Category: Python projects

Welcome to the Python Projects Section.

Find the below some projects we have developed using Python.

  1. Google Finance currency calculator API.
  2. using aprori on website to get relevent data
  3. Game like tictactoe,2048,
  4. Note/content sharing platform for college students
  5. book information parsing tool developed for opencart
  6. AI Crooswords
  7. An algorithm for steganography of multiple heterogeneous files on a single video carrier.
  8. college-administration
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Audio Classification On Cat’s And Dog’s Python Project

Our Audio Classification project illustrates a straightforward audio classification model supported by deep learning. we tend to address the matter of classifying the sort of sound-supported short audio signals and their generated spectrograms, from classifying dog’s audio to cat’s audio throughout model training. So as to satisfy this challenge, we tend to use a model-supported Convolutional Neural Network (CNN). The audio was processed with Mel-frequency Cepstral Coefficients (MFCC) into what is unremarkably called Mel spectrograms, and hence, was reworked into a picture. Our final CNN model achieved 89% accuracy on the testing dataset.

Project Overview :

The input to our model, in this project, is cats and associated dogs recording audio go in WAV kind. It lies below the supervised machine learning class. Thus, a dataset is also present as well as a target class. Hence, the intention here is to classify if the given input wav file is that of a cat or dog. Each of the dog and cat sounds is incredibly distinguished like in their pitch and frequency level since completely different| sounds have different sample rates. By default, Librosa mixes all audio to mono and resamples them to 22050 cycles/second at load time. For music and audio analysis, Librosa is associated ASCII text file python package. The info and the sampling rate are provided by Librosa. Audio or sound is in its raw kind, and the data provided should be pre-processed to extract significant and meaningful features so we implemented an algorithm i.e., MFCC (Mel Frequency Cepstral Coefficients) rule. Then, when audio extraction is done, the information is fed and the dataset is split into training and test set. So, after the preprocessing, a Convolutional Neural Network model is designed using tensor flow. For every code and model building, Keras API was used to implement Google colab.

Motivation

Machine learning can be used in image processing, understanding speech, and musical instruments, speech-to-text, environmental sound classification, and many more. And as for our project, we implemented a class of speech processing i.e, audio classification. Converting sound waves into audio and spectrograms which is a visual representation of frequencies with the help of function provided by machine learning.

There are many techniques to classify images as many different in-built neural networks under CNN are already there, especially if it is related to images. And it’s straightforward to extract options from pictures as a result of pictures already being available in the shape of numbers, because the formation of a picture may be an assortment of pixels, and pixels area units within the sort of numbers. When we have data as text, we use the sequential encoder and decoder-based techniques to find features. But if it is to sound recognition or audio it is more difficult compared to text because it is based on frequency and time. Therefore a proper model is to be made to extract the frequency and pitch of that audio so as to make it easier to later recognize it.

Flow Chart:

Preliminaries and Background 

Related work

Machine learning: Image classification of cats and dogs – Before a decade, in computer notion, many problems had been saturating in accordance with their precision. However, the accuracy of those troubles significantly stepped forward with the boom of deep gaining knowledge of strategies. The majority of the problems that arise from image class is that it is defined as predicting the distinct categories a photo can belong to. Hence, for the supplied enter/ photograph detection with the aim of accomplishing high precision, a state-of-the-art approach is incorporated, i.e., a convolutional neural network turned into the build for the photo category mission of puppies and cats. A dataset become given from Kaggle comprising a total of 25000 pix of each dog and cat.

Machine learning: Audio classification of different bird species – Here, the methodology and results of using deep learning to assist in the classification of birds by their sounds are presented. As birds indicate the health of an ecosystem, hence this topic is of high importance. Random Forest Classification and custom-made six CNN models from the literature were performed on a dataset of ten birds that were composed of xeno-canto.org. The highest accuracy was achieved at around 65% by the Random Forest and at about 58% for the CNN model.

conclusion and future work 

In this report, we first briefly explained the overview of this project and showed some referred project work already established. Then, we precisely illustrated our task, including the learning task and the performance task. After that, we explained the approach we are heading toward in order to classify the datasets. The approach/model we used is a neural network which is an implementation of the deep network which is a trainable model by which we were able to classify the dog’s and cat’s audio. The highest accuracy we got was 89.6%.

  1. In the future, we will try to implement the different high-level models in order to achieve much higher
  2. We’ll build a system that can directly intake a live raw
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Fake Disaster Tweet Detection Web-App Python Machine Learning Project

This project “Fake Disaster Tweet Detection” aims to help predict, whether a tweet weather it is fake or real. It uses the Multinomial Naïve Bayes approach for detecting fake or real tweets from existing datasets available on Kaggle. The classifier will be trained only on text data. Traditionally text analysis is performed using Natural Language Processing also known as NLP. Natural language processing is a field that comes under Artificial Intelligence. Its main focus is on letting computers understand human language and process it. NLP helps recognize and predict diseases using speech, it helps in sentiment analysis, cognitive assistant, spam detection, the healthcare industry, etc. In this project Training Data is pre-processed, then sent to the classifier, then and the classifier predicts weather the tweet is real or fake.

