Traffic Accidents Analysis using Correlation Analysis & Feature Analysis

This project demonstrates various data preprocessing and visualization techniques using a traffic accidents dataset. The dataset is downloaded from OpenML (data_id=42803) and analyzed using Python libraries such as Pandas, Matplotlib, Seaborn, and Missingno.

Dataset

The dataset is used is here

https://www.openml.org/search?type=data&sort=runs&id=42803&status=active

Requirements

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Missingno

You can install the required libraries using pip:

pip install pandas numpy scikit-learn matplotlib seaborn missingno

Usage

1. Clone the repository:

git clone https://github.com/your-username/EDA-analysis.git

2. Navigate to the project directory:

cd EDA-analysis

3. Open the Jupyter notebook:

jupyter notebook

4. Run the notebook edamone.ipynb to see the data analysis in action.

Notebook Overview

Data Download and Basic Exploration

• The dataset is downloaded from OpenML.
• Basic exploration includes displaying random samples, dataset size, and data types.

Data Type Conversion and Descriptive Statistics

• Conversion of data types for specific columns.
• Descriptive statistics for non-numerical features.

Unique Values and Duplicates

• Computation of unique values for numerical features.
• Identification and removal of duplicate rows.

Missing Values

• Visualization and handling of missing values.

Data Visualization

• Various plots for numerical and non-numerical features.

Feature Analysis

• Analysis of numerical and categorical features.
• Identification of continuous and discrete features.
• Pair plots and strip plots.

Correlation Analysis

• Computation and visualization of correlation matrix.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to contribute to this project by submitting issues or pull requests. Happy analyzing!