Exploratory data analysis (EDA) is a critical step in the data science process. It involves analyzing and summarizing data to gain insights and understand its underlying patterns, relationships, and distributions. EDA can help you to identify outliers, missing values, and other data quality issues. It can also help you to identify relationships between different features in your data. This information can be used to improve the performance of machine learning models and other data science tasks.
Benefits of Exploratory Data Analysis (EDA)
- It can help you to identify outliers and missing values. Outliers are data points that are significantly different from the rest of the data. Missing values are data points that are not present in the dataset. Both outliers and missing values can impact the performance of machine learning models. EDA can help you to identify these issues so that you can take steps to address them.
- It can help you to identify relationships between different features. EDA can help you to find relationships between different features in your data. This information can be used to improve the performance of machine learning models. For example, if you are trying to predict the price of a house, you might find that the size of the house is a good predictor of the price.
- It can help you to understand the distribution of your data. EDA can help you to understand the distribution of your data. This information can be used to select the right machine-learning algorithm for your task. For example, if your data is normally distributed, you might want to use a linear regression model. However, if your data is not normally distributed, you might want to use a different model, such as a decision tree or a random forest.
EDA is an essential step in the data science process. By performing EDA, you can gain insights into your data and improve the performance of your machine-learning models.