Update visualization.py

Updated Visualizations and requirements using seaborn

visualizations/visualization.py

@@ -1,7 +1,10 @@
+import seaborn as sns
 import matplotlib.pyplot as plt
 import numpy as np
 import os
 
+sns.set(style="whitegrid")  # Set the seaborn style
+
 def plot_feature_importance(model, features, dataset_name):
     """Generate a bar plot for feature importance."""
     importance = model.coef_
@@ -12,27 +15,24 @@ def plot_feature_importance(model, features, dataset_name):
     sorted_importance = importance[indices]
 
     plt.figure(figsize=(12, 8))
+    sns.barplot(x=sorted_importance, y=sorted_features, palette="coolwarm")
     plt.title(f"Feature Importance for {dataset_name}", fontsize=16)
-    plt.barh(range(len(indices)), sorted_importance, align='center', color='skyblue')
-    plt.yticks(range(len(indices)), sorted_features, fontsize=12)
     plt.xlabel('Relative Importance', fontsize=14)
-    plt.gca().invert_yaxis()  # Most important feature at the top
-    plt.grid(axis='x', linestyle='--', alpha=0.7)
+    plt.ylabel('Features', fontsize=14)
 
     plt.savefig(f'static/images/feature_importance_{dataset_name}.png', bbox_inches='tight')
     plt.close()
 
 def plot_predictions_vs_true(y_true, y_pred, dataset_name):
     """Generate a scatter plot comparing true vs. predicted values."""
     plt.figure(figsize=(12, 8))
-    plt.scatter(y_true, y_pred, edgecolors='k', color='dodgerblue', s=100, alpha=0.6, label='Predictions')
-    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--', lw=2, label='Perfect Prediction Line')
+    sns.scatterplot(x=y_true, y=y_pred, color='dodgerblue', s=100, edgecolor='k', alpha=0.6)
+    sns.lineplot(x=y_true, y=y_true, color='red', linestyle='--', lw=2)  # Perfect prediction line
 
     plt.title(f"Predictions vs. True Values for {dataset_name}", fontsize=16)
     plt.xlabel('True Values', fontsize=14)
     plt.ylabel('Predictions', fontsize=14)
     plt.grid(True, linestyle='--', alpha=0.7)
-    plt.legend(fontsize=12)
 
     plt.savefig(f'static/images/predictions_vs_true_{dataset_name}.png', bbox_inches='tight')
     plt.close()