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https://github.com/yash22222/analysis-of-amcat-aspiring-minds

Analyzing employment outcomes for engineering graduates based on a provided dataset. Specifically, it aims to verify a claim regarding the salary range for fresh graduates in certain engineering roles and investigate potential relationships between gender and specialization preferences.
https://github.com/yash22222/analysis-of-amcat-aspiring-minds

amcat aspiring-minds charts computer-engineering data-analytics data-vizualisation dataset graph machine-learning matplotlib numpy pandas python seaborn

Last synced: 4 days ago
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Host: GitHub
URL: https://github.com/yash22222/analysis-of-amcat-aspiring-minds
Owner: Yash22222
Created: 2024-02-18T11:11:41.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-02-18T17:58:26.000Z (9 months ago)
Last Synced: 2024-02-19T12:36:23.260Z (9 months ago)
Topics: amcat, aspiring-minds, charts, computer-engineering, data-analytics, data-vizualisation, dataset, graph, machine-learning, matplotlib, numpy, pandas, python, seaborn
Language: Jupyter Notebook
Homepage: https://yashashokshirsath.netlify.app/
Size: 4.03 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# Engineering Graduates Employment Analysis

## Overview

This project aims to analyze employment outcomes for engineering graduates using Python. The dataset contains information on salaries, job titles, gender, specialization, and other relevant variables. The analysis focuses on verifying claims regarding salary ranges for specific engineering roles and investigating potential relationships between gender and specialization preferences. Insights gained from this analysis can inform decision-making processes in the engineering industry and contribute to promoting diversity and inclusion.

## Dataset

The dataset used for this analysis is sourced from [Aspiring Minds Employment Outcome 2015 (AMEO)](https://dl.acm.org/doi/10.1145/2888451.2892037). It contains around 40 independent variables and 4000 data points, primarily limited to students with engineering disciplines. The variables include continuous and categorical features such as salary, job titles, gender, specialization, College CGPA, and more.

## Analysis Steps

1. **Introduction**: Provides a detailed overview of the dataset and project objectives.
2. **Data Import and Description**: Loads the dataset, displays its head, and shape, and describes the data.
3. **Univariate Analysis**: Conducts exploratory analysis on individual variables, including histograms, boxplots, and countplots.
4. **Bivariate Analysis**: Explores relationships between variables using scatter plots, bar plots, and stacked bar plots.
5. **Research Questions**: Investigate specific research questions related to salary claims and gender-specialization relationships.
6. **Conclusion**: Summarizes key findings and insights from the analysis.
7. **Bonus**: Proposes additional research questions for further exploration.

## Getting Started

To run the analysis:

1. Clone the repository:

```bash
git clone https://github.com/Yash22222/ANALYSIS-OF-AMCAT-ASPIRING-MINDS.git
```

2. Install the required dependencies:

```bash
pip install -r requirements.txt
```

3. Run the Jupyter Notebook:

```bash
jupyter notebook AMCAT_ANALYSIS.ipynb
```

## Contributors

- [Yash Ashok Shirsath](https://yashashokshirsath.netlify.app/)

## License

This project is licensed under the [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT).

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