Category Archives: Machine Learning Projects With Source Code

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

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Crop Recommendation using Random Forest flask web app

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Buy Now ₹1501

Buy Now Project Report ₹1001

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

Rainfall Prediction using LogisticRegression Flask Web App

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Buy Now ₹1501

The Rainfall Prediction using LogisticRegression Flask Web App project is a web application that predicts the amount of rainfall based on historical weather data. The project involves building a logistic regression model that can predict the amount of rainfall based on several weather parameters, and integrating this model into a Flask web application.

Here's a general overview of the project:

  1. Data collection: Collect historical weather data from reliable sources such as the National Oceanic and Atmospheric Administration (NOAA) or India Meteorological Department (IMD).
  2. Data preprocessing: Clean and prepare the weather data for use in the logistic regression model.
  3. Feature selection: Select the most important features that can affect the rainfall prediction, such as temperature, humidity, wind speed, and cloud cover.
  4. Model training: Train a logistic regression model using the preprocessed weather data and the selected features.
  5. Model evaluation: Evaluate the performance of the logistic regression model to ensure it can accurately predict the amount of rainfall.
  6. Flask app development: Develop a Flask web application that allows users to input weather parameters and get a prediction of the amount of rainfall.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Rainfall Prediction using LogisticRegression Flask Web App project can be a valuable tool for farmers and other industries that rely on weather predictions.

Explanation of the logistic regression model:

Logistic regression is a statistical model that is used for binary classification problems, where the goal is to predict whether an observation belongs to a particular class or not. The logistic regression model uses a logistic function to map the input features to a probability output. The logistic function is a sigmoid function that outputs a value between 0 and 1, which can be interpreted as the probability of the observation belonging to the positive class.

Here are the key components of the logistic regression model:

  1. Input features: The input features are the variables that are used to predict the outcome. In the case of rainfall prediction, the input features could include temperature, humidity, wind speed, and cloud cover.
  2. Weights: Each input feature is assigned a weight, which reflects the strength of the relationship between that feature and the outcome variable. The weights are learned during the training process and are used to make predictions.
  3. Bias term: The logistic regression model also includes a bias term, which is added to the weighted sum of the input features to produce the final prediction.
  4. Logistic function: The logistic function is a sigmoid function that is used to map the input features to a probability output. The logistic function has a characteristic S-shaped curve and outputs a value between 0 and 1.
  5. Decision boundary: The decision boundary is the threshold value that is used to determine whether an observation belongs to the positive class or the negative class. The decision boundary is typically set to 0.5, meaning that any observation with a predicted probability greater than 0.5 is classified as belonging to the positive class, while any observation with a predicted probability less than 0.5 is classified as belonging to the negative class.
  6. Training: During training, the logistic regression model is fed a set of labeled data and adjusts its weights to minimize the difference between the predicted output and the actual output. This process is typically done using an optimization algorithm such as gradient descent.

Overall, the logistic regression model is a simple and interpretable model that can be used for binary classification tasks, such as rainfall 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

 

Plant Disease Prediction using CNN Flask Web App

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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

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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

Download

Automated Answer Grading System machine learning project

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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

 

Tomato Leaf Disease Prediction CNN Python Flask Project

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Buy Now Source Code ₹1501

Buy Now Project Report ₹1001

The Tomato Leaf Disease Predictor is a flask web application which classifies a plant/leaf image into 10 categories viz. 'Tomato_mosaic_virus', 'Early_blight', 'Septoria_leaf_spot', 'Bacterial_spot', 'Target_Spot', 'Spider_mites Two spotted_spider_mite', 'Tomato_Yellow_Leaf_Curl_Virus', 'Late_blight', 'Healthy', and 'Leaf_Mold'. The code is written in Python 3.6.10 and makes use of Keras and Tensorflow libraries in developing an InceptionV3 based image classification web application.