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

System Design

System Flowchart

System Flowchart

Problem: To detect disaster tweets whether it’s fake or real using a machine learning algorithm. In this, the concept of Natural language Processing is used.

Identification of data: In this project, I have used a dataset available on Kaggle competition based on Natural language processing. This project works only on text data. It has five columns:

  1. Id: It tells the unique identification of each tweet
  2. Text: It tells the tweet in text form
  3. Location: It tells the place from where the tweet was sent and it can be blank
  4. Keyword: It tells a particular word in the tweet and it can be blank
  5. Target: It tells the actual value of the tweet weather it’s a real tweet or Fake

Data-preprocessing: First the preprocessing is done in the dataset which includes the removal of punctuations, then the removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data.

Definition of Training Data: The training dataset which contains 70% of the whole dataset is used for training the model.

Algorithm Section: In this project Multinomial Naïve Bayes classifier algorithm is used for detecting disaster tweets whether they are fake or real.

Evaluation with test set: Several text samples are passed through the model to check whether the classification algorithm gives the correct result or not.

Prediction Model

Implementation Work Details

The data-set which is used in this project “Fake disaster tweet detection” is taken from the Kaggle competition “Natural Language Processing with Disaster Tweets”. The data set contains 7613 samples. This project works only on text data. It has five columns:

  • Id: It tells the unique identification of each tweet
  • Text: It tells the tweet in text form
  • Location: It tells the place from where the tweet was sent and it can be blank
  • Keyword: It tells a particular word in the tweet and it can be blank
  • Target: It tells the actual value of the tweet weather it’s a real tweet

Step 2: Data-Preprocessing

  1. Removing Punctuations: Punctuations are removed with the help of the following python code
  1. Removing URLs, digits, non-alphabets, _: True means it has HTTP, and False means it does not have HTTP
  1. Removing Contraction: It expands the words which are written in short form like can’t is expanded into cannot, I’ll is expanded into I will, etc.
  1. Lowercase the text, tokenize them, and remove Stopwords: Tokenizing means splitting the text into a list of tokens. Stopwords are the words in the text which does not provide additional meaning to the text.
  1. Lemmatizing: It converts any word into its root form like running, ran into a run.
  1. Countvectorizer:

Text cannot be used to train our model, it has to be converted into numbers that our computer can understand, so far in this project, Countvectorizer is used. Countvectorizer counts the number of times each word appears in a document. Countvectorizer works as:

Step1: It first identifies unique words in the complete dataset.

Step 2: then it will create an array of zeros for each sample of the same length as above Step 3: It then takes each word at a time and find its occurrence in each sample in the dataset. The number of times the word appears in the sample will replace the zero positioned at the word in the list. This will repeat for every word. 

Step 3: Model Used:

In this project, the Multinomial Naïve Bayes approach is used for detecting fake or real tweets from existing datasets available on Kaggle. Naïve Bayes classifier is based on the probability theorem “Bayes Theorem” and also has an assumption of conditional independence among every pair.

System Testing

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The machine learning model is evaluated we normally use classification accuracy which is the number of correct predictions divided by the total number of predictions.

This accuracy measuring technique works well when there is an equal number of samples in the dataset belonging to each class. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

  • Precision = True Positives / (True Positives + False Positives)
  • Recall = True Positives / (True Positives + False Negatives)

Conclusion

In this project only one classification algorithm is used which is Multinomial Naïve Bayes. First, the preprocessing is done in the dataset which includes the removal of punctuations, then removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average f1 score is 0.775.

Future Scope

In the future, some other classification algorithms can also be tried on this dataset like KNN, Support vector machine (SVM), Logistic Regression, and even Deep learning algorithms can also be used which give very high accuracy. Vectorizing can be done using other methods like word2vec, Tf-Idf vectorizer, etc.

Download the Complete Project on ake Disaster Tweet Detection Web Application Python-based Machine Learning Project.

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Covid-19 Outbreak Prediction Using Machine Learning Python Project

The aim of this Covid-19 Outbreak Prediction project is to make a model which will forecast the number of confirmed cases covid-19 virus in the upcoming days. Covid-19 is an infectious disease that is affecting a huge number of people all around the world.

This virus was first identified in Wuhan, China, and later spread throughout the world causing a pandemic that forced most countries to go into lockdown.

Various machine learning models and time series forecasting models.

The predictive model will be created using machine learning and using the dataset obtained from Kaggle. Machine learning automates the formation of analytical models. It is a branch of artificial intelligence focused on the principle that data can be learned from processes, It can find patterns and take decisions.

Time series forecasting will be used which is a type of predictive model. Time series forecasting is the use of a model centered on earlier observed values to evaluate future values. 

INTRODUCTION

The aim of this project is to make a predictive model which will predict the trajectory of the outbreak of the covid-19 virus in the upcoming days. Covid-19 is an infectious disease that is affecting a huge number of people all around the world.

It was first identified in Wuhan, China, and then later spread all over the world causing a pandemic.

Since no vaccine is developed which can be available all throughout the world, we have to take preventive measures which can stop the spread of the disease. Since a lockdown cannot last forever, we have to know how fast the spread is and how much more people will be infected.

