Heart Disease Prediction using Machine Learning Project, Web App, Flask...

Buy Now ₹1501

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).

Diabetes Prediction using Machine Learning Project Code

Buy Now ₹1501

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.)

• Data gathering
• Descriptive Analysis
• Data Visualizations
• Data Preprocessing
• Data Modelling
• Model Evaluation
• Model Deployment

Technical Aspect : -

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

Technologies Used : -

• Python 3.7
• Pandas
• Numpy
• Flask

 

Datasets

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

 

Installation

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

RUN

• Execute the command: python app.py

Diabetes Prediction using Machine Learning Project Code

Categories: Machine Learning Projects With Source Code, Python Projects

Tags: diabetes prediction using machine learning github, diabetes prediction using machine learning ppt, diabetes prediction using machine learning project, diabetes prediction using machine learning project code, diabetes prediction using machine learning project documentation, diabetes prediction using machine learning project github, diabetes prediction using machine learning project ppt, diabetes prediction using machine learning project report, diabetes prediction using machine learning project report pdf, diabetes prediction using machine learning projects kaggle, diabetes prediction using machine learning python

Post navigation

Online Medicine Shop using NodeJS MYSQL

Buy Now  ₹1001

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.

 

Black Friday Sales Prediction project with source code | Machine Learnin...

Buy Now ₹1501

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.

Bus Reservation System Project Python Django

Buy Now ₹501

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