Data wrangling is process of preparing raw data for analysis. It involves cleaning, structuring and enriching of raw data. It is also known as data preprocessing. Pandas is great tool which provides with all the features required for data preprocessing. The process includes: Data Cleaning Data Transformation Data Integration Data Filtering Data Validation Data Formatting …
Removing duplicate elements from a Python list is a common task in data processing and manipulation. Here are several effective methods to achieve this: Methods to Remove Duplicates from a Python List: Using set(): Using OrderedDict(): Using List Comprehension with not in: Using a Loop and a Temporary Set: 1.Using set(): Principle: Sets in Python …
Hey fellas! Let us create a movie recommendation system based on what the user likes using Python and Pandas library. We shall process the movie dataset imported online consisting of thousands of movies including the genre, date of release, and other essential features to obtain a set of movies with a score that defines the …
Creating a movie recommendation system on the user-like basis Read More »
Hey fellas! Let us dive into today’s topic of Leave-One-Out Cross Validation used in Machine Learning to assess a model’s performance and provide reliable information to further improve the performance of the model. Leave-One-Out Cross Validation or LOOCV is a type of k-fold cross-validation method where k is equal to the number of data points. …
Using Pandas and Seaborn, this lesson explores exploratory data analysis (EDA) in Python, covering fundamental ideas, methods for exploring data, and visualization using real-world examples. It’s intended to improve your capacity for efficient data trend analysis and interpretation. EDA in Python Using Pandas and Seaborn The steps involved are illustrated in the code below. Follow …
Pandas and Seaborn: Exploratory Data Analysis (EDA) in Python Read More »
This tutorial explores the ARIMA model for time series forecasting in Python, covering fundamentals, model building, and tuning with practical examples. It’s designed to help you effectively forecast and analyze trends in time series data. ARIMA Model For Time Series The steps which involved are shown below: Step 1: Import Libraries import pandas as pd …
import requests from bs4 import BeautifulSoup def get_youtube_title(url): response = requests.get(url) soup = BeautifulSoup(response.text, ‘html.parser’) title = soup.find(‘title’).text return title url = “https://youtu.be/2PQ0WnXzPdY?si=vxuesrpr9CAx7AL7” title = get_youtube_title(url) print(title)
def count_spaces(file_name):with open(file_name, ‘r’) as file:text = file.read()num_spaces = text.count(‘ ‘)return num_spacesfile_name = r’C:\Desktop\ajay.txt’ # Use raw string literalnum_spaces = count_spaces(file_name)print(f’The number of spaces in {file_name} is {num_spaces}.’)
Clustering text documents is a typical issue in natural language processing (NLP). Based on their content, related documents are to be grouped. The k-means clustering technique is a well-liked solution to this issue. In this article, we’ll demonstrate how to cluster text documents using k-means using Scikit Learn K-means clustering algorithm The k-means algorithm is a well-liked unsupervised learning …
Introduction: When working with machine learning models, especially in a dynamic environment, being able to save the progress and reload it later is very important. This capability not only saves time but also ensures that the work is safe and can be easily shared or deployed. In TensorFlow, this process is streamlined with functions that …