The predictive model will be created using machine learning and using the dataset obtained from Kaggle. Machine learning automates the formation of analytical models. It is a branch of artificial intelligence focused on the principle that data can be learned from processes, It can find patterns and take decisions.

Time series forecasting will be used which is a type of predictive model. Time series forecasting is the use of a model centered on earlier observed values to evaluate future values.

PRESENT SYSTEM

Various work on this problem related to covid-19 is being done. Officials all over the world are using several outbreak prediction models for covid-19 to make informed decisions and implement relevant control measures. Simple statistical models have received greater attention from authorities among the standard models for covid-19 global pandemic prediction. One of the works suggests using SEIR models. SEIR means susceptible-exposed-infected-recovered model.

This model aims to forecast factors like the spread of a disease, the total number of infected, and the span of an outbreak, and estimate different epidemiological parameters like the number of reproductive. Such models can illustrate how the outcome of the disease can be affected by various public health measures.

PROPOSED SYSTEM 

In this project, we will first collect and evaluate the dataset. We will transform the raw data into an accessible format and visualize it using data preprocessing. Various machine learning algorithms such as Linear regression, polynomial regression, SVM, holt’s linear model, Holt’s winter model, AR model, ARIMA model, and SARIMA model are used. The tools used in this project are mainly sklean for model selection, and NumPy library which is used to work with the arrays and pandas that use a key data structure called a data frame that allows us to store and manipulate tabular data in observation rows and variable columns, matplotlib is a library of plotting that is used to plot graphs. After implementing the model, the model with the least mean square error will be considered the best-fit model.

System Design 

The dataset is first preprocessed and visualized so that it is in a usable format for analysis. After this, we model the data using Linear regression, polynomial regression, SVM, holt’s linear model, Holt’s winter model, AR model, ARIMA model, and SARIMA. Then we evaluate the model and choose the best one according to its root mean square.

The flowchart depicts the following

Dataset 

The dataset involves the collection of data from various sources.

Data Pre-processing and visualization 

In order to obtain accurate results, data preprocessing is done to check if there is any inconsistency in the data, if there is it is handled accordingly. We then visualize the data to study the pattern and trends in the data.

Model Building 

Various models are used in this project-: Linear Regression

Polynomial Regression SVM

Holt’s Linear

Holt’s Winter Model

Auto Regressive Model (AR)

Moving Average Model (MA) ARIMA Model

SARIMA Model

DATASET

In this project, the dataset is taken from Kaggle which is the Novel Corona Virus 2019 Dataset and the goal is to study the effect and spread of COVID-19 in the coming days, and conduct predictions and time series forecasting.

Hardware and Software Details 

  •  Software Details Python 3.7(64-bit) Jupyter notebook

Implementation work details  

First, the data is pre-processed and visualization is done and analyzed. Afterward, various models are used to train the data and the model with the least root mean squared error is selected as the best fit model. Various machine learning models are used and time series forecasting models such as holt’s linear model and ARIMA model are used. The dataset is obtained from Kaggle.

Real-life applications 

It can be used by the government for predicting the extent of the spread of the infectious disease and take action accordingly.

Data implementation and program execution 

The data is analyzed and visualized afterward. On different models, the data is trained and the one with the least mean square error is considered to be the best fit model and can be used for forecasting. The program is executed on a Jupyter notebook.

Output Screens 

Fig: Growth of different types of cases in India

Fig: Confirmed cases Linear Regression Prediction

Fig: Polynomial Regression Prediction for confirmed cases

Fig: SVM regressor Prediction for confirmed cases

Fig: Holts Linear Model Prediction for confirmed cases

Fig: Holt’s Winter model prediction for confirmed cases

Fig: AR model prediction for confirmed cases

Fig: SARIMA model Prediction

System Testing 

In this project, the model evaluation part is very important as by the means of it we can identify which model can best fit the problem.

Here the models are evaluated on the basis of their root mean square error(rmse).

The root-mean-square variance (RMSD) or root-mean-square error (RMSE) is a commonly used calculation of the differences expected by the model or estimator between values (sample or population values) and the values observed.

According to the rmse values of all the models tested in the project, the one with the least rmse value was the SARIMA model. So it can be considered the best fit model for this problem.

Conclusion

 It is concluded that machine learning models can be used to forecast the spread of infectious diseases like Covid-19. In the project, we used various algorithms to forecast the rise of confirmed cases. It was observed among all the algorithms used, SARIMA had the least rmse so it was considered the best fit model for the data that was available.

Limitations

It is a new virus so only a year worth of dataset is available. Generally, the more data we have the better accuracy we get and we have to keep updating the data.

Scope for future work

 It can be implemented such that it can update its graphs or predictions according to real-time values.

Download the Complete project on Covid-19 Outbreak Prediction Using Machine Learning Python Project Code & Report.

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Prediction of the growth of Corona Virus Python Project

The upsurge of this disease is CORONA VIRUS has created a life-and-death situation in the world of the living. The virus is increasing day by day and effective lives. Machine Learning can be established very effectively in tracing the disease predicting its growth and forming an effective strategy in order to manage the effect of the virus. This report gives us a full glance and the best mathematical computation with modeling for predicting growth.

