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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.
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