https://github.com/abhinav330/unsupervised-learning-groping-of-schools

This Python notebook demonstrates an exploratory data analysis (EDA) and clustering exercise using the pandas, seaborn, and matplotlib libraries. The code works with a dataset called 'College_Data' and explores college-related attributes, including 'Private' status, graduation rates, and enrollment data.
https://github.com/abhinav330/unsupervised-learning-groping-of-schools

data-science kmeans-algorithm kmeans-clustering kmeans-clustering-algorithm machine-learning python unsupervised-learning

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This Python notebook demonstrates an exploratory data analysis (EDA) and clustering exercise using the pandas, seaborn, and matplotlib libraries. The code works with a dataset called 'College_Data' and explores college-related attributes, including 'Private' status, graduation rates, and enrollment data.

• Host: GitHub
• URL: https://github.com/abhinav330/unsupervised-learning-groping-of-schools
• Owner: Abhinav330
• Created: 2024-08-25T22:56:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-31T20:48:49.000Z (about 1 year ago)
• Last Synced: 2025-03-03T08:16:35.336Z (8 months ago)
• Topics: data-science, kmeans-algorithm, kmeans-clustering, kmeans-clustering-algorithm, machine-learning, python, unsupervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 6.56 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Unsupervised-learning-Groping-of-schools

# Code Summary

TThis Python notebook demonstrates an exploratory data analysis (EDA) and clustering exercise using the pandas, seaborn, and matplotlib libraries. The code works with a dataset called 'College_Data' and explores college-related attributes, including 'Private' status, graduation rates, and enrollment data.

## Data Exploration and Visualization

The code begins by importing the necessary libraries and loading the dataset 'College_Data' using pandas. It then conducts several data exploration and visualization tasks:

- Displays the first few rows of the dataset using `df.head()`.

- Provides dataset information using `df.info()`.

- Generates summary statistics of the dataset using `df.describe()`.

- Creates scatterplots and pairplots to visualize relationships between variables, with points color-coded by 'Private' status.

## Data Preprocessing

The code performs some data preprocessing steps, including:

- Filtering and visualizing data points where 'Grad.Rate' is greater than 100. This might be a data error, and the code appears to set the 'Grad.Rate' of 'Cazenovia College' to 100.

- Using the 'KMeans' clustering algorithm from scikit-learn to cluster colleges into two groups based on their features, excluding the 'Private' column.

- Displaying a scatterplot of 'Enroll' vs. 'F.Undergrad' with points colored by cluster labels.

## Clustering

The code uses the K-means clustering algorithm to cluster colleges into two groups based on their features. It computes cluster centers and assigns each college to a cluster label. The scatterplot at the end visualizes the clustering results.

Overall, this code showcases the use of EDA and K-means clustering for analyzing college data, with a focus on distinguishing between private and non-private institutions based on various features.