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https://github.com/saro0307/unemployment-analysis
This data science project delves into unemployment trends, using data analysis and machine learning to identify key factors and predict future joblessness rates, aiding policymakers and businesses in informed decision-making.
https://github.com/saro0307/unemployment-analysis
data-science datavisualization machine-learning matplotlib numpy pandas python seaborn skit-learn
Last synced: 3 days ago
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This data science project delves into unemployment trends, using data analysis and machine learning to identify key factors and predict future joblessness rates, aiding policymakers and businesses in informed decision-making.
- Host: GitHub
- URL: https://github.com/saro0307/unemployment-analysis
- Owner: saro0307
- License: mit
- Created: 2023-09-23T13:32:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-19T11:30:50.000Z (8 months ago)
- Last Synced: 2024-03-19T12:42:41.750Z (8 months ago)
- Topics: data-science, datavisualization, machine-learning, matplotlib, numpy, pandas, python, seaborn, skit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 78.1 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Unemployment Analysis Project
This README file provides an overview of the "Unemployment Analysis" project, which was conducted using Machine Learning techniques in the Google Colaboratory environment. The project utilizes a dataset downloaded from Kaggle to analyze and understand unemployment trends.
## Table of Contents
1. [Project Overview](#project-overview)
2. [Dataset](#dataset)
3. [Notebook](#notebook)
4. [Dependencies](#dependencies)
5. [Instructions for Reproduction](#instructions-for-reproduction)
6. [Contributors](#contributors)
7. [License](#license)
---
## 1. Project Overview
The "Unemployment Analysis" project aims to analyze and gain insights into unemployment trends using Machine Learning techniques. Unemployment is a critical economic indicator that impacts individuals, communities, and entire nations. By leveraging a dataset from Kaggle, this project seeks to answer questions such as:
- What are the historical unemployment trends?
- What factors contribute to unemployment rates?
- Can we build a predictive model for unemployment rates?
The project is implemented in a Jupyter Notebook on Google Colaboratory for easy collaboration and access to powerful computing resources.
---
## 2. Dataset
The dataset used in this project can be found on Kaggle and is titled [Insert Dataset Title and Link Here]. This dataset provides historical unemployment data along with various socio-economic and demographic variables. The dataset is crucial for our analysis and model development.
---
## 3. Notebook
The core of this project is the Jupyter Notebook located in the repository. The notebook is titled `Unemployment_Analysis.ipynb` and contains all the code, analysis, and visualizations related to the project. It is organized into sections, making it easy to follow the workflow of the analysis.
---
## 4. Dependencies
To run the Jupyter Notebook successfully, you will need the following Python libraries and dependencies:
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
Ensure that you have these libraries installed in your environment before running the notebook.
---
## 5. Instructions for Reproduction
To reproduce the results of this project, follow these steps:
1. Clone this repository to your local machine using `git clone`.
2. Install the required dependencies mentioned in the `requirements.txt` file using a package manager like pip:
```
pip install -r requirements.txt
```
3. Open the Jupyter Notebook in Google Colaboratory or any other Jupyter Notebook environment.
4. Execute each cell in the notebook sequentially to replicate the analysis and generate visualizations.
5. Feel free to modify the code or experiment with different approaches to gain further insights.
6. If you wish to use a different dataset, ensure it is compatible with the notebook's structure and update the necessary code accordingly.
---
## 6. Contributions
Please feel free to contribute to this project by submitting issues, suggesting improvements, or making pull requests.
---
7. License
This project is licensed under the MIT License - see the LICENSE file for details.
The MIT License is a permissive open-source license that allows you to use, modify, and distribute the code for both commercial and non-commercial purposes. For full details, please refer to the LICENSE file in the project's root directory.