From the previous blog, we got an overview of machine
learning & linear regression. Here we can try to address usecase of very
basic stock forecasting.
Usecase: In stock market, some
stocks tend to change linearly with the entire markets valuation.
That means a particular stock is
sensitive to the Market changes. We need to predict the stocks price based on
markets valuation.
Approach:
We can use simple linear regression
to solve this since we feel that the relationship is linear.
Predicting Equation: y=mx+b
Lets say the points in Exchange as X
&
lets try predicting the value of Y,
a single stock present in the stock exchange X
Lets take the history data set in R
first.
x <- c(350, 320, 410, 370, 290, 330, 340, 350, 340, 370)
y <- c(75, 68, 82, 76, 47, 70, 76, 72, 69, 74)
Now simply apply the linear function
lm() on this dataset to find the relationship between these 2 datasets.
relationship <- lm(y~x)
Just print the results
print(relationship)
Coefficients:
(Intercept) x
-15.2784
0.2484
This will print the coefficents
Now we can use the predict function(
equation of straight line) to predict the results.
inputX <- data.frame(x = 450)
resultY <- predict(relationship, inputX)
print(resultY)
1
96.48034
This result can be plotted in graph
for better understanding.
plot(y~x,col="red")
abline(lm(relationship))