Merge pull request #326 from Aditi22Bansal/main

Create code.py solved #304

Students' stress and coping model/code.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import statsmodels.api as sm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the dataset
+data = pd.read_csv('student_data.csv')
+
+# Display basic statistics
+print("Data Summary:\n", data.describe())
+
+# Check for missing values
+print("Missing Values:\n", data.isnull().sum())
+
+# Fill missing values (if any) with the mean of the column
+data.fillna(data.mean(), inplace=True)
+
+# Feature scaling
+scaler = StandardScaler()
+scaled_features = scaler.fit_transform(data.drop(['mental_health', 'academic_performance', 'physical_health'], axis=1))
+
+# Convert scaled features back to a DataFrame
+scaled_data = pd.DataFrame(scaled_features, columns=data.columns[:-3])
+
+# Add the outcome columns back to the scaled DataFrame
+scaled_data[['mental_health', 'academic_performance', 'physical_health']] = data[['mental_health', 'academic_performance', 'physical_health']]
+
+# Split the data into training and testing sets
+train_data, test_data = train_test_split(scaled_data, test_size=0.3, random_state=42)
+
+# Separate features and targets
+X_train = train_data.drop(['mental_health', 'academic_performance', 'physical_health'], axis=1)
+Y_train = train_data[['mental_health', 'academic_performance', 'physical_health']]
+X_test = test_data.drop(['mental_health', 'academic_performance', 'physical_health'], axis=1)
+Y_test = test_data[['mental_health', 'academic_performance', 'physical_health']]
+
+# Add a constant to the model (for the intercept)
+X_train_const = sm.add_constant(X_train)
+X_test_const = sm.add_constant(X_test)
+
+# Fit the model for mental health
+model_mental_health = sm.OLS(Y_train['mental_health'], X_train_const).fit()
+print("Mental Health Model Summary:\n", model_mental_health.summary())
+
+# Fit the model for academic performance
+model_academic_performance = sm.OLS(Y_train['academic_performance'], X_train_const).fit()
+print("Academic Performance Model Summary:\n", model_academic_performance.summary())
+
+# Fit the model for physical health
+model_physical_health = sm.OLS(Y_train['physical_health'], X_train_const).fit()
+print("Physical Health Model Summary:\n", model_physical_health.summary())
+
+# Predictions
+Y_pred_mental_health = model_mental_health.predict(X_test_const)
+Y_pred_academic_performance = model_academic_performance.predict(X_test_const)
+Y_pred_physical_health = model_physical_health.predict(X_test_const)
+
+# Evaluate the model
+def evaluate_model(Y_true, Y_pred, label):
+ mse = np.mean((Y_true - Y_pred) ** 2)
+ print(f"{label} MSE: {mse}")
+
+evaluate_model(Y_test['mental_health'], Y_pred_mental_health, "Mental Health")
+evaluate_model(Y_test['academic_performance'], Y_pred_academic_performance, "Academic Performance")
+evaluate_model(Y_test['physical_health'], Y_pred_physical_health, "Physical Health")
+
+# Plotting predictions vs actuals
+def plot_predictions(Y_true, Y_pred, label):
+ plt.figure(figsize=(10, 6))
+ plt.scatter(Y_true, Y_pred)
+ plt.plot([Y_true.min(), Y_true.max()], [Y_true.min(), Y_true.max()], 'k--', lw=2)
+ plt.xlabel('Actual')
+ plt.ylabel('Predicted')
+ plt.title(f'{label} - Actual vs Predicted')
+ plt.show()
+
+plot_predictions(Y_test['mental_health'], Y_pred_mental_health, "Mental Health")
+plot_predictions(Y_test['academic_performance'], Y_pred_academic_performance, "Academic Performance")
+plot_predictions(Y_test['physical_health'], Y_pred_physical_health, "Physical Health")