Effective Techniques for Managing Missing Values in Datasets
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Chapter 1: Understanding Missing Values
This article delves into the concept of missing values, the reasons for their importance, popular methods to manage them, and the acceptable limits of missing data. It is part of a series that guides readers through solving fintech challenges using different machine learning techniques, specifically with the "All Lending Club Loan" dataset. This comprehensive project serves as a learning tool for beginners in data science.
In this discussion, we will focus on missing values, a crucial aspect of preparing your data for future machine learning applications. If you are just starting out, this guide will help you formulate the right questions for your business context, manage missing data effectively, and prepare for relevant interview inquiries. Common interview questions addressed include:
- What constitutes a missing value?
- Why is it crucial to recognize missing values?
- What strategies are available to handle missing values?
- What are the prevalent techniques?
- What proportion of missing values is deemed acceptable?
What Are Missing Values?
Missing data refers to the absence of certain values in a dataset.
For instance, in the sample below, the columns 'loan_amnt', 'term', 'annual_inc', 'dti', 'mths_since_recent_inq', and 'bc_open_to_buy' contain missing values.
The Importance of Addressing Missing Data
In real-world datasets, missing values are common and can arise from various factors, such as:
- Data Collection: In surveys, non-mandatory fields may lead to intentional omissions by respondents or simply the lack of available information.
- Data Transfer: Corruption can occur during data manipulation or human error.
- Feature Updates: Missing values may stem from software applications that lack certain features until recently added.
The implications of missing data can include:
- Diminished model effectiveness, resulting in erroneous conclusions.
- Disruptions in model training, as algorithms like logistic regression in scikit-learn require complete data.
- Reduced model accuracy if critical features contain significant missing values.
Strategies for Managing Missing Data
Method 1 — Removal of Incomplete Rows or Columns: As a general guideline, consider cutoffs for missing data. While there is no universally accepted threshold, research indicates that a missing rate of 5% or less is typically inconsequential, allowing for the removal of those rows. However, this approach can lead to data loss and may not be viable depending on the extent of missing values. Analyze the observations before deletion to ensure you are not excluding essential populations.
Method 2 — Imputation of Missing Values: This involves replacing missing values for continuous features with mean or median values, and for categorical features, with the most frequent category. While this method mitigates data loss, it may compromise model performance since simple calculations may not accurately reflect the true missing values.
Imputation Techniques
- Mean/Median Substitution: Calculate the mean or median of a column and replace missing entries accordingly. Useful libraries include Impyute and SimpleImputer from sklearn.
- Time Series Imputation: Techniques like .fillna() or .interpolate() can fill gaps based on adjacent values.
- Advanced Methods: More sophisticated models such as KNN Imputation or Multivariate Imputation can also be employed, albeit requiring more time and resources.
Practical Application of Missing Data Management
To demonstrate these techniques, we will work with a fintech dataset containing information on past loan applicants, including credit grades and income. The aim is to identify patterns and utilize machine learning to predict loan default likelihood, aiding businesses in their decision-making processes.
To examine missing values in your dataset, you can leverage the Pandas library's isnull() function:
# Load dataset
loan = pd.read_csv('../input/lending-club/accepted_2007_to_2018Q4.csv.gz', compression='gzip', low_memory=True)
loans = loan[['loan_amnt', 'term', 'int_rate', 'sub_grade', 'emp_length', 'home_ownership', 'annual_inc', 'loan_status', 'addr_state', 'dti', 'mths_since_recent_inq', 'revol_util', 'bc_open_to_buy', 'bc_util', 'num_op_rev_tl']]
# View percentage of missing values
missing_data = pd.DataFrame({'total_missing': loans.isnull().sum(), '%_missing': (loans.isnull().sum()/2260701)*100})
We will observe that most features have minimal missing values, typically below 5%.
Alternatively, you can utilize the Pandas Profiling library for a comprehensive view of missing values through various visualizations.
Handling Missing Data by Deletion
Given the relatively low percentage of missing values and the substantial number of observations (~2 million), the most straightforward approach is to remove rows with missing data using the dropna() function.
loans = loans.dropna()
While this method is straightforward, it is crucial to understand the reasons behind the missing data, as this can affect the integrity of your model.
Imputation Using the SimpleImputer
Next, we will use the SimpleImputer from sklearn to handle missing values. For example, consider the emp_length feature, which has 6% of its values missing. The SimpleImputer calculates the mean value and fills in the gaps.
from sklearn.impute import SimpleImputer
imput_mean = loans.copy()
mean_imputer = SimpleImputer(strategy='mean')
imput_mean['emp_length'] = mean_imputer.fit_transform(imput_mean['emp_length'].values.reshape(-1,1))
After running this, you will notice that the emp_length column no longer contains any missing values. However, always question whether using the mean value is appropriate, especially if the feature is critical to your model's predictions.
Conclusion and Further Learning
For more insights on managing missing values, refer to the Kaggle notebook on this topic.
If you wish to deepen your understanding of data science, consider completing a full end-to-end project that encompasses the entire data science lifecycle. This experience will equip you with practical skills and prepare you for prevalent interview questions in the field.
What challenges do you face in your early data science journey? Feel free to reach out—I’m here to help! Subscribe to my newsletter for more content tailored to your learning path.
Chapter 2: Practical Video Tutorials on Missing Data
The first video provides a comprehensive overview of handling missing data and implementing imputation techniques in R programming.
The second video explores simple methods in Python to address missing values effectively.