Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is the process of visualizing and understanding your data before building models. It reveals patterns, anomalies, relationships, and assumptions that guide your preprocessing and algorithm choices.
EDA is detective work – you ask questions of the data and let it answer through statistics and plots.
Key EDA Questions
- How many rows and columns? (
df.shape) - What are the data types? (
df.dtypes) - Are there missing values? (
df.isnull().sum()) - What is the distribution of each feature? (histograms)
- Are features correlated? (correlation matrix, heatmap)
- Are there outliers? (box plots)
Essential Visualizations
import matplotlib.pyplot as plt
import seaborn as sns
# Histogram for a single feature
df['age'].hist(bins=20)
plt.title('Age Distribution')
# Boxplot to detect outliers
sns.boxplot(x=df['income'])
# Scatter plot between two features
plt.scatter(df['age'], df['income'])
plt.xlabel('Age'); plt.ylabel('Income')
# Correlation heatmap
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')What to Look For
- Skewed distributions: May need log transformation.
- Outliers: Could be errors or rare events; decide to cap or remove.
- High correlation: Two features highly correlated → maybe drop one.
- Class imbalance: In classification, check target distribution.
EDA Saves Time Later
Spotting issues early (e.g., data entry errors, wrong data types) prevents wasted effort on flawed models. Spend time on EDA – it pays off.
Two Minute Drill
- EDA explores data through statistics and visualizations.
- Use histograms, boxplots, scatter plots, heatmaps.
- Identify missing values, outliers, distributions, correlations.
- EDA guides preprocessing and feature engineering.
Need more clarification?
Drop us an email at career@quipoinfotech.com