Upload _1402.py

_1402.py

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+# -*- coding: utf-8 -*-
+""".1402
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-MyRdtV24jTgS9BfGaWSD_PPnzPW5R3E
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+
+data = pd.read_csv('/content/psychological_state_dataset.csv')
+
+data.sample(20)
+
+data.info()
+
+numerical_columns = data.select_dtypes(include=['int64', 'float64']).columns.tolist()
+categorical_columns = data.select_dtypes(include=['object']).columns.tolist()
+
+print("Numerical Columns:", numerical_columns)
+print("Categorical Columns:", categorical_columns)
+
+for col in categorical_columns:
+  print(f"Value counts for {col}:\n{data[col].value_counts()}\n")
+
+for col in numerical_columns:
+  sns.histplot(data[col], kde=True, bins=30)
+  plt.title(f'Distribution of {col}')
+  plt.show()
+
+for col in numerical_columns:
+  sns.boxplot(x=data[col])
+  plt.title(f'Boxplot of {col}')
+  plt.show()
+
+for col in categorical_columns:
+  sns.countplot(x=data[col], order=data[col].value_counts().index)
+  plt.title(f'Distribution of {col}')
+  plt.xticks(rotation=45)
+  plt.show()
+
+corr_matrix = data[numerical_columns].corr()
+sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Heatmap')
+plt.show
+
+sns.pairplot(data[numerical_columns])
+plt.show()
+
+for cat_col in categorical_columns:
+  if data[cat_col].nunique() < 5:
+    g = sns.FacetGrid(data, col=cat_col, sharey=False)
+    g.map(sns.histplot, "Skin Temp (°C)")
+    plt.show()
+
+if 'Time' in categorical_columns:
+  data['Time'] = pd.to_datetime(data['Time'])
+
+  for col in numerical_columns:
+    sns.lineplot(x=data['Time'], y=data[col])
+    plt.title(f'{col} Over Time')
+    plt.show()
+
+for col in ['Mood State', 'Psychological State']:
+  if col in categorical_columns:
+    sns.countplot(x=col, data=data, order=data[col].value_counts().index)
+    plt.title(f'{col} Distribution')
+    plt.show()
+
+from math import pi
+
+radar_data = data[numerical_columns].mean()
+categories = radar_data.index
+values = radar_data.values.tolist()
+values += values[:1]
+
+angles = [n / float(len(categories)) * 2 * pi for n in range(len(categories))]
+angles += angles[:1]
+
+plt.figure(figsize=(8, 8))
+ax = plt.subplot(111, polar=True)
+plt.xticks(angles[:-1], categories)
+
+ax.plot(angles, values, linewidth=2, linestyle='solid')
+ax.fill(angles, values, alpha=0.4)
+plt.title('Radar Chart of Numerical Data')
+plt.show()
+
+if 'Cognitive Load' in numerical_columns:
+  sns.scatterplot(x=data['Age'], y=data['Focus Duration (s)'], size=data['Cognitive Load'], sizes=(20, 200))
+
+for cat_col in categorical_columns:
+  grouped_stats = data.groupby(cat_col)[numerical_columns].mean()
+  print(f"Grouped statistics for {cat_col}:\n", grouped_stats)
+
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+
+scaler = StandardScaler()
+scaled_data = scaler.fit_transform(data[numerical_columns])
+
+kmeans = KMeans(n_clusters=3, random_state=42)
+data['Cluster'] = kmeans.fit_predict(scaled_data)
+
+pca = PCA(n_components=2)
+pca_data = pca.fit_transform(scaled_data)
+
+plt.figure(figsize=(10, 6))
+sns.scatterplot(x=pca_data[:, 0], y=pca_data[:, 1], hue=data['Cluster'], palette='viridis', s=100)
+plt.title('K-Means Clusters Visualized in 2D')
+plt.xlabel('PCA Component 1')
+plt.ylabel('PCA Component 2')
+plt.show()
+
+from scipy.cluster.hierarchy import linkage, dendrogram
+
+linked = linkage(scaled_data, method='ward')
+
+plt.figure(figsize=(12, 6))
+dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=False)
+plt.title('Hierarchiacal Clustering Dendrogram')
+plt.xlabel('Samples')
+plt.ylabel('Distance')
+plt.show()
+
+from sklearn.manifold import TSNE
+
+tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=300)
+tsne_results = tsne.fit_transform(scaled_data)
+
+plt.figure(figsize=(10, 6))
+sns.scatterplot(x=tsne_results[:, 0], y=tsne_results[:,1], hue=data['Cluster'], palette='coolwarm', s=100)
+plt.title('T-SNE Clustering Visualization')
+plt.xlabel('TSNE Component 1')
+plt.ylabel('TSNE Component 2')
+plt.show()
+
+for cat_col in categorical_columns:
+  grouped_data = data.groupby(cat_col)[numerical_columns].mean()
+  plt.figure(figsize=(12, 6))
+  sns.heatmap(grouped_data, annot=True, fmt=".2f", cmap='coolwarm')
+  plt.title(f'Mean Values of Numerical Features by {cat_col}')
+  plt.ylabel(cat_col)
+  plt.xlabel('Numerical Features')
+  plt.show()
+
+from pandas.plotting import parallel_coordinates
+
+parallel_data = data[numerical_columns].copy()
+parallel_data['Cluster'] = data['Cluster']
+
+plt.figure(figsize=(12, 6))
+parallel_coordinates(parallel_data, class_column='Cluster', colormap='viridis')
+plt.title('Parallel Coordinates Plot by Clusters')
+plt.xticks(rotation=45)
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, accuracy_score
+
+data_encoded = data.copy()
+for col in categorical_columns:
+  le = LabelEncoder()
+  data_encoded[col] = le.fit_transform(data[col])
+
+X = data_encoded.drop(['Cognitive Load'], axis=1)
+y = data_encoded['Cognitive Load']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+scaler = StandardScaler()
+X_train[numerical_columns] = scaler.fit_transform(X_train[numerical_columns])
+X_test[numerical_columns] = scaler.transform(X_test[numerical_columns])
+
+clf = RandomForestClassifier(n_estimators=100, random_state=42)
+clf.fit(X_train, y_train)
+
+y_pred = clf.predict(X_test)
+
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+
+sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d')
+
+accuracy = accuracy_score(y_test, y_pred)
+print(f"accuracy_score: {accuracy * 100:.2f}%")
+
+feature_importances = pd.DataFrame({'Feature': X.columns, 'Importance': clf.feature_importances_})
+feature_importances = feature_importances.sort_values(by='Importance', ascending=False)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Importance', y='Feature', data=feature_importances)
+plt.title('Feature Importance')
+plt.show()