Project - Bike Rental Forecasting

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End to End Project - Bikes Assessment - Analyzing and visualizing the dataset

Analyzing and visualizing the dataset

These tasks shall be performed as a part of analyzing and visualizing the dataset:

  1. Hourly count of bikes with trend and without trend

  2. Correlation matrix between the features and the dependent variable

  3. Plotting correlation among selected variables - 'yr','mnth','isWorking','xformWorkHr','dayCount','temp','hum','windspeed','cntDeTrended'

  4. Plotting the count pattern with daycount for specific hours

  5. Plotting box plot for different attributes: 'hr', 'mnth', 'weathersit', 'isWorking', 'dayWeek', 'xformHr'

  6. Plotting scatter matrix for selected attributes: 'temp', 'hum', 'windspeed', 'hr', 'xformHr', 'cntDeTrended'

  7. Plotting box plots for 0900 and 1800 hrs working and non-working days

  8. Plotting the demand counts for each of the transformed work hours which include working and non-working hours

Task: Complete the statement to complete plotting the hourly count with trend and without trend.

Hint

plt.plot(bikesData.loc[:,'cnt'])
plt.plot(bikesData.loc[:,'cntDeTrended'])
plt.plot(bike_lm.predict(X))
plt.legend(['With trend','Detrended','Trend'])
plt.show()


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