Category Archives: Machine Learning Projects With Source Code

Android Human Activity Recognition Tensorflow Project with Source Code

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This is the source code for a sensor-based human activity recognition android app. The model has been built with Keras deep learning library. The classifier has been trained and validated on "Sensors Activity Dataset" by Shoaib et al. which is available for download from here. The dataset contains data for seven activities of daily living including biking, downstairs, jogging, sitting, standing, upstairs, and walking. An LSTM learner has been employed for classification task which achieved an accuracy of 98% on valdiation data. Finally the model has been exported in protobuf format to be used in android app for on-device inference. You can check out the jupyter notebook that goes along to follow all the steps which have been taken to build and export the model.

Dependencies

  1. Python 3.6
  2. Tensorflow 1.13.1
  3. Keras

Android Requirements :

  1. Android Studio Latest.
  2. Grandle version 4.2.2, 7.0.2
  3. Emulator

Read Before Purchase  :

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

Netflix Movie Recommendation System Project on Machine Learning

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A flask web-app which can be used to get recommendations for a tv-show/movie, the app recommends a list of media according to the input.

Problem Description

Netflix is all about connecting people to the movies they love. To help customers find those movies, they developed world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. Netflix use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better.

Now there are a lot of interesting alternative approaches to how Cinematch works that netflix haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business.

Problem Statement

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

Data Overview

Source of Data : https://www.kaggle.com/netflix-inc/netflix-prize-data

Data files : combined_data_1.txt combined_data_2.txt combined_data_3.txt combined_data_4.txt movie_titles.csv

The first line of each file [combined_data_1.txt, combined_data_2.txt, combined_data_3.txt, combined_data_4.txt] contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

MovieIDs range from 1 to 17770 sequentially. CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users. Ratings are on a five star (integral) scale from 1 to 5. Dates have the format YYYY-MM-DD.

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Artificial Intelligence Project Handwritten Digits Recognition

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The handwritten digit recognition is the capability of computer applications to recognize the human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different shapes and sizes. The handwritten digit recognition system is a way to tackle this
problem which uses the image of a digit and recognizes the digit present in the image. Convolutional Neural Network model created using PyTorch library over the MNIST dataset to recognize handwritten digits .

Handwritten Digit Recognition is the capability of a computer to fete the mortal handwritten integers from different sources like images, papers, touch defenses, etc, and classify.  them into 10 predefined classes (0-9). This has been a  Content of bottomless- exploration in the field of deep literacy.  Number recognition has numerous operations like number plate recognition, postal correspondence sorting, bank check processing, etc . (2). In Handwritten number recognition,   we face numerous challenges . because of different styles of jotting of different peoples as it .  is not an Optic character recognition. This exploration provides
a comprehensive comparison between different machine literacy and deep literacy algorithms for the purpose of handwritten number recognition. For this, we've used Support . Vector Machine, Multilayer Perceptron, and Convolutional . Neural Network. The comparison between these algorithms is carried out on the base of their delicacy, crimes, and .testing- training time corroborated by plots and maps that have been constructed using matplotlib for visualization.

Datasets Details : -

The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset.

It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9.

The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively.

It is a widely used and deeply understood dataset and, for the most part, is “solved.” Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold out test dataset.

Handwritten character recognition is an extensive exploration area that formerly contains detailed ways of perpetration which include major literacy datasets, popular algorithms,  . features scaling and point birth styles. MNIST dataset ( Modified National Institute of Norms and Technology database) is the subset of the NIST dataset which is a combination of two of NIST’s databases Special.  Database 1 and Special Database 3. Special Database 1 and Special Database 3 correspond of integers written by high academy scholars and workers of the United States Census Bureau,.  independently. MNIST contains a aggregate of handwritten . number images (- training set and- test set) in .  28x28 pixel bounding box andanti-aliased. All these images have corresponding Y values which apprises what the number

Implementation Steps : -

  1. Import the libraries and load the dataset
  2. Preprocess the data
  3. Create the model
  4. Train the model
  5. Evaluate the model
  6. Create GUI to predict digits

Requirements .txt file :-

  1. torch
  2. numpy==1.16.5
  3. flask==1.1.1
  4. gunicorn
  5. matplotlib==3.3.1
  6. pillow==6.2.0
  7. flake8
  8. pip
  9. pylint

 

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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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

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Image to Cartoon Python OpenCV Machine Learning

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Image to Cartoon Python OpenCV Machine Learning Free Source Code . This Project web app project you can directly select image then you can convert any image to cartoon . its very interesting project . This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project.

