Tag Archives: flask

Age and Gender Detection Using Deep Learning Python Flask

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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 

Handwritten digit recognition Python Flask

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Handwritten digit recognition is an important application of machine learning, particularly in the field of computer vision. The task involves identifying handwritten digits from an image and classifying them into the corresponding numerical values. In this project, we have developed a Flask-based application that recognizes handwritten digits using a pre-trained machine learning model.

Objective:

The objective of this project is to build a machine learning model that can accurately recognize handwritten digits and to develop a Flask-based web application that utilizes the model to recognize digits entered by users.

Methodology:

We used the MNIST dataset for training and testing our machine learning model. This dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. We used a convolutional neural network (CNN) architecture to train our model on this dataset.

Once the model was trained and tested, we saved it as a serialized object using the joblib library. We then developed a Flask-based web application that allows users to draw a digit using their mouse or touchscreen and submit the image to the model for recognition.

Results:

Our machine learning model achieved an accuracy of 99.1% on the MNIST test set. When integrated with the Flask application, the model is able to accurately recognize handwritten digits drawn by users in real-time.

Technology Overview:

  1. Machine Learning – Machine learning is a subfield of artificial intelligence that enables machines to learn from data, without being explicitly programmed. In this project, we used machine learning algorithms to recognize handwritten digits.
  2. Convolutional Neural Networks – Convolutional Neural Networks (CNNs) are a class of deep neural networks commonly used in image processing and computer vision tasks. CNNs are designed to recognize visual patterns directly from pixel images, making them well-suited for tasks like image classification and object detection.
  3. Flask – Flask is a lightweight web framework that enables the development of web applications in Python. In this project, we used Flask to develop a web application that allows users to input handwritten digits and receive predictions from the trained machine learning model.
  4. MNIST Dataset – The MNIST dataset is a large database of handwritten digits commonly used for training and testing machine learning models. The dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9.
  5. Joblib – Joblib is a library for Python that enables the efficient serialization and deserialization of Python objects. In this project, we used Joblib to save and load the trained machine learning model.

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

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Iris Flower Classification with Decision Trees Web App

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Objective:

To build a web application that can accurately classify Iris flower species based on their sepal and petal characteristics using a Decision Tree machine learning algorithm.

Dataset: The Iris flower dataset, which contains 150 samples of Iris flowers, each with measurements for sepal length, sepal width, petal length, and petal width. The dataset is labeled with the species of each flower: Iris setosa, Iris versicolor, and Iris virginica.

Methodology:

  1. Data Preprocessing: Load the dataset and split it into training and testing sets. Perform feature scaling to normalize the data.
  2. Decision Tree Model Building: Train a decision tree model on the training data using scikit-learn library. Tune the hyperparameters of the model to obtain the best performance.
  3. Web App Development: Use Flask web framework to create a web app that allows users to input the sepal and petal measurements of an Iris flower and displays the predicted species using the trained decision tree model.
  4. Model Interpretation: Interpret the decision tree to gain insights into which features are most important in classifying the Iris flower species.

Tools and Technologies:

  1. Python
  2. scikit-learn
  3. Flask
  4. HTML
  5. CSS
  6. pandas
  7. numpy
  8. matplotlib.

Conclusion:

Decision Trees are a simple yet powerful machine learning algorithm for classification tasks. In this project, we have built a decision tree model to classify Iris flower species with high accuracy and developed a web application that allows users to interactively predict the species of an Iris flower based on its sepal and petal measurements. The web app can be used for real-world applications such as plant identification, environmental monitoring, and plant breeding.

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

Detecting Fraudulent Transactions using Random Forest Project Proposal

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Project Title: Detecting Fraudulent Transactions using Random Forest

Project Description: The objective of this project is to develop a machine learning model using Random Forest to detect fraudulent transactions. Fraudulent transactions can cause significant financial losses to organizations, and machine learning models can help identify such transactions in real-time.

As a student, you can start by collecting a dataset of transactions that includes both legitimate and fraudulent transactions. You can then preprocess the data, perform exploratory data analysis, and engineer relevant features that may help the model identify fraudulent transactions.

You can then use Random Forest, an ensemble learning method that combines multiple decision trees, to build a model that can learn the patterns of fraudulent transactions. You can train the model on the labeled dataset and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.

Once the model is trained and tested, you can deploy it in a real-time environment using web technologies such as Flask or Django. The model can be integrated into an application that can monitor transactions and flag any that are deemed suspicious.

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

Expected Deliverables:

  1. A detailed analysis of the transaction dataset
  2. A machine learning model using Random Forest to detect fraudulent transactions
  3. An evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1 score
  4. A web application that can flag fraudulent transactions in real-time
  5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

  1. Python
  2. Scikit-learn
  3. Pandas
  4. NumPy
  5. Flask or Django

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 fraud detection and transaction datasets
  2. Week 2-3: Data Collection and Preprocessing
  3. Week 4-5: Model Development and Training
  4. Week 6-7: Model Evaluation and Deployment
  5. Week 8: Report Writing and Presentation.

Hypo Thyroid Disease prediction Machine Learning Project

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Hypo Thyroid Disease prediction Machine Learning Project

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Hypothyroid diseases (underactive thyroid) is a condition in which the body doesn't produce enough of important thyroid hormones. The condition may lead to various symptoms at late ages. More information about the disease is available at https://www.mayoclinic.org/diseases-conditions/hypothyroidism/symptoms-causes/syc-20350284 .

The Data

The data was from: http://archive.ics.uci.edu/ml/datasets/thyroid+disease. I used "allhypo.data" for the analysis. "allhypo.names" contains the column names of the data. Include the info about primary data processing in the Jupyter notebook list below.

set of algorithms performed to carry out the analysis of the "thyroid-disease" database published in the UCI page
URL data source
data: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.data
names: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.names

Algorithms

  • Naıve Bayes
  • KNN
  • ANN
  • Random Forest
  • SVM
  • FSF
  • PCA
  • LCA

Related sources

Ionita, Irina. (2016). Prediction of Thyroid Disease Using Data Mining Techniques. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol.7. pp.115-124.
URL: https://www.researchgate.net/publication/321145710_Prediction_of_Thyroid_Disease_Using_Data_Mining_Techniques

Ammulu K., Venugopal. (2017). Thyroid Data Prediction using Data Classification Algorithm. IJIRST –International Journal for Innovative Research in Science & Technology. Vol.4. Issue 2. July 2017. ISSN (online): 2349-6010
URL: http://www.ijirst.org/articles/IJIRSTV4I2054.pdf

Geetha K., Santosh S. Eficient Thyroid Disease Classification Using Differential Evolution with SVM. Journal of Theoretical and Applied Information Technology. Vol.88. No.3. E-ISSN: 1817-3195
URL: http://www.jatit.org/volumes/Vol88No3/4Vol88No3.pdf

Banu, Gulmohamed. (2016). Predicting Thyroid Disease using Linear Discriminant Analysis (LDA) Data Mining Technique. Communications on Applied Electronics. 4. 4-6. 10.5120/cae2016651990. URL: https://www.caeaccess.org/research/volume4/number1/banu-2016-cae-651990.pdf

Lou H, Wang L, Duan D, Yang C,Mammadov M (2018) RDE: A novel approach to improve the classification performance and expressivity of KDB. PLoS ONE 13(7): e0199822. URL: https://doi.org/10.1371/journal.pone.0199822

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