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# End to End Project - Bikes Assessment - Basic - Evaluate the model on test - Make Predictions on the Test dataset using Final Model

Let us use the Final Model to make predictions on the 'Test' data set and calculate the RMSE. Then compare the predicted values to the actual values.

INSTRUCTIONS

• Make the predictions using the `final_model` on `X_test` data set and store the output in a new column `predictedCounts_test` in the `test_set` dataframe.

``````test_set.loc[:,<<your code comes here>>] = final_model.<<your code comes here>>(X_test)
``````
• Calculate the `mean squared error` using `mean_squared_error()` function by passing to it the Test 'target' dataset (y_test) and the Test dataset predictions ('predictedCounts_test' column data of test_set dataframe) to it, and store the output in 'mse' variable

``````mse = <<your code comes here>>(<<your code comes here>>, test_set.loc[:,'predictedCounts_test'])
``````
• Calculate the 'root mean square error' from the 'mse' and store it in `final_mse` variable.

• Print `final_mse`

• For a better understanding, you can have a look at the data inside `test_set` dataframe.

• Please plot the predicted values v/s actual values using the below code:

``````times = [9,18]
for time in times:
fig = plt.figure(figsize=(8, 6))
fig.clf()
ax = fig.gca()
test_set_freg_time = test_set[test_set.hr == time]
test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'cnt', ax = ax)
test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'predictedCounts_test', ax =ax)
plt.show()
``````

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