# 机器学习汇总

**Repository Path**: rottengeek/machine_learning_summary

## Basic Information

- **Project Name**: 机器学习汇总
- **Description**: 机器学习算法实战汇总
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 11
- **Forks**: 6
- **Created**: 2019-11-07
- **Last Updated**: 2025-05-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 机器学习汇总

#### 介绍
本模块包含自己学习机器学习过程中学习过的内容，包含基础案例和实战案例，大部分以ipynb的格式展示，包含数据集和源代码，可以下载使用练习，包含机器学习的常见算法，如逻辑斯蒂回归，决策树，支持向量机，集成算法，贝叶斯，聚类等等。



由于包含数据集内容比较大，鉴于网速没有上传到github，而且码云自带ipynb文件渲染，可以在线看代码，由于码云自带的图片无法渲染，有些内容是通过jupyter官网的nbviewer渲染的，下面是该项目的链接。

[项目码云链接](https://gitee.com/rottengeek/machine_learning_summary/tree/master)


#### 目录

- 0-数据预处理
  - [数值特征处理](https://gitee.com/rottengeek/machine_learning_summary/blob/master/0-数据预处理/特征标准化.ipynb)
  - [离散特征处理](https://gitee.com/rottengeek/machine_learning_summary/blob/master/0-数据预处理/离散特征处理.ipynb)
-  [1-K近邻算法](https://gitee.com/rottengeek/machine_learning_summary/blob/master/0-数据预处理/离散特征处理.ipynb)
- [2-梯度下降求解逻辑斯蒂回归](https://gitee.com/rottengeek/machine_learning_summary/blob/master/2-梯度下降/梯度下降求解逻辑回归.ipynb)
- [3-逻辑回归-信用卡欺诈检测](https://gitee.com/rottengeek/machine_learning_summary/blob/master/3-逻辑回归-信用卡欺诈检测/信用卡欺诈检测.ipynb)
- [4-决策树](https://gitee.com/rottengeek/machine_learning_summary/blob/master/4-决策树/决策树demo.ipynb)
- 5-随机森林
  - [随机森林](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/5-随机森林/1-随机森林.ipynb)
  - [数据特征对随机森林的影响](https://gitee.com/rottengeek/machine_learning_summary/blob/master/5-随机森林/2-数据与特征对随机森林的影响.ipynb)
  - [随机森林参数选择](https://gitee.com/rottengeek/machine_learning_summary/blob/master/5-随机森林/3-随机森林参数选择.ipynb)
- 6-特征工程
  - [特征预处理](https://gitee.com/rottengeek/machine_learning_summary/blob/master/6-特征工程/特征预处理.ipynb)
  - [数值特征](https://gitee.com/rottengeek/machine_learning_summary/blob/master/6-特征工程/数值特征.ipynb)
  - [文本特征](https://gitee.com/rottengeek/machine_learning_summary/blob/master/6-特征工程/文本特征.ipynb)
  - [图像特征](https://gitee.com/rottengeek/machine_learning_summary/blob/master/6-特征工程/图像特征.ipynb)
- [7-集成算法](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/7-集成算法/集成算法.ipynb)
- [8-贝叶斯-拼写检查器](https://gitee.com/rottengeek/machine_learning_summary/blob/master/8-贝叶斯-拼写检查器/Untitled.ipynb)
- [9-贝叶斯-新闻分类](https://gitee.com/rottengeek/machine_learning_summary/blob/master/9-贝叶斯-新闻分类/news_C.ipynb)
- [10-聚类算法实验分析](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/10-聚类算法实验分析/聚类算法-实验/聚类.ipynb)
- [11-GMM聚类](https://gitee.com/rottengeek/machine_learning_summary/blob/master/11-GMM聚类/GMM.ipynb)
- [12-支持向量机](https://gitee.com/rottengeek/machine_learning_summary/blob/master/12-支持向量机/notebooks/support_vector_machines.ipynb)
- 13-降维算法
  - [LDA](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/13-降维算法/LDA/LDA.ipynb)
  - [PCA](https://gitee.com/rottengeek/machine_learning_summary/blob/master/13-降维算法/PCA/PCA.ipynb)
- 14-Xgboost
  - [数据探索](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/14-Xgboost/part1_data_discovery.ipynb)
  - [建模](https://nbviewer.jupyter.org/urls/gitee.com/rottengeek/machine_learning_summary/raw/master/14-Xgboost/part1_data_discovery.ipynb)
- [15-案例：商品销售回归分析](https://gitee.com/rottengeek/machine_learning_summary/blob/master/15-案例：商品销售额回归分析/商品销售额预测.ipynb)
- [16-GBDT_XGBoost_LightGBM对比](https://gitee.com/rottengeek/machine_learning_summary/blob/master/16-GBDT_XGBoost_LightGBM/GBDT_XGBoost_LightGBM.ipynb)
- [17-使用lightgbm进行饭店流量预测](https://gitee.com/rottengeek/machine_learning_summary/blob/master/17-使用lightgbm进行饭店流量预测/饭店流量预测.ipynb)
- 20-HMM案例实战
  - [HMM实践](https://gitee.com/rottengeek/machine_learning_summary/blob/master/20.HMM案例实战/hmm实践.ipynb)
  - [时间序列](https://gitee.com/rottengeek/machine_learning_summary/blob/master/20.HMM案例实战/时间序列.ipynb)
- 21-推荐系统
  - [电影数据集推荐系统](https://gitee.com/rottengeek/machine_learning_summary/blob/master/21-推荐系统/rs_1.ipynb)




如果觉得我资料搜集和整体的还行，希望大佬打赏一下

<img src="https://images.gitee.com/uploads/images/2019/1107/013456_bf94ad3a_2024231.jpeg" height="300px" width="200px" >

<img src="https://images.gitee.com/uploads/images/2019/1107/013717_cd068b3e_2024231.jpeg" height="300px" width="200px" >