bilaudaud/K-Means-Clustering-Project-Private-and-public-university-students- bilaudaud/K-Means-Clustering-Project-Private-and-public-university-students-

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 3 years ago
  • Updated over 3 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
bilaudaud/K-Means-Clustering-Project-Private-and-public-university-students-
github

bilaudaud

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public.

More Repositories

1

Boson-USA-Housing-Price-Prediction

housing prices for regions in the USA
Jupyter Notebook
1
star
2

Covid19-Data-Analysis-Project-using-Tableau

Covid19 data Analysis Project using Tableau
1
star
3

End-to-end-dog-vision-Different-dog-breeds-classification-

Classify different dog breeds
Jupyter Notebook
1
star
4

End-to-end-bluebook-bulldozer-price-regression

# 🚜 Predicting the Sale Price of Bulldozers using Machine Learning In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers. Since we're trying to predict a number, this kind of problem is known as a **regression problem**. The data and evaluation metric we'll be using (root mean square log error or RMSLE) is from the [Kaggle Bluebook for Bulldozers competition](https://www.kaggle.com/c/bluebook-for-bulldozers/overview). The techniques used in here have
Jupyter Notebook
1
star
5

BREAST-CANCER-CLASSIFICATION

- Predicting if the cancer diagnosis is benign or malignant based on several observations/features - 30 features are used, examples: - radius (mean of distances from center to points on the perimeter) - texture (standard deviation of gray-scale values) - perimeter - area - smoothness (local variation in radius lengths) - compactness (perimeter^2 / area - 1.0) - concavity (severity of concave portions of the contour) - concave points (number of concave portions of the contour) - symmetry - fractal dimension ("coastline approximation" - 1) - Datasets are linearly separable using all 30 input features - Number of Instances: 569 - Class Distribution: 212 Malignant, 357 Benign - Target class: - Malignant - Benign https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Jupyter Notebook
1
star