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50 lines
2.3 KiB
Python
50 lines
2.3 KiB
Python
"""
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EDA Process Flow Diagram
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Shows the exploratory data analysis workflow and key insights
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"""
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from diagrams import Diagram, Cluster, Edge
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from diagrams.generic.storage import Storage
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from diagrams.generic.compute import Rack
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from diagrams.programming.language import Python
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from diagrams.onprem.analytics import Tableau
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from diagrams.onprem.monitoring import Grafana
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with Diagram("Exploratory Data Analysis Process Flow",
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filename="diagrams/02_eda_process_flow",
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show=False,
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direction="TB"):
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# Data Input
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raw_dataset = Storage("Raw Dataset\ndata.csv\n4,424 rows × 37 columns")
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# EDA Steps
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with Cluster("Data Quality Assessment"):
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data_info = Rack("Data Info\n• No missing values\n• No duplicates\n• Mixed data types")
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column_analysis = Rack("Column Analysis\n• 36 predictors\n• 1 target variable\n• Column name cleanup")
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with Cluster("Statistical Analysis"):
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descriptive_stats = Grafana("Descriptive Statistics\n• Central tendencies\n• Distributions\n• Outlier detection")
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correlation_analysis = Grafana("Correlation Analysis\n• Feature relationships\n• Multicollinearity check")
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with Cluster("Visualization & Insights"):
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target_distribution = Tableau("Target Distribution\n• Graduate: 50%\n• Dropout: 32%\n• Enrolled: 17%")
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feature_plots = Tableau("Feature Analysis\n• Histograms\n• Box plots\n• Categorical counts")
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pattern_discovery = Tableau("Pattern Discovery\n• Key predictors\n• Risk factors\n• Success indicators")
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# Key Findings
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with Cluster("Key Findings"):
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class_imbalance = Rack("Class Imbalance\nIdentified for modeling")
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high_cardinality = Rack("High Cardinality\nCourse (51 values)\nApplication order (9 values)")
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data_quality = Rack("Data Quality\nClean dataset\nReady for modeling")
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# Data Flow
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raw_dataset >> data_info >> descriptive_stats
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raw_dataset >> column_analysis >> correlation_analysis
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descriptive_stats >> target_distribution
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correlation_analysis >> feature_plots
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target_distribution >> pattern_discovery
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pattern_discovery >> class_imbalance
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pattern_discovery >> high_cardinality
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pattern_discovery >> data_quality |