Upload 1077_252_49.py

1077_252_49.py

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+# -*- coding: utf-8 -*-
+"""1077_252_49
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Oc-ciFRATiivfVFWe8Dd7m5LvNeQIQZT
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import warnings
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+# %matplotlib inline
+
+import seaborn as sns
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.inspection import permutation_importance
+
+sns.set(style='whitegrid')
+
+plt.switch_backend('Agg')
+
+file_path = '/content/air_quality_health_dataset.csv'
+try:
+  df = pd.read_csv(file_path, encoding='ISO-8859-1', delimiter=',')
+  print('Data loaded successfully.')
+except Expection as e:
+  print('Error loading data:', e)
+
+df.head()
+
+missing_values = df.isnull().sum()
+print('Missing values in each column:')
+print(missing_values)
+
+# Handling missing values (basic strategy): drop rows with critical missing data
+df.dropna(subset=['date', 'aqi', 'pm2_5', 'hospital_admissions'], inplace=True)
+
+# Convert population_density to a numeric value if possible; if not possible, leave it as is
+try:
+    df['population_density_numeric'] = pd.to_numeric(df['population_density'], errors='coerce')
+    print('Converted population_density to numeric where possible.')
+except Exception as e:
+    print('Error converting population_density:', e)
+
+# For any remaining non-numeric entries in population_density_numeric, we can fill them with the median
+if 'population_density_numeric' in df.columns:
+    median_val = df['population_density_numeric'].median()
+    df['population_density_numeric'].fillna(median_val, inplace=True)
+
+# Final sanity check
+print('Data types after processing:')
+print(df.dtypes)
+
+numeric_df = df.select_dtypes(include=[np.number])
+
+if numeric_df.shape[1] >= 4:
+  plt.figure(figsize=(10, 8))
+  corr = numeric_df.corr()
+  sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
+  plt.title('Correlation Heatmap')
+  plt.tight_layout()
+  plt.savefig('correlation_heatmap.png')
+  plt.show()
+
+else:
+  print('Not enough numeric columns for a correlation heatmap.')
+
+sns.pairplot(df[['aqi', 'pm2_5', 'pm10', 'hospital_admissions']])
+plt.suptitle('Pair Plot of Selected Variables', y=1.02)
+plt.savefig('pairplot.png')
+plt.show()
+
+numeric_cols = numeric_df.columns
+for col in numeric_cols:
+  plt.figure(figsize=(6, 4))
+  sns.histplot(df[col].dropna(), kde=True, bins=30)
+  plt.title(f'Histogram of {col}')
+  plt.savefig(f'histogram_{col}.png')
+  plt.show()
+
+plt.figure(figsize=(10, 6))
+sns.countplot(data=df, x='city', order=df['city'].value_counts().index)
+plt.title('Count Plot of Cities')
+plt.xticks(rotation=45, ha='right')
+plt.savefig('countplot_cities.png')
+plt.show()
+
+target = 'hospital_admissions'
+features = ['aqi', 'pm2_5', 'pm10', 'no2', 'o3', 'temperature', 'humidity', 'hospital_capacity']
+
+model_df = df.dropna(subset=features + [target]).copy()
+
+X = model_df[features]
+y = model_df[target]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+print(f'Mean Squared Error: {mse:2f}')
+print(f'R*2 Score: {r2:.2F}')
+
+perm_importance = permutation_importance(model, X_test, y_test, n_repeats=10, random_state=42)
+
+feature_importance = pd.Series(perm_importance.importances_mean, index=features)
+feature_importance = feature_importance.sort_values()
+
+plt.figure(figsize=(8,6))
+plt.barh(feature_importance.index, feature_importance.values, color='skyblue')
+plt.xlabel('Permutation Importance')
+plt.title('Feature Importance')
+plt.savefig('feature_importance.png')
+plt.show()