Here are 10 hands-on exercises on list slicing in Python along with their solutions. Exercise 1: Given a list numbers, create a new list containing the first three elements. Solution 1: Output: [1, 2, 3] Exercise 2: Given a list letters, extract the last two elements. Solution 2: Output: [‘d’, ‘e’] Exercise 3: Given a …
50+ interview questions along with their answers on Python
Here are 50+ interview questions along with their answers on Python. What is Python, and what are its key features? Answer: Python is a high-level, interpreted programming language known for its simplicity and readability. Key features include easy syntax, dynamic typing, automatic memory management, and a large standard library. 2. What is the difference between …
How to Implement Linear Regression With Multiple variables?
The following article describes How to Implement Linear Regression With Multiple variables. Problem Statement Use the sklearn library to create a linear regression with multiple variables. Load a well known dataset split it into training and testing sets, and then train the model to predict a target variable based on one or more features. For …
How to Implement Gradient Descent Algorithm for Linear Regression?
The following program demonstrates How to Implement Gradient Descent Algorithm for Linear Regression. Problem Statement Implement the gradient descent algorithm for linear regression with one variable from scratch in vectorize form. Train a linear regression model using gradient descent to find the optimal coefficients (slope and intercept) for a given dataset. Also Plot the Gradient …
How to Implement Linear Regression from Scratch?
The following program demonstrates How to Implement Linear Regression from Scratch. Problem Statement Implement a linear regression with one variable algorithm from scratch using Python. Given a dataset of X and Y values, create a linear regression model that predicts Y based on X without using any machine learning libraries like sklearn. Solution The following …
How to Perform Dataset Preprocessing in Python?
The following article demonstrates How to Perform Dataset Preprocessing in Python. Basically, dataset preprocessing is a crucial step before training a machine learning model. For this purpose, we need to handle the missing values and the categorical data. Further, we need to split the dataset into training and test datasets. The following program demonstrates how …
Logistic Regression from Scratch
In this blog, we will describe Logistic Regression from Scratch. Basically, Logistic regression, a fundamental machine learning algorithm, serves as the cornerstone for binary classification tasks, spam email detection, and so much more. As a matter of fact, implementing logistic regression from scratch for binary classification involves several steps, including defining the logistic function, implementing …
Getting Started with Data Analysis in Python
In this article on Getting Started with Data Analysis in Python, we will cover common data manipulation tasks. Particularly, we will cover common tasks such as loading CSV files and extracting specific columns, filtering, sorting, and merging datasets Getting Started with Data Analysis in Python: Loading and Displaying CSV Files Welcome to our comprehensive guide …
Data Visualization Practice Exercise
The following article on Data Visualization Practice Exercise shows few practical problems on Data Visualization in Python. Also, their solutions are provided with these problems. The upcoming problems may appear elementary, yet they hold value for anyone seeking to grasp these concepts. Whether you’re taking your initial steps into data visualization or already possess some …
How to Create a Dataset in Python Using Some Random Values?
The following article explains How to Create a Dataset. Basically, here we use a small set of values. The dataset will be generated by using these values randomly. Python Program to Generate the Dataset The following python program shows how to generate this dataset. Output When we run the above program, a csv file with …