Tejas Chechar tejaschechar

Machine Learning, Deep Learning, AWS.

tejaschechar / building_kmeans.py

Created June 28, 2023 04:57 — forked from aishwarya-singh25/building_kmeans.py

Gaussian Mixture Models Implementation

	#training k-means model
	from sklearn.cluster import KMeans
	kmeans = KMeans(n_clusters=4)
	kmeans.fit(data)

	#predictions from kmeans
	pred = kmeans.predict(data)
	frame = pd.DataFrame(data)
	frame['cluster'] = pred
	frame.columns = ['Weight', 'Height', 'cluster']

tejaschechar / Logistic Regression

Created April 30, 2023 05:04 — forked from sparshs413/Logistic Regression

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt

	#Loading dataset – User_Data
	dataset = pd.read_csv('...\\User_Data.csv')

	#Now, to predict whether a user will purchase the product or not, one needs to find out the relationship between Age and Estimated Salary. Here User ID and Gender are not important factors for finding out this.
	# input
	x = dataset.iloc[:, [2, 3]].values

tejaschechar / Logistic Regression

Created April 30, 2023 05:04 — forked from sparshs413/Logistic Regression

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt

	#Loading dataset – User_Data
	dataset = pd.read_csv('...\\User_Data.csv')

	#Now, to predict whether a user will purchase the product or not, one needs to find out the relationship between Age and Estimated Salary. Here User ID and Gender are not important factors for finding out this.
	# input
	x = dataset.iloc[:, [2, 3]].values