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📊 Income Analysis Project

Overview

This project analyzes income data based on demographic features such as age. The objective is to uncover patterns and insights that can help understand how factors like age impact income levels. The dataset used for this analysis contains records of 22 individuals, including their names, ages, and annual incomes.

📁 Dataset

The dataset includes the following fields:

  • Name: The individual's name.
  • Age: The individual's age.
  • Income ($): The individual's annual income in USD.

Sample Data:

Name Age Income ($)
Rob 27 70,000
Michael 29 90,000
Mohan 29 61,000
Ismail 28 60,000
Kory 42 150,000
Gautam 39 155,000
... ... ...

🔧 Requirements

To run this project, ensure you have the following installed:

- **Python 3.x**
- **Libraries**: 
  - Pandas
  - NumPy
  - Matplotlib

Install the necessary libraries using:

pip install pandas numpy matplotlib

🛠️ Methodology

Data Preparation: Cleaned the dataset by handling missing values and removing duplicates.

Exploratory Data Analysis (EDA): Explored the data distribution and visualized key patterns using various plotting techniques.

Correlation Analysis: Examined relationships between age and income to identify trends.

Modeling (if applicable): Applied regression techniques to predict income based on demographic factors.

alt text

🚀 Getting Started

To run the analysis locally, follow these steps:

Clone the repository:

bash

git clone https://github.com/Madhusudan0626/Machine-Learning-Lab

Navigate to the project directory:

cd kmeans

Open '.ipynb' in Jupyter NoteBook or Equivalent

📚 Conclusion

This analysis offers a preliminary exploration into income distribution based on age. Future work could extend this analysis with a larger, more diverse dataset and additional demographic features to draw more comprehensive conclusions.

🤝 Acknowledgments

Pandas Documentation Matplotlib Documentation Special thanks to the open-source community for their valuable tools and resources.

Feel free to reach out if you have any questions or suggestions! 😊

Enhancements Made:

  1. Professional Tone: Refined the language to sound more professional.
  2. Icons and Emojis: Added emojis to make the document visually appealing and easier to navigate.
  3. Structured Sections: Organized content into clear, concise sections for better readability.
  4. Emphasized Key Points: Used bold text to highlight key terms.
  5. Instructions and Clarity: Clarified instructions under "Getting Started" and "Requirements."

This format will make your project stand out by providing a clear, engaging overview while maintaining a professional appearance!Cleaned the dataset by handling missing values and removing duplicates.

Exploratory Data Analysis (EDA): Explored the data distribution and visualized key patterns using various plotting techniques.

Correlation Analysis: Examined relationships between age and income to identify trends.

Modeling (if applicable): Applied regression techniques to predict income based on demographic factors.

📈 Results

alt text

K-means Clustering and Elbow Method

K-means clustering is a popular unsupervised machine learning algorithm used to group data into k clusters. The key challenge in K-means is determining the optimal number of clusters (k). The Elbow Method is a heuristic used to find this optimal number.

The Elbow Method involves running the K-means algorithm with different values of k and plotting the sum of squared distances (also known as inertia) between data points and their corresponding cluster centers. The "elbow" point on the plot, where the rate of decrease sharply slows down, indicates the optimal number of clusters.

In this project, the Elbow Method was applied to determine the ideal number of clusters for the dataset. The sum of squared distances was calculated for k values ranging from 1 to 10, and the results were plotted.

The plot shows a noticeable "elbow" at k = X (replace X with your actual value), suggesting that this is the optimal number of clusters for the dataset. Adding more clusters beyond this point does not significantly reduce the sum of squared distances, indicating diminishing returns and a well-separated clustering at this k value.

Insights: Younger individuals (ages 25-35) show varied income levels. Higher income levels tend to plateau or decrease slightly beyond middle age.

Model Performance (if applicable): Evaluation Metrics: Sum of Squared Errors Method