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Loan Defaulter Prediction Machine Learning Projects

 

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Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

 

Goals/Outcome

  • Determining probability of user liability
  • Creating an interactive UI that will take users input and return an output
  • To determine if a neural network vs logistic regression is the better model for classification

Models Created

  • Logistic Regression
  • Random Forest Model
  • Deep Neural Network

About

Probability of Credit Card Default, Machine Learning

Technologies Used : -

  • beautifulsoup4==4.6.0
  • certifi==2018.4.16
  • chardet==3.0.4
  • click==6.7
  • Flask==1.0
  • gunicorn==19.8.0
  • idna==2.6
  • itsdangerous==0.24
  • Jinja2==2.10
  • MarkupSafe==1.0
  • numpy==1.14.3
  • pandas==0.22.0
  • python-dateutil==2.7.2
  • pytz==2018.4
  • requests==2.18.4
  • scikit-learn==0.19.1
  • scipy==1.0.1
  • six==1.11.0
  • SQLAlchemy==1.2.7
  • urllib3==1.22
  • Werkzeug==0.14.1

 

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Used Car Price Prediction Using Machine Learning

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Car Price Prediction is a really an interesting machine learning problem as there are many factors that influence the price of a car in the second-hand market. In this competition, we will be looking at a dataset based on sale/purchase of cars where our end goal will be to predict the price of the car given its features to maximize the profit.

Datasets Link - Kaggle Data 

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt
  • Run flask web app
    python app.py
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Skin cancer Detection using Machine learning

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Skin cancer Detection using Machine learning .The purpose of this project is to create a tool that considering the image of a mole, can calculate the probability that a mole can be malign.

Skin cancer is a common disease that affect a big amount of peoples. Some facts about skin cancer:

Every year there are more new cases of skin cancer than the combined incidence of cancers of the breast, prostate, lung and colon.

An estimated 87,110 new cases of invasive melanoma will be diagnosed in the U.S. in 2017.

The estimated 5-year survival rate for patients whose melanoma is detected early is about 98 percent in the U.S. The survival rate falls to 62 percent when the disease reaches the lymph nodes, and 18 percent when the disease metastasizes to distant organs.

Development process and Data

The idea of this project is to construct a CNN model that can predict the probability that a specific mole can be malign.

Data: Skin cancer Detection using Machine learning

To train this model I'm planning to use a set of images from the International Skin Imaging Collaboration:

Mellanoma Project ISIC https://isic-archive.com.

The specific datasets to use are:

ISICUDA-21: Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.

Benign: 23

Malign: 37

ISICUDA-11 Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and

benign lesions are included.

Benign: 398

Malign: 159

ISICMSK-21: Benign and malignant skin lesions. Biopsy-confirmed melanocytic and non-melanocytic lesions.

Benign: 1167 (Not used)

Malign: 352

ISICMSK-12: Both malignant and benign melanocytic and non-melanocytic lesions. Almost all images confirmed by histopathology. Images not taken with modern digital cameras.

Benign: 339

Malign: 77

ISICMSK-11: Moles and melanomas. Biopsy-confirmed melanocytic lesions, both malignant and benign.

Benign: 448 Malign: 224

As summary the total images to use are:

Benign ImagesMalign Images
1208849

Some sample images are shown below: 1. Sample images of benign moles:

Sample images of malign moles:

Preprocessing:

The following preprocessing tasks are going to be developed for each image: 1. Visual inspection to detect images with low quality or not representative 2. Image resizing: Transform images to 128x128x3 3. Crop images: Automatic or manual Crop 4. Other to define later in order to improve model quality

CNN Model:

The idea is to develop a simple CNN model from scratch, and evaluate the performance to set a baseline. The following steps to improve the model are: 1. Data augmentation: Rotations, noising, scaling to avoid overfitting 2. Transferred Learning: Using a pre-trained network construct some additional layer at the end to fine tuning our model. (VGG-16, or other) 3. Others to define.

Model Evaluation:

To evaluate the different models we will use ROC Curves and AUC score. To choose the correct model we will evaluate the precision and accuracy to set the threshold level that represent a good tradeoff between TPR and FPR.

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Heart Disease Prediction using Machine Learning Project

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Introduction

Heart diseases is a term covering any disorder of the heart. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases have increased significantly over the past few decades in India, in fact it has become the leading cause of death in India.