In an Corona Virus Prediction ML-based project, we come up with various computations and modeling to suspect or predict the growth of a particular dataset. Although this concept can be used on a dynamic dataset that is changing day to day, here in this report we will study a particular dataset.

Working on the dataset led to various challenges such as modeling different algorithms of machine learning but finally worked on them in order to get the best result. This report is an insight into the working brief of the project such as descriptive information about machine learning, algorithms, statistical description, and most important the programming language used here which is python.

INTRODUCTION

This deadly disease is caused by the spread of various germs and harmful bacteria(pathogens) which transmits from one human to many humans, from one animal to many, and from animal to human. Early diagnoses are curable, while the patients suffering from it with a maximum number of days are not 100% curable.

There is a need for innovation in predicting the growth with deep thorough analysis, of huge global data on the rise of the virus.

The Corona Virus Prediction project comprises two main features or methods we can say, first predicting and analyzing cumulative confirmed cases and then representing with visuals that are data visualization. The second one is predicting the growth of total, confirmed, and new cases and finding accuracy.

  • PRESENT SYSTEM

Many employers are working on the same data and with the same idea of predicting the growth of the virus by analyzing cases. The COVID crisis has led many colleges and students to work in teams to get into a solution against corona.

There are many ongoing types of research and many projects have already been developed in predicting creating awareness on the same

  • PROPOSED SYSTEM

Working on the dataset led to various challenges such as modeling different algorithms of machine learning but finally worked on them in order to get the best result. It is an insight into the working brief of the project such as descriptive information about machine learning, algorithms, statistical description, and most important the programming language used here which is python. 

System Design 

System Flow Chart

Data Dictionary 

Data Pre-Processing: Our dataset needs to be pre-processed. Therefore, data pre-processing is required in this project.

Definition of Training Set: The training set is the data that the algorithm will learn from. Learning looks different depending on which algorithm you are using.

Algorithm Selection: Our project has been implemented using various algorithms such as linear regression, random forest, and decision trees.

Decision Tree: In python, we use a decision tree to observe and figure out the trained data in the structure of the tree in order for any future implementation. Decision Tree, here the target variables take continuous values called regression tree. 

Implementation Work Details 

Libraries used

Numpy

It contains among other things:

  • a powerful N-dimensional array object
  • broadcasting Functions
  • Tools for integrating
  • Useful linear algebra etc.

Pandas

Pandas is an open-source, BSD-authorized library giving superior, simple-to-utilize information structures and information investigation apparatuses for the Python programming language.

  • Benefits:

Python has for some time been incredible for information munging and planning, however less so for information examination and display. pandas help fill this hole, empowering you to do your whole information examination work process in Python without changing to a more space-explicit language like R.

Joined with the amazing IPython toolbox and different libraries, the earth for doing information examination in Python exceeds expectations in execution, profitability, and the capacity to work together.

More work is as yet expected to make Python a top-notch measurable displaying condition.

Download the Complete Project on Prediction of the growth of Corona Virus Python Project Code and Report

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Vehicle Management System Project using Python and SQLite

 

The complete development of this Vehicle Management System project using Django as the backend and sqlite3 as the database. This Python project has main sections Login/Signup, Dashboard, Vehicle, Driver, Booking, Repair, and Report which are explained in the coming slides in detail.

Functions Below:

Login/Signup

Users can signup/log in to the portal with this page. It takes in the necessary fields required for the user details

Dashboard

This is the dashboard in which you can view your details is shown and the user can edit the details.

Driver

Driver Section has two pages, one is to add a driver and the other is to view the list of drivers available. This section is only visible to the users which have admin access. The Driver list has the features to search and sort lists according to the fields.

Vehicles

In this section there are two pages one is to add vehicles and the other is to list the vehicles owned by the user. On the vehicle list page, the user can view individual details, edit the details and delete the vehicle.

Booking

The booking section has four pages one is the form to book a trip the second one is the success page where the booking details including the distance, cost, and duration are displayed using google maps API. On the success page, you have the option to pay which will take you to the payment page.

The payment page lets you enter the card details to pay. There is a page to display all the bookings made by the user wherein the user can search for the bookings. The admin will have another option which is to confirm the booking and a driver will be allotted and a mail will be sent to the user saying the mail is confirmed. The map option in the booking list page displays the route using google maps API. When the admin confirms the booking a confirmation mail will be sent to the user.

Repair

The repair section has two pages, one for reporting the issue and another page to show the issues made by the user. On the issues page, the admin has an option to solve the issue made by all the users.

Report

Each user can have a report of the trips that he has made. There is an option to mail the user the report for further use. Demo Here is the live demo of our project. The quality of the gif is a bit low.

