Delete visualize.py

visualize.py

@@ -1,40 +0,0 @@
-# visualize.py
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-import os
-
-def plot_column_distributions(df: pd.DataFrame, output_dir="charts"):
-    os.makedirs(output_dir, exist_ok=True)
-
-    for col in df.columns:
-        plt.figure(figsize=(6, 4))
-
-        if pd.api.types.is_numeric_dtype(df[col]):
-            sns.histplot(df[col].dropna(), kde=True)
-            plt.title(f"Distribution of {col}")
-        elif pd.api.types.is_categorical_dtype(df[col]) or df[col].dtype == object:
-            top_vals = df[col].value_counts().nlargest(10)
-            sns.barplot(x=top_vals.values, y=top_vals.index)
-            plt.title(f"Top categories in {col}")
-        else:
-            continue
-
-        plt.tight_layout()
-        plt.savefig(f"{output_dir}/{col}.png")
-        plt.close()
-
-def plot_relationships(df, target_col='income', output_dir="charts"):
-    os.makedirs(output_dir, exist_ok=True)
-
-    for col in df.columns:
-        if col == target_col:
-            continue
-
-        if pd.api.types.is_numeric_dtype(df[col]) and target_col in df.columns:
-            plt.figure(figsize=(6, 4))
-            sns.boxplot(x=target_col, y=col, data=df)
-            plt.title(f"{col} by {target_col}")
-            plt.tight_layout()
-            plt.savefig(f"{output_dir}/{col}_by_{target_col}.png")
-            plt.close()