A study shows that from 1990 to 2016 the death rate due to heart diseases have increased around 34 per cent from 155.7 to 209.1 deaths per one lakh population in India.

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

Problem Description :

A dataset is formed by taking into consideration some of the information of 920 individuals. The problem is : based on the given information about each individual we have to calculate that whether that individual will suffer from heart disease.

Dataset :

The Heart disease data set consists of patient data from Cleveland, Hungary, Long Beach and Switzerland. The combined dataset consists of 14 features and 916 samples with many missing values. The features used in here are,

  1. Age : displays the age of the individual.
  2. Sex : displays the gender of the individual using the following format : 1 = male 0 = female.
  3. Chest-pain type : displays the type of chest-pain experienced by the individual using the following format : 1 = typical angina 2 = atypical angina 3 = non - anginal pain 4 = asymptotic
  4. Resting Blood Pressure : displays the resting blood pressure value of an individual in mmHg (unit)
  5. Serum Cholestrol : displays the serum cholestrol in mg/dl (unit)
  6. Fasting Blood Sugar : compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false)
  7. Resting ECG : 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy
  8. Max heart rate achieved : displays the max heart rate achieved by an individual.
  9. Exercise induced angina : 1 = yes 0 = no
  10. ST depression induced by exercise relative to rest : displays the value which is integer or float.
  11. Peak exercise ST segment : 1 = upsloping 2 = flat 3 = downsloping
  12. Number of major vessels (0-3) colored by flourosopy : displays the value as integer or float.
  13. Thal : displays the thalassemia : 3 = normal 6 = fixed defect 7 = reversable defect
  14. Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not : 0 = absence 1,2,3,4 = present.

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt
  • Run flask web app
    python main_file.py

Models used and accuracy

A Random forest classifier achieves an average multi-class classification accuracy of 56-60%(183 test samples). It gets 75-80% average binary classification accuracy(heart disease or no heart disease).

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Diabetes Prediction using Machine Learning Project Code

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In this Diabetes Prediction using Machine Learning Project Code, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.The data set that has used in this project has taken from the kaggle . "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage." and used a simple random forest classifier.

Learning Objectives : -

The following points were the objective of the project (The main intention was to create an end-to-end ML project.)

  1. Data gathering
  2. Descriptive Analysis
  3. Data Visualizations
  4. Data Preprocessing
  5. Data Modelling
  6. Model Evaluation
  7. Model Deployment

Technical Aspect : -

  1. Training a machine learning model using scikit-learn.
  2. Building and hosting a Flask web app.
  3. A user has to put details like Number of Pregnancies, Insulin Level, Age, BMI etc .
  4. Once it get all the fields information , the prediction is displyed on a new page .

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

 

Datasets

https://www.kaggle.com/uciml/pima-indians-diabetes-database

 

Installation

  1. Download  and unzip it.
  2. After downloading, cd into the flask directory.
  3. Begin a new virtual environment with Python 3 and activate it.
  4. Install the required packages using pip install -r requirements.txt

RUN

  1. Execute the command: python app.py
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Online Medicine Shop using NodeJS MYSQL

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Online Medicine Shop using NodeJS MYSQL  . The purpose of the system is to ease the process of ordering/purchasing medicines online as well as directly. The system manages the customers and their orders as well as all the details of medicines (Manufacturer info, Expiry Date, etc.). The system is being developed in NodeJs and is based on relational database MySQL. The system will be made available across all the branches of the store along with the users.

Admin Features : 

  • Add Medicine , Delete Medicine, Edit Medicine.
  • Add Category , Delete Category ,  Edit Category
  • Add Brand, Delete Brand, Edit brand.
  • See All Order.
  • Update order Status.
  • Customer Activate , Deactivate.

User Features :

  • Register.
  • Login .
  • Edit Profile.
  • Add To Cart.
  • Purchase Medicine.
  • Check Purchase Status.

Technologies in Used :-

  • NodeJs.
  • JavaScript.
  • CSS.
  • HTML.
  • Mysql.
  • Bootstrap.

Installation Steps :-

  • create new mysql database import sql file.
  • run command npm install .
  • run Command - npm start .

