This tutorial shows how to link a cell in an excel sheet to another excel sheet. How to link excel sheet to another spreadsheet
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Neural Network Prediction Modeling for Predicting Graduate Admissions And Using Confusion Matrix to Check Its Validity in R
The program code is used for predicting Graduate Admissions which uses the Neural Network Prediction Model and Cross-validation to split the dataset into training and testing data and the Confusion Matrix to check its validity. The dataset was downloaded from www.kaggle.com, below is the dataset download link; https://www.kaggle.com/mohansacharya/graduate-admissions library(neuralnet) setwd("C:\\Users\\LENOVO\\Desktop\\Neural") admission <- read.csv("admission.csv", header = TRUE) admission str(admission) normalize <- function(x) { return((x - min(x)) / (max(x) - min(x))) } maxin <- as.data.frame(lapply(admission, normalize)) maxin trainset <- maxin[1:400,] #trainset testset <- maxin[401:500,] nn <- neuralnet(Chance ~ GRE + TOEFL + SOP + LOR + CGPA + Research, data=trainset, hidden =c(4,2), linear.output = FALSE) nn$result.matrix plot(nn) temp_test <- subset(testset, select = c(...
Linear Regression Prediction Model for predicting Graduate Admissions in Python with Scikit-Learn
The program code is used for prediction of Graduate Admissions which uses the Linear Regression Model and cross-validation to split the dataset into training and testing data, including R squared to show how the data fit the model. The program code also allows the user to input it performs or scores to check whether he or she has the chance of admission. The dataset was downloaded from www.kaggle.com, below is the dataset download link; https://www.kaggle.com/mohansacharya/graduate-admissions #importing the essential libraries import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split #importing dataset df=pd.read_csv('Admission_Predict_Ver1.1.csv',index_col=0) #independent or explanatory variables feature_cols=['GRE Score','TOEFL Score','LOR','CGPA','Research'] X=np.array(df[feature_cols],dtype=np.float64) #dependent or response variable y...
Survival Analysis in Leukemia Patients Dataset in R (Practical) library(survival) library(KMsurv) library(MASS) setwd("C:\\Users\\LENOVO\\Desktop\\leukemia") leukemia <- read.delim("leukemia.txt", header = TRUE, sep = " ") leukemia #### KAPLAN-MEIER km= Surv(time = leukemia$Survtime, event = leukemia$Binary) km km1= survfit(km~1, data = leukemia) summary(km1) #### ESTIMATION OF CUMULATIVE HAZARD Km2= survfit(coxph(km~1), type="aalen") summary(Km2) plot(Km2, main=expression(paste("Kaplan-Meier-estimate ", hat(Lambda)(t))), xlab="time(weeks)", ylab="cumulative hazard", fun="cumhaz", lwd=2) #### KAPLAN-MEIER for both group of the AG variables km3= survfit(km~AG, data = leukemia) print(km3) summary(km3) ### Logrank Test survdiff(km ~ AG,data=leukemia,rho=0) ### Using Cox-PH to model the survival time fit = coxph(Surv(Survtime, Binary) ~ AG + WBC ,data=leukemia ,ties="b...
UNDERSTANDING EPIDEMIOLOGY STUDY IN MEDICAL STATISTICS Who is an Epidemiologist? An epidemiologist is someone who studies the distribution of diseases within populations of people and factors related to them. Epidemiologist analyzes what causes disease outbreaks in order to treat existing diseases and prevent future outbreaks. What are some of the things an epidemiologist is interested in studying? 1. Epidemiologists identify the cause of disease and determine the extent of disease. 2. Epidemiologists evaluate preventive and therapeutic measures for a disease or condition. 3. Epidemiologists determine the crucial difference between those who get the disease and those who are spared. 4. Epidemiologists study exposed and non-exposed people. 5. Epidemiologists also determine the crucial effect of the exposure. The difference between the two broad types of epidemiology. Descriptive epidemiology examines the distribution of disease in a population and observes the...
Time Series Analysis Using ARIMA Model For Forecasting In R (Practical) Time series refers to a set of observations on a particular variable recorded in time sequence. This time sequence or space can be hourly, daily, weekly, monthly, quarterly or yearly. Why time series analysis 1. Its emphasis is to support decision making 2. To fit a mathematical model and then proceed to forecast the future. The tool that we will be using for the practice is the RStudio you can click on the link below and download the RStudio setup and install it; https://rstudio.com/products/rstudio/download/ The dataset that will be used is the daily-minimum-temperatures-in-me.csv you can download it from Kaggle. Below is the link to download the daily minimum temperatures in me dataset; https://www.kaggle.com/paulbrabban/daily-minimum-temperatures-in-melbourne The libraries that will be used for the model in time se...