Month: February 2021

Implementing KMeans Clustering in Python

Created By: Debasis Das(23-Feb-2021) In this post we will see how KMeans clustering works by using sklearn KMeans Cluster and by manually writing the KMeans Clustering logic and comparing the results. The manual approach code is a sample to demonstrate

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Pandas DataFrame – Pivot, Groupby, Filter, Query

Created By: Debasis Das (18-Feb-2021) http://www.knowstack.com/notebooks/DataFrame_Pivot_GroupBy_Filter.html In this post we will explore the following DataFrame functions pivot_table groupby filter query import pandas as pd import numpy as np df = pd.read_csv(“SalesData.csv”,low_memory=False) df The working DataFrame is as below and we

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Pandas DataFrame – Different ways to Create and Edit

By Debasis Das (17-Feb-2021) In this post we will see Different ways of creating a pandas DataFrame and editing it Lets first import the Python Pandas and numpy module import pandas as pd import numpy as np import random pd.set_option(‘display.width’,

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KMeans Clustering

By: Debasis Das (17-Feb-2021) KMeans Clustering using SKLearn Plotting the cluster centroid with the cluster points import pandas as pd import numpy as np from sklearn.cluster import KMeans from sklearn.decomposition import PCA import matplotlib.pyplot as plt A = [ [10,10],

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SSE Calculation for a Clustering

By Debasis Das: (17-Feb-2021) How to manually calculate the SSE for a Clustering. Clustering such as KMeans has a inertia_ function that gives the total SSE for the clustering, however clustering such as DBScan lacks an inertia_ function and in

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DBScan Clustering Sample

Written By: Debasis Das  In this post we will generate a DBScan cluster using SKLearn DBSCAN module and will generate the following List of noise/outlier points (not readily available in DBSCAN model output) Index of noise/outlier points View the clusters

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