Working with Email in Go

https://zetcode.com/golang/email-smtp/ Tags: go, applications, emails, smtp https://github.com/go-gomail/gomail Intro Go provides an smtp package which uses the SMTP standard to send email. Sending…

Working with Scope in Go

Intro Scope in programming languages defines the region where a variable exists. In Go, we have three places to declare variables, local, global, and formal parameters. In this article, we will learn…

Working with Type Conversion in Go

Intro Type conversion lets you easily change the types of your variables in Go. We can do this using the cast operator. In this article, we will learn how to use type conversion in Go. The Case…

Working with Logs in Go

Intro Logs allow you to send info or errors during your program for debugging purposes. In practice, logging is often used in conjunction with something like Elastic Search to monitor and track down…

Working with Channels in Go

Intro Channels allow you to shared data between goroutines. When executing multiple concurrent tasks, you often want to send and receive data from these tasks. Channels allow you to do this. In this…

Working with WaitGroups in Go

Intro When using goroutines, you often need to wait for many goroutines to complete. You can set a timer, but this can be inconsistent as you don't always know how long the goroutines will take. To…

Working with GoRoutines in Go

Intro Go routines allow you to execute operations or functions concurrently. These are Go's way of handling multi-threading or asynchronous programming. In this article, we will learn how to us…

Working with Panic in Go

Intro Go doesn't have an exception mechanism, instead we have panic. During run time, if there is an execution problem, Go will throw a panic. This will stop code execution, completed all deferred…

Working with Deferred Functions in Go

Intro Go doesn't have an exception mechanism, instead we have panic. During run time, if there is an execution problem, Go will throw a panic. This will stop code execution, completed all deferred…

Working with Variadic Functions in Go

Intro Variadic functions allow you to pass a variable number of arguments. If you are creating a function, but want to allow for various arguments, you can use a variadic function. For example. The…

Working with Switch in Go

Intro In go, the switch statement allows us to direct program flow in a slightly different way. Instead of multiple if else statements, we can use switch on a single expression. In this article, we…

Working with Loop Control in Go

Intro Interfaces in Go work a bit different than in other languages. First of all, you specify only the methods that an interface should conform to and not the properties. Also, interfaces are…

Working with Interfaces in Go

Intro Interfaces in Go work a bit different than in other languages. First of all, you specify only the methods that an interface should conform to and not the properties. Also, interfaces are…

Working with If Else in Go

Intro Go provides us with the common , , and control statements that you would expect from programming languages. These statements allow you to branch and run code based on conditions. In this…

Working with Struct in Go

Intro In Go, structs allow you to create your own data types by grouping multiple types together. For example, we can create a person struct to hold a first name, last name, and more. In this article…

Working with Range in Go

Intro The range keyword can be used with the for keyword to loop through Go data types. We can loop through arrays, slices, maps, and channels. In this article, we will learn how to use Go range…

Working with Maps in Go

Intro Maps allow us to build a map of keys to values. For example, we can map a list of users to phone numbers to quickly retrieve the information. In this article, we will learn how to use Maps in Go…

Working with Slices in Go

Intro Slices are super charged arrays. They allow you to dynamically build, add, and modify lists. Similar to arrays, they must have the same site, but they don't require a static size. In this…

Working with Arrays in Go

Intro Arrays are a fixed list of variables all with the same size. For example, we can have an array of 3 integers, or 4 strings, but we cannot mix them. Because arrays are fixed, their size cannot…

Working with strings in Go

Intro In Go, strings are a read-only sequences of bytes. If you know other programming languages, you are probably used to managing a sequence of characters. Go uses bytes to support many characters…

Working with Functions in Go

Intro Function allow you to group code together and easily reuse code. In Go, we always have a main function where the program starts. From then on, we can build out functions to keep our code…

Working with For Loops in Go

Intro Loops allows you to easily repeat code by telling the compiler how many times to execute a block of code. In this article, we will learn how to use loops in Go. Loops in Go Go is slightly…

