Diabetes Predictor In Machine Learning Webapp

Last Updated on May 3, 2021

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  • Dataset used consists of 2,000 records with 8 independent features.
  • Performed Exploratory Data Analysis, Data Cleaning, Data Visualization, and developed various models for classifying whether a person is having diabetes or not.
  • Algorithms and Technologies used: Random Forest, Python, Jupyter Notebook, Matplotlib, Seaborn.
  • Successfully achieved an accuracy of 99%
  • Completed End-to-End implementation with Heroku by creating a Web App
More Details: DIABETES PREDICTOR in MACHINE LEARNING webapp

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A Review On Weather Forecasting Techniques Using Machine Learning

Last Updated on May 3, 2021

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Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.

 

Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.

 

In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.

 

Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller


More Details: A REVIEW ON WEATHER FORECASTING TECHNIQUES USING MACHINE LEARNING

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

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HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

More Details: Human Computer Interaction using iris,head and eye detection

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Password Checker

Last Updated on May 3, 2021

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This can be the most secure way for you to check if your password has ever been hacked. This is a password checker which checks whether this password has been used before or not. and if it has been used then the number of times it has been found. It makes it easy for you to understand that your password is strong enough to keep or is it too light. Its working is pretty simple, in my terminal i write the python file with my code checkmypass.py followed by the password to check if its ever been hacked , its gonna check as many passwords as we list in the terminal. I have used passwords API (pawned password) and SHAH1 (algorithm) to hash the given password into some complex output which is hard to hack also only the first five characters of hash version of password has been used for super privacy so that the real one is safe. The concept of k-anonymity is used  it provides privacy protection by guaranteeing that each record relates to at least k individuals even if the released records are directly linked (or matched) to external information. I have added this on my Github repository.

password-checker/checkmypass.py at main · THC1111/password-checker (github.com)

THIS CAN BE REALLY EFFECTIVE FOR SOME PERSONEL USE.

More Details: PASSWORD CHECKER

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Identify The Best Model For Class Imbalance Data In Multiclass Problem

Last Updated on May 3, 2021

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In Robust model for Imbalanced class of data, a research on an Infinite possibility of imbalanced class of data and we would like to investigate what are the best models through all possible imbalanced situation of a data set. Usually we do Up-Sampling Or Down-Sampling of the imbalanced data and make it balanced before applying machine learning models. In both the cases, We lose information about that data set. In this project, we would like to investigate what are the best models through all possible imbalanced situation of a data set. There is no particular definition for imbalanced class of data. In general, data that is not balanced is called imbalanced. Generating Data Points in Square Pattern Keeping a boundary classifying the data points as belongs to multiple class and name them as class_1, class_2 and class_3. Adding some jitter points to every data points to make every data points fall under different class and make them misclassify in itself. Make the balance dataset to imbalance by making one class with the proportion of samples like 1%,2%,3%.......10% keeping other classes same. Referring to the above that at least one of the class having significantly less number of training examples or the examples in the training data belonging to one class heavily outnumber the examples in the other class. Currently, most of the Machine learning algorithms assume the training data to be balanced like SVM, Logistic-Regression, Naïve-Bayes etc., Last few decades ,some effective methods have been proposed to attack this problem like upsampling, down-sampling, Smote etc…

More Details: Identify The Best Model For Class Imbalance Data in Multiclass Problem

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Personal Assistance

Last Updated on May 3, 2021

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PERSONAL COMPUTER ASSISTANT

This project work same as siri in iphone google assistance in android.

It will take input from user voice and will manage many things like youtube, google, stackoverflow, date, time

can play music and can shut-down your pc as well.

.


Necessary pip installation requires are


1. pip install pyttsx3

2. pip install speechRecognition

3. pip install wikipedia

4. pip install webbrowser

5. pip install pipwin

6. pip install PyAudio


What is it capable of doing ?


     Input format :

       This piece of code tak input from the voice of user


     Output :

     

     1. strongly assist and open Google,Youtube,Tell date, time, open stackoverflow,gmail and play music for user.


     2. This piece of code can also shut down your pc if you ask it 

     to shutdown and give premission of 'yes'.


Module installation instruction:


    1. Please install the modules written at the beggining of the 

    code in case it throw error.(in mine pc it's working smoothly).

     

    2. Use pip install module_name to install packages or modules.


NOTE:

  1. Please run this code on jupyter notebook or any offline text edidtor ie (VS Code, Pycharm, Atom, Sublime)
  2. Please install the module in case not installed using pip install modulename.
  3. Module list are written at the begging of the code

More Details: Personal Assistance

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