Author: Yugesh Verma

Posted on by — Leave a comment

Crop Recommendation using Random Forest flask web app

Buy Now ₹1501

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
Posted on by — Leave a comment

Rainfall Prediction using LogisticRegression Flask Web App

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

 

Posted on by — Leave a comment

Plant Disease Prediction using CNN Flask Web App

Buy Now ₹2501

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

 

Posted on by

Fire Detection Using Surveillence Camera web app Project with Source Code

Buy Now ₹1501

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
Posted on by

Iris Flower Classification with Decision Trees Web App

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.

Download

Posted on by

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.
Posted on by

Anomaly Detection in Time Series Data using Autoencoder Project Proposal

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.
Posted on by

100 unique machine learning project proposal

  1. Predicting Customer Churn in a Telecom Company using Machine Learning
  2. Anomaly Detection in Time Series Data using Autoencoder
  3. Detecting Fraudulent Transactions using Random Forest
  4. Building a Recommendation System using Collaborative Filtering
  5. Predicting House Prices using Regression Techniques
  6. Classifying Images using Convolutional Neural Networks (CNNs)
  7. Sentiment Analysis of Social Media Posts using Natural Language Processing (NLP)
  8. Detecting Parkinson’s Disease using Machine Learning Techniques
  9. Credit Risk Assessment using Decision Trees
  10. Predicting Stock Prices using Time Series Analysis
  11. Predicting the Success of a Marketing Campaign using Decision Trees
  12. Recognizing Handwritten Digits using Neural Networks
  13. Facial Recognition using Convolutional Neural Networks (CNNs)
  14. Customer Segmentation using K-Means Clustering
  15. Predicting the Likelihood of Customer Loan Default using Logistic Regression
  16. Identifying Wildlife using Image Recognition Techniques
  17. Fraud Detection in Insurance Claims using Machine Learning
  18. Predicting the Outcome of a Football Match using Regression Techniques
  19. Recommending Movies based on User Ratings using Collaborative Filtering
  20. Classifying Emails as Spam or Non-Spam using Naive Bayes
  21. Building a Chatbot using Natural Language Processing (NLP)
  22. Detecting Heart Disease using Machine Learning Techniques
  23. Building a Music Recommendation System using Collaborative Filtering
  24. Predicting Flight Delays using Regression Techniques
  25. Identifying Plant Species using Image Recognition Techniques
  26. Predicting Customer Lifetime Value using Regression Techniques
  27. Detecting Cyber Attacks using Machine Learning Techniques
  28. Building a News Recommendation System using Collaborative Filtering
  29. Sentiment Analysis of Customer Reviews using Natural Language Processing (NLP)
  30. Predicting the Outcome of a Basketball Game using Regression Techniques
  31. Classifying Images of Food using Convolutional Neural Networks (CNNs)
  32. Detecting Breast Cancer using Machine Learning Techniques
  33. Building a Product Recommendation System using Collaborative Filtering
  34. Predicting Traffic Congestion using Regression Techniques
  35. Detecting Spam Calls using Machine Learning Techniques
  36. Building a Book Recommendation System using Collaborative Filtering
  37. Sentiment Analysis of Movie Reviews using Natural Language Processing (NLP)
  38. Detecting Alzheimer's Disease using Machine Learning Techniques
  39. Predicting the Outcome of a Baseball Game using Regression Techniques
  40. Recognizing Objects in Images using Convolutional Neural Networks (CNNs)
  41. Identifying Fraudulent Financial Statements using Machine Learning
  42. Building a Restaurant Recommendation System using Collaborative Filtering
  43. Predicting Customer Satisfaction using Regression Techniques
  44. Identifying the Best Route for Delivery using Machine Learning Techniques
  45. Building a Course Recommendation System using Collaborative Filtering
  46. Sentiment Analysis of Product Reviews using Natural Language Processing (NLP)
  47. Detecting Autism using Machine Learning Techniques
  48. Predicting the Outcome of a Tennis Match using Regression Techniques
  49. Recognizing Text in Images using Optical Character Recognition (OCR)
  50. Identifying Malicious Websites using Machine Learning Techniques
  51. Building a Travel Recommendation System using Collaborative Filtering
  52. Predicting the Success of a Kickstarter Campaign using Machine Learning Techniques
  53. Detecting Depression using Machine Learning Techniques
  54. Predicting the Outcome of a Hockey Game using Regression Techniques
  55. Identifying Traffic Signs using Image Recognition Techniques
  56. Building a Real Estate Recommendation System using Collaborative Filtering
  57. Sentiment Analysis of Restaurant Reviews using Natural Language Processing (NLP)
  58. Detecting Lung Cancer using Machine Learning Techniques
  59. Predicting the Outcome of a Soccer Game using Regression Techniques
  60. Classifying Images of Animals using Convolution
  61. Predicting Online Purchases using Machine Learning Techniques
  62. Image Segmentation using Convolutional Neural Networks (CNNs)
  63. Medical Image Analysis using Deep Learning
  64. Fraud Detection in Credit Card Transactions using Machine Learning
  65. Predicting Sales using Time Series Analysis
  66. Building a Music Genre Classifier using Machine Learning
  67. Building a Chatbot using Sequence-to-Sequence Models
  68. Identifying Fake News using Natural Language Processing (NLP)
  69. Predicting Patient Readmission using Machine Learning Techniques
  70. Predicting Solar Power Generation using Machine Learning
  71. Building a Recipe Recommendation System using Collaborative Filtering
  72. Detecting Pneumonia using Machine Learning Techniques
  73. Traffic Sign Detection and Recognition using Deep Learning
  74. Sentiment Analysis of Hotel Reviews using Natural Language Processing (NLP)
  75. Building a Social Network Recommendation System using Collaborative Filtering
  76. Identifying Depression using Social Media Posts using Natural Language Processing (NLP)
  77. Predicting the Success of a Movie using Machine Learning Techniques
  78. Identifying Hand Gestures using Deep Learning
  79. Building a Restaurant Rating Prediction System using Collaborative Filtering
  80. Predicting Customer Lifetime Value using Machine Learning Techniques
  81. Identifying Sarcasm in Text using Natural Language Processing (NLP)
  82. Detecting Brain Tumors using Machine Learning Techniques
  83. Building a Fashion Recommendation System using Collaborative Filtering
  84. Identifying Emotional States using Facial Expression Recognition
  85. Predicting Product Returns using Machine Learning Techniques
  86. Building a Car Recommendation System using Collaborative Filtering
  87. Building a News Article Classification System using Machine Learning
  88. Predicting Traffic Accidents using Machine Learning Techniques
  89. Identifying Bot Accounts in Social Media using Machine Learning
  90. Predicting the Success of a Book using Machine Learning Techniques
  91. Building a Travel Package Recommendation System using Collaborative Filtering
  92. Detecting Glaucoma using Machine Learning Techniques
  93. Predicting Customer Complaints using Natural Language Processing (NLP)
  94. Building a Beer Recommendation System using Collaborative Filtering
  95. Classifying Speech Emotions using Machine Learning Techniques
  96. Identifying Cyberbullying in Social Media using Natural Language Processing (NLP)
  97. Predicting the Success of a Video Game using Machine Learning Techniques
  98. Building a Hotel Room Recommendation System using Collaborative Filtering
  99. Detecting Skin Cancer using Machine Learning Techniques
  100. Building a Ride-Sharing Recommendation System using Collaborative Filtering.
Posted on by

