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05: Linear regression outputs, null hypothesis, t-test & p-value interview Q&As

Q1. How do you produce & interpret Linear Regression output? A1. Scatter plots can only detect obvious relationships between variables by looking at the graph, but we can use statistics to comment about the variable relationships as outlined below. The link 11A: Databricks – Spark ML – Pandas Dataframe &...

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11A: Databricks – Spark ML – Pandas Dataframe & Matplotlib

Prerequisite: Extends 11: Databricks – Spark ML – Multivariate Linear Regression. How do you convert Pyspark dataframe to Pndas Dataframe? df.toPandas() converts Pyspark Dataframe to Pandas Dataframe. Output: How to perform statistical data exploration?… Read more ›...

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03: Simple Linear Regression interview Q&As

Q01. What is a gradient? A01. In algebra we can represent a straight line with: y = mx + c A parabola is represented as: y = m1x2 + m2x + c, and so on. … Read more ›...

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04: Residuals, Cost/Loss functions, R-squared & Gradient Descent interview Q&As

Q01. What do you understand by the terms mean, variance, and standard deviation of the sample Vs. the population? A01. Given that the following are the number of job applications sent by 6 individuals: Where X is the Sample. … Read more ›...

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21: Databricks – Spark ML Naive Bayes

Prerequisite: Extends 16: Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Problem statement: Predict the iris flower species based on its sepal & petal length & width. Independent Variables (aka features): sepal length in cm, … Read more ›...

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20: Databricks – Spark ML K-Means

Prerequisite: Extends Databricks – Spark ML Random Forrest Classifier. Q. What is K-Means algorithm? A. K-Means is an unsupervised algorithm, hence it does only have “features“, … Read more ›...

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19: Databricks – Spark ML Random Forrest Classifier

Prerequisite: Extends Databricks – Spark ML Decision Tree Classifier. Q. What is the difference between a decision tree classifier and a random forrest classifier? A. As is implied by the names “Tree” … Read more ›...

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18: Databricks – Spark ML Decision Tree Classifier

Prerequisite: Extends Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Q: What is a Decision Tree Classifier? A: A Decision Tree a Supervised Machine Learning where the data is continuously split into Root Node, … Read more ›...

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17: Databricks – Spark ML K-Folds Cross Validation

Prerequisite: Extends Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Q: What is K-Folds Cross Validation in ML? How does it differ from the train-split validation? A: Once we are done with training our model, … Read more ›...

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16: Databricks – Spark ML Multiclass Logistic Regression & Pipeline

Prerequisite: Extends 15: Databricks – Spark ML – Classification with Logistic Regression & Databricks – Spark ML – StringIndexer & OneHotEncoder. Problem statement: Predict the likelihood of leaning towards a political party based on age & … Read more ›...

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15: Databricks – Spark ML – Classification with Logistic Regression

Prerequisite: Extends Spark ML – StringIndexer & OneHotEncoder – LinearRegression. Q. What is a Classification type prediction? How does it differ from Linear Regression? A. Classification type predictions are 1) Is Email spam or not? … Read more ›...

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13: Databricks – Spark ML – Dummy Variables

Prerequisite: Extends Databricks – Spark ML – Categorical Features – Linear Regression. Problem statement: Predict the house prices by land area in square feet, house color as in “White”, “Grey”, and “Cream”, and house locality as in “Eastwood”, … Read more ›...

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12: Databricks – Spark ML – Categorical Features

Prerequisite: Extends Databricks – Spark ML – Linear Regression. Problem statement: Predict the house prices by land area in square feet, house condition as in “Bad”, “Average”, and “Good”, and house locality as in “Sydney”, … Read more ›...

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