Data exploration, or exploratory data analysis (EDA), examines variables before modeling to find patterns, trends, relationships, and unexpected behavior. Its outputs are initial insights and recommendations, supported by reports or visualizations. Discuss those findings with business stakeholders: they can refine the problem or change the modeling plan. section 11.9 section 1.4
A practical exploration starts with checks and moves toward interpretation:
- Establish which datasets and variables address the business problem. Check that key business figures agree with stakeholder expectations. section 11.3
- Inspect missing values, duplicates, sparse variables, and values that carry little information; clean the data before relying on its patterns. section 3.8
- Examine each variable’s spread, distribution, and typical values, then look for relationships, differences between groups, seasonality, and aberrations. section 3.8 section 11.3
- Use charts to make findings visible, and carry useful insights into model development rather than treating exploration as a separate deliverable. section 11.9 section 11.19
Inspecting vehicles before clustering
Load the vehicle CSV into a pandas DataFrame, inspect its shape and columns, preview rows, count class values, and check missing values. Plot class counts and a scatter plot before standardizing variables and testing k-means cluster counts. The initial inspection gives context for interpreting the resulting clusters; it does not choose a clustering solution by itself. section 2.3.5
vehicle_df.shape vehicle_df.info() vehicle_df.head() pd.value_counts(vehicle_df['class'])
A compact pandas inspection sequence shows the dataset size, column information, sample rows, and class counts before modeling. section 2.3.5
For customer segmentation, exploration can reveal ranges, variation, cycles, and aberrations before clustering customers by their attributes. For high-dimensional subscriber data, begin after basic cleaning with individual-variable distributions and summary measures; these checks also help identify uninformative variables before dimensionality reduction. section 1.5.2 section 3.8
