Most neglected steps by ML engineers: Key steps to Data Preprocessing
Data preprocessing is a critical step in building an effective machine learning model.
While many ML engineers tend to focus on model selection and tuning, neglecting the importance of data preprocessing can lead to suboptimal performance or even outright failure of the model.
Let’s dive in and explore the key steps of data preprocessing and how they connect to machine learning.
Steps to EDA:
Before diving into data preprocessing, we need to perform exploratory data analysis (EDA) to understand the structure and quality of our data. EDA consists of the following steps:
1. Data inspection:
We need to check for data completeness, accuracy, consistency, relevance, format, quality, outliers, distributions, relationships, and biases.
This helps to identify potential issues with the data and determine the appropriate data cleaning steps.
2. Data cleaning:
Cleaning data involves handling missing values, duplicate data, inconsistent data, and outliers.
We may also need to transform the data to prepare it for modeling.
3. Data visualization:
Visualization is a powerful tool for exploring relationships between variables and identifying patterns and trends.
We can create histograms, box plots, scatter plots, heatmaps, and other visualizations to gain insights into the data. Mainly for the following purposes:
a. Relationship between each feature and the target variable:
It’s important to visualize the distribution of the target variable and each feature to see if there are any clear patterns or trends. This helps us to identify relevant features and understand their relationship with the target variable.
b. Relationship between pairs of features:
We can also visualize the relationship between pairs of features to identify potential correlations or interactions between them. This can help us to identify opportunities for feature engineering.
c. Relationship between multiple features and the target variable:
Visualizing the relationship between multiple features and the target variable can help us to identify nonlinear relationships or interactions that may require more complex modeling techniques.
d. Outliers and anomalies:
Visualizing the data for outliers or anomalies helps to identify potential issues that may need to be handled before building the model.
e. Data imbalance:
Visualizing the distribution of the target variable is important to check for data imbalance, which can affect the accuracy of the model and may require resampling techniques.
4. Data analysis:
Once we have cleaned and visualized the data, we can apply statistical methods and models to test hypotheses and draw conclusions. We look for correlations, dependencies, and causation to understand the relationship between variables.
5. Communicate results:
Finally, we summarize our findings in a clear, concise, and visual way. We provide insights, recommendations, and limitations of the analysis to help others understand the data and its implications for machine learning.
With this, we won’t be having a hard time on figuring out why is the ML model not performing as good as expected, with you having a good understanding of data.
Data preprocessing is a critical step in building an effective machine learning model. By performing EDA, cleaning, and visualizing the data, we can identify issues, select relevant features, and prepare the data for modeling.
