Suicide Rate Analysis

This repository contains a comprehensive data analysis project focused on the suicide rates in India, with an emphasis on statistical and predictive modeling. The analysis is based on the Indian Suicide Rates dataset, aiming to uncover patterns and contributing factors, as well as develop predictive models. This project uses various data analysis and statistical techniques including descriptive statistics, chi-square tests, and logistic regression models.

Repository Structure

Notebooks

1. Binary Logistic Regression.ipynb
   This notebook implements a binary logistic regression model to predict factors associated with suicide rates. The model assesses significant predictors and provides insights into the likelihood of suicide occurrences based on demographic and socio-economic features.

2. Chi Square Test.ipynb
   This notebook performs a chi-square test to examine the association between categorical variables within the dataset. It explores relationships between different features, identifying significant dependencies that may contribute to suicide rates.

3. Descriptive Statistical Data Analysis.ipynb
   A foundational analysis of the dataset through descriptive statistics, providing an overview of key variables and trends within the data. This notebook includes visualizations to illustrate distribution patterns and central tendencies of various features related to suicide rates.

4. Logistic Regression.ipynb
   This notebook presents another logistic regression model with feature selection and interpretation steps specific to the dataset. It helps to understand the risk factors in suicide rates across different population segments and locations within India.

Dataset

Dataset

The dataset used in this project contains official suicide statistics collected across various Indian states and demographic groups. It includes attributes such as age group, gender, state, year, means adopted, education status, and profession, which are essential for exploring patterns and building predictive models.

Getting Started

1. Clone this repository to your local environment.
2. Install the required packages as specified in requirements.txt.
3. Open each notebook in Jupyter or a similar environment, and follow the analysis flow provided within each notebook.

Objective

The primary objective of this project is to gain a deeper understanding of the underlying factors influencing suicide rates in India through both descriptive and predictive approaches. The findings are intended to aid policymakers and researchers in identifying high-risk factors and regions that may benefit from targeted intervention.

Contact

For any queries or further information, feel free to reach out to the project contributors.