Data visualization in machine learning transforms complex data into intuitive graphs and charts, helping uncover patterns, trends, and insights. It enhances model interpretability, supports decision-making, and communicates findings effectively to both technical and non-technical audiences.

1. www.iabac.org

2. Data visualization transforms complex data into visual
forms like charts and heatmaps, making it easier to
identify patterns, trends, and outliers at a glance. This
process enhances data understanding, enabling faster
insights and clearer communication, especially in fields
requiring quick decision-making based on large data sets.
What is Data Visualization?
www.iabac.org

3. Data visualization boosts understanding by
uncovering relationships, distributions, and data
quality issues. It also aids in model evaluation,
allowing for a clearer view of performance and
simplifying the communication of insights for better
decision-making.
Why is Data Visualization Important?
www.iabac.org

4. Histograms: Show distribution of single variables.
Scatter Plots: Reveal relationships between two
variables.
Line Plots: Track changes over time (useful for time
series).
Heatmaps: Display correlations across multiple
features.
Box Plots: Identify data spread and outliers.
Pair Plots: Explore relationships among multiple
features
Types of Data Visualizations
www.iabac.org

5. Visualization in the ML Pipeline
Exploratory Data Analysis (EDA): Identify
missing data, outliers, distributions.
Feature Selection: Spot relevant features using
correlation matrices.
Model Evaluation: Use confusion matrices, ROC
curves, etc., to gauge performance.
www.iabac.org

6. Tools for Data Visualization in ML
Matplotlib: Basic, versatile plotting library.
Seaborn: Built on Matplotlib, adds appealing charts for EDA.
Plotly: Interactive visuals for exploring complex data.
www.iabac.org

7. Examples of Visuals by ML Task
Classification: Confusion matrices show correct and incorrect predictions.
Regression: Scatter plots reveal prediction accuracy.
Clustering: Visualize clusters with color-coded scatter plots.
NLP: Word clouds and bar charts highlight word frequencies.
www.iabac.org

8. Interpreting ML Models with Visualization
Feature Importance: Highlights impactful features.
Partial Dependence Plots (PDP): Shows effect of one feature on
predictions.
SHAP Values: Explains individual predictions by feature impact.
www.iabac.org

9. Best Practices for Effective Visualization
Select the appropriate chart type based on data needs, prioritize
simplicity by avoiding overly complex visuals, and ensure visuals
are suited to the audience's knowledge level. Effective
visualization highlights key points and enhances understanding.
www.iabac.org

10. Visit www.iabac.org
THANKYOU