This project involves the analysis of country data using machine learning clustering algorithms. The dataset used contains information about various countries, including socio-economic indicators such as GDP per capita, income, child mortality rate, and life expectancy.
Python 3.x Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn Setup: Clone the repository to your local machine. Ensure that you have Python and the required libraries installed. Install the dependencies using the following command: Copy code pip install -r requirements.txt Make sure to have the 'Country-data.csv' file in the project directory.
The 'country_data_analysis.py' script contains the code for data loading, preprocessing, exploratory data analysis, and clustering. It imports necessary libraries and loads the dataset using pandas.
The data is preprocessed by handling missing values and scaling numeric features.
Exploratory data analysis includes summary statistics, correlation analysis, and visualization using seaborn and matplotlib.
K-Means clustering and Hierarchical clustering algorithms are applied to the dataset.
Results from both clustering algorithms are compared and visualized to understand the cluster distributions.
The analysis provides insights into the socio-economic characteristics of different countries.
Clustering results help in identifying similar groups of countries based on their features.
Use the visualizations and analysis to draw conclusions and make further decisions.
Make sure to have the required permissions to access the dataset and install dependencies.
Feel free to modify the code and experiment with different clustering algorithms or features for analysis.
Author: Kedhareswer Naidu
Contact: For any inquiries or suggestions, please contact (https://www.linkedin.com/in/kedhareswer-naidu/)