update

README.md

@@ -36,11 +36,20 @@ The dataset comprises 20 participant records with:
 The recommended sample size is 52 participants; however, this dataset currently contains 20, representing a shortfall of 32 participants.
 
 ## Exploratory Data Analysis
-Score Distribution:
-- Mean total score: 2.65
-- Most common diagnosis: Absence of brain impairment (14 cases)
-- Borderline cases: 5
-- Strong evidence for brain impairment: 1 case
+### Dataset Overview:
+The dataset consists of 20 records, each representing a participant's test results and demographic information:
+- Mean Total Score: 2.65
+- Most Common Diagnosis: Absence of brain impairment (14 cases)
+- Borderline Cases: 5
+- Strong Evidence for Brain Impairment: 1 case
+
+
+### Visualizations:
+- Bar chart for diagnosis: A bar chart was created to visualize the distribution of diagnoses, with the majority of participants having no brain impairment.
+- Boxplot of Total Scores by Education Level: Shows how total scores vary across different education levels, providing insight into potential patterns related to cognitive skills and educational background.
+- Boxplot of Total Scores by Previous Test Experience: Examines how prior experience with the test might influence scores, revealing differences between those who have taken the test before and those who have not.
+- Most common error: A bar plot comparing the total value per error, showing which factors tend to have the most significant impact on the scores.
+
 
 ## Data Collection Protocol
 Participants were recruited from Duke University and through social media to ensure broad outreach. Only candidates aged 18 and older were accepted. The data collection process involved the following steps:

dataset-eda.py

@@ -0,0 +1,110 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pytest
+
+# Paths to the dataset files
+scores_file = 'dataset/scores_summary.csv'
+metadata_file = 'dataset/metadata.csv'
+
+# Read both datasets
+scores_df = pd.read_csv(scores_file)
+metadata_df = pd.read_csv(metadata_file)
+
+# Merge the datasets on 'id'
+df = pd.merge(scores_df, metadata_df, on='id')
+
+# Basic dataset info
+print("Dataset Info:")
+print(df.info())
+print("\nSummary Statistics:")
+print(df.describe())
+print("\nMissing Values:")
+print(df.isnull().sum())
+
+# Distribution of diagnoses
+plt.figure(figsize=(10, 6))
+df['diagnose'].value_counts().plot(kind='bar')
+plt.title('Distribution of Diagnoses')
+plt.xlabel('Diagnosis')
+plt.ylabel('Count')
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.show()
+
+# Create the boxplot
+plt.figure(figsize=(10, 6))
+sns.boxplot(data=df, x='education_level', y='total_score')
+plt.title('Total Scores by Education Level')
+plt.xticks(rotation=45)
+plt.xlabel('Education Level')
+plt.ylabel('Total Score')
+plt.tight_layout()
+plt.show()
+
+# Taken test before
+plt.figure(figsize=(10, 6))
+sns.boxplot(data=df, x='taken_test_before', y='total_score')
+plt.title('Total Scores by Previous Test Experience')
+plt.xlabel('Has Taken Test Before')
+plt.ylabel('Total Score')
+plt.tight_layout()
+plt.show()
+
+# Most common error
+columns_to_plot = [
+    'rotation', 'overlapping_difficulty', 'simplication', 'fragmentation', 'retrogression',
+    'perseveration', 'collision', 'impotence', 'closure_difficulty', 'motor_incoordination',
+    'angulation', 'cohesion', 'time'
+]
+
+mean_values = df[columns_to_plot].sum().sort_values(ascending=False)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x=mean_values.index, y=mean_values.values)
+plt.title('Mean Values of Parameters Ordered from Largest to Smallest')
+plt.xlabel('Parameter')
+plt.ylabel('Mean Value')
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.show()
+
+# --- Pytest for CSV reading ---
+def test_pd_read_scores():
+    """Test if the scores CSV is read correctly"""
+    scores_df = pd.read_csv(scores_file)
+    
+    # Check if the DataFrame is not empty
+    assert not scores_df.empty, "Scores DataFrame is empty"
+    
+    # Check if the required columns exist
+    required_columns = [
+        'id', 'rotation', 'overlapping_difficulty', 'simplication', 'fragmentation',
+        'retrogression', 'perseveration', 'collision', 'impotence', 'closure_difficulty',
+        'motor_incoordination', 'angulation', 'cohesion', 'time', 'total_score', 'diagnose'
+    ]
+    for column in required_columns:
+        assert column in scores_df.columns, f"Column '{column}' not found in scores DataFrame"
+    
+    # Check if the number of rows matches the expected value (use len to check row count)
+    expected_row_count = 20 
+    assert len(scores_df) == expected_row_count, f"Expected {expected_row_count} rows, but found {len(scores_df)}"
+
+def test_pd_read_metadata():
+    """Test if the metadata CSV is read correctly"""
+    metadata_df = pd.read_csv(metadata_file)
+    
+    # Check if the DataFrame is not empty
+    assert not metadata_df.empty, "Metadata DataFrame is empty"
+    
+    # Check if the required columns exist
+    required_columns = ['id', 'age', 'nacionality', 'education_level', 'taken_test_before']
+    for column in required_columns:
+        assert column in metadata_df.columns, f"Column '{column}' not found in metadata DataFrame"
+    
+    # Check if the number of rows matches the expected value
+    expected_row_count = 20  
+    assert len(metadata_df) == expected_row_count, f"Expected {expected_row_count} rows, but found {len(metadata_df)}"
+
+if __name__ == "__main__":
+    pytest.main()

dataset/scores_summary.csv

@@ -18,10 +18,4 @@ s16,0,1,1,0,0,0,0,0,0,0,0,0,0,2,absence of brain impairment
 s17,0,1,1,0,0,1,0,0,1,0,0,0,0,4,borderline
 s18,0,1,1,0,0,1,0,0,1,0,0,0,0,4,borderline
 s19,0,1,1,0,0,1,0,0,0,0,0,0,0,3,absence of brain impairment
-s20,0,1,1,0,0,1,0,0,1,0,0,0,0,4,borderline
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+s20,0,1,1,0,0,1,0,0,1,0,0,0,0,4,borderline