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Karan Jakhar karanjakhar

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Computer Vision | Edge Computing | Deep Learning | Backend Developer | Robotics

karanjakhar / LinearRegression.py

Last active June 4, 2019 17:21

sample code for linear regression

	#Importing required libraries
	from sklearn.linear_model import LinearRegression
	from sklearn.datasets import california_housing
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import mean_squared_error

	#Downloading dataset
	data = california_housing.fetch_california_housing()

	#Getting target and features

karanjakhar / LinearRegression_loading_data.py

Last active June 4, 2019 20:45

Importing wine data and predicting quality

	#importing libraries
	from sklearn.linear_model import LinearRegression
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import mean_squared_error
	import pandas as pd

	#loading data
	data = pd.read_csv('path of your csv file here.csv')

	#getting information

karanjakhar / LogisticRegression.py

Last active June 15, 2019 12:20

Logistic Regression code

	#importing required libraries
	import pandas as pd
	from sklearn.linear_model import LogisticRegression
	from sklearn.model_selection import train_test_split

	#loading data from a csv file to a pandas Dataframe
	df = pd.read_csv('https://query.data.world/s/nsyvxagzhkssbiwytst5vpuvxpwgtb')

	#looking at first 5 rows of the data
	df.head()

karanjakhar / SupportVectorMachine(SVC).py

Created June 15, 2019 13:01

	#loading required libraries
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from sklearn.svm import SVC

	#loading data for classification
	df = pd.read_csv('https://query.data.world/s/67p5gkjye5vocfiqm2cuxnrkx4ijim')

	#looking at first five rows
	df.head()

karanjakhar / SupportVectorMachine(SVR).py

Created June 18, 2019 16:18

svm for regression

	#loading required libraries
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import mean_squared_error
	from sklearn.svm import SVR

	#loading data
	df = pd.read_csv('boston_train.csv')

	#looking at first five rows

karanjakhar / knn (classification).py

Last active June 21, 2019 14:38

	#importing required libraries
	from sklearn.neighbors import KNeighborsClassifier
	import pandas as pd
	from sklearn.model_selection import train_test_split

	#loading data into dataframe
	df = pd.read_csv('https://query.data.world/s/67p5gkjye5vocfiqm2cuxnrkx4ijim')

	#printig first five rows
	df.head()

karanjakhar / knn (regression).py

Created June 21, 2019 14:37

	#importing required libraries
	from sklearn.neighbors import KNeighborsRegressor
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import mean_squared_error

	#loading data for regression
	r_df = pd.read_csv('boston_train.csv')

	#printing first five rows

karanjakhar / naive bayes.py

Created June 21, 2019 14:40

	#importing required libraries
	from sklearn.naive_bayes import GaussianNB
	import pandas as pd
	from sklearn.model_selection import train_test_split

	#loading data into dataframe
	df = pd.read_csv('https://query.data.world/s/67p5gkjye5vocfiqm2cuxnrkx4ijim')

	#printig first five rows
	df.head()

karanjakhar / kmeans.py

Created July 1, 2019 16:34

k-means with dummy data.

	#importing required libraries
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.cluster import KMeans

	#creating data
	x1 = np.concatenate((np.random.normal(10,2,(100,1)),np.random.normal(20,5,(100,1))))
	x2 = np.concatenate((np.random.normal(10,2,(100,1)), np.random.normal(30,3,(100,1))))

	#visualizing the data

karanjakhar / full implementation.py

Last active July 2, 2019 04:20

Testing the result of different classifiers

	#importing required libraries
	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.linear_model import LogisticRegression
	from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
	from sklearn.ensemble import RandomForestClassifier
	from sklearn.svm import SVC
	from sklearn.naive_bayes import GaussianNB
	from sklearn.neighbors import KNeighborsClassifier