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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Black Friday Sales Prediction project with source code

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Black Friday Sales Prediction project with source code . In this project, we are getting to predict what proportion the purchasers will spend during Black Friday, using various features like age, gender, legal status . The dataset we are going to use is the Black Friday dataset from Kaggle which contains about 550068 rows and 12 features that can be downloaded here. We will follow all the steps of a Data Science lifecycle from data collection to model deployment.

This Project contains a jupyter notebook file used to train a CatBoostRegressor model for predicting the amount of sales on a black friday based on several feautures.
The model was then integrated into a flask web application

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Dataset Link: https://www.kaggle.com/c/black-friday/data

Training Model File 

model.ipynb

model-checkpoint.ipynb

Output Generated File

catBoost.pkl

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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Bus Reservation System Project Python Django

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Bus Reservation System is a preety basic system developed in Django,SQLite,Python, which is designed to automate the online ticket purchasing through an easy online bus booking system.With the bus ticket reservation system you can manage/book reservations, clients data and passengers lists through its Django admin and book tickets effortlessly through the Bus reservation Website.

Features

  1. Built with Python 3.6, Djang0 2.0 Framework
  2. Styled using Bootstrap4
  3. Uses SQLite
  4. Sign in with the application to start using.
  5. Set up a profile about and manage your details
  6. Search for buses based on source and destination
  7. Booking buses
  8. Cancel bus reservations
  9. View buses booked and cancelled
  10. View available buses listing
  11. Login , Registration
  12. Admin Panel

Technology Used 

  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. SQLite : SQLite database has been used as database for the project
  7. Django : Project has been developed over the Django Framework

Supported Operating System

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 2.7, PIP, Django.
  • 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 Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 2.2.0
  • Finally run cmd - python manage.py runserver

 

 

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Online Food Ordering Project JAVA JSP MYSQL

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An online food ordering system JAVA JSP MYSQL is a web-based application that stimulates the foodies (customers) to put food orders through internet by locating their favorite restaurant or nearest one.  This application is based on the JAVA, JSP (Java Server Page ), MYSQL  platform.

The online food ordering system JAVA JSP MYSQL provides convenience for the customers. It overcomes the disadvantages of the traditional queuing system. This system increases the takeaway of foods than visitors. Therefore, this system enhances the speed and standardization of taking the order from the customer. The online food ordering system JAVA JSP MYSQL set up menu and the customers easily places the order with a simple mouse click. This system allows the user to select the desired food items from the displayed menu. The use r’s details are maintained confidential because it maintains a separate account for each user. An id and password is provided for each user. Therefore it provides a more secured ordering.

Modules and their requirements :-

User Module :

  • Login
  • Registration
  • View Menu
  • Order
  • Order History
  • Feedback

Admin Module :

  • Login
  • View Menu
  • Add Menu Item
  • Delete Menu
  • View All User
  • View All Order
  • View All Feedbacks
  • View Specific User all Order

 

Software Requirements

Technology used:

  • Front end – JSP
  • Backend – MYSQL

Software:

  •  IDE - Netbeans 8.2
  •  Database support - MySQL 5.7
  •  Operating system – Windows 8 and above
  •  Server deployment - Glassfish server

Technology:

  • HTML is integrated in JSP. It provides a means to structure text based information in a document. It allows users to produce web pages that include text, graphics and
    hyperlinks.
  •  Javascript is a scripting language which supports the development of both client and server applications. It is preferred at client side to write programs that can be
    executed by a web browser within the context of a web page.
  •  CSS(Cascading Style Sheets) is a style sheet language used for describing the presentation of a document written in a markup language. Although most often used to set the visual style of web pages and user interfaces written in HTML and XHTML, the language can be applied to any XML document,
  •  SQL is the language used to manipulate relational databases. It is tied closely with the relational model. It is issued for the purpose of data definition and data
    manipulation.
  • Java Server pages is a simple yet powerful technology for creating and maintaining dynamic-content web pages. It is based on the Java programming language. It can be thought of as an extension to servlet because it provides more functionality than servlet A JSP page consists of HTML tags and JSP tags. The jsp pages are easier to maintain than servlet because we can separate designing and
    development.

We require a JDBC connection between the front end and back end components to write

to the database and fetch required data.

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