Why EDA is Crucial Before Applying Machine Learning Algorithms

Why EDA is Crucial Before Applying Machine Learning Algorithms

In today’s world, data is the new gold and businesses are increasingly investing in data-driven decision making to gain a competitive edge. With the exponential increase in the volume of data, Machine Learning (ML) has become the go-to tool for analyzing large datasets and extracting valuable insights. However, before applying ML algorithms to any dataset, a crucial step that needs to be taken is Exploratory Data Analysis (EDA).

Exploratory Data Analysis is a statistical approach to analyze and summarize data to gain insights into the data’s properties. EDA helps to understand the data’s underlying structure, identify hidden patterns, detect outliers, and prepare cleaned and preprocessed data for the ML algorithms.

Benefits of EDA Before Applying Machine Learning Algorithms

Identifying Data Structure

EDA helps identify the data structure, making it easier to choose the suitable ML algorithm for the analysis. Different ML algorithms perform better on a particular type of data like regression works on continuous data.

Detecting Outliers

Outliers can severely affect the ML algorithms’ accuracy, and EDA helps identify outliers, and the data can be cleaned or removed accordingly.

Data Cleaning and Preprocessing

EDA identifies missing values, incorrect or irrelevant data, and data inconsistencies. EDA helps to preprocess data, thereby improving the quality of the data and increasing the accuracy of the model.

Feature Selection

Feature selection is an important step in ML. It helps identify the most relevant features from the data, which in turn improves model performance. EDA helps to identify the most prominent features, which make the models more efficient.

Model Selection

EDA helps to choose the right ML models, parameters, and metrics. This step is crucial for the success of ML models, and EDA helps businesses to make data-driven decisions while selecting the right model or algorithm.

Real-World Examples of EDA and Machine Learning

Let’s consider an example of predicting customer churn in the telecom industry. Suppose we have a dataset containing customer demographics, billing information, and call details. Before applying ML models, we need to perform EDA to analyze the data’s underlying structure, identify missing values and outliers, preprocess the data, and select the relevant features for the model. This step helps to improve the model’s accuracy and decrease the number of false positives and false negatives and, in turn, reduce customer churn.

Another example is credit risk analysis, where ML models are used to predict the likelihood of default or payment delays. EDA helps to preprocess and clean the data, identify outliers, and select important features, which improves the accuracy of the model and reduces the risk of credit losses.

Conclusion

Exploratory Data Analysis is a crucial step before applying Machine Learning algorithms to any dataset. EDA helps to identify the data structure, detect outliers, clean and preprocess data, select relevant features, and choose the right ML models. These actions are essential to improve model accuracy and assist businesses in making data-driven decisions. By conducting proper EDA before applying ML algorithms, businesses can avoid costly mistakes, reduce risks, and improve overall performance.