Blog Website Project Using Python Django Project

The project aimed to develop a fully functional blog website using the Django web framework. Key features included user authentication, CRUD operations for managing blog posts, a commenting system, rich text editing capabilities, responsive design, and pagination. By leveraging Python, Django, HTML/CSS, and SQLite, the project provided valuable insights into web development. Future improvements were suggested, such as implementing search functionality, categories and tags, social media integration, security enhancements, and performance optimization. Overall, the project offered hands-on experience in building dynamic web applications with Django, empowering users to share their ideas and stories effectively.

Features:

  1. User Authentication: Registration, login, and logout functionalities.
  2. CRUD Operations: Users can create, read, update, and delete blog posts.
  3. Commenting System: Users can comment on posts.
  4. Responsive Design: Ensured the website is accessible across devices.
  5. Pagination: Implemented pagination for listing posts.
  6. Blogs are updated and deleted only by author of that blog.
  7. Profile is only changed by logging user.

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. SQLite : SQLite database has been used as database for the project
  7. Django : Project has been developed over the Django 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 3.10, 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.

Project on Django Installation Steps :-

  1. Install Python 3.10
  2. Install Django version 4.1.6
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

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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

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Online Taxi Booking Python Django with Real time Map

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An online taxi booking Python Django project is a web-based application that allows users to book a taxi ride online through a user-friendly interface. The application integrates with real-time maps to show the location of available taxis and their estimated arrival times.

The project involves designing a database using Django's built-in ORM to store user and ride data. The application's frontend is created using HTML, CSS, and JavaScript to provide an interactive user interface for booking rides, tracking ride progress, and paying for rides.

The Python code interacts with the database to retrieve user data, such as name, email, and payment information, and with real-time map APIs to display the location of available taxis and their estimated arrival times. It uses algorithms to match user requests with available taxis, manage ride progress, and calculate fare amounts.

Some key features of an online taxi booking Python Django project may include:

  1. User registration and login
  2. Ride booking and tracking
  3. Real-time map integration
  4. Payment processing and receipt generation
  5. Ride history and user reviews
  6. Driver and vehicle management
  7. Ride cancellation and refund management
  8. Mobile app integration

Overall, an online taxi booking Python Django project provides a convenient and user-friendly way for users to book and track taxi rides, while providing drivers with a simple way to manage their bookings and pickups. The real-time map integration helps to minimize wait times and improve the overall user experience.

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. Django: Project has been developed over the Django 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 3.7.0, 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 manage.py runserver

Detecting Fraudulent Transactions using Random Forest Project Proposal

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

Hypo Thyroid Disease prediction Machine Learning Project

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Latest Machine Learning Project with Source Code

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

Loan Defaulter Prediction Machine Learning Projects

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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

 

Crime Data Analysis Project in Machine Learning

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Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions.
The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

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

Read Before Purchase  :

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

Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

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( Note : Project Included with Complete Source code Database Plus Documentation, Synopsis, Report)

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Project Features :-

  1. User can register and login.
  2. User can search through various movies and look through its details.
  3. User can give rating to the movies.
  4. User can add movie to their watch list.
  5. User can get movie recommendation (Recommendation algorithm (Collaborative Filtering) which suggests new movies based on the ratings given by user.)

Algorithm :

Collabortive Filtering (Recommender Algorithm)
  1. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future.

  2. When we want to find a new movie to watch we'll often ask our friends for recommendations. Naturally, we have greater trust in the recommendations from friends who share tastes similar to our own.

  3. Collaborative-filtering systems focus on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.

  4. There are two types of collaborative filtering

    1. User-based, which measures the similarity between target users and other users.
    2. Item-based, which measures the similarity between the items that target users rate or interact with and other items.

    I have used user based collaborative filtering in this project.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.7

pip3

virtualenv

Installation

pip install -r requirements.txt --user

Run server locally

$ python manage.py runserver

Go to localhost:8000

  1. Admin email - admin@admin.com
  2. admin pass - admin

Read Before Purchase  :

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