MMS: Data Mining (2018-I)

EngPracII: Systematic Review > EngPracII: Estructuración del problema > HPC:Tensorflow > MMS: Data Mining (2018-I)

Course contents

Chapter 1. Practical Machine Learning with PYTHON

• Downloading and installing PYTHON
• Installing and loading packages
• Reading and writing data
• Using PYTHON to manipulate data
• Applying basic statistics
• Visualizing data
• Getting a dataset for machine learning

Chapter 2. Data Exploration with Real Live Data

• Reading a dataset from a CSV file
• Converting types on character variables
• Detecting missing values
• Imputing missing values
• Exploring and visualizing data
• Predicting passenger survival with a decision tree
• Validating the power of prediction with a confusion matrix
• Assessing performance with the ROC curve

Chapter 3. PYTHON and Statistics

• In this chapter, we will cover the following topics:
• Understanding data sampling in PYTHON
• Operating a probability distribution in PYTHON
• Working with univariate descriptive statistics in PYTHON
• Performing correlations.
• Conducting an exact binomial test
• Performing the Kolmogorov-Smirnov test
• Understanding the Wilcoxon Rank Sum and Signed Rank test
• Working with Pearson’s Chi-squared test
• Conducting a one-way ANOVA
• Performing a two-way ANOVA

Chapter 4. Understanding Regression Analysis

• Fitting a linear regression model with Python
• Summarizing linear model fits
• Using linear regression to predict unknown values
• Generating a diagnostic plot of a fitted model
• Fitting a polynomial regression model with Python
• Fitting a robust linear regression model with Python
• Studying a case of linear regression on SLID data

Chapter 5. Classification (I) – Tree, Lazy, and Probabilistic

In this chapter, we will cover the following recipes:

Preparing the training and testing datasets Building a classification model with recursive partitioning trees Visualizing a recursive partitioning tree Measuring the prediction performance of a recursive partitioning tree Pruning a recursive partitioning tree Building a classification model with a conditional inference tree Visualizing a conditional inference tree Measuring the prediction performance of a conditional inference tree Classifying data with logistic regression

Chapter 6. Working with Spatial Data

• Motivation: What's so great about spatial data?
• Spatial data structures.
• Making maps.
  • Static maps with Python.
  • Projections.
  • Geocoding, routes, and distances.
  • Dynamic maps with leaflet.
• Extended example: Congressional districts.
  • Election results.
  • Congressional districts.
  • Putting it all together.
  • Using leaflet.
• Effective maps: How (not) to lie.
• Extended example: Historical airline route maps.
• Projecting polygons.