In this section, we will explore data by performing Exploratory Data Analysis (EDA). EDA is the most critical and most important component of the data analysis process. EDA offers the following benefits:
- It provides an initial glimpse of data and its context.
- It captures quick insights and identifies the potential drivers from the data for predictive analysis. It finds the queries and questions that can be answered for decision-making purposes.
- It assesses the quality of the data and helps us build the road map for data cleaning and preprocessing.
- It finds missing values, outliers, and the importance of features for analysis.
- EDA uses descriptive statistics and visualization techniques to explore data.
In EDA, the first step is to read the dataset. We can read the dataset using pandas. The pandas library offers various options for reading data. It can read files in various formats, such as CSV, Excel, JSON, parquet, HTML, and pickle. All these methods were covered in...