Database tables:

Vehicle Table

Owner
Cost per KM
Price
Registration Plate
Vehicle Status
Insurance Status
Total KM traveled
Fuel Type
Mileage
Vehicle Type
Image

Report Table

Registered Date
Registered User
Vehicle Mileage
Issue

Driver Table

First Name
Last Name
National ID
Address
Email
Phone Number
License Category

BookingTable

Source
Destination
Distance
Booking Date
Start Date
End Date
Security Deposit
Allotted User
Allotted Driver
Vehicle
Cost
Duration

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Temperature and Air Quality Monitoring System Project for Pet lovers

Introduction:

In this modern world, there are many pet lovers who would like to carry their pets to places wherever they go. It’s the responsibility of the same person to ensure the safety of their pets. There are some public places where they can’t take their pets. For example, if a person visits a shopping mall he can’t carry his pet into the shopping mall. Hence, he/she has to park his car in the parking lot, leave his pet inside the car, slide down the window a little bit for air circulation and continue his shopping.

For suppose he/she forgot to slide down the window and left for shopping then the pet gets suffocated due to lack of air circulation and a rise in temperature. Even though he/she slides down the window and leaves for shopping there is a possibility that one of the many people inside the parking lot may smoke a cigarette. The smoke released may enter the car and damage the air quality which in turn may have effects on pets.

This is where our project finds its scope. We are developing a “Temperature and Air quality monitoring system for Pet lovers” in which we are monitoring the temperature levels, humidity, pressure, and air quality of the air inside our automobile and present them in an attractive dashboard so that the pet owner can monitor the atmospheric conditions inside his automobile through all of his gadgets having internet connection.

High-level architecture of the project:

Hardware Requirements:

  • Raspberry Pi Zero
  • 32 GB or larger Micro–SD Card
  • Power Supply and cable
  • BME680 Sensor
  • Connecting cables

Software Requirements:

  • Balena Cloud to create dashboards using sensor data
  • Balena Etcher to flash our SD card
  • Balena CLI for command line interface
  • Balena Sense code for installing the services

Project Implementation:

Step-1:

  • The first step of our implementation is to flash the operating system is to flash balena operating system into our Raspberry pi zero board.
  • For this initially, we have to create a balena cloud
  • Once we signed up and login into our balena cloud account then we have to create an application as shown below with our Wifi SSID and password and then we have to download Balena operating system image
  • Once we download the operating system image file then we will insert our SD card into card reader and connect the card reader to our
  • Then we will flash the OS image file into an SD card by means of balena Etcher as shown
  • By end of this system, our SD card should be ready with the flashed operating system for insertion into our Raspberry Pi zero board.

Step-2:

  • The main aim of this step is to complete the hardware
  • Please find the pin configuration of the Raspberry Pi Zero
  • Please find the pin configuration of the BME680 sensor
  • The connections are listed below:

Pin1 of Raspberry Pi zero——- CC pin of BME680

Pin3 of Raspberry Pi zero—– SDA pin of BME680

Pin 5 of Raspberry Pi zero—– SCL pin of BME680

Pin 9 of Raspberry Pi zero—– GND pin of BME680

  • Once we complete the connections to the BME680 sensor then we have to insert the flashed SD card into the SD card slot of our Raspberry Pi Zero
  • Please find the Raspberry Pi zero board after the connections are done as below:

Step-3:

  • Once we completed step 2 then we have to power up our Raspberry Pi zero board and then we have to open balena
  • If everything goes right our device must automatically be listed in balena cloud as shown
  • Then we have to install Balena command line interface for pushing the services
  • Then we have to push balena sense code into our board by using push
  • Please find the balena CLI below:
  • Once the push is successful then automatically the services get installed as shown below:

Step-4:

  • When the above three steps are successful then our cloud starts pulling the data from the sensor
  • To see the readings in dashboards we need to enable the public device URL and we can copy the URL we can access the dashboards on any device on which a web browser is installed across any geographic location.
  • Please find the screenshot of the dashboards below:
  • Then for testing purposes, I started breathing on the sensor. As we all know human breath contains CO2 and it is warm we can see on the dashboards as Indoor Air Quality showing Unhealthy and temperature is also raised as
  • After I have stopped breathing on the sensor within some time the IAQ returned to Good and also we can see the temperature started dropping as
  • As I have mentioned earlier every individual having a public device URL can monitor the dashboards from any electronic device which has a web browser installed in it. Please find the dashboards opened from the mobile phone
  • Hence the device is placed in a car with wifi module connected to it our device starts sending the data to the cloud. Hence even though pet owners leave their pets in cars and left for shopping can monitor the temperature and air quality and can make sure their pet is safe.
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Corona Virus Prediction and Analysis Machine Learning Project

1.  Introduction 

Background 

Currently, there are many people, who are being affected by CoronaVirus. It started in China and now it is spreading all over the world. Till now, there is no medicine for this virus, and it’s killing millions of millions of people. So, it is a big question among all of us of how many people are going to be affected.

Problem Statement 

Currently, there is no application that can predict the spread of CoronaVirus for the future 30 days. So, with this project, we would like to create awareness among the people, by showing them how the corona rises for the future 30 days so that they can take some preventive measures by staying indoors.

Project Goal 

The main objective of this Corona Virus Prediction project is :

  • Future prediction of the increase/decrease in the number of active Coronavirus Cases for the next 30 days – for the whole world as well as for the United States of America. We have chosen the USA among all the counties as it is the highly affected country due to corona.
  • Future prediction of the increase/decrease in the number of deaths due to Coronavirus for the next 30 days.
  • Future prediction of the increase/decrease in the number of recovered cases due to Coronavirus for the next 30 days.

