1812_159_252.py

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}%')