Machine Learning Project: Anemia Detection “Detect anemia we must, with the force of machine learning, progress we achieve.”- Not Yoda but close. Introduction Anemia is no laughing matter (although my puns might be). Affecting over 1.6 billion people globally, anemia is a condition where the body doesn’t produce enough red blood cells, leading to fatigue, …
Prediction of wine quality is a great way to practice end-to-end machine learning workflows, including data cleaning, model training, and optimization. Steps involving to solve the question: Step 1: Set Up Your Environment First step is to ensure we have python and necessary libraries installed. Or we can install essential packages using: pip install pandas numpy …
This Python script matches company names between two datasets using fuzzy matching with the thefuzz library. It ensures that company names are standardized and finds the best match from another dataset. Key Features: Loads two CSV datasets containing company names. Standardize the names by converting them to lowercase and removing extra spaces. It uses fuzzy …
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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 …
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from googlesearch import search def company_website(cname): query = f”{cname} official website” res = search(query, num=1, stop=1) return res cname = input(“Enter company name: “) url = get_company_website(cname) print(f”Website URL is: {url}”)
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 …
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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 …
In this tutorial, we explore how to calculate Minimum Detectable Effect (MDE) using Python. This guide introduces you to the concept of MDE in A/B testing, providing a step-by-step approach with code examples, making it easy to determine the smallest change that can be statistically detected in your experiments. Minimum-Detectable-Effect-(MDE)-Using-Python The Approach can be discussed …
In this tutorial, we will learn how to match the strings in large datasets When working with huge datasets, especially in cases where data comes from different sources, it’s common to encounter slightly different versions of the same string. This post will guide you through performing fuzzy matching to identify and link similar strings between …
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In this blog spot, we will explore an advanced implementation of Lasso Regression in Python using Scikit_learn. This implementation includes additional steps such as hyperparameter tuning. What is Lasso Regression? Lasso regression is a type of linear regression that uses shrinkage, shrinkage is where data values are shrunk towards a point, like the mean. This …