2.  Literature Review

 There is an outbreak of Corona in early December. This is caused due to severe acute respiratory syndrome coronavirus 2, which is basically the family of SARS virus. Many governments all over the world are issuing their own preventive measures to control the spread of coronavirus. So, we have conducted a literature review regarding this virus, based on the information that is publicly available.

Background of Literature Review:

China alerted WHO on 31st December 2019 that many people are reported to be suffering from Pneumonia, in Wuhan City. They reported that it started on Dec 8th, 2019, and there were an increasing number of patients who are working or living around the Huanan Seafood Wholesale Market.

When we started working on this project at the start of February, the Coronavirus was majorly prevalent in China. Initially, at the time of our project proposal, the mortality rate in China among all the confirmed cases is around 1.2% as of February 2020. And the mortality rate in all other countries, other than china was around only 0.2%. Among all the patients, who were admitted to the hospitals, the mortality rate, was around 11%. COVID-19 is increasing with great speed, and now there is a relatively very high mortality rate

A Way to Further Research :

So, we have performed this literature review, to analyze the spread of coronavirus. After analyzing how increasingly it’s spreading all over the world, we thought of performing our own prediction regarding this virus, so as to make people aware of its spread, and with this, they can take their own preventive measures, so that they do not fall prey to this dangerous virus.

We had very little amount of data when we started this project. It is a very trending topic all over the world. And millions of millions of people are losing their lives due to this virus. So, we are very curious to analyze this pandemic and so we have taken up this project.

We have found many datasets to collect data regarding the corona cases. Some of them include Kaggle, John Hoppkins, etc. So, we thought of choosing the dataset from John Hoppkins, as it’s updating the dataset on a daily basis. So, we collected the data and performed our own future predictions.

3. Methodology 

Approach

 So, basically, we have followed the below approach to kick-start our Corona Virus Prediction project:

  1. Firstly, we have started with research on choosing the datasets. On performing research on various datasets, we have finalized with John Hoppkins data set, as it gives us the live data on coronavirus.
  2. Secondly, we have collected the data and performed our preprocessing operation, so as to make our data ready for future predictions.
  3. Next, coming to choosing the machine learning algorithm. We have chosen appropriate machine learning(we will discuss below regarding this).
  4. Finally, we have performed our predictions to analyze the active cases, deaths, and recoveries for the next 30 days, based on the data available from the datasets and the chosen machine learning algorithm.

Figure: Approach

4.  Implications 

Benefits of the Project: 

  • This Corona Virus Prediction project helps in the prediction of coronavirus cases for the next 30 days, all over the world.
  • With this, we can also predict the increase in corona cases in the world.
  • By this, we can know how fast the coronavirus is spreading all over the world.
  • We can create awareness among people.
  • We can also create awareness in government so that they can take preventive measures to stop the spread of corona.

Lessons Learned:

Initially, I had no idea of a Machine learning algorithm. I started learning about machines from scratch. I bought some Udemy tutorials and through that, I learned everything step by step. At the start of the project, I am not even aware of what machine learning algorithm to use.

It was really an exciting experience doing this project. I am inspired to take up a Machine Learning Course for my next semester to learn deeply about Machine Learning Algorithms.

I tried my level best and contributed my 100% to this project.

Now, I came to know about machine learning, different types of machine learning Algorithms, and the differences between classification and regression algorithms -when to use what, creating test and train sets, building up the model, choosing the appropriate parameters, and performing future predictions. In the future, I would also love to take up a project related to Classification Algorithms.

5.  Conclusion 

  • Finally, to conclude, we have performed prediction using SVR and Polynomial Regression Algorithm.
  • SVR predictions are mainly for predicting the world case scenario, which includes confirmed, death, and recovered cases.
  • Polynomial Regression is used for the prediction of US Cases.
  • Based on the results, we believe that our predictions were almost accurate, with some little differences from the actual values.
  • This project can be further scalable, to include the predictions for various individual

6.  Appendix 

  • We have used Google Collab for our project. As we are two members of the team, we have chosen this, because it enables us to simultaneously work on the project from different
  • No Installation is Required.
  • We just need to have a google account. And we can easily create a Google Collaboratory file in our google drive, just like Google docs.
  • We will provide both .py files as well as .ipynb files along with this report, so as to run on google collab.
  • .ipynb can be uploaded to google collab directly and the results of the projects can be easily checked.
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Predicting Life Expectancy Using Machine Learning Python Project

Project Name: Predicting Life Expectancy Using Machine Learning

Project scope: The scope of this project is ” Predicting Life Expectancy Using Machine Learning” in this project we are given the task to predict life expectancy, life expectancy is the average time period for which the subject lives.

