Mental Health Prediction using Machine Learning

This project aims to predict mental health outcomes using various machine learning algorithms. The goal is to explore and compare the effectiveness of multiple models in predicting mental health conditions based on various factors. The following machine learning algorithms are implemented:

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Random Forest
• Bagging
• Boosting

Project Overview

Mental health is an important issue, and early intervention can have a significant impact on improving treatment outcomes. This project uses machine learning techniques to predict the likelihood of mental health conditions, based on demographic, behavioral, and health-related data.

Dataset

The dataset used for this project contains multiple features (e.g., age, gender, lifestyle factors) that are analyzed to predict the mental health status of individuals. The data is preprocessed and cleaned before training the models, with steps like handling missing values, encoding categorical features, and normalizing numerical values.

Algorithms Used

1. Logistic Regression

Logistic Regression is a linear model used for binary classification tasks, predicting the presence or absence of mental health conditions.

2. K-Nearest Neighbors (KNN)

KNN is a non-parametric classification algorithm that classifies data based on the majority class of its nearest neighbors.

3. Random Forest

Random Forest is an ensemble method that uses multiple decision trees to improve prediction accuracy and reduce overfitting.

4. Bagging

Bagging (Bootstrap Aggregating) improves the performance of machine learning models by creating multiple versions of the same model using different subsets of the training data.

5. Boosting

Boosting is an ensemble technique that trains models sequentially, each focusing on correcting errors made by the previous model, improving overall model performance.

Features

• Data Preprocessing: Missing values are handled, categorical features are encoded, and numerical features are normalized.
• Model Comparison: Evaluates the performance of the five algorithms based on various metrics such as accuracy, precision, recall, and F1-score.
• Model Evaluation: Visualizations and performance metrics are used to compare the effectiveness of each model.