https://github.com/usk2003/income-testing-hypothesis

This repository analyzes data scientists' income using t-tests, providing insights into salary distributions and company ratings. Key features include data cleaning, statistical analysis, visualizations, and company suggestions for freshers. Practical advice helps guide career decisions effectively!
https://github.com/usk2003/income-testing-hypothesis

hypothesis-testing matplotlib pandas python statistical-analysis t-test

Last synced: 3 months ago
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This repository analyzes data scientists' income using t-tests, providing insights into salary distributions and company ratings. Key features include data cleaning, statistical analysis, visualizations, and company suggestions for freshers. Practical advice helps guide career decisions effectively!

• Host: GitHub
• URL: https://github.com/usk2003/income-testing-hypothesis
• Owner: usk2003
• Created: 2024-12-23T20:10:06.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-01-10T19:59:57.000Z (4 months ago)
• Last Synced: 2025-01-10T20:35:50.655Z (4 months ago)
• Topics: hypothesis-testing, matplotlib, pandas, python, statistical-analysis, t-test
• Language: Python
• Homepage:
• Size: 0 Bytes
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🚀 Data Scientist Income T-Test Hypothesis Analysis

This project involves analyzing and testing hypotheses regarding data scientists' income using statistical methods, specifically t-tests. The dataset includes salary information from various companies, and we aim to provide actionable insights for freshers looking for job opportunities. 💼

## 📊 Project Overview

1. **Data Cleaning** 🧹  

   - Cleaning salary columns (`Average`, `Lowest`, `Highest`).

   - Filtering data based on frequency `/yr`.

   - Removing outliers using the IQR method.

2. **Statistical Analysis** 🧮  

   - Calculation of population and sample statistics (mean, standard deviation).

   - Hypothesis testing:

     - Two-tailed t-test.

     - One-tailed t-tests (greater/less).

3. **Visualizations** 📈  

   - Normal distribution plots for population and sample salaries.

   - Scatter plots: Rating vs. Average Salary for population and sample.

4. **Company Suggestions** 🏢  

   - Suggesting companies based on user-specified expected salary.

5. **Conclusions and Practical Advice** 📝  

   - Insights derived from hypothesis testing.

   - Tips for freshers choosing companies based on salary and ratings.

## 💻 Prerequisites

- Python 3.12

- Libraries: pandas, numpy, matplotlib, seaborn, scipy

## 🌟 Key Features

- Data cleaning and preprocessing.

- Statistical analysis using t-tests.

- Visualizations for clear data interpretation.

- Company recommendations based on salary expectations.

## 📊 Visualizations

### 1. Normal Distribution Plot

A comparison of the population and sample average salary distributions.

### 2. Scatter Plot: Rating vs. Average Salary

Insights into how company ratings relate to salaries.

## 🏆 Results and Conclusions

- Statistical tests reveal whether sample salaries significantly differ from the population mean.

- Practical advice provided for freshers based on salary expectations and company ratings.

## 💡 Suggested Companies for Freshers

Enter your expected salary during the script execution to get a list of recommended companies that meet your salary criteria. 💰

## 📬 Contact

For any questions or issues, feel free to reach out via email: [[email protected]] ✉️