文件名称：颜色分类leetcode-Machine-Learning-using-sklearn:机器学习使用sklearn
文件大小：12.08MB
文件格式：ZIP
更新时间：2024-07-26 15:41:32
系统开源
颜色分类leetcode 机器学习使用 sklearn 项目涉及 网络抓取 Cricinfo 数据以预测特定年份击球手得分的次数 使用不同的特征来预测水果，例如重量、高度、颜色分数等。 使用 TFID Vectoriser 根据其强度进行密码分类
【文件预览】：
Machine-Learning-using-sklearn-master
----EDA_Exploratory_Data_Analysis.ipynb(152KB)
----Logistic Regression()
--------Social_Network_Ads.csv(11KB)
--------Task 1 - Gender Classification using Decision Tree.ipynb(17KB)
--------P1 - Logistic Regression (Social Network).ipynb(125KB)
--------Task 2 - Label Encoding with Classification Algorithms.ipynb(11KB)
--------Logistic Regression from scratch with Normalization and Random Values.ipynb(93KB)
----Linear Regression()
--------Salary_Data.csv(454B)
--------Linear Regression-Scratch-using Numpy.ipynb(41KB)
--------Linear Regression - [Salary vs Exp].ipynb(70KB)
----Logistic_Regression.ipynb(19KB)
----Pandas_Time_Series_Analysis.ipynb(3.03MB)
----Creating your own Images in Matplotlib and Digits.ipynb(88KB)
----Panda - Exercise-with-solution.ipynb(36KB)
----Datasets()
--------Salary_Data.csv(454B)
--------Cars.csv(21KB)
--------Images()
--------spam.tsv(502KB)
--------covid-19-new.csv(57KB)
--------titanic.csv(60KB)
--------synthetic.csv(7KB)
--------Readme.md(3B)
--------cancer_classification.csv(144KB)
--------news.csv(26KB)
--------amazonreviews.tsv(4.25MB)
--------Mall_Customers.csv(4KB)
--------Sample-Superstore.xls(3.22MB)
--------Social_Network.csv(11KB)
--------flavors_of_cacao.csv(125KB)
--------Fruit_with_color.zip(810B)
----KNN()
--------P3 - KNN (Underweight).ipynb(17KB)
--------Underweight-knn.csv(174B)
----Chipotle.ipynb(91KB)
----fruits-with-colors()
--------fruit_data_with_colors.txt(2KB)
--------load_json.py(280B)
--------saving_json.py(2KB)
--------Fruits with color.ipynb(92KB)
----Fruits_with_color_OpenSearchCV.ipynb(255KB)
----Visualization_SImple_Time_Series.ipynb(2.84MB)
----Web Scraping()
-------- Cricinfo ( Dhoni) Linear Regression.ipynb(163KB)
----Saving_models()
--------sk-json.py(168B)
--------json_file.py(234B)
----README.md(310B)
----password()
--------password-classification(1KB)
----Missing Values.ipynb(17KB)
----P2 - K Means - Clustering ( Malls_Customers).ipynb(73KB)
----Day_5 DS.ipynb(167KB)