User Registration Website [Login/Logout Website] Using Python,Django,Html,Css,Bootstrap,Dbsqlite3.

Last Updated on May 3, 2021

About

This project consists of a home page ,a registration page , a login page,and a admin page.

After filling the registration form the user can login to the website and will not be logged out until the user logs out using logout button.

The website is hosted on pythonanywhere.

More Details: User registration website [Login/logout website] using python,django,html,css,bootstrap,dbsqlite3.

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Iris Flower Prediction

Last Updated on May 3, 2021

About

Understanding the scenario

Let’s assume that a hobby botanist is interested in distinguishing the species of some iris flowers that she has found. She has collected some measurements associated with each iris, which are:

  • the length and width of the petals
  • the length and width of the sepals, all measured in centimetres.

She also has the measurements of some irises that have been previously identified by an expert botanist as belonging to the species setosa, versicolor, or virginica. For these measurements, she can be certain of which species each iris belongs to. We will consider that these are the only species our botanist will encounter.

The goal is to create a machine learning model that can learn from the measurements of these irises whose species are already known, so that we can predict the species for the new irises that she has found.

Modules imported

  • SkLearn is a pack of Python modules built for data science applications (which includes machine learning). Here, we’ll be using three particular modules:
  • load_iris: The classic dataset for the iris classification problem. (NumPy array)
  • train_test_split: method for splitting our dataset.
  • KNeighborsClassifier: method for classifying using the K-Nearest Neighbor approach.
  • NumPy is a Python library that makes it easier to work with N-dimensional arrays and has a large collection of mathematical functions at its disposal. It’s’ base data type is the “numpy.ndarray”.

Building our model

As we have measurements for which we know the correct species of iris, this is a supervised learning problem. We want to predict one of several options (the species of iris), making it an example of a classification problem. The possible outputs (different species of irises) are called classes. Every iris in the dataset belongs to one of three classes considered in the model, so this problem is a three-class classification problem. The desired output for a single data point (an iris) is the species of the flower considering it’s features. For a particular data point, the class / species it belongs to is called its label.

As already stated, we will use the Iris Dataset already included in scikit-learn.

Now, let’s print some interesting data about our dataset:

ACCURACY we get an accuracy of 93%

OUTPUT IN THIS CASE    as we have 2 samples [[3,5,4,2], [2,3,5,4]]

so the iris type predicted by our model based on the given features are

predictions:  ['versicolor', 'virginica']

for more details this is my Github repository

ml-2/iris_flower.ipynb at main · THC1111/ml-2 (github.com)




More Details: Iris Flower Prediction

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Chess Game

Last Updated on May 3, 2021

About

Implementation of chess in python language in which 2 players can play this game. Using "R" key we can undo this game and "Q" the game ends in between. There is 2 Classes in code.

  1. One is GameState() which will store the positions of the pieces.
  2. It has changePosition() function which will change the position of the pieces as per request.
  3. undoSteps() will undo to the previous state.
  4. availablePositions() will give all the possible moves a piece can move at that position. availablePositions() will call respective functions of pieces like bishopAvailablePositions(), queenAvailablePositions(), etc. Pawn can move in forward direction and in left or right only if opposite color piece is there. For rook, we create directions=((-1,0),(0,-1),(1,0),(0,1)) where each set() represents the direction that piece can move. Similar we use different logic for other pieces as well.
  5. Another Class is Steps() which is used as a data structure to store initial and final positions of rows and columns as well as unique number(unique ID), attacking piece and piece captured.

To get input from user, pygame is used. Whenever user clicks on game window, we get the position, where we clicked, using mouse.get_pos(). Some graphics is also used to make the game more enjoyable.

More Details: Chess Game

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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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Automated Plant Watering System (Iot ,Html)

Last Updated on May 3, 2021

About

The automated plant watering system is a project under the domain                                                          ‘Internet of Things’ that can detect the water requirements of plants using a soil moisture detection sensor and can automatically turn on and off the supply of water accordingly.

The user has the convenience of setting up the system to either automated or manual mode and can check the status of soil and the details of the last time the plant has been watered.

This is accomplished by creating a webserver that contains certain buttons which provides the choice of the mode of supply and provides access to every other detail.

Tools:

1. Raspberry Pi

2. Soil Moisture Sensor

3. Water Pump

4. Relay Module 5V

Languages:

1. Python for Raspberry Pi (flask and psutil libraries)

2. HTML for application interface

 Design and algorithms used:

The code for the project was written in the python language. Various libraries were installed to serve different purposes such as interaction with RPi GPIO, connecting the program to the web server etc. For maintaining the control on entire working, a web page is created and all the actions are controlled using the web interaction page using HTML

Working:

This is the project designed for the Agricultural purpose. In general a person has to monitor the water content in fields and switch on/off the water pump regularly, Using this project the soil moisture sensor itself senses the water content and automatically switches the motor ON if water content is low as per the conditions mentioned. All this procedure is controlled by Raspberry Pi as per the Python Code. Other than this, we have coded such that the last watered time, date also get displayed on the desktop.

    The code contained 4 sections, one of which includes the html code for a web page and the others for running, automated mode and interaction with the web page

My role in the project:

I worked mostly on the coding part in the project, especially the code for establishing the connection between the components using Python 


More Details: Automated Plant watering system (IoT ,HTML)

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Determination Of A Person’S Health

Last Updated on May 3, 2021

About

Determination of person’s health

The project was built with the intend of helping the society. It has been calculated that approx. 1.9 billion people die due to health-related problems every year. This rate is very high, and the disease is easily preventable

The project has been made with the help of Data Analysis and Machine Learning using Python with a GUI output page. In this project, the machine will analyse the already present data first and then conclude upon a person’s health on his/her given factors.

In this project, gender and either height or weight will be given to the machine. If the height is given then the weight will be predicted and vice-versa. Through these predictions the machine will tell us about the health of a person.

The main goal is to help the society for its betterment as far as health is concerned.

The data set used is from UCI repository. It includes four attributes-

1.     Gender

2.     Height

3.     Weight

4.     Index

The machine will be trained in these aspects to determine a person’s health or weight and the category it will lie in.

The categories are-

1.     0 – Underweight

2.     1 – Normal weight

3.     2 – Healthy

4.     3 – Over weight

5.     4 – Obesity

The methods followed in chronological form are-

1.     Loading dataset (using pandas library)

2.     Dataset cleaning (using pandas and numpy libraries)

3.     Dataset pre-processing

4.     Data visualization (using seaborn, matplotlib and matplotlib.pyplot libraries)

4.1  Univariate analysis

4.2  Bivariate analysis

5.     Correlation matrix 

The machine learning algorithms applied were-

1.     Linear Regression

2.     Logistic Regression

3.     KNN Classifier

4.     Decision Tree Classifier

5.     Random Forest Classifier

Random Forest Classifier gave highest accuracy of about 95% while logistic regression gave the leas with about 76%.

The user in the GUI page will be asked:

1. Full name

2. Gender

3. Whether they know their height or weight

4. Their height or weight

More Details: Determination of a Person’s Health

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