Saurabh620 / Mental_Health_Analysis_Machine-_Learning

Analyzing a dataset to understand mental health factors, this project employs Python tools for preprocessing, exploration, segmentation, trend analysis, and modeling.

Mental_Health_Analysis_Machine-_Learning

Project Description

The Mental Health Analysis project aims to explore and analyze mental health-related data to gain insights into various factors that might influence mental health outcomes. By leveraging data analysis and machine learning techniques, the project seeks to identify patterns, trends, and potential predictive factors associated with mental health conditions. The project is designed to provide valuable insights for mental health professionals, policymakers, and individuals seeking to understand and address mental health challenges effectively.

Key Features

Data preprocessing:

Includes data cleaning, handling missing values, and encoding categorical variables.

Exploratory data analysis (EDA):

Visualizes and analyzes the distribution and relationships between different features in the dataset.

Segmentation analysis:

Utilizes clustering techniques to segment the data into meaningful groups based on specific characteristics.

Trend analysis:

Examines temporal trends and patterns in mental health data over time.