Advanced Visual EDA

🎯 Purpose¶

This guide provides best practices for conducting visual exploratory data analysis (EDA) before fitting a logistic regression model. The goal is to understand variable relationships, class balance, separation, and possible violations of assumptions.

🔍 1. Class Balance Check¶

📊 Count Plot or Bar Chart¶

Shows the distribution of the binary target variable (e.g., 0 vs 1).

sns.countplot(x="target", data=df)

Why: - Identify class imbalance - Important for model evaluation (e.g., precision vs. recall focus)

🧪 2. Continuous Predictor vs Binary Outcome¶

📈 Disc Plot (a.k.a. Strip/Swarm Plot)¶

Shows how a continuous variable relates to the binary target.

sns.stripplot(x="feature", y="target", data=df, jitter=True, hue="target")

Why: - Visualize class separation - Detect non-linear patterns

📊 Box Plot / Violin Plot¶

Summarizes feature distributions per class.

sns.boxplot(x="target", y="feature", data=df)

Why: - Compare median, spread, and outliers - Spot potential predictors with clear group separation

📌 Disc Plot vs Confusion Matrix¶

A disc plot provides a feature-level preview of separation before classification. A confusion matrix summarizes final model performance. Use both to: - Anticipate separability before training - Validate if visual trends translate into predictive accuracy

🧠 3. Feature Correlation (to Detect Multicollinearity)¶

🔗 Heatmap of Correlation Matrix¶

sns.heatmap(df.corr(), annot=True, cmap="coolwarm")

Why: - Detect highly correlated predictors - Guide feature selection or regularization (Ridge, Lasso)

🌀 4. Relationships Among Features¶

📊 Pair Plot (for small feature sets)¶

Explore interactions between continuous predictors colored by class.

sns.pairplot(df, hue="target")

Why: - Understand interaction patterns - See class clustering/separation

📐 5. Logistic Curve Fit (Optional Visual Check)¶

Fit a logistic curve between a single continuous predictor and the binary target.

sns.regplot(x="feature", y="target", data=df, logistic=True)

Why: - Assess linearity of the logit visually - Detect saturation or threshold effects

📌 Summary Table¶

Visualization	Purpose
Count Plot	Check class imbalance
Disc/Strip Plot	See class separation by feature
Box/Violin Plot	Compare distributions between classes
Correlation Heatmap	Detect multicollinearity
Pair Plot	Explore predictor relationships and class overlap
Logistic Curve	Visualize logit relationship to a feature
Disc Plot + Confusion Matrix	Compare predicted vs visual separability

✅ Next Step¶

Once you complete visual EDA: - Scale numeric features if needed - Encode categorical variables - Begin model building and check for assumptions (linearity of logit, multicollinearity)

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