Task 1: Complete the code to plot cntDeTrended versus dayCount for these hours: [7, 9, 12, 15, 18, 20, 22]
Hint
times = [7, 9, 12, 15, 18, 20, 22]
for time in times:
fig = plt.figure(figsize=(4,4))
tsToPlot = bikesData[bikesData.hr==time]
fig.clf()
ax = fig.gca()
tsToPlot.plot(kind='line', x='dayCount', y='cntDeTrended', ax =ax)
plt.xlabel("Days from start of plot")
plt.ylabel("Count of bikes rented")
plt.title("Bikes rented at hour " + str(time))
plt.show()
Observation:
Significant differences in the shape of these curves at the two different hours.
Some of the outliers can be a source of bias when training machine learning models.
Action:
We will keep these outlier observation in mind when we need to fine-tune the model.
Hours can be a good datetime feature to train the model on. We need to explore the impact of other datetime features like month and year.
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