Category Archives: Python Projects

Crop Recommendation using Random Forest flask web app

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The Crop Recommendation Flask Web App is a web application that recommends the best crop to grow based on soil and climate conditions. The project involves building a machine learning model that can predict the crop yield based on several parameters such as soil pH, temperature, rainfall, humidity, and crop type. The machine learning model is then integrated into a Flask web application to provide farmers with a simple and easy-to-use tool for crop selection.

Here's a general overview of the project:

  1. Data collection: Collect soil and climate data from reliable sources such as the National Soil Information System and the National Oceanic and Atmospheric Administration (NOAA).
  2. Data preprocessing: Clean and prepare the data for use in the machine learning model.
  3. Feature selection: Select the most important features that can affect the crop yield, such as soil pH, temperature, rainfall, humidity, and crop type.
  4. Model training: Train a machine learning model using the preprocessed data and the selected features.
  5. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately predict the crop yield.
  6. Flask app development: Develop a Flask web application that allows users to input soil and climate parameters and get a recommendation for the best crop to grow.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Crop Recommendation Flask Web App project can be a valuable tool for farmers to increase their crop yield and improve their farming practices.

 Algorithm :

  1. *Random Forest Classifier* is used for development of model.
  2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
    1. Accuracy of the model using Logistic Regression is 95%.
    2. Accuracy of the model using Random Forest Classifier is 99%.
    3. Accuracy of the model using XGBoost Classifier is 99%.

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

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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

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

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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.

Automated Answer Grading System machine learning project

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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

 

Online Gas Booking Project in Python Django

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The Online Gas Booking System Project in Python Django has been developed to override the problems prevailing in the practicing manual system. This Project is Supported to eliminate and in some cases reduce the hardship faced by this existing system. More over this Project is designed for the particular need of the company to carry out operations in a smooth and effective manner

User Panel :

(Users first fill the signup form then login into their panel and do the following activities )

  1. Dashboard: This is the Welcome page for the customer.
  2. New Connection: In this section, customer sends the request for LPG connection to the organization
  3. Book Cylinder: When the organization provides a connection number then the customer can book his/her cylinder.
  4. Booking History: In this section, customer can view the history of gas booking.
  5. Search: In this section, customer can search gas booking records by entering the booking number.
  6. The customer can also update his profile, change the password and recover the password.

Admin Panel :

  1. Admin is the superuser of the website who can manage everything on the website. Admin can log in through the login page
  2. Dashboard: In this section, admin can see all detail in brief like the total new connection, total new connection,
  3. total on-hold connection, total approved connection, total rejected connection, total new booking, total confirmed booking, total canceled booking, total assign booking, total delivered LPG, total staff and total registered users
  4. Delivery Staff: In this section, admin can manage staff (add/update).
  5. Reg Users: In this section, admin can view the detail of registered users.
  6. Connection: In this section, admin can view the connection request admin also has the right to change connection status according to the current status and add his/her remarks.
  7. Booking: In this section, admin can view booking request and assign to delivery staff or cancel the booking.
  8. Assigned Booking: In this section, admin can change the status of booking according to the current status and add his/her remarks.
  9. Reports: In this section, admin can view booking and connection requests in a particular period.
  10. Search: In this section, admin can search booking and connection details with the help of booking number and connection number respectively.
  11. Admin can also update his profile, change the password and recover the password.

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 2.7, 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.

Django Installation Steps :-

  1. Install Python 3.7 Or Higher
  2. Install Django version 3.0.8
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

Online Tiffin Service Mangement System Using Django

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Online Tiffin Service System is a Django based web application which strives to make an online portal for both vendors and customers. Using Interactive GUI anyone can quickly learn to use the complete system. This system will give power and flexibility to the administrator to manage the entire system from a single online portal.

In Online Tiffin Service System we use Python Django and SQLite Database. This project keeps the records of Tiffin’s orders. This project has three modules i.e. admin, vendor and customers.

Admin Module

  1. Dashboard: In this section, admin can briefly view all the databases users and vendors.
  2. Users: In this section, admin can manage Users (Add/Update/Delete/Edit).
  3. Customers:In this section, admin can manage Users (Add/Update/Delete/Edit).
  4. Vendors:In this section, admin can manage Users (Add/Update/Delete/Edit).
  5. User Groups:In this section, admin can manage Users (Add/Update/Delete/Edit).

Admin can also update his profile, change the password and recover the password.

Vendor Module

  1. Dashboard: In this section, Vendor can briefly view all orders.

User Module

  1. Home Page: In this section, user can view the home page of the web application and also view which food available in Tiffin service and order that food.
  2. My Profile: In this section, user can view and update his/her profile.
  3. Setting: In this section, user can change his/her password.
  4. Log out: The user can be logged off the system using this module.

