Upload DataVisualization.py

DataVisualization.py

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+# DataVisualization.py
+# Purpose: Script to create visualizations for chat data and machine learning model results.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load Data
+# Assuming you have a CSV file with your model's predictions and actual scores
+datafile_path = "data/model_predictions.csv"
+df = pd.read_csv(datafile_path)
+
+# Visualization Functions
+
+def plot_feature_importances(model):
+    """
+    Plots feature importances of a trained model.
+    """
+    feat_importances = pd.Series(model.feature_importances_, index=df.columns[:-1])
+    feat_importances.nlargest(10).plot(kind='barh')
+    plt.title('Feature Importances')
+    plt.show()
+
+def plot_actual_vs_predicted(y_actual, y_pred, title='Actual vs Predicted'):
+    """
+    Scatter plot for actual vs predicted values.
+    """
+    plt.figure(figsize=(10, 6))
+    sns.scatterplot(x=y_actual, y=y_pred, alpha=0.6)
+    plt.plot([y_actual.min(), y_actual.max()], [y_actual.min(), y_actual.max()], '--r')
+    plt.xlabel('Actual')
+    plt.ylabel('Predicted')
+    plt.title(title)
+    plt.show()
+
+def plot_error_distribution(y_actual, y_pred, title='Error Distribution'):
+    """
+    Histogram for prediction errors.
+    """
+    errors = y_actual - y_pred
+    plt.figure(figsize=(10, 6))
+    sns.histplot(errors, bins=20, kde=True)
+    plt.xlabel('Prediction Error')
+    plt.title(title)
+    plt.show()
+
+# Example Usage
+# These are just examples. Replace 'your_model' with your actual trained model
+# and 'y_actual', 'y_pred' with your actual data.
+
+# plot_feature_importances(your_model)
+# plot_actual_vs_predicted(df['ActualScore'], df['PredictedScore'])
+# plot_error_distribution(df['ActualScore'], df['PredictedScore'])
+
+# Note to Users:
+# - Adjust the data paths, column names, and model variables as per your data and model.
+# - Feel free to add more visualization functions based on your specific needs.