Sparsh Sihotiya sparshs413

🏠

Working from home

sparshs413 / Codes.cpp

Last active January 17, 2021 02:40

Contains the Code snippets for Session on C++

	// Cout
	// #include <iostream>
	// using namespace std;

	// int main()
	// {
	// char sample[] = "Electronics Club";
	// cout << sample << " , IIT Kanpur";

	// return 0;

sparshs413 / KNN

Created October 3, 2020 16:05

	# Let’s start with importing libraries:
	import numpy as np
	import pandas as pd

	import matplotlib.pyplot as plt #data visualization

	from sklearn.datasets import make_blobs #synthetic dataset
	from sklearn.neighbors import KNeighborsClassifier #kNN classifier
	from sklearn.model_selection import train_test_split #train and test sets

sparshs413 / Non-Linear SVM

Created October 3, 2020 15:57

	#Non-Linear SVM Example
	#For this example, we'll use a slightly more complicated dataset to show one of the areas SVMs shine in. Let's import some packages.

	import matplotlib.pyplot as plt
	import numpy as np
	from sklearn import datasets
	from sklearn import svm
	#This set of imports is similar to those in the linear example, except it imports one more thing. Now we can use a dataset directly from the Scikit-learn library.

	# non-linear data

sparshs413 / Linear-SVM

Created October 3, 2020 15:55

	# We'll start by importing a few libraries that will make it easy to work with most machine learning projects.

	import matplotlib.pyplot as plt
	import numpy as np
	from sklearn import svm

	# For a simple linear example, we'll just make some dummy data and that will act in the place of importing a dataset.

	# linear data
	X = np.array([1, 5, 1.5, 8, 1, 9, 7, 8.7, 2.3, 5.5, 7.7, 6.1])

sparshs413 / Logistic Regression

Created October 3, 2020 15:47

	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

sparshs413 / Linear-Regression Using Scikit Learn

Last active October 3, 2020 15:20

	# imports
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.linear_model import LinearRegression
	from sklearn.metrics import mean_squared_error, r2_score

	# generate random data-set
	np.random.seed(0)
	x = np.random.rand(100, 1)
	y = 2 + 3 * x + np.random.rand(100, 1)

sparshs413 / Linear-Regression

Created October 3, 2020 15:15

	# imports
	import numpy as np


	class LinearRegressionUsingGD:
	"""Linear Regression Using Gradient Descent.
	Parameters
	----------
	eta : float
	Learning rate