Merge pull request #486 from adi271001/Nuclear-Weapons-Analysis

Nuclear weapons analysis

Nuclear Weapons Analysis/Dataset/README.md

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+# Nuclear Weapons Datasets
+
+## Overview
+
+This repository contains datasets related to nuclear weapons, providing valuable insights into various aspects such as historical trends, stockpiles, tests, and considerations by different entities. The datasets have been curated for analysis and modeling purposes.
+## Link to Dataset
+1. https://www.kaggle.com/datasets/michaelbryantds/nuclear-weapons-dataset/
+
+## Datasets
+
+### 1. Nuclear Weapons Distribution Over Time (df1.csv)
+
+- **Columns:**
+  - `country_name`: Name of the country.
+  - `year`: Year of observation.
+  - `nuclear_weapons_status`: Current status of nuclear weapons.
+  - `nuclear_weapons_consideration`: Consideration of nuclear weapons.
+  - `nuclear_weapons_pursuit`: Pursuit of nuclear weapons.
+  - `nuclear_weapons_possession`: Possession of nuclear weapons.
+
+### 2. Relationship Between Features (df2.csv)
+
+- **Columns:**
+  - `entity_name`: Name of the entity.
+  - `year`: Year of observation.
+  - `number_nuclweap_consideration`: Number of nuclear weapons under consideration.
+  - `number_nuclweap_pursuit`: Number of nuclear weapons under pursuit.
+  - `number_nuclweap_possession`: Number of nuclear weapons in possession.
+
+### 3. Nuclear Weapons Stockpile Trend (df3.csv)
+
+- **Columns:**
+  - `country_name`: Name of the country.
+  - `year`: Year of observation.
+  - `nuclear_weapons_stockpile`: Number of nuclear weapons in the stockpile.
+
+### 4. Nuclear Weapons Tests Over Time (df4.csv)
+
+- **Columns:**
+  - `country_name`: Name of the country.
+  - `year`: Year of observation.
+  - `nuclear_weapons_tests`: Number of nuclear weapons tests.
+
+## Usage
+
+These datasets are valuable for exploratory data analysis (EDA), clustering, and predictive modeling. The associated code in this repository demonstrates how to perform EDA, clustering using K-Means, t-tests, PCA, and build machine learning models.
+
+
+
+## Contributions
+
+Contributions to enhance the datasets or provide additional insights are welcome. Feel free to create issues or pull requests.
+
+## Acknowledgments
+
+Special thanks to Kaggle for providing these datasets.
+