Moving averages in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the simple moving average of a list of numbers.

ML Python

def simple_moving_average(data, window_size):
    moving_averages = []
    for i in range(len(data) - window_size + 1):
        window = data[i:i + window_size]
        average = sum(window) / [1]
        moving_averages.append(average)
    return moving_averages

Drag options to blanks, or click blank then click option'

Ai

Blen(data)

Cwindow_size

Dlen(window)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length of the whole data list instead of the window size.

Dividing by the index variable instead of the window size.

The simple moving average divides the sum of the window by the window size to get the average.

2fill in blank

medium

Complete the code to calculate the exponential moving average (EMA) smoothing factor.

ML Python

def calculate_ema_smoothing_factor(window_size):
    smoothing_factor = 2 / ([1] + 1)
    return smoothing_factor

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Awindow_size * 2

Bwindow_size - 1

Cwindow_size + 1

Dwindow_size

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting 1 instead of adding 1 to the window size.

Multiplying the window size instead of adding.

The smoothing factor for EMA is calculated as 2 divided by (window size plus 1).

3fill in blank

hard

Fix the error in the code to update the EMA value correctly.

ML Python

def update_ema(current_value, previous_ema, smoothing_factor):
    ema = [1] * current_value + (1 - smoothing_factor) * previous_ema
    return ema

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Asmoothing_factor

Bprevious_ema

Ccurrent_value

D1 - smoothing_factor

Attempts:

3 left

💡 Hint

Common Mistakes

Using previous EMA instead of smoothing factor for the current value.

Using current value without multiplying by smoothing factor.

The EMA update formula multiplies the current value by the smoothing factor.

4fill in blank

hard

Fill both blanks to create a dictionary of moving averages for each window size.

ML Python

def moving_averages_dict(data, window_sizes):
    averages = {size: simple_moving_average(data, [1]) for size in [2]
    return averages

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Asize

Bdata

Cwindow_sizes

Daverages

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'data' instead of 'window_sizes' in the loop.

Using 'averages' inside the comprehension which is not defined yet.

The dictionary comprehension uses 'size' as the window size for each key and iterates over 'window_sizes'.

5fill in blank

hard

Fill all three blanks to filter data points and compute their moving averages.

ML Python

filtered_data = [x for x in data if x [1] threshold]
moving_avg = simple_moving_average(filtered_data, [2])
result = {i: moving_avg[i] for i in range(len(moving_avg)) if moving_avg[i] [3] 0}

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A>

Bwindow_size

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering data points.

Using wrong variable for window size.

Filtering moving averages less than zero instead of greater.

The code filters data points greater than the threshold, uses the window size to compute moving averages, and selects positive averages.