PySpark Binarizer

Intro The PySpark Binarizer allows you to convert a continuous variable into a discrete 0 or 1. This is helpful when you want to simply convert a column to check if a value exists based on a threshold…

PySpark Statistics Package

Intro PySpark provides a stats library located in that gives us a few tests and classes to use to do common statistical flows. The are based on vectors and provide scalable operations. In this…

PySpark Vectors

Intro PySpark provides several methods for working with linear algebra methods in the machine learning library. Specifically, we have a few ways to build and work with vectors at scale. In this…

PySpark Difference Between Two Dates

Intro PySpark provides us with and that allows us to get the time differences between two dates. This is helpful when wanting to calculate the age of observations or time since an event occurred. In…

PySpark Date_Format

Intro The PySpark function allows use to convert date columns into string columns using a specific output. A common task would be to convert to and iso8601 standard for exporting to other systems. In…

PySpark To_Date

Intro The PySpark method allows us to convert timestamps to a date type. This is useful if you import csv data that has date strings, but you want to perform date options on it. In this article, we…

PySpark Substring

Intro The PySpark method allows us to extract a substring from a column in a DataFrame. In this article, we will learn how to use substring in PySpark. Setting Up The quickest way to get started…

Working with Operators in Go

Intro Operators allow us to perform mathematics, comparisons, and much more in programming. Go provides a great set of operators for your daily use. In this article, we will learn how to use operators…

Working with Constants in Go

Intro Constants are variables that have a fixed value and cannot be changed. They are often use to keep static values that you don't want changed later on. For example, you may have a size for a cache…

C# Type Conversion

Intro Type conversion or type casting is a common feature in programming levels that allows you to go from one type to another. This is common when converting types to strings for logging or…

PySpark ConcatWS

Intro The PySpark function allows you to concatenate an array field into a single Sting field. This serves as the opposite of the function. This allows you to perform string operations on a column…

PySpark Split

Intro The PySpark method allows us to split a column that contains a string by a delimiter. For example, we have a column that combines a date string, we can split this string into an Array Column…

PySpark Fillna

Intro The PySpark and methods allow you to replace empty or null values in your dataframes. This helps when you need to run your data through algorithms or plotting that does not allow for empty…

Working with Variables in Go

Intro Declaring variables is a fundamental task in all programming languages. To do this in Go, we can use the following formula: is a reserved keyword that tells go we are going to declare a…

PySpark ForEach

Intro The PySpark method allows us to take small samples from large data sets. This allows us to analyze datasets that are too large to review completely. Setting Up The quickest way to get started…

PySpark ForEach

Intro The PySpark method allows us to iterate over the rows in a DataFrame. Unlike methods like map and flatMap, the method does not transform or returna any values. In this article, we will learn…

PySpark FlatMap

Intro The PySpark method allows use to iterate over rows in an RDD and transform each item. This method is similar to method, but will produce a flat list or array of data instead of mapping to new…

PySpark Map

Intro The PySpark method allows use to iterate over rows in an RDD and transform each item. Mapping is a common functional operation and PySpark allows us to use this at scale. In this article, we…

PySpark UDF (User Defined Function)

Intro Similar to most SQL database such as Postgres, MySQL and SQL server, PySpark allows for user defined functions on its scalable platform. These functions can be run on dataframes or registers to…

PySpark UnionByName

Intro When merging two dataframes with , we sometimes have a different order of columns, or sometimes, we have one dataframe missing columns. In these cases, PySpark provides us with the method. In…

PySpark Union

Intro PySpark provides us with the function to merge two or more data frames together. There also exists a method that was deprecated since Spark 2.0, but can be used if you have an older version…

PySpark GroupBy

Intro When working we often want to group data to view distributions or aggregations. PySpark provides us with the method to group our dataframes. In this particle, we will learn how to work with…

PySpark Drop OrderBy

Intro When working with data and viewing, we often want to sort or order our data for easier review. PySpark provides the and functions to sort dataframes. In this article, we will learn how to use…