Customer Churn in a Telecom Company using Machine Learning project proposal

Project Title: Predicting Customer Churn in a Telecom Company using Machine Learning

Project Description:

The aim of this project is to predict customer churn in a telecom company using machine learning techniques. Customer churn is the rate at which customers stop using a company's services, and predicting it can help the company identify customers who are at risk of leaving, and take proactive measures to retain them.

As a student, you can start by understanding the concept of customer churn and how it affects a telecom company's business. You can then collect and preprocess a dataset of customer information, such as demographic data, call and text usage, billing information, and other customer data.

After preprocessing the data, you can perform exploratory data analysis to identify patterns and trends that may indicate a likelihood of churn. You can then use various machine learning techniques, such as logistic regression, decision trees, random forests, and support vector machines (SVMs) to build predictive models.

You can evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1 score. Once the models have been trained and evaluated, you can deploy them to predict customer churn in real-time.

The final deliverable can be a report detailing the methodology, findings, and recommendations for the telecom company.

Expected Deliverables:

  1. A detailed analysis of the customer data and the factors that contribute to customer churn in the telecom industry.
  2. A set of machine learning models that can predict customer churn with high accuracy.
  3. A user-friendly web interface that allows the telecom company to input customer data and get predictions in real-time.
  4. A comprehensive report that details the methodology, findings, and recommendations for the telecom company.

Tools and Technologies:

  1. Python
  2. Scikit-learn
  3. Pandas
  4. NumPy

Project Timeline:

As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline: Week 1: Understanding the concept of customer churn and the telecom industry Week 2-3: Data Collection and Preprocessing Week 4-5: Exploratory Data Analysis and Feature Engineering Week 6-7: Model Development and Evaluation Week 8: Report Writing and Presentation.

Posted on by

Automated Answer Grading System machine learning project

Buy Source Code ₹1501

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