Python3 data Analysis and mining modeling combat video

The 1th chapter introduces "free related ebook + accompanying code" this chapter first introduces the course is what, what characteristics, can learn what, content arrangement, need what foundation, is suitable to study this course and so on. Then we summarize the data analysis, so that we have a whole understanding of the meaning and function of data analysis, so that we have a basic concept and understanding of what we are going to do next. ... 1-1 Course Guide 1-2 Data Analysis Overview 2nd Chapter Data acquisition data from where? How come? In this chapter, we will introduce the general means of data acquisition. Mainly include Data Warehouse, crawl, data filling, log, buried point, calculation and other means. At the same time, we will introduce several commonly used data sites for your reference and learning. 2-1 Data Warehouse 2-2 monitoring and crawling 2-3 fill, Bury, log, calculate 2-4 Data Learning website Chapter 3rd single Factor Exploration analysis and data visualization with data, how to get started? In this chapter, we will describe part of the exploratory analysis---single factor exploration for analysis and visualization of content. We will take the basic knowledge of statistical theory as the starting point, learning outlier analysis, comparative analysis, structural analysis, distribution analysis. At the same time, the introduction of the following chapters will be used in the case of-HR Human Resources analysis table, and the theoretical and visual methods to complete the preliminary analysis of this table. ... 3-1 Data Case Introduction 3-2 concentration trend, trend 3-3 data Distribution-skewness and kurtosis 3-4 sampling theory 3-5 coding implementation (based on python2.7) 3-6 data classification 3-7 outlier analysis 3-8 comparative analysis 3-9 Structural Analysis 3-10 Distribution Analysis 3-11 SATISFA Analysis of ction level 3-12 lastevaluation analysis 3-13 numberproject analysis 3-14 averagemonthlyhours analysis of 3-15 Timespendcompany 3-16 Analysis of Workaccident 3-17 left analysis 3-18 promotionlast5years Analysis 3-19 Salary analysis of 3-20 department 3-21 Simple Contrast analysis Operation 3-22 visualization-Histogram 3-23 Visualization-Histogram 3-24 visualization-box line figure 3-25 Visualization-Line chart 3-26 visualization-Pie Chart 3-27 Chapter 4th Chapter Summary Multi-factor exploration and analysis on the hand, then? In this chapter, we introduce another part of exploratory analysis---Multi-factor complex exploration analysis. We also take the basic statistical knowledge as the starting point, learning the multi-factor interaction and coordination of the analysis methods, such as cross-analysis, group analysis, correlation analysis, composition and so on. At the same time, the HR Human Resources Analysis table as an example, to further explore. ... 4-1 hypothesis Test 4-2 cardSquare Test 4-3 Variance test 4-4 correlation coefficient 4-5 linear regression 4-6 principal component Analysis 4-7 coding Implementation 4-8 cross-analysis method and implementation 4-9 analysis and implementation of 4-10-factor analysis and implementation 4-11 factorial analyses and implementations 4-12 the 5th Chapter summary of preprocessing theory data has been understood, used up ！ No hurry, first processing. In this chapter, we will introduce the main content of feature engineering, focusing on the main content of data cleansing and data feature preprocessing, including data cleansing, feature acquisition, feature processing (include pointing, normalization, normalization, etc.), feature dimensionality reduction and feature derivation. The quality of pretreatment directly affects the effect of the next model. ... 5-1 Feature Engineering Overview 5-2 Data Sample acquisition 5-3 outlier handling 5-4 Callout 5-5 Feature selection 5-6 feature transform-alignment 5-7 feature transform-discretization 5-8 feature transform-normalization and normalization 5-9 feature transform-numerical 5-10 feature transform-normalized 5-11 feature reduced dimension-lda 5-12 feature-derived 5-13 HR table feature preprocessing -15-14 HR table feature preprocessing-25-15 Chapter Summary 6th Chapter Mining Modeling to use data together! In this chapter, we will introduce the main content of data mining and modeling. It mainly includes the establishment and practice of five kinds of models: Classification model (KNN, naive Bayesian, decision tree, SVM, integration method, GBDT ...). ), regression model and regression thought classification (linear regression, logical return "also called Reggie Regression, logistic regression. Transliteration difference ", neural network, regression tree), cluster model (K-means, DBSCAN, hierarchical clustering 、... 6-1 machine learning and data modeling 6-2 training set, validation set, test set 6-3 classification-knn6-4 classification-naive Bayes 6-5 classification-decision tree 6-6 classification-support vector machine 6-7 classification-integration-random forest 6-8 classification-integrated-adaboost6-9 regression-linear regression 6-10 Regression-Classification-Logistic regression 6-11 regression-classification-artificial neural network-16-12 regression-classification-artificial neural network-26-13 regression-regression tree and lifting tree 6-14 cluster-kmeans-16-15 cluster-kmeans-26-16 cluster-DBSCAN6-17 Clustering-Hierarchical clustering 6-18 clustering-graph splitting 6-19 Association-association Rules-16-20 Association-association rules-26-21 semi-supervised-label propagation algorithm 6-22 This chapter summarizes the 7th Chapter model evaluation which model is good? In the previous chapter, we learned a lot of models, a dataset, which can be modeled with a variety of models, so what's good is that it needs some sort of indicator to help us make decisions. In this chapter, we will introduce the use of confusion matrices and corresponding indicators, ROC curves and AUC values to evaluate the classification model;The regression model was evaluated by Mae, MSE and R2, and the clustering model was evaluated by RMS and contour coefficients. ... 7-1 Classification Evaluation-Confusion matrix 7-2 classification evaluation-roc, AUC, lift chart and KS Figure 7-3 Regression Assessment 7-4 The 8th chapter summarizes and forecasts the non-supervisory assessment, we will review the whole content of this course and take a look at our data analysis work from many perspectives. In the end, we will learn what we can do to develop this course. 8-1 Course review and multi-perspective data Analysis 8-2 Big Data and study what else can you do after this class?

Baidu Network disk Baidu Network disk download

Python3 data Analysis and mining modeling combat video