Why data preparation consumes most ML time in ML Python - Experiment to Prove It

Experiment - Why data preparation consumes most ML time

Problem:You want to build a machine learning model to predict house prices. However, you notice that most of your time is spent cleaning and preparing the data rather than training the model.

Current Metrics:Not applicable since the focus is on time spent, not model accuracy.

Issue:Data preparation takes up to 80% of the total project time, delaying model development and evaluation.

Your Task

Understand why data preparation consumes most of the ML project time and identify key steps that take the longest.

Do not change the model architecture or training process.

Focus only on data-related tasks.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import time
import pandas as pd

# Simulate loading raw data
start_time = time.time()
data = pd.DataFrame({
    'size': [1500, 2000, None, 1800, 2400],
    'bedrooms': [3, 4, 3, None, 5],
    'age': [10, 15, 20, 5, None],
    'price': [300000, 400000, 350000, 320000, 450000]
})
load_time = time.time() - start_time

# Cleaning missing values
start_clean = time.time()
data['size'].fillna(data['size'].mean(), inplace=True)
data['bedrooms'].fillna(data['bedrooms'].mode()[0], inplace=True)
data['age'].fillna(data['age'].median(), inplace=True)
clean_time = time.time() - start_clean

# Feature engineering: create price per square foot
start_feat = time.time()
data['price_per_sqft'] = data['price'] / data['size']
feat_time = time.time() - start_feat

# Data formatting: convert to float
start_format = time.time()
data = data.astype(float)
format_time = time.time() - start_format

# Summarize times
print(f"Loading data time: {load_time:.4f} seconds")
print(f"Cleaning data time: {clean_time:.4f} seconds")
print(f"Feature engineering time: {feat_time:.4f} seconds")
print(f"Data formatting time: {format_time:.4f} seconds")

# Output shows cleaning takes the longest time in this example

Measured time spent on each data preparation step.

Identified cleaning missing values as the most time-consuming step.

Demonstrated simple feature engineering and formatting steps.

Results Interpretation

Time spent on data preparation steps:

• Loading data: very fast
• Cleaning data: longest step
• Feature engineering: short
• Data formatting: short

Most ML project time is spent cleaning and preparing data because raw data often has missing or inconsistent values. This step is crucial to ensure the model learns from good quality data.

Bonus Experiment

Try using automated data cleaning tools or libraries to reduce time spent on cleaning.

💡 Hint

Explore libraries like pandas-profiling or AutoML tools that include data preparation steps.

Practice

1. Why does data preparation usually take the most time in a machine learning project?

easy

A. Because testing the model takes a long time

B. Because training the model requires many calculations

C. Because raw data is often messy and needs cleaning before use

D. Because deploying the model to production is complex

Solution

1. Step 1: Understand the nature of raw data

   Raw data collected from real sources often contains errors, missing values, and inconsistencies.

2. Step 2: Recognize the need for cleaning and organizing

   Before training, data must be cleaned and formatted properly to ensure the model learns correctly.

3. Final Answer:

   Because raw data is often messy and needs cleaning before use -> Option C

4. Quick Check:

   Messy data = More preparation time [OK]

Hint: Messy raw data means more cleaning time [OK]

Common Mistakes:

• Confusing model training time with data cleaning time
• Thinking deployment takes the most time
• Assuming testing is the longest step

2. Which of the following is the correct Python code snippet to check for missing values in a pandas DataFrame named df?

easy

A. df.isnull().sum()

B. df.missing_values()

C. df.check_na()

D. df.nulls_count()

Solution

1. Step 1: Recall pandas method for missing values

   The method isnull() returns a DataFrame of booleans indicating missing values.

2. Step 2: Summing missing values per column

   Using sum() on the boolean DataFrame counts missing values per column.

3. Final Answer:

   df.isnull().sum() -> Option A

4. Quick Check:

   pandas isnull + sum = count missing [OK]

Hint: Use df.isnull().sum() to count missing data [OK]

Common Mistakes:

• Using non-existent pandas methods
• Confusing isnull() with isna() (both work but only isnull() shown)
• Trying to call methods that don't exist on DataFrame

3. Consider this Python code for data preparation:

import pandas as pd

data = {'age': [25, None, 30], 'salary': [50000, 60000, None]}
df = pd.DataFrame(data)
df_filled = df.fillna(df.mean())
print(df_filled)

What will be the output?

medium

A. DataFrame with missing values replaced by zeros

B. DataFrame unchanged with null values still present

C. Error because df.mean() cannot be used with fillna

D. DataFrame with missing values replaced by column means

Solution

1. Step 1: Understand df.mean()

   Calculates mean of each numeric column ignoring null (NaN) values.

2. Step 2: fillna with means

   fillna replaces missing values with the corresponding column mean.

3. Final Answer:

   DataFrame with missing values replaced by column means -> Option D

4. Quick Check:

   fillna(df.mean()) fills missing with mean [OK]

Hint: fillna(df.mean()) replaces missing with average [OK]

Common Mistakes:

• Thinking fillna(df.mean()) causes error
• Assuming missing values remain unchanged
• Believing missing values become zero

4. You have this code snippet to drop rows with missing values:

import pandas as pd
data = {'height': [170, None, 160], 'weight': [70, 65, None]}
df = pd.DataFrame(data)
df_clean = df.dropna(axis=1)
print(df_clean)

What is wrong with this code if the goal is to remove rows with missing data?

medium

A. dropna() requires a threshold parameter to work

B. dropna(axis=1) removes columns, not rows

C. dropna() cannot be used on DataFrames

D. dropna(axis=1) removes rows, not columns

Solution

1. Step 1: Understand dropna axis parameter

   axis=0 drops rows; axis=1 drops columns with missing values.

2. Step 2: Identify the mistake

   Using axis=1 removes columns, but the goal is to remove rows.

3. Final Answer:

   dropna(axis=1) removes columns, not rows -> Option B

4. Quick Check:

   axis=1 means columns, not rows [OK]

Hint: Use dropna(axis=0) to remove rows with missing data [OK]

Common Mistakes:

• Confusing axis=1 with rows
• Thinking dropna always removes rows by default
• Believing dropna needs threshold parameter

5. You have a dataset with many missing values and inconsistent formats. Which two data preparation steps combined will most improve model quality and reduce errors?

hard

A. Filling missing values with mean and converting all text to lowercase

B. Randomly shuffling data and dropping duplicate rows

C. Ignoring missing values and training the model directly

D. Removing all rows with any missing data and normalizing numeric columns

Solution

1. Step 1: Consider filling missing values

   Replacing missing data with mean keeps data size and reduces bias from dropping rows.

2. Step 2: Standardize text format

   Converting text to lowercase fixes inconsistencies and helps model learn better.

3. Final Answer:

   Filling missing values with mean and converting all text to lowercase -> Option A

4. Quick Check:

   Fill missing + text clean = better data [OK]

Hint: Fill missing + clean text improves data quality [OK]

Common Mistakes:

• Dropping all rows loses too much data
• Ignoring missing values causes errors
• Shuffling or dropping duplicates alone won't fix missing data