https://youtu.be/XXJdZD0SCTI#Python supports the object-oriented programming paradigm through classes#class is like the blueprint for a house#create several houses and even a complete neighborhood. Each concrete house is an object or instance that’s derived from the blueprint #Each instance can have its own properties, such as color, owner, and interior design#properties carry what’s commonly known as the […]
Python F String
https://youtu.be/8GIqXqHDNfY#f string#not a string generated by user#double quotes the best#12 examples#Ex 1: Printing a string #ex 2: multiple strings #Ex 3: functions or methods in the f string #Ex 4: (SORT OF ADVANCED CAN SKIP) #Ex: 5 Inline Expressions #ex: 9 datetime #ex 12 loops
time series stationary python
#https://www.kaggle.com/datasets/camnugent/sandp500
time series seasonality python
Lets look at a specific year nowdf[“Date”] = pd.to_datetime(df[“Date”]) Summary Statistics by a Period (week, month, quarter etc)
machine learning imbalanced classes
#Read over#data professor#emma Ding#mahesh huddar#ritvik mathPart 1 Load a Dataset Part 2 SIMPLE EDA Part 3 Set Up the Data Part 4 BASELINE MODEL – NO FIXING THE IMBALANCE part 5Oversampling ExampleOversampling Example 1 RandomOverSampler To start we’re going to create a simple dataframe in python led to overfitting part 6Oversampling Method Example 2 SMOTE […]
simpsons paradox in Python
2nd example Running 1st plot just the mile time/miles per week
two sample z test scipy
#Example 1 More Manual – From Slides Quicker way to test it – Not Entirely preciseThe ztest function in statsmodels.stats.weightstatsdoes not explicitly allow for directlypassing the population standard deviation. Instead, it estimates the standard error based on thesample standard deviations unless the sample variance is explicitly pooled example 2 marathon times of two running clubs […]
paired sign test in Python
Example 1 Example 2 #one tail #zero value #shapiro #ordinal data Example statsmodel
Column Transformer
#drop #Example Passthrough some columns, drop offthers
extra trees classifier
Aggregates the results from group of decision trees (Like a random forest) Difference 1. ETC randomly selects the value to split features unlike a DTC which looks for the best2. Makes ETC More random + Faster Algorithm which can help with noisy data