Tag Archives: Neural networks

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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Anomaly Detection in Time Series Data using Autoencoder Project Proposal

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Project Title: Anomaly Detection in Time Series Data using Autoencoder

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

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

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

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

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

Expected Deliverables:

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

Tools and Technologies:

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

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

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

Automated Answer Grading System machine learning project

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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