PySpark Drop Duplicates

Intro During the data cleaning process, we would like to remove duplicate rows. PySpark provides us with the and that let's us remove duplicates on large amounts of data. In this article, we will…

PySpark Filter

Intro Often when working with dataframes we want to filter our data to a subset. PySpark provides us with the and the alias to filter our data frames. In this article, we will learn how to use…

PySpark WithColumnRenamed

Intro The allows us to easily change the column names in our PySpark dataframes. In this article, we will learn how to change column names with PySpark withColumnRenamed. Setting Up The quickest way…

PySpark WithColumn

Intro The method allow us to add columns, modify their types, modify their values and more. It is one of the most commonly used methods for PySpark. In this article, we will learn how to use PySpark…

PySpark Collect

Intro The dataframe collect method is used to return the rows in a dataframe as a list of PySpark Row classes. This is used to retrieve data on small dataframes so that you can inspect and iterate…

PySpark Select

Intro Selecting columns is one of the most common operations when working with dataframes. We can select by position or name. We can also select a single or multiple columns. In this article, we will…

PySpark Row

Intro The PySpark Row class is located in the module and provides a simple way to create rows or observations in a dataframe or an RDD. In this article, we will learn how to use PySpark Row. Let's…

PySpark StructType and StructField

Intro PySpark provides two major classes, and several other minor classes, to help defined schemas. This allows us to interact with Spark's distributed environment in a type safe way. In this article…

PySpark Show

Intro The show function allows us to preview a data frame. The show method provides us with a few options to edit the output. In this article, we will learn how to use the PySpark show function. We…

PySpark Window Functions

Intro Computing operations over a window of data, or a subset, is a common task. Often we want to rank information or subsets of data. For example, we may want to see the top sales per each month. In…

PySpark Pivot (rows to columns)

Intro Often when viewing data, we have it stored in an observation format. Sometimes, we would like to turn a category feature into columns. We can use the Pivot method for this. In this article, we…

PySpark DataFrame Join

Intro Often you will have multiple datasets, tables, or dataframes that you would like to combine. For example, you may have customers and their purchases and would like to see these in a single…

PySpark DataFrame Aggregations

Intro One main feature you will use in Spark is aggregation. This will help with exploratory data analysis and building dashboards that scale. In this article, we will learn how to use pyspark…

PySpark DataFrame Select, Filter, Where

Intro Filtering and subsetting your data is a common task in Data Science. Thanks to spark, we can do similar operation to sql and pandas at scale. In this article, we will learn how to use pyspark…

PySpark Handle Null Data

Intro Often when working with data you will find null values. It is a common task to work with and know how to manage these null values. The decision to drop or to impute is important in the model…

PySpark Create Dataframe

Intro There are many ways to create a data frame in spark. You can supply the data yourself, use a pandas data frame, or read from a number of sources such as a database or even a Kafka stream. In…

Redis Sort with Node

Intro Sorting is a common programming task, and you may be tempted to pull data from Redis and sort it client side. However, using Redis's built in sort function will be more performant and general…

Redis Sort with Python

Intro Sorting is a common programming task, and you may be tempted to pull data from Redis and sort it client side. However, using Redis's built in sort function will be more performant and general…

Redis Server Sessions in Python

Intro Sessions are usually short lived data, or at least have an expiration date, used to transfer state accross RESTful applications. REST applications are stateless per their spec, yet sometimes we…

Backing Up and Restoring Redis

Intro Redis uses two methods for persistence, snapshotting and append only file. Both have different use cases and can be used separately or in conjunction. In this article, we will learn how to…

Redis Server Sessions in Node

Intro Sessions are usually short lived data, or at least have an expiration date, used to transfer state accross RESTful applications. REST applications are stateless per their spec, yet sometimes we…

Redis Transactions in Node

Intro Transactions are a common database requirement for when you need to make multiple insert or update operations together. That is, if one operation fails, you don't want to execute either…

Redis Transactions in Python

Intro Transactions are a common database requirement for when you need to make multiple insert or update operations together. That is, if one operation fails, you don't want to execute either…

