# Behavioural Model on solving mental depression

## Description

This code performs an analysis of the dataset containing information about root causes of depression among college students. Here's a breakdown of what each part of the code does: Loading the Dataset: The code uses Pandas to read a CSV file named 'root_causes_depression_data.csv' into a DataFrame named df. This dataset likely contains information about various factors contributing to depression among college students. Computing the Correlation Matrix: The code computes the correlation matrix of the DataFrame df using the corr() method. This matrix provides insights into the relationships between different variables in the dataset. Plotting the Correlation Matrix: It uses Seaborn and Matplotlib to create a heatmap visualization of the correlation matrix. The heatmap annotates each cell with the correlation coefficient, providing a visual representation of the strength and direction of correlations between pairs of variables. Generating Scatter Plots: For each pair of variables in the dataset, the code generates scatter plots using Seaborn's scatterplot function. These scatter plots visually represent the relationship between each pair of variables, helping to identify potential patterns or trends. Performing Statistical Analysis: Finally, the code conducts statistical analysis for each pair of variables. It calculates correlation coefficients and p-values using the pearsonr function from the scipy.stats module. These values quantify the strength and significance of the linear relationship between pairs of variables. Overall, this code provides a comprehensive analysis of the dataset, including visualizations of correlations and scatter plots, as well as statistical insights into the relationships between different root causes of depression among college students.

## Files

## Steps to reproduce

import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt # Load the dataset df = pd.read_csv('root_causes_depression_data.csv') # Compute the correlation matrix corr_matrix = df.corr() # Plot correlation matrix plt.figure(figsize=(12, 10)) sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=0.5) plt.title('Correlation Matrix of Root Causes of Depression') plt.show() # Generate scatter plots for each pair of variables for i, col1 in enumerate(df.columns): for j, col2 in enumerate(df.columns): if i != j: plt.figure(figsize=(6, 4)) sns.scatterplot(x=df[col1], y=df[col2]) plt.title(f'Scatter Plot: {col1} vs {col2}') plt.xlabel(col1) plt.ylabel(col2) plt.grid(True) plt.show() # Perform statistical analysis for each pair of variables for i, col1 in enumerate(df.columns): for j, col2 in enumerate(df.columns): if i != j: # Perform statistical analysis here # For example, you can calculate correlation coefficient, p-value, etc. corr_coefficient, p_value = stats.pearsonr(df[col1], df[col2]) print(f"Correlation between {col1} and {col2}: {corr_coefficient:.2f}, p-value: {p_value:.2f}")