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Task-1.R

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+#Install the 'Metrics' package if you do not have that installed
+
+#Loading the required libraries
+library(Metrics)
+
+#Reading the dataset
+s_data<-read.csv('http://bit.ly/w-data',header=T)
+
+#Dimensions of the dataset
+dim(s_data)
+
+#A glimpse of the dataset
+head(s_data,n=5)
+
+#Column names of the dataset
+names(s_data)
+
+#Plotting the dataset to get a clear view of the dataset
+par(bg='#CCFFFF')
+plot(s_data$Hours,s_data$Scores,pch=16,col='dark blue',
+	xlab='Hours studied',ylab='Precentage Score',
+	main='Hours vs. Percentage')
+legend(x=1,y=95,'Scores',pch=16,col='dark blue')
+
+#Seperate the train and test data
+X<-s_data$Hours;Y<-s_data$Scores
+t_sample<-sample(nrow(s_data),floor(0.8*nrow(s_data)),replace=F)
+X_train<-X[t_sample];Y_train<-Y[t_sample]
+X_test<-X[-t_sample];Y_test<-Y[-t_sample]
+
+#Fit a linear model
+model<-lm(Y_train~X_train)
+par(bg='#CCFFCC')
+plot(s_data$Hours,s_data$Scores,pch=16,col='dark blue',
+	xlab='Hours studied',ylab='Precentage Score',
+	main='Hours vs. Percentage')
+abline(model,col='red',lwd=2)
+
+#Predictions
+X_test#Testing data
+Y_predicted<-predict(model,newdata=data.frame(X_train=X_test))#Predicting the scores
+df<-data.frame('Hours'=X_test,'Actual score'=Y_test,'Predicted score'=Y_predicted)
+df
+pred<-predict(model,newdata=data.frame(X_train=9.25))
+pr_data<-data.frame('Hours'=9.25,'Predicted Score'=pred)
+pr_data
+
+#Accuracy
+mae(df$Actual,df$Predicted)