Node Redis Expire

Intro Expiring keys allows you to set automatic time limits for keys in Redis. When you set a TTL (time to live), Redis will clean up and remove the key when time has run out. This can be helpful for…

Python Redis Expire

Intro Expiring keys allows you to set automatic time limits for keys in Redis. When you set a TTL (time to live), Redis will clean up and remove the key when time has run out. This can be helpful for…

Python Redis Pub Sub

Intro Redis provides a Pub/Sub api that scales well and allows for quick real time connections. Real time apps are very popular, so redis can help you solve these business problems. If you are…

Redis Pipeline in Nodejs

Intro Redis offers a feature called pipeline that allows you to bulk send commands. This can drastically improved performance if you are running queries that can be batched together. The reason for…

Redis Pipeline in Python

Intro Redis offers a feature called pipeline that allows you to bulk send commands. This can drastically improved performance if you are running queries that can be batched together. The reason for…

Node Redis Pubsub

Intro Redis provides a Pub/Sub api that scales well and allows for quick real time connections. Real time apps are very popular, so redis can help you solve these business problems. If you are…

Redis Bitmap in Python

Intro Bitmaps are a pattern in redis, not actualy a data type as they are just using string, that can help save space when using redis. For example, you can store 4 billion users subscribed to a…

Redis Bitmap in Nodejs

Intro Bitmaps are a pattern in redis, not actualy a data type as they are just using string, that can help save space when using redis. For example, you can store 4 billion users subscribed to a…

Python Redis HyperLogLog Commands

Intro Tracking unique visits to a page or user vists is a common requirement for business applications. Doing this with large volumes can be very difficult as the data requirements are high. Thus, we…

Node Redis HyperLogLog Commands

Intro Tracking unique visits to a page or user vists is a common requirement for business applications. Doing this with large volumes can be very difficult as the data requirements are high. Thus, we…

Python Redis Geo Commands

Intro Geocoding and coordinates are a common use case in modern applications. These computations can be very heavy in terms of lookups so it is often desired to cache. Thus, Redis provides us with the…

Node Redis Geo Commands

Intro Geocoding and coordinates are a common use case in modern applications. These computations can be very heavy in terms of lookups so it is often desired to cache. Thus, Redis provides us with the…

Python Redis Sorted Set Commands

Intro Sorted sets are a powerful data set used in redis. If you are familiar with binary search, you know the importance of having a presorted set to access items in log(n). This data type is often…

Node Redis Sorted Set Commands

Intro Sorted sets are a powerful data set used in redis. If you are familiar with binary search, you know the importance of having a presorted set to access items in log(n). This data type is often…

Python Redis Set Commands

Intro Sets are lists filled with unique items. The set data type is helpfuly when you want to work with unique data types, thus they help with features where you want to easily deuplicate values. In…

Node Redis Set Commands

Intro Sets are lists filled with unique items. The set data type is helpfuly when you want to work with unique data types, thus they help with features where you want to easily deuplicate values. In…

Python Redis HSET and other Hash Commands

Intro Hash data types are used in many alogrithms to increase speed. They usually take more memory but improved the processing speed. This makes them an asset in the redis database which takes the…

Node Redis HSET and other Hash Commands

Intro Hash data types are used in many alogrithms to increase speed. They usually take more memory but improved the processing speed. This makes them an asset in the redis database which takes the…

Python Redis Lists

Intro Lists are one of the fundemental data types in Redis. You will often use this data type to manage many features. In this article, we look at many of the common list commands in Redis using…

Node js Redis Lists

Intro Lists are one of the fundemental data types in Redis. You will often use this data type to manage many features. In this article, we look at many of the common list commands in Redis using Node…

Python Redis

Intro When building large scale applications, there comes a need for scaling. There are many places to start with scaling, but one place my be scaling your reads. Let's say that you have a read heavy…

Node js redis

Intro When building large scale applications, there comes a need for scaling. There are many places to start with scaling, but one place my be scaling your reads. Let's say that you have a read heavy…

