Exploratory Data Analysis (EDA) is a crucial step in the data science process that involves analyzing and summarizing the main characteristics of a data set. The goal of EDA is to identify patterns, anomalies, and relationships in the data, as well as to make inferences and hypotheses about the underlying structure of the data. Techniques used in EDA include visualization, statistical analysis, and data cleaning.
EDA is an iterative process and it is an important step to perform before building any model. It helps to understand the data and its characteristics, which in turn will help to select the appropriate model and to improve the model performance.
In this repo, I'll use different types of datasets to explore and implement various EDA approaches.
For any query, ping me on
- LinkedIn: @jabhij
- Twitter: @jabhij
- Web: LetUsTweak
Hope, it helps!! ヅ