ML-Rainfall prediction using linear regression
Rainfall prediction
Rainfall prediction is the application of scientific knowledge and technological resources to determine the volume and inches of rain for a particular period of time and location. Rainfall prediction is vital to plan power production, crop irrigation, and educate people on weather dangers.
Linear regression
Linear Regression is a method that describes the relationship between a dependent variable and a set of independent variables. The equation of the line is given as
Example
Here is extracted data from Kaggle-Link
| Rainfall | Average Temp | Humidity |
|---|---|---|
| 0 | 16.15 | 48.5 |
| 3.6 | 20.45 | 58 |
| 3.6 | 14.4 | 75.5 |
| 39.8 | 11.85 | 59 |
| 2.8 | 11.55 | 58.5 |
| 0 | 12.15 | 63.5 |
| 0.2 | 12.65 | 55 |
| 0 | 14.15 | 61 |
| 0 | 15.6 | 59 |
| 16.2 | 17.15 | 57 |
| 0 | 17.9 | 54 |
| 0.2 | 19 | 44.5 |
| 0 | 21.5 | 45.5 |
| 0 | 20.65 | 43.5 |
We will plot a line that fits our scatter plot (with minimum errors) that shows the relationship between each
#importing librariesimport numpy as npimport matplotlib.pyplot as pltfrom scipy import stats#create arrays that show the x and y valuesx = [16.15, 20.45, 14.4, 11.85, 11.55, 12.15, 12.65, 14.15, 15.6, 17.15, 17.9, 19, 21.5, 20.65]y = [0, 3.6, 3.6, 39.8, 2.8, 0, 0.2, 0, 0, 16.2, 0, 0.2, 0, 0]#create a method that provides the key values of linear regressionslope, intercept, r, p, std_err = stats.linregress(x, y)#create a function that makes use of the slope and intercept to provide new values.def myfunc(x):return slope * x + intercept#to create a new array with new values on the y-axis, run each value of the x array through the functionmymodel = list(map(myfunc, x))#draw the scatter plotplt.scatter(x, y)#draw the line of regressionplt.plot(x, mymodel)#label the x axis and y axisplt.xlabel('Temperature')plt.ylabel('Rainfall')#show the diagramplt.show()plt.savefig('output/legend.png')#view the correlation between the x and y variablesprint (r)
#importing librariesimport numpy as npimport matplotlib.pyplot as pltfrom scipy import stats#create arrays that show the x and y valuesx = [48.5, 58, 75.5, 59, 58.5, 63.5, 55, 61, 59, 57, 54, 44.5, 45.5, 43.5]y = [0, 3.6, 3.6, 39.8, 2.8, 0, 0.2, 0, 0, 16.2, 0, 0.2, 0, 0]#create a method that provides the key values of linear regressionslope, intercept, r, p, std_err = stats.linregress(x, y)#create a function that makes use of the slope and interceot to provide new valuesdef myfunc(x):return slope * x + intercept#to create a new array with new values on the y axis, run each value of the x array through the functionmymodel = list(map(myfunc, x))#draw the scatter diagramplt.scatter(x, y)#draw the line of regressionplt.plot(x, mymodel)#label the x and y axisplt.xlabel('Humidity')plt.ylabel('Rainfall')#show the diagramplt.show()plt.savefig('output/legend.png')#view the correlation betwwen the x and y variablesprint (r)
Conclusion
We can conclude from the results above that when the temperature is high, then the precipitation is low. However, when the temperature is low, then precipitation is high. This is not the same for humidity because the precipitation is high with higher humidity and low with lower humidity. This goes to show that rainfall can be predicted using average temperature and average humidity.
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