Random Forest using Scikit-Learn

𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 is a supervised machine learning algorithm which has been used for a classification task in this example. It uses multiple decision trees and takes a majority vote on them to reach a single decision. 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 is an 𝒆𝒏𝒔𝒆𝒎𝒃𝒍𝒆 𝒍𝒆𝒂𝒓𝒏𝒊𝒏𝒈 method. I used 𝘀𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆_𝗺𝘂𝘀𝗵𝗿𝗼𝗼𝗺_𝗱𝗮𝘁𝗮𝘀𝗲𝘁.𝗰𝘀𝘃 for this example. The dataset is available in the repository. This dataset contains two types of mushrooms: 𝗲𝗱𝗶𝗯𝗹𝗲 & 𝗽𝗼𝗶𝘀𝗼𝗻𝗼𝘂𝘀. It has 20 features. But, 9 of them contain null values and I dropped them before fitting the dataset to the model. I used 5 trees to train the 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 model. 𝘎𝘪𝘵𝘏𝘶𝘣 𝘢𝘥𝘥𝘳𝘦𝘴𝘴: https://github.com/randomaccess2023/MG2023/tree/main/Video%2061 𝙸𝚖𝚙𝚘𝚛𝚝𝚊𝚗𝚝 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚜: 00:45 - Import required libraries 02:14 - Load 𝐬𝐞𝐜𝐨𝐧𝐝𝐚𝐫𝐲_𝐦𝐮𝐬𝐡𝐫𝐨𝐨𝐦_𝐝𝐚𝐭𝐚𝐬𝐞𝐭 04:38 - Drop null values 07:46 - Perform preprocessing 10:30 - Visualize selected features 15:20 - Separate features and labels 16:01 - Split the dataset 17:23 - Apply 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 19:26 - Plot 𝐜𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧_𝐦𝐚𝐭𝐫𝐢𝐱 24:48 - Print 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧_𝐫𝐞𝐩𝐨𝐫𝐭 25:24 - Plot 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 #datascience #machinelearning #pythonprogramming #python #jupyternotebook #jupyter #supervisedclassification #supervisedlearning #randomforest