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))