Project schedule: 

  1. Understanding what to do in the above-given Predicting Life Expectancy Project
  2. Identify and get familiar with the tools needed to complete this project
  3. Writing codes
  4. Collecting Data sets
  5. The time duration is 5 days

 Deliverables: 

  1. Predicting Life Expectancy Using Machine Learning.
  2. Making a user interface too as front-end work and writing code as backend work to make the user interact and calculate the Life

Setting The Development Environment

  1. Creating GitHub account
  2. Creating Slack account
  3. Signing up for cloud services
    1. Node-Red for front end
    2. Watson Studio for coding
  • Machine Learning services

1.  INTRODUCTION

  1.1          Overview:

 This project is based on predicting the life expectancy of a person. It is the statistical average of the number of years a person is expected to live. Factors affecting life expectancy are Country, Mental and Physical Illness, lifestyle, diet, health care services, financial condition, BMI, alcohol consumption, Diseases, etc.

Here in this Predicting Life Expectancy project, our motive is to find the life expectancy of a person after providing details such as the country he is living in is developed or is developing, BMI of the person, Disease history, Income, Population of that country, Expenditure, etc. So here I have used Machine learning and Artificial Intelligence to predict life expectancy. The data used in the training of the model was the data by WHO taken from Kaggle.

There were almost 22 columns stating different factors affecting Life expectancy and 2939 rows comprising data of different persons from different countries. Based on the results we got on Watson Studio some factors which were not affecting the Life expectancy much were removed and the scoring endpoint was obtained after running full code. This scoring endpoint is the URL that helps us to send payload data to a model or function development for analysis (such as to classify the data or to make predictions).

After obtaining the endpoint the next step is to work on Node-red which is the platform, we can use for developing our front-end page that will have a form asking you your details such as year of birth, adult mortality, infant deaths, BMI, etc. rest we’ll discuss in details later on.

Requirements: IBM Cloud, GitHub, Slack, IBM Watson, Node-Red

1.2          Purpose:

 The purpose of this project is to build a model that will predict the Life Expectancy of a person after giving the details of the BMI, Expenditure, Disease history, etc.

2.  Literature Survey

2.1          Proposed Solution:

 The project tries to create a model based on data provided by the World Health Organization (WHO) to evaluate the life expectancy for different countries in years. The data offers data on different person’s Physical health, Mental health, etc from the time frame 2000 to 2015. The data was taken from the website: https://www.kaggle.com/kumarajarshi/life-expectancy- who/data. 

3.  Theoretical Survey

 3.1          Block diagram

 

Block Diagram for Predicting Life Expectancy with Python

3.2          Hardware/Software Designing:

·       GitHub

GitHub is the largest community of developers in the world with millions of people sharing their projects, and ideas for benefiting many people in a very unique way. Any person living in any corner of this world can access this platform for his/her benefit. They can share their problem, their ideas, and solution to some problems. In simple words, it is basically a platform in which anyone can come and share their problems and solutions. It is easy to manage. A team working on the same project can easily monitor the progress and can easily access their work anywhere.

·       Slack

It is a messaging tool that is intended to contact your internal team easily. As it gives you a platform through which we can communicate to our team members easily under one roof. It is not as hectic as sending emails and reading them. It directly comes as a message to you from the group created having your team members. It is great if you are having a team of more than 2 members. Searching for messages becomes an easy, fast medium, searching old messages.

·       IBM Cloud

It is the platform that enables us to use its various features such as Watson Studio which provides a platform where we can write our python code and observe our results in the form of heat maps, graphs, and tables. In this project, we used it and got our scoring endpoint. It is the URL that helps us to send payload data to a model or function development for analysis (such as to classify the data or to make predictions).

·       Node-Red

Node-Red helps us to create a front-end window on which we can get the data from the user such as his Year, BMI, Alcohol intake, etc. and it will then connect to the code written on Watson Studio via the scoring endpoint created after running the python code.

4.  Experimental Investigations

 The graphs of various Factors affecting the prediction of life expectancy are shown in the figure given below:

Curves of life expectancy v/s different factors

Heat map of different factors

Shown above is the heat map of the various factors affecting other various factors some of them have positive values some of them have negative but the thing we have to keep in mind is that we can’t neglect the factors having negative values because they will have the adverse effect which will affect the life expectancy. After some observations, I decided not to include 6 factors that are not affecting life expectancy much and will reduce the calculations and make our model less complex.

5.  Flow Chart

  1. Result

 After filling in all the necessary details asked in the UI form, we got the prediction of life expectancy. The accuracy of our model was 94.41%

Screenshot of the prediction of life expectancy obtained

Advantages and Disadvantages

 Advantages:

  • Easily identifies trends and patterns
  • Wide Applications
  • Handling multi-dimensional and multi-variety data
  • No human intervention is needed (automation)
  • Continuous Improvement

Disadvantages:

  • High error-susceptibility
  • Needs a lot of time to implement
  • Interpreting the results accurately
  • Data set collection is a complex task

Applications 

  1. The form created is easy to understand and is easy to fill by anyone.
  2. It can be used for monitoring health conditions in a particular country
  3. It can be used to get the data about the factor affecting Life expectancy the most in order to work in the direction of obtaining a high life expectancy
  4. It can be used to develop statistics for a country’s development process

Conclusions

This user interface enables any user to predict the life expectancy value of anyone on the basis of the details asked in the form.

