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Disease Prediction Project in Python GUI using ML
WebLet us start the project, we will learn about the three different algorithms in machine learning. The first algorithm is a Decision Tree, second is a Random Forest and the last one is Naive Bayes. We are going to import Pandas for manipulating the CSV file, Numpy, Sklearn for the algorithms and Tkinter for our GUI stuff.
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URL: https://www.codespeedy.com/disease-prediction-using-machine-learning-in-python-using-gui/
Python program to calculate BMI
WebThe unit of BMI is Kg/m 2. The BMI is calculated to distinguish whether the person is Underweight, Normal weight, Overweight or Obese. A table is given below which contains the data about the above 4 categories. BMI is calculated using the following formula:-. BMI = [mass/ (height*height)] where, mass = mass of the body in Kg, height = height
Predict the Heart Disease Using SVM using Python
WebIn this tutorial, we will be predicting heart disease by training on a Kaggle Dataset using machine learning (Support Vector Machine) in Python. We aim to classify the heartbeats extracted from an ECG using machine learning, based only on the lineshape (morphology) of the individual heartbeats. To achieve this, we will have to import various
The Rise of Artificial Intelligence: what to look for in 2023
WebAI’s ability to analyze vast datasets can aid in disease diagnosis, drug discovery, patient care, and health management. In 2023, AI’s role in healthcare is expected to expand. From AI-powered robots assisting in surgeries to AI algorithms predicting patient health outcomes, the technology could significantly improve healthcare delivery and
BMI Calculator in Java Swing
WebHere, we are going to make a BMI Calculator which is a simple Java Swing project that Calculates BMI. BMI stands for “Body mass index” BMI is derived from the height and weight of a person. We can also say that BMI is nothing but mass divided by height square because the formula to calculate BMI is (kg/m*m) where weight in kilograms and
Height-Weight Prediction By Using Linear Regression in Python
WebHi everyone, in this tutorial we are going to discuss “Height-Weight Prediction By Using Linear Regression in Python“. What is a Linear Regression? In statistics, linear regression is a linear approach to modeling the relationship between a scalar response(or dependent variable ) and one or more explanatory variables(or independent variables). The case of …
Build BMI Calculator in JavaScript
WebNow we need to write a function named bmi to calculate the BMI of the user from the entered values. So add the following code in “bmi.js”. We can classify the BMI into the following categories: function bmi() {. var w = parseFloat(document.getElementById("weight").value); // To get weight from the input …
Python: COVID-19 live update for india
WebIt will generate data from This website which is the official website of the Ministry of Health in India and it has COVID-19 data for every state which is being updated regularly. To achieve this we need to first install a few libraries in our Python environment. Type following commands into your terminal window to install all these libraries.
Predict Population Growth Using Machine Learning in Python
WebWe change the values of countries to numerical values. And lastly, we normalize the data to scale using the function from scikit library to ease out the prediction of growth rate with machine learning. To know more about the normalize function, do give this a read: sklearn.preprocessing.normalize in Python. data = pd.read_csv('population.csv')
Collision Detection in pygame using Python
WebHere we are going to use colliderect () function whose functionality is to return True if a collision occurs between two rectangles else return False. It functions with pygame.Rect object. Here we will use movement keys like up arrow, down arrow, left arrow, right arrow for the movement of the rectangle. The syntax for colliderect () is as follows.
Naive Bayes Algorithm in Python
Webnv = GaussianNB() # create a classifier. nv.fit(X_train,y_train) # fitting the data. Output: GaussianNB(priors=None, var_smoothing=1e-09) Explain: Here we create a gaussian naive bayes classifier as nv. And we fit the data of X_train,y_train int the classifier model. from sklearn.metrics import accuracy_score.
re.sub() function in python with examples
WebExample 3. By providing the value count parameter we can control the no of replacements. In this example, the value of count is 1. Therefore after one replacement the re.sub () will not make any further replacements. import re. origional_str="I LOVE CODESPEEDY". new_str=re.sub("\s",".",origional_str,1)
Linear Regression in Python with Large Dataset Example
WebOutput: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) Here we split our ‘X’ and ‘y’ dataset into ‘X_train’, ‘X_test’ and ‘y_train’, ‘y_test’. Here we take 25% data as test dataset and remaining as train dataset. We take the random_state value as 15 for our better prediction.
Learn Fingerprint Detection in Python
WebMatching with the database . We are going to use SIFT (Scale-Invariant Feature Transform) algorithm here. To learn more about this topic, you can visit this site.We use it to extract key-points and detect descriptors for the best-retained features.
Find Battery Percentage and Charging status in Windows and
Websyntax: psutil.sensors_battery () The method sensors_battery () returns the battery status information in the form of a named tuple. The battery status information includes: percent: It is the percentage of battery left. secsleft: It is the capacity of the battery in seconds. power_plugged: It indicates the status of charging of the battery.
Alphabetic Pattern Programs in Python
WebAlphabetical (A-Z) pattern is a series of alphabet which forms a pattern or any shape like triangle, square, rhombus etc. These patterns are created with the help of nested for loop. To create the alphabetic pattern, you should know that how to convert the ASCII value into the ASCII character. chr (): This function is used to convert the ASCII
Income tax calculator using Python
WebIf income is more than Rs. 15,00,000 then tax will be 30% of total income exceeding Rs. 15,00,000 with an additional cost of Rs. 1,87,500. And so we can print the calculated income tax in Rupees. if income <= 250000: #2 Lakh 50 thousand. tax = 0. elif income <= 500000: #5 Lakh. tax = (income - 250000) * 0.05.
Download datasets into Google Drive using Google Colab
WebStep 1: Get the API key from your account: Visit Kaggle, login to your account, go to My Account, and then Create New API Token. I have used Kaggle because this is one of the most popular websites for datasets. After completing the above process a file with the name “kaggle.json” will be automatically downloaded.
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