Image to Cartoon Python OpenCV Machine Learning Free Source Code

Required modules in this Projects

  1. CV2: Imported to use OpenCV for image processing
  2. easygui: Imported to open a file box. It allows us to select any file from our system.
  3. Numpy: Images are stored and processed as numbers. These are taken as arrays. We use NumPy to deal with arrays.
  4. Imageio: Used to read the file which is chosen by file box using a path.
  5. Matplotlib: This library is used for visualization and plotting. Thus, it is imported to form the plot of images.
  6. OS: For OS interaction. Here, to read the path and save images to that path.
  7. Flask: Flask is a micro web framework written in Python.

Steps to develop Image to cartoon 

  1. Importing the required modules
  2. Building a File Box to choose a particular file
  3. How is an image stored?
  4. Transforming an image to grayscale
  5. Smoothening a grayscale image
  6. Retrieving the edges of an image
  7. Preparing a Mask Image
  8. Giving a Cartoon Effect
  9. Plotting all the transitions together
  10. Functionally of save or download  button

Application tested on:

  1. python 3.7
  2. tensorflow 2.1.0
  3. tf_slim 1.1.0
  4. ffmpeg 3.4.8
  5. Cuda version 10.1
  6. OS: Windows 10

Algorithmia For Video Convert 

We used the Serveless AI Layer product of Algorithmia for inference on videos. To learn more on how to deploy your model in Algorithmia, check here - https://algorithmia.com/developers

 

Installation Step : -

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

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Book Recommendation System Project Machine Learning

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A recommendation system is one of the top applications of data science. Every consumer Internet company requires a recommendation system like Netflix, Youtube, a news feed, etc. What you want to show out of a huge range of items is a recommendation system.  recommendation engine is a class of machine learning which offers relevant suggestions to the customer.  Before the recommendation system, the major tendency to buy was to take a suggestion from friends. But Now Google knows what news you will read, Youtube knows what type of videos you will watch based on your search history, watch history, or purchase history.

Book Recommendation System Development Steps:

  1. Collect the data by scraping the web using beautifulsoup
  2. Encode the data using tensorflow-hub
  3. Build a nearest neighbor model using scikit-learn
  4. Make a flask web app to see recommendations
  5. Make a REST API using flask to get recommendations

Book Recommendation Methods:

  1. Euclidean distance.
  2. cosine similarity.

Requirements

All the code is written in python 3.7 and the following packages are used:

  1. tensorflow-hub
  2. tensorflow-text
  3. scikit-learn
  4. numpy
  5. flask
  6. beautifulsoup4
  7. requests
  8. gunicorn

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

Read Before Purchase  :

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

Insurance Claim Prediction Machine Learning Project with Source Code

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Latest Machine Learning Project with Source Code

insurance claim prediction machine learning. Insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Furthermore, because of the payment errors, processing the claims again accounts for a significant portion of administrative costs.

Dataset :

This dataset contains 7 features as shown below:

age: age of the policyholder
sex: gender of policyholder (female=0, male=1)
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25
steps: average walking steps per day of the policyholder
children: number of children/dependents of the policyholder
smoker: smoking state of policyholder (non-smoke=0;smoker=1)
region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)
charges: individual medical costs billed by health insurance.

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

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Medical Insurance Cost Prediction Project in Python Flask

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Latest Machine Learning Project with Source Code

Medical Insurance Cost Prediction using Random Forest Regressor.

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!

Features

  1. Exploring the dataset
  2. Converting Categorical values to Numerical
  3. Plotting Heatmap to see dependency of Dependent valeu on Independent features
  4. Data Visualization (Plots of feature vs feature)
  5. Plotting Skew and Kurtosis
  6. Data Preparation
  7. Prediction using Linear Regression
  8. Prediction using SVR
  9. Prediction using Ridge Regressor
  10. Prediction using Random Forest Regressor
  11. Performing Hyper tuning for above mentioned models
  12. Plotting Graph for all Models to compare performance
  13. Preparing model for deployment
  14. Deployed model using Flask

Results

Model gave 86% accuracy for Medical Insurance Amount Prediction using Random Forest Regressor

Dataset

The dataset used can be downloaded here (Kaggle) - Click to Download

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

 

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