Future Scope 

  1. Increase model accuracy
  2. Gives suggestions on how to increase Life Expectancy
  3. Mental health data was missing from the WHO data set which also plays the important role in affecting life expectancy
  4. The scalability and flexibility of the application can be
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Energy Management System Python Project

ENERGY MANAGEMENT

  • This is a project on energy management.
  • The project helps to save energy.

Description

  • First, create an IBM cloud account with an e-mail address and password.
  • Then create an IoT platform and node-red platform.
  • We need to write a python code for it because it is a real-time example.
  • Create an MIT app.

First, create an IBM cloud account with an e-mail address and password

Go to google and search for IBM and create an account using your email address and log in to the page. we will get the interface as an IBM dashboard.

CREATING IBM PLATFORM

After creating an IBM account there will be a search option then we can able to see it. Type Internet of things there will be a platform called the internet of thing in that u can able connect the so many services in that internet of things and create a service in that it will ask u to launch it and for creating an internet of things u need some keys as API keys, Device connects and so many things will fill and u can able to see an option as the add device click on new device u can see the device has been created for that u need to keep the API keys, Device information safely in the note pad because it will help u at the need for connecting it.

WRITE PYTHON CODE

we are writing python code because we are going to deal with a real-time example so we need to use python. actually, our project is about how much electricity is causing monthly and its estimated cost
In this, we are writing about fan, light, washing machine, tv, and ac let s take some random values in that because we can’t able take the exact values, and then we need to add all of the to know the charge and then we need to find out the estimation money for it. after completion of this code, u can able to know the code is running.
we need to send it to the IoT platform so we need to install pip by command prompt and then write the code separately so u can able to run and send information to the IoT platform. For sending to the IoT platform we need to give information in a notepad.

python code:

python running code

Information on IoT platform

Creating node-red platform

For creating a node-red platform we need to search in IBM as a node-red app then u can able to see the node app then create it as the local u can create node-red is nothing but we will get the information in the form of a flow chart.
we need to install IoT in, IOT out will be in the node-red app itself and then u can able to use it for better ability and experience we will use the gauge tool and for doing it on the web we will use the HTTP in, HTTP response and then we will use the payload option to print in and I will show u the flow chat I had created and when we run the program u can see the outputs are coming in the debug option.

Create an MIT app.

Search for the MIT app and click on create apps.

First, we should design the app page with alignments given on the left side of MIT. with horizontal alignments drag them to the screen and edit those with suitable markings.
And next insert a text box into the screen. As we have taken energy management the as a fan, ac, light, tv, etc.
Now insert 5 text boxes to the screen and assign the markings. Take two buttons one as checked and another as not checked.

And the two text boxes with one as charge gives the charge of every applicant and another as cost, which gives the summation of all applicants.

This is done and next tap to blocks button. In that create the blocks with URL.

Paste the URL which we have I IBM as:

Now tap on the build option and get the QR code and scan the code you will receive an apk file and install the file and app.

Now the click on the checked button now the values, cost, and charges will be displayed on the screen in their respective blocks.

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Covid-19 Testing Management System Python Project

Covid-19 Testing Management System is a small project developed using Python programming. Here are the application features below.

Project features:

• Adding new testing centers
• Search for available testing centers by locality
• Update and delete testing centers
• Show all testing centers by city, state
• Shows the number and type of testing kits available at each center.

Software to be used to develop this application:

• Python
• SQL for creating a database
• Library to interface between Python and SQL
• Either Django (to create a web app) or a GUI library to create the UI (not decided yet)

Implementation

1. Login page (This has a simple login prompt designed using a GUI library that asks for the user id and password.)
2. Menu:
a. Add a testing center
B. Delete testing center
C. Edit the number of testing kits available
D. Search for the testing center by district/city/state
E. Show all testing centers
3. Separate sections for:
a. Adding a new center
b. Deleting center
c. Updating center information
d. Searching for center
e. Nationwide data

Covid-19 Testing Management System can be developed by using PHP & MySQL Server with different functionalities below.

User Characteristics

The Covid 19 Testing Management system has 2 types of users they are Admin and user(patient)

General Constraints

The tools and technologies that are used to develop this project are:
The language used in this project is PHP5.6 and PHP7.x.
The database used in this project is MySQL 5.x.
The web browsers that are used in this project are Mozilla, Google Chrome, IE8, and OPERA.

Operational Scenarios

Scenario A:
How your application starts
Our application covid19 testing management system starts by login into the application, if the user is a new user, the user needs to register by providing the needed credentials.
New users need to provide testing information. A registered user needs to provide test information.

Scenario B:
Usage Scenarios like Customer Check-out
When a customer visits our application, he/she needs to log in by providing their credentials according to their role i.e., admin and user.
If the user is new, he needs to register if he is an old user, he can directly enter the login details and login directly.

Scenario C:
Database

The data that we are going to store in the database
1) Admin Login
Username
Password

2) New admin login
Admin name
Username
Password

3) User login
Username
Password

4) New user login
Name
Phone number
Username
Password

5) User Registration
Name
Current address
Gender
D.O.B
E-mail id
Phone number
Age
Aadhar card number