Project title: Prediction using Unsupervised ML
The Project link is:
Description
In this project, I tried to find optimum number of clusters in an Iris dataset using K_Means. I used libraries like pandas, matplotlib, seaborn and numpy.
About the Dataset
The given dataset was given as a task from the Sparks Foundation https://www.thesparksfoundationsingapore.org/. With this dataset I am tried to find the optimum number of clusters for K_Means and then value of K for K_Means classification. The name of the Dataset used for this project is Iris.csv. There are 150 rows and 6 columns with each column containing data about Sepal length and widhth; Petal length and width and Species. .
I have used be using Python 3 for this analysis. The Libraries/Packages I used in this projects are as follows:
- numpy (as np is one of the very famous packages for working with arrays in python)
- pandas (Is greatly used in analysis of data and making dataframe)
- matplotlib (Lets make our Analyzation fun and interative with the visualization library matplotlib)
- seaborn (Adding more colours into matplotlib visualization)