Lec 04 -Visualization dataset using Python - Copy.pptx

Lec 04 -Visualization dataset using Python - Copy.pptx

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  Engr. Atta MuhammadPanhyar 1 QUAID E AWAM UNIVERSITY OF ENGINEERING SCIENCE AND TECHNOLOGY DEPARTMENT OF ARTIFICIAL INTELLIGENCE
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  Visualizing datasets using Pythonlibraries Machine Learning (03 ch) Engr. Atta Muhammad Panhyar attapanhyar@quest.edu.pk | +923331971110
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  Engr. Atta MuhammadPanhyar 3 Why is Visualization Important? • Understanding Data: Visualization helps in comprehending the structure of the dataset. • Identifying Patterns: Visual representation allows us to see trends, correlations, and anomalies. • Making Decisions: Supports informed preprocessing, feature selection, and feature engineering. • Model Insights: Helps debug and interpret machine learning models.
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  Engr. Atta MuhammadPanhyar 4 Introduction to Matplotlib • A foundational library for creating static, interactive, and animated visualizations in Python. • Supports various plot types: line, scatter, bar, histogram, etc. • Highly customizable for labels, titles, colours, and more.
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  Matplotlib Engr. Atta MuhammadPanhyar 5
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  Engr. Atta MuhammadPanhyar 6 Why is Visualization Important? • Helps identify trends, correlations, and patterns. • Assists in understanding the dataset's structure. • Supports decision-making during preprocessing and feature selection.
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  Engr. Atta MuhammadPanhyar 7 Introduction to Matplotlib • Matplotlib Features: • Basic 2D plotting (line, scatter, bar). • Fine control over chart elements (titles, labels).
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  Engr. Atta MuhammadPanhyar 8 Seaborn • Seaborn Features: • High-level interface for statistical graphics. • Built on top of Matplotlib for ease of use.
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  Seaborn Engr. Atta MuhammadPanhyar 9
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  Engr. Atta MuhammadPanhyar 10 Identifying Trends and Patterns • Common Techniques: • Histogram: Distribution of a single variable. • Box Plot: Summary of data variation and outliers.
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  Box Plot Engr. AttaMuhammad Panhyar 11
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  Correlation Heatmaps • Used tovisualize relationships between numerical variables. Engr. Atta Muhammad Panhyar 12
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  Pair Plot for Multivariate Analysis •Highlights pairwise relationships in datasets. Engr. Atta Muhammad Panhyar 13
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  Best Practices in Visualization •Choose the Right Plot: Match the plot type to the data and objective. • Avoid Overcrowding: Display only relevant information to maintain clarity. • Use Labels and Titles: Always annotate your visualizations. • Consistency: Maintain consistent styles and colours for better interpretation. • Interactivity: Use libraries like Plotly for interactive visualizations in real-world applications. Engr. Atta Muhammad Panhyar 14
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  Engr. Atta MuhammadPanhyar 15 The End