rizolli2000/indian-road-accidents-analysis

Indian-Road-Accidents-analysis

Sure, here's an extended version of your project description that includes the information about using SQL in your project. Feel free to adjust it according to your project's specifics:

Indian Road Accidents Analysis This project focuses on an in-depth analysis of road accident data in India, utilizing Python and SQL to uncover insights, trends, and contributing factors to road accidents. The project aims to contribute to road safety improvements by identifying actionable recommendations based on data-driven analysis.

Project Objectives The main objectives of the project include:

Data Collection: The project collects road accident data from various open sources, such as Kaggle, to create a comprehensive dataset. The dataset includes information about road accidents, road types, vehicle details, and related factors.

Data Cleaning and Preparation: The collected data undergoes thorough preprocessing to handle missing values, outliers, and inconsistencies. Data transformations and feature engineering are performed to make the dataset suitable for analysis.

Exploratory Data Analysis (EDA): EDA is conducted to gain valuable insights into the nature of road accidents in India. Through visualizations, statistical summaries, and correlation analyses, the project identifies patterns, trends, and relationships between different variables.

Feature Extraction: Relevant features are extracted from the dataset that could potentially contribute to road accidents. These features encompass factors such as road conditions, vehicle speeds, driver ages, and the presence of traffic signals.

Predictive Modeling: Utilizing machine learning algorithms and SQL queries, the project builds predictive models to identify factors that significantly influence road accidents. Regression, classification, and clustering techniques are employed based on analysis goals.

Visualization: Interactive visualizations are created using Python libraries like Matplotlib, Seaborn, and Plotly. These visualizations effectively communicate the results of the analysis, making insights accessible to a wider audience.

Recommendations: Building on analysis findings, the project formulates actionable recommendations and potential solutions to address road accidents in India. These suggestions may span infrastructure enhancements, awareness campaigns, stringent law enforcement, or driver education initiatives.

Integration of SQL: The project also incorporates SQL into the analysis. SQL queries are employed to manipulate and extract relevant data from the dataset, enhancing the analysis's depth and accuracy.