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1812_159_252.py

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+# -*- coding: utf-8 -*-
+"""1812.159.252
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/18Q9tMQvVHXk2Rs_3EoKpdRhFuOyv4iDk
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+warnings.filterwarnings('ignore')
+
+df = pd.read_csv('/content/Raw Data.csv')
+
+df.info()
+
+cols_index = list(range(7, 14)) + list(range(16, 26)) + list(range(28,37))
+
+df.drop(columns=df.columns[cols_index], inplace=True)
+
+df.info()
+
+df.columns = df.columns.str.replace(r'^\d+\.\s*','', regex=True)
+df.rename(columns = {'Did you receiver a waiver or scholarship at your university?': 'Waiver/Scholarship'}, inplace=True)
+
+df.info()
+
+value_cols = ['Anxiety Value', 'Stress Value', 'Depression Value']
+
+for col in value_cols:
+  plt.figure(figsize=(10,6))
+  plt.title(f'{col}, Distribution')
+  sns.histplot(data=df, x=col, bins=10)
+  plt.show()
+
+label_cols = ['Anxiety Label', 'Stress Label', 'Depression Label']
+
+for col in label_cols:
+  data = df[col].value_counts().reset_index()
+  plt.figure(figsize=(10,6))
+  plt.title(f'{col} Distribution')
+  sns.barplot(data=data, x=col, y='count', palette='viridis', width=.4)
+  plt.xticks(rotation=45)
+  plt.xlabel('')
+  plt.ylabel('')
+  plt.show()
+
+features = df.drop(columns=value_cols+label_cols).columns
+
+for label in label_cols:
+  label_name = label.split(' ')[0]
+  print(f'\n{label_name} Analysis\n')
+  for col in features:
+    pivot_table = pd.crosstab(df[col], df[label])
+    pivot_tabel = pivot_table.div(pivot_table.sum(axis=1), axis=0) * 100
+    plt.figure(figsize=(10,8))
+    sns.heatmap(pivot_table, annot=True, fmt='.2f', cmap='YlGnBu')
+    plt.title(f'{label_name} vs {col}')
+    plt.xlabel('')
+    plt.ylabel('')
+    plt.yticks(rotation=0)
+    plt.show()
+
+from sklearn.preprocessing import OrdinalEncoder
+
+df.drop(columns=value_cols, inplace=True)
+
+ordinal_encoder = OrdinalEncoder()
+df[df.columns] = ordinal_encoder.fit_transform(df[df.columns])
+
+df.head()
+
+for label in label_cols:
+  cols = list(features).copy()
+  cols.append(label)
+  data = df[cols].corr()
+  plt.figure(figsize=(10,8))
+  sns.heatmap(data, annot=True, fmt='.3f', cmap='coolwarm')
+  plt.title(f'{label} Correlation')
+  plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+print('Model Accuracy')
+for label in label_cols:
+  X = df.drop(columns=label_cols)
+  y = df[label]
+
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 0)
+
+  model = RandomForestClassifier(random_state=0, n_estimators=30, max_depth=8)
+  model.fit(X_train, y_train)
+  preds = model.predict(X_test)
+
+  print(f'{label}: {accuracy_score(y_test, preds) * 100:.2f}%')