Sneha Pandey

Import a word using Word2Vec and show how it performs in terms of the similarity?

For Wordage, we need to import the library from the gen sim.Here are some points sentences: the list of split sentences. size: the dimensionality of the embedding vector window the number of context words you are looking at mincount: tells the model to ignore words wit

Data Science

Answered Dec 24, 2019 · 1.1K Views Read answer →

Explain with a case study how to visualize a KNN classification model in R.

First we import the data and convert the target feature as factor # Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] Now we split, scale and fit the data into a KNN model # Splitting the dataset into the Training set and Test set # install.packages('caTools') library(caTools)…

Data Science

Answered Dec 20, 2019 · 1.4K Views Read answer →

How to create a basic dataframe using R?

A data frame is a 2 dimensional structure containing data in the form of rows and columns. A data frame is used to easily represent data in the form of spreadsheet and can be easily accessed in every algorithm. Also a dataframe contains a series of dictionaries with particular keys and values that are represented in…

Data Science

Answered Dec 10, 2019 · 1.4K Views Read answer →

A user tries to run logistic regression on my data (6 categorical, 1 integer) using scikit learn. He is following the scikit learn documentation but when trying to fit my data he is getting the…

#Below are the variables of my data. train_data.dtypes OUTPUT TripType category VisitNumber category Weekday category Upc category ScanCount int64

Data Science

Answered Nov 30, 2019 · 1.4K Views Read answer →

How can ROC and AUC can help us evaluate our model?

ROC curve can give us a clear idea to set a threshold value to classify the label and also help in model optimization. A low threshold value we will put most of the predicted observations under the positive category, even when some of them should be placed under the negative category. On the other hand, keeping the…

Data Science

Answered Nov 30, 2019 · 1.1K Views Read answer →

Explain maximum likelihood estimation.

Maximum likelihood estimation is a method of estimating the parameters of a model given observations, by finding the parameter values that maximize the likelihood of making the observations, this means finding parameters that maximize the probability p of event 1 and (1-p) of non-event 0, as we know: probability…

Data Science

Answered Nov 28, 2019 · 1.3K Views Read answer →

A text data requires preprocessing and it contains non-alphanumeric symbols. How to remove them?

To remove non alphanumeric symbols from text data, regex is used. A regex basically used .compile() method to remove all symbols and sub() method to separate all the words with spaces. Suppose a text contains the following A B C D ,5 .. AAA55AAA aaa.bbb.ccc And the output should be 'A' 'B' 'C' 'D' 'AAA' 'AAA' 'aaa'…

Data Science

Answered Nov 12, 2019 · 1.4K Views Read answer →

Explain implementation of decision tree regression in Python

First we import the data # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Position_Salaries.csv') X = dataset.iloc[:, 1:2].values y = dataset.iloc[:, 2].values Then we split the data # Splitting the dataset into the Training…

Data Science

Answered Nov 9, 2019 · 1.4K Views Read answer →

Explain Gini in decision tree algorithm

It is an impurity which is also a measure of misclassification which applies in a context of multi class classifier. It works similar to entropy but is quicker and easy to calculate. It works on the blow formula.Where i=number of classes. The similarity between Gini and entropy is shown below

Data Science

Answered Nov 7, 2019 · 1.1K Views Read answer →

A user runs the following script and receives an error. How to fix that?

import pandas as pd from sklearn.tree import DecisionTreeClassifier data = pd.DataFrame() data['A'] = ['a','a','b','a'] data['B'] = ['b','b','a','b'] data['C'] = [0, 0, 1, 0] data['Class'] = ['n','n','y','n'] tree =

Data Science

Answered Nov 4, 2019 · 1.0K Views Read answer →