Technology Used in the project Online Tiffin Service Mangement

  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 2.7, 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.

Online Tiffin Service Mangement Django Installation Steps :-

  1. Install Python 3.7 Or Higher
  2. Install Django version 2.2.0
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

 

  1. Admin Email - admin@admin.com
  2. Admin username - admin
  3. Admin Password -admin123
  4. Vendor ID
  5. 11910617
  6. 11910616
  7. 11910615

Download Link 

Road Lane Detection Computer Vision Python Flask Web app

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Road Lane Detection Computer Vision Python Flask Web app. Lane Detection on Road Images which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines.

The Steps Involved are:

  1. Computing the camera calibration matrix and distortion coefficients given a set of chessboard images. (9x6)
  2. Apply a distortion correction to raw images.
  3. Use color transforms, gradients, etc., to create a thresholded binary image.
  4. Apply a perspective transform to rectify binary image ("birds-eye view") to get a warped image.
  5. Detect lane pixels and fit to find the lane boundary.
  6. Warp the detected lane boundaries back onto the original image.

Python Packages Used:

  1. Flask==1.1.0
  2. gunicorn==19.6.0
  3. pandas==0.22.0
  4. numpy==1.11.2
  5. scipy==0.18.1
  6. scikit-learn>=0.18
  7. opencv-python==3.1.0.4

Pipeline:

Camera Calibration:

The first step in the pipeline is to undistort the camera. Some images of a 9x6 chessboard are given and are distorted. Our task is to find the Chessboard corners an plot them. For this, after loading the images we calibrate the camera. Open CV functions like findChessboardCorners(), drawChessboardCorners() and calibrateCamera() help us do this.

Undistortion of Input Image:

The images uploaded are initially undistorted using cv2.undistort() which takes in an image and returns the undistorted one.

Color transforms, gradients or other methods to create a thresholded binary image:

Detecting edges around trees or cars is okay because these lines can be mostly filtered out by applying a mask to the image and essentially cropping out the area outside of the lane lines. It's most important that we reliably detect different colors of lane lines under varying degrees of daylight and shadow. So, that our self driving car does not become blind in extreme daylight hours or under the shadow of a tree.

I performed gradient threshold and color threshold individually and then created a binary combination of these two images to map out where either the color or gradient thresholds were met called the combined_binary in the code.

Perspective Transform:

Perspective Transform is the Bird's eye view for Lane images. We want to look at the lanes from the top and have a clear picture about their curves. Implementing Perspective Transform was the most interesting one for me. I used values of src and dst as shown below:

src = np.float32([[590,450],[687,450],[1100,720],[200,720]])

dst = np.float32([[300,0],[900,0],[900,720],[300,720]])

Also, made a function warper(img, src, dst) which takes in the Binary Warped Image and return the perspective transform using cv2.getPerspectiveTransform(src, dst) and cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST).

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Online movie ticket booking Project in python django

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Movie Ticket Booking System is to manage the details of Shows, Booking, Payment,Movie, Customer. It manages all the information about Shows, Seats, Customer, Shows. The project is totally built at administrative end and thus only the administrator is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the Shows, Booking,
Seats, Payment. It tracks all the details about the Payment,Movie, Customer.

Online movie ticket booking Project in python django Features :-

User Features :

  1. Login
  2. Register.
  3. Search Movies.
  4. Upcomming Movies.
  5. Current Shows.
  6. Book Movies.
  7. Pay.

Admin Features :

  1. Add Movies .
  2. Edit Movies.
  3. Delete Movies
  4. Booking Hiostory.
  5. User details.
  6. All Crud Operations.

Technology Used :

  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

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 3.7, PIP, Django.
  • 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 Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 2.2.0
  • pip install Pillow
  • Finally run cmd - python manage.py runserver

User Login :-

  1. User Id- ram@gmail.com
  2. Password- ram12345

Admin Login :-

  1. Admmin Login-admin@gmail.com
  2. Password- admin@12345

Artificial Intelligence Project Chess Game Python Flask with Source Code

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This is a simple chess engine/interface created using flask.  It uses chessboard.js and chess.js for the logic of the frontend chessboard, and python chess for the
logic of the backend chessboard. All calculation is done on the backend using python. In order to run this application on your own machine, please install flask and python chess.

Features

  1. Play against Artificial Intelligence bot with multi level .
  2. See game moves in a pretty formatted table. (Standard Algebraic Notation).
  3. Reset the game whenever you want.
  4. Undo and redo your moves.

Installation Step : 

  1. You have to install the required packages, you can do it:
  2. Install flask by running:
    pip install flask

Install python chess by running:
    pip install python-chess[uci,gaviota]
  3. Run command - python flask_app.py

Download Source Code

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