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Artificial Intelligence Project Handwritten Digits Recognition

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 imagespaperstouch defenses, etc, and classify.  them into 10 predefined classes (0-9). This has beenContent of bottomlessexploration in the field of deep literacyNumber 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
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 delicacycrimes, 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

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Artificial Intelligence Project Chess Game Python Flask with Source Code

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.


  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

Download Source Code

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Image to Cartoon Python OpenCV Machine Learning

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 -

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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.


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

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.
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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

Download Link 

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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!


  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


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


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


Download Link 

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Breast Cancer Prediction Machine Learning Project Source Code

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Latest Machine Learning Project with Source Code

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Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.


Logistic Regression model is developed based on 10 features that classify whether the breast cancer is benign or malignant. For classifying the patient, users are requested to submit their data on this following form as per the value range.

Languages  Used

  • Python: language
  • NumPy: library for numerical calculations
  • Pandas: library for data manipulation and analysis
  • SkLearn: library which features various classification, regression and clustering algorithms
  • Flask: microframework for building web applications using Python.

Installation Steps :-

  • Install Python 3.7.0
  • Install all dependencies cmd -python -m pip install –-user -r requirements.txt
  • Finally run cmd - python


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IMDB Sentiment Analysis based on comment Machine Learning

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Latest Machine Learning Project with Source Code

his is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.

Computer Vision is the branch of the science of computers and software systems which can recognize as well as understand images and scenes. Computer Vision is consists of various aspects such as image recognition, object detection, image generation, image super-resolution and many more. Object detection is widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and self-driving cars. In this project, we are using highly accurate object detection-algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding by using dependencies such as TensorFlow, OpenCV, imageai etc, we can detect each and every object in image by the area object in an highlighted rectangular boxes and identify each and every object and assign its tag to the object. This also includes the accuracy of each method for identifying objects.


  1. flasgger==0.9.4
  2. Flask==1.0.3
  3. gunicorn==19.9.0
  4. itsdangerous==1.1.0
  5. Jinja2==2.10.1
  6. MarkupSafe==1.1.1
  7. Werkzeug==0.15.5
  8. numpy==1.18.1
  9. scipy==1.4.1
  10. scikit-learn==0.22.1
  11. matplotlib==3.2.1
  12. pandas==1.0.3
  13. nltk==3.4.5

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Salary Prediction using Machine Learning Web App

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Latest Machine Learning Project with Source Code

Salary Prediction Based on work experience ML Web App. The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type. The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type.

Data The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries.

Information Used To Predict Salaries Years Experience: How many years of experience .

This model can be used as a guide when determining salaries since it shows reasonable predictions when given information on years of experience.

Methods Used

  1. Data Analysis and Visualization
  2. Linear Regression
  3. Polynomial Transformation
  4. Ridge Regression
  5. Random Forest

Technologies/Libraries Used

  1. Python 3
  2. Pandas
  3. NumPy
  4. Seaborn
  5. Scikit-learn
  6. Matplotlib
  7. SciPy
  8. Jupyter


The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries. Twenty percent of this training dataset was split into a test dataset with corresponding salaries.

There is also a testing dataset that does not have any salary information available and was used as a substitute for real-world data.

Information Used To Predict Salaries

  1. Years Experience: How many years of experience


  1. This is project predicts the salary of the employee based on the experience.

Model Training :- trains and saves the model to the disk.
    model.pkb the pickle model

Run App :- contains all the requiered for flask and to manage APIs.

Open command Prompt and go to given directory and then run python

Download Link