Datasets Link - https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf

About Dataset

The data has different types of diseases for tomato leaves.
Here goes the list:

  1. Tomatomosaicvirus
  2. Target_Spot
  3. Bacterial_spot
  4. TomatoYellowLeafCurlVirus
  5. Late_blight
  6. Leaf_Mold
  7. Early_blight
  8. Spidermites Two-spottedspider_mite
  9. Tomato___healthy
  10. Septorialeafspot

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

 

Music Recommendation Based on Facial Expression Sporify api Python

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The emotion recognition model is trained on FER 2013 dataset. It can detect 7 emotions. The project works by getting live video feed from web cam, pass it through the model to get a prediction of emotion. Then according to the emotion predicted, the app will fetch playlist of songs from Spotify through spotipy wrapper and recommend the songs by displaying them on the screen.

Features:

  • Real time expression detection and song recommendations.
  • Playlists fetched from Spotify using API.
  • Neumorphism UI for website.

Installation Steps:

Flask:

  • Run pip install -r requirements.txt to install all dependencies.
  • In Spotipy.py enter your credentials generated by your Spotify Developer account in 'auth_manager'. Note: - This is only required if you want to update recommendation playlists. Also uncomment import statement in 'camera.py'.
  • Run python app.py and give camera permission if asked.

Technology Overview:

  1. Keras
  2. Tensorflow
  3. Spotipy
  4. flask
  5. Flask

Dataset:

The dataset used for this project is the famous FER2013 dataset. Models trained on this dataset can classify 7 emotions. The dataset can be found here.

Note that the dataset is highly imbalanced with happy class having maxiumum representation. This might be a factor resulting in okaysish accuracy after training.

Model Architecture:

  • The model architecture is a sequential model consisting of Conv2d, Maxpool2d, Dropout and Dense layers:
  1. Conv2D layers throughout the model have different filter size from 32 to 128, all with activation 'relu'
  2. Pooling layers have pool size (2,2)
  3. Dropout is set to 0.25 as anything above results in poor performance
  4. Final Dense layer has 'softmax' activation for classifying 7 emotions
  • Used 'categorical_crossentropy' for loss with 'Adam' optimizer with 'accuracy' metric

Note:- Tried Implementing various other models like VGG16 but accuracy was far too low. This model architecture gives good enough accuracy. A bit more tinkering with hyper parameters might lead to a better accuracy

Image Processing and Training:

  • The images were normalised, resized to (48,48) and converted to grayscale in batches of 64 with help of 'ImageDataGenerator' in Keras API.
  • Training took around 13 hours locally for 75 epochs with an accuracy of ~66 %

Issue:

The app in current state can't be deployed on web as:

  1. Opencv tries to open the camera on whatever device the app is running on. Code in current state makes use of webcam if available on server side not client side. So when app is run locally on a laptop Video Streaming through webcam is possible. But if it's deployed to a cloud, the app is stored in a data center somewhere which obviously doesn't have web camera connected to it and hence it doesn't work.

Further Work:

  1. Instead of CSVs, create a databse and connect it to application. The DB will fetch songs for recommendations and new songs can be updated directly onto database
  2. Add a feature which will update specified playlists for better and more recent recommendations, a specific day over a fixed duration say every sunday and append it to database
  3. Directly play the song or redirect to the song on Spotify when user clicks on it.
  4. Rewrite code such that Video Streaming is done on client side instead of server side so as it make the app deployable

Note: Model accuracy is not that great. It is ~66%. Further training and finetuning required. May try Vision Transformer Model.

Download Link

Object detection Python Machine Learning Web App

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Buy Now ₹1501

Buy Now Project Report ₹1001

This Project is a web application built with the python-flask framework, that uses YOLO weights to detect the Objects. Using YOLO object detection algorithm.

YOLOv5  is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

YOLO an acronym for 'You only look once', is an object detection algorithm that divides images into a grid system. Each cell in the grid is responsible for detecting objects within itself.

YOLO is one of the most famous object detection algorithms due to its speed and accuracy.

How Does it Work

To put it simply, the back-end receives an image from a user and runs an object detection algorithm on the image (YOLO v3). Once the predictions are obtained, they are drawn on the image, which is, then, sent back to the user (to the front-end). In this README I provided the environment setup for the computing machine, which runs the detection algorithm. However, setting up the back-end machine is just the tip of an iceberg. The whole engineering pipeline includes many other steps full of caveats. If you are interested in the details of each step, checkout How Did You Build Your Object Detector?.

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 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python webapp.py