zirong326's Stars
jingxinfu/WebCrawlerLearning
Practice on web crawling
TeamHG-Memex/deep-deep
Adaptive crawler which uses Reinforcement Learning methods
dataabc/weiboSpider
新浪微博爬虫,用python爬取新浪微博数据
shengqiangzhang/examples-of-web-crawlers
一些非常有趣的python爬虫例子,对新手比较友好,主要爬取淘宝、天猫、微信、微信读书、豆瓣、QQ等网站。(Some interesting examples of python crawlers that are friendly to beginners. )
fchollet/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
hanbt/learn_dl
Deep learning algorithms source code for beginners
xiaohuiduan/data_mining
《Python数据挖掘入门与实践》 代码,数据以及教程
datawhalechina/statistical-learning-method-solutions-manual
统计学习方法习题解答,在线阅读地址:https://datawhalechina.github.io/statistical-learning-method-solutions-manual
ludwig-ai/ludwig
Low-code framework for building custom LLMs, neural networks, and other AI models
Mikoto10032/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
lawlite19/MachineLearning_Python
机器学习算法python实现
ljpzzz/machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
hanmq/MachineLearning_Zhouzhihua_ProblemSets
Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题,个人解答。各算法都拿numpy和pandas实现了一遍
PnYuan/Machine-Learning_ZhouZhihua
Exercises answers to the book "machine-learning" written by Prof. Zhou Zhihua of Nanjing University
fsrt16/Introduction-to-Genomic-Data-Sciences---Breast-cancer-Detection
# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
smazzanti/mrmr
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
chansonZ/book-ml-sem
《机器学习:软件工程方法与实现》Method and implementation of machine learning software engineering
obaidM/FeatureSelectionPython
Includes Filter based, wrapper based, Embedded and Hybrid techniques
yuanjie-ai/FeatureSelector
Feature selector is a tool for dimensionality reduction of machine learning datasets.
Alxe1/FeatureSelectionsAndExtractions
feature selections and extractions
chr2117216003/machine_learning
aleromualdi/mRMR
information based feature selection algorithm
kr-prince/mRMR
This is an App developed in Python to implement the algorithm for minimum redundancy maximum ralevance. The formulation was based on a research paper from Chris Ding and Hanchuan Peng (Minimum Redundancy Feature Selection from Microarray Gene Expression Data).
fbrundu/pymrmr
Python3 binding to mRMR Feature Selection algorithm (currently not maintained)
jundongl/scikit-feature
open-source feature selection repository in python
WebDevSimplified/Learn-SQL
Exercises for beginners to learn SQL
tirthajyoti/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
firmai/industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
Nyandwi/machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
dair-ai/ML-Notebooks
:fire: Machine Learning Notebooks