Upload application21.py

application21.py

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+from ast import increment_lineno
+from statistics import LinearRegression
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+plt.switch_backend('Agg')
+import seaborn as sns
+import warnings
+warnings.filterwarnings('ignore')
+
+sns.set(style='whitegrid')
+
+print('Import and setup completed successfully.')
+
+file_path = ''
+
+file_path = r'C:\Users\Donte Patton\Downloads\dataset_2191_sleep.csv'
+df = pd.read_csv(file_path, encoding='ascii', delimiter=',')
+
+print('Dataset loaded successfull. Showing first few rows:')
+print(df.head())
+
+print('Dataset Info:')
+df.info()
+
+print('\nMissing values in each column:')
+print(df.isnull().sum())
+
+df.dropna(inplace=True)
+print('\nDataframe shape after dropping missing values:', df.shape)
+
+# Removed Year conversion as the column doesn't exist in the dataset
+
+print('\nData types after conversion:')
+print(df.dtypes)
+
+numeric_df = df.select_dtypes(include=[np.number])
+
+if numeric_df.shape[1] >= 4:
+    plt.figure(figsize=(12, 10))
+    corr = numeric_df.corr()
+    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
+    plt.title('Correlation Heatmap of Numeric Variables')
+    plt.show()
+else:
+    print('Not enough numeric columns for a correlation heatmap.')
+
+# Using available numeric columns for pairplot
+numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
+if len(numeric_cols) > 1:
+    sns.pairplot(df[numeric_cols])
+    plt.suptitle('Pair Plot of Numeric Features', y=1.02)
+    plt.show()
+else:
+    print('Not enough numeric columns for pair plot.')
+
+# Plotting distribution of body_weight instead of CO2
+plt.figure(figsize=(8, 6))
+sns.histplot(df['body_weight'], kde=True, bins=30)
+plt.title('Distribution of Body Weight')
+plt.xlabel('Body Weight (kg)')
+plt.ylabel('Frequency')
+plt.show()
+
+# Plotting mean body weight by predation index
+plt.figure(figsize=(10, 6))
+body_weight_by_predation = df.groupby('predation_index')['body_weight'].mean().reset_index()
+sns.barplot(x='predation_index', y='body_weight', data=body_weight_by_predation, palette='viridis')
+plt.title('Average Body Weight by Predation Index')
+plt.xlabel('Predation Index')
+plt.ylabel('Average Body Weight (kg)')
+plt.show()
+
+# Create a count plot for predation_index instead of Emissions Category
+plt.figure(figsize=(8, 6))
+sns.countplot(x='predation_index', data=df, palette='Set2')
+plt.title('Count of Records by Predation Index')
+plt.xlabel('Predation Index')
+plt.ylabel('Count')
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score, mean_squared_error
+
+# Update features to use existing numeric columns
+features = ['body_weight', 'brain_weight', 'predation_index', 'sleep_exposure_index', 'danger_index']
+
+# Convert string columns to numeric where needed
+model_df = df.copy()
+
+# Convert total_sleep to numeric (it's currently an object/string)
+model_df['total_sleep'] = pd.to_numeric(model_df['total_sleep'], errors='coerce')
+
+# Drop any rows with missing values
+model_df = model_df.dropna()
+
+# Use available numeric features for prediction
+# We'll predict 'total_sleep' using other numeric features
+X = model_df[['body_weight', 'brain_weight', 'predation_index', 'sleep_exposure_index', 'danger_index']]
+y = model_df['total_sleep']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+print('Training set shape:', X_train.shape)
+print('Testing set shape:', X_test.shape)
+
+# Train the model
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Make predictions
+y_pred = regressor.predict(X_test)
+
+r2 = r2_score(y_test, y_pred)
+rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+
+print(f'R^2 score for the predictor: {r2:.3f}')
+print(f'RMSE for the predictor: {rmse:.3f}')
+
+plt.figure(figsize=(8, 6))
+plt.scatter(y_test, y_pred, alpha=0.5, color='teal')
+plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')
+plt.xlabel('Actual CO2')
+plt.ylabel('Predicted CO2')
+plt.title('Actual vs Predicted CO2 Emissions')
+plt.show()
+