The college dataset contains the undergraduate population percentage of eight different races for over 7,500 colleges. It would be interesting to find the race with the highest undergrad population for each school and then find the distribution of this result for the entire dataset. We would be able to answer a question like, What percentage of institutions have more white students than any other race?
Finding the most common maximum
by Aldrin Yim, Claire Chung, Allen Yu, Pratap Dangeti
Numerical Computing with Python
- Title Page
- Copyright
- Contributors
- About Packt
- Preface
- Journey from Statistics to Machine Learning
- Tree-Based Machine Learning Models
- Introducing decision tree classifiers
- Comparison between logistic regression and decision trees
- Comparison of error components across various styles of models
- Remedial actions to push the model towards the ideal region
- HR attrition data example
- Decision tree classifier
- Tuning class weights in decision tree classifier
- Bagging classifier
- Random forest classifier
- Random forest classifier - grid search
- AdaBoost classifier
- Gradient boosting classifier
- Comparison between AdaBoosting versus gradient boosting
- Extreme gradient boosting - XGBoost classifier
- Ensemble of ensembles - model stacking
- Ensemble of ensembles with different types of classifiers
- Ensemble of ensembles with bootstrap samples using a single type of classifier
- Summary
- K-Nearest Neighbors and Naive Bayes
- K-nearest neighbors
- KNN classifier with breast cancer Wisconsin data example
- Tuning of k-value in KNN classifier
- Naive Bayes
- Probability fundamentals
- Understanding Bayes theorem with conditional probability
- Naive Bayes classification
- Laplace estimator
- Naive Bayes SMS spam classification example
- Summary
- Unsupervised Learning
- Reinforcement Learning
- Reinforcement learning basics
- Markov decision processes and Bellman equations
- Dynamic programming
- Grid world example using value and policy iteration algorithms with basic Python
- Monte Carlo methods
- Temporal difference learning
- SARSA on-policy TD control
- Q-learning - off-policy TD control
- Cliff walking example of on-policy and off-policy of TD control
- Further reading
- Summary
- Hello Plotting World!
- Hello Matplotlib!
- Plotting our first graph
- Summary
- Visualizing Online Data
- Visualizing Multivariate Data
- Adding Interactivity and Animating Plots
- Selecting Subsets of Data
- Boolean Indexing
- Calculating boolean statistics
- Constructing multiple boolean conditions
- Filtering with boolean indexing
- Replicating boolean indexing with index selection
- Selecting with unique and sorted indexes
- Gaining perspective on stock prices
- Translating SQL WHERE clauses
- Determining the normality of stock market returns
- Improving readability of boolean indexing with the query method
- Preserving Series with the where method
- Masking DataFrame rows
- Selecting with booleans, integer location, and labels
- Index Alignment
- Grouping for Aggregation, Filtration, and Transformation
- Defining an aggregation
- Grouping and aggregating with multiple columns and functions
- Removing the MultiIndex after grouping
- Customizing an aggregation function
- Customizing aggregating functions with *args and **kwargs
- Examining the groupby object
- Filtering for states with a minority majority
- Transforming through a weight loss bet
- Calculating weighted mean SAT scores per state with apply
- Grouping by continuous variables
- Counting the total number of flights between cities
- Finding the longest streak of on-time flights
- Restructuring Data into a Tidy Form
- Tidying variable values as column names with stack
- Tidying variable values as column names with melt
- Stacking multiple groups of variables simultaneously
- Inverting stacked data
- Unstacking after a groupby aggregation
- Replicating pivot_table with a groupby aggregation
- Renaming axis levels for easy reshaping
- Tidying when multiple variables are stored as column names
- Tidying when multiple variables are stored as column values
- Tidying when two or more values are stored in the same cell
- Tidying when variables are stored in column names and values
- Tidying when multiple observational units are stored in the same table
- Combining Pandas Objects
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Finding the most common maximum
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