{"id":24274406,"url":"https://github.com/usk2003/income-testing-hypothesis","last_synced_at":"2026-05-15T20:31:36.742Z","repository":{"id":271931662,"uuid":"907535252","full_name":"usk2003/Income-Testing-Hypothesis","owner":"usk2003","description":" 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! 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The dataset includes salary information from various companies, and we aim to provide actionable insights for freshers looking for job opportunities. 💼\n\n## 📊 Project Overview\n\n1. **Data Cleaning** 🧹  \n   - Cleaning salary columns (`Average`, `Lowest`, `Highest`).\n   - Filtering data based on frequency `/yr`.\n   - Removing outliers using the IQR method.\n\n2. **Statistical Analysis** 🧮  \n   - Calculation of population and sample statistics (mean, standard deviation).\n   - Hypothesis testing:\n     - Two-tailed t-test.\n     - One-tailed t-tests (greater/less).\n\n3. **Visualizations** 📈  \n   - Normal distribution plots for population and sample salaries.\n   - Scatter plots: Rating vs. Average Salary for population and sample.\n\n4. **Company Suggestions** 🏢  \n   - Suggesting companies based on user-specified expected salary.\n\n5. **Conclusions and Practical Advice** 📝  \n   - Insights derived from hypothesis testing.\n   - Tips for freshers choosing companies based on salary and ratings.\n\n## 💻 Prerequisites\n\n- Python 3.12\n- Libraries: pandas, numpy, matplotlib, seaborn, scipy\n\n## 🌟 Key Features\n\n- Data cleaning and preprocessing.\n- Statistical analysis using t-tests.\n- Visualizations for clear data interpretation.\n- Company recommendations based on salary expectations.\n\n## 📊 Visualizations\n\n### 1. Normal Distribution Plot\nA comparison of the population and sample average salary distributions.\n\n### 2. Scatter Plot: Rating vs. Average Salary\nInsights into how company ratings relate to salaries.\n\n## 🏆 Results and Conclusions\n\n- Statistical tests reveal whether sample salaries significantly differ from the population mean.\n- Practical advice provided for freshers based on salary expectations and company ratings.\n\n## 💡 Suggested Companies for Freshers\n\nEnter your expected salary during the script execution to get a list of recommended companies that meet your salary criteria. 💰\n\n## 📬 Contact\n\nFor any questions or issues, feel free to reach out via email: [urlanasureshkumar@gmail.com] ✉️\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fusk2003%2Fincome-testing-hypothesis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fusk2003%2Fincome-testing-hypothesis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fusk2003%2Fincome-testing-hypothesis/lists"}