CODEWITHSUNDEEP
pythonscikit-learnlinear-regressiondata-sciencepredict
Peachh Salts
Peachh Salts

Reputation: 45

How to predict value?

I have plotted a scatter plot graph with linear regression for the Relative Humidity over a number of days. The given number of days were 244. Now im supposed to predict the the Relative Humidity Value for Day 245.

--> this is the sample value of the data set.

index RH

0 78.80

1 80.80

2 78.60

3 76.10

4 73.85

5 71.40

x=linear.index
y=linear["RH"]
plt.title('Air Temperature vs. Relative Humidity')
plt.title(' Relative Humidity over Days')
plt.ylabel('Relative Humidity')
plt.xlabel('Days')

fit = np.polyfit(x,y,1)
fit_fn = np.poly1d(fit)
reg= plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
reg

now to find the prediction value

from sklearn.linear_model import LinearRegression

regr = LinearRegression()
regr.fit(linear[["RH"]],linear.index)
regr.predict(linear[245])

Errors im getting are usually among "'list' object has no attribute 'predict'" as i've already tried a few different methods and codes but none seemed to work.

Upvotes: 2

Views: 4577

Answers (1)

James Phillips
James Phillips

Reputation: 4647

Here is a graphical Python polynomial fitter using numpy.polyfit() for the fitting and numpy.polyval() for evaluation, and this example includes a single value. The polynomial order is set at the top of the code, this can be set to "1" for a straight line.

plot

import numpy, matplotlib
import matplotlib.pyplot as plt

xData = numpy.array([1.1, 2.2, 3.3, 4.4, 5.0, 6.6, 7.7, 0.0])
yData = numpy.array([1.1, 20.2, 30.3, 40.4, 50.0, 60.6, 70.7, 0.1])


polynomialOrder = 2 # example quadratic equation


# curve fit the test data
fittedParameters = numpy.polyfit(xData, yData, polynomialOrder)
print('Fitted Parameters:', fittedParameters)

# predict a single value
print('Single value prediction:', numpy.polyval(fittedParameters, 3.0))

# Use polyval to find model predictions
modelPredictions = numpy.polyval(fittedParameters, xData)
absError = modelPredictions - yData

SE = numpy.square(absError) # squared errors
MSE = numpy.mean(SE) # mean squared errors
RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))
print('RMSE:', RMSE)
print('R-squared:', Rsquared)

print()


##########################################################
# graphics output section
def ModelAndScatterPlot(graphWidth, graphHeight):
    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
    axes = f.add_subplot(111)

    # first the raw data as a scatter plot
    axes.plot(xData, yData,  'D')

    # create data for the fitted equation plot
    xModel = numpy.linspace(min(xData), max(xData))
    yModel = numpy.polyval(fittedParameters, xModel)

    # now the model as a line plot
    axes.plot(xModel, yModel)

    axes.set_title('numpy.polyval example') # add a title
    axes.set_xlabel('X Data') # X axis data label
    axes.set_ylabel('Y Data') # Y axis data label

    plt.show()
    plt.close('all') # clean up after using pyplot

graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)

Upvotes: 2

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