An evaluation of the model's performance using metrics such as precision, recall, and F1 score
  4. A visualization of the anomalies to understand their patterns and characteristics
  5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

  1. Python
  2. TensorFlow or Keras
  3. Pandas
  4. NumPy
  5. Matplotlib or Seaborn

Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:

  1. Week 1: Understanding time series data and anomalies
  2. Week 2-3: Data Collection and Preprocessing
  3. Week 4-5: Model Development and Training
  4. Week 6-7: Model Evaluation and Visualization of Anomalies Week 8: Report Writing and Presentation.

Automated Answer Grading System machine learning project

Buy Source Code ₹1501

Buy Project Report ₹1001

An Automated Answer Grading System is a machine learning-based Django project that allows teachers to automatically grade student answers in a fast and efficient manner. The system will use natural language processing techniques to analyze and compare the student's answer to the correct answer and assign a grade based on how closely the two match.

The project will consist of a web-based interface that teachers can use to upload student answers and view the results. Teachers will also have the ability to view detailed reports on student performance, including overall scores and breakdowns of individual question scores.

The system will be trained using a dataset of correct and incorrect answers, which will be used to develop the machine learning model that will be used to grade the student's answers. The model will use various natural language processing techniques such as text similarity, sentiment analysis, and topic modeling to compare the student's answer to the correct answer.

The project will be built using the Django web framework and will be hosted on a cloud platform such as AWS or Google Cloud. The frontend of the system will be designed using HTML, CSS, and JavaScript and will provide an easy-to-use and intuitive interface for teachers to interact with.

Overall, the Automated Answer Grading System will be a powerful tool for teachers that will allow them to grade student answers quickly and accurately, freeing up more time for other important teaching tasks.

Dataset

The dataset used is the Kaggle’s Automatic Essay Scoring dataset,can be downloaded from https://www.kaggle.com/c/asap-aes/data

Results

The models were tested using kappa statistic which is intending to compare labelling by different human annotators, not a classifier versus a ground truth. The kappa score is a number between -1 and 1. Scores above .8 are generally considered good agreement,zero or lower means no agreement For this project we have used an Algorithm in which we Combine all the topics into a single model and predicted the score using bi-directional LSTM. kappa score obtained is 0.74

 

Pneumonia Prediction Using chest x-ray Image Machine Learning

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Latest Machine Learning Project with Source Code

Buy Now ₹1501

Buy Now Project Report ₹1001

Chest x-ray: An x-ray exam will allow your doctor to see your lungs, heart and blood vessels to help determine if you have pneumonia. When interpreting the x-ray, the radiologist will look for white spots in the lungs (called infiltrates) that identify an infection. Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake. This is a Flask web app designed to analyze a chest x-ray and predict whether a person has TB/pneumonia or not.

Models : 

The model is based on a  convolutional neural network that has been trained on a dataset of 800 images from two sources

The model has an overall accuracy of 83% and an F1 score of 80%.

A negative prediction means that the chest X-ray is most likely normal while the contrary is implied by a positive prediction

Environment and tools

  1. flask
  2. tensorflow

Runtime Python Version  : python-3.8.2

Datasets Link

Read Before Purchase  :

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  3. We offer Paid Customization installation Support
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  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.

 

Loan Defaulter Prediction Machine Learning Projects

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Latest Machine Learning Project with Source Code

Buy Now ₹1501

Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

 

Goals/Outcome

  • Determining probability of user liability
  • Creating an interactive UI that will take users input and return an output
  • To determine if a neural network vs logistic regression is the better model for classification

Models Created

  • Logistic Regression
  • Random Forest Model
  • Deep Neural Network

About

Probability of Credit Card Default, Machine Learning

Technologies Used : -

  • beautifulsoup4==4.6.0
  • certifi==2018.4.16
  • chardet==3.0.4
  • click==6.7
  • Flask==1.0
  • gunicorn==19.8.0
  • idna==2.6
  • itsdangerous==0.24
  • Jinja2==2.10
  • MarkupSafe==1.0
  • numpy==1.14.3
  • pandas==0.22.0
  • python-dateutil==2.7.2
  • pytz==2018.4
  • requests==2.18.4
  • scikit-learn==0.19.1
  • scipy==1.0.1
  • six==1.11.0
  • SQLAlchemy==1.2.7
  • urllib3==1.22
  • Werkzeug==0.14.1

 

Fake News Detection using Machine Learning Natural Language Processing

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Buy Source Code ₹1501

Buy Project Report  ₹1001

Fake News Detection using Machine Learning Natural Language Processing . A NLP and Machine Learning based web application used for detecting fake news. Uses NLP for preprocessing the input text. Uses XGBoost model for predicting whether the input news is Fake or Real.

here are tons of stories articles, where the news is fake or cooked up. With numerous advances in tongue Processing and machine learning, we will actually build an ml model which is in a position to detect if a bit of stories ... Here we'll be using artificial neural network models to verify the genuinity of the article.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Dataset Link: https://www.kaggle.com/c/fake-news/data

Training Model File 

Fake_News_Classifier_Using_LSTM.ipynb

Fake_News_Classifier_using_Machine_Learning.ipynb

Output Generated File

xgb_fake_news_predictor.pkl