Observability: Python Health Check Example

Intro Building out health checks is a common task when building a web server. You may have seen status sites, such as Github status, where we can see the update time of each service that is offered…

Observability: Node js Health Check Example

Intro Building out health checks is a common task when building a web server. You may have seen status sites, such as Github status, where we can see the update time of each service that is offered…

Observability: Python Distributed Tracing with Open Zipkin Example

Intro Eventually apps get complicated and make many requests. When building out services such as microservices or even just multi services, debugging our apps get a bit harder. The services will…

Observability: Node Distributed Tracing with Open Zipkin Example

Intro Eventually apps get complicated and make many requests. When building out services such as microservices or even just multi services, debugging our apps get a bit harder. The services will…

Observability: Python Grafana and Statsd for Performance Monitoring

Intro Monitoring performance and uptime is a common task in server development. We often want to know how fast our endpoints are performing and if they are responding at all. With the help of statsd…

Observability: Nodejs Grafana and Statsd for Performance Monitoring

Intro Monitoring performance and uptime is a common task in server development. We often want to know how fast our endpoints are performing and if they are responding at all. With the help of statsd…

Observability: Python Elasticsearch Example

Intro Logging is one of the most fundemental observability concepts needed in Python programming. Whether we have a cli app, REST api app, or graphql app, we use logs to make sure things are going…

Observability: Node js Elasticsearch Example

Intro Logging is one of the most fundemental observability concepts needed in Node.js programming. Whether we have a cli app, REST api app, or graphql app, we use logs to make sure things are going…

MA Model in Python

Intro The moving average model, or MA model, predicts a value at a particular time using previous errors. The model relies on the average of previous time serries and correlations between errors that…

AR Model in Python

Intro The auto regression model, or AR model, predicts a value at a particular time using previous lags (values at previous times). The model relies on the correlations between lags, or auto…

AR Model in R

Intro The auto regression model, or AR model, predicts a value at a particular time using previous lags (values at previous times). The model relies on the correlations between lags, or auto…

Python White Noise Simuation

Intro White noise is a base line model that appears when we have removed correlations and difference. The model is a simple list of random errors and serves as a base for many time series models. In…

R White Noise Simuation

Intro White noise is a base line model that appears when we have removed correlations and difference. The model is a simple list of random errors and serves as a base for many time series models. In…

Python Random Walk Simuation

Intro Random walks are one of the fundamental time series models. Despite this simplicity, they are able to model many real world scenarios. In this article, we will learn how to simulate a random…

R Random Walk Simuation

Intro Random walks are one of the fundamental time series models. Despite this simplicity, they are able to model many real world scenarios. In this article, we will learn how to simulate a random…

How to Check Stationarity of Time Series Data in Python

Intro Before modeling a time series data set, we often want to check if the data is stationary. Many models assume stationary time series, and if this assumption is violated, our forcast will not be…

How to Check Stationarity of Time Series data in R

Intro Before modeling a time series data set, we often want to check if the data is stationary. Many models assume stationary time series, and if this assumption is violated, our forcast will not be…

Second Order Exponential Smoothing in R

Intro Second Order Exponential Smoothing extends Simple Exponential Smoothing by adding a Trend Smoother. If SES doesn’t work well, we can see if there is a trend and add another component to our…

Second Order Exponential Smoothing in Python

Intro Second Order Exponential Smoothing extends Simple Exponential Smoothing by adding a Trend Smoother. If SES doesn't work well, we can see if there is a trend and add another component to our…

Simple Exponential Smoothing in R

Intro Simple Exponential Smoothing is a forecasting model that extends the basic moving average by adding weights to previous lags. As the lags grow, the weight, alpha, is decreased which leads to…

Simple Exponential Smoothing in Python

Intro Simple Exponential Smoothing is a forecasting model that extends the basic moving average by adding weights to previous lags. As the lags grow, the weight, alpha, is decreased which leads to…

Time Series Decomposition in R

Intro When working with time series data, we often want to decompose a time series into several components. We usually want to break out the trend, seasonality, and noise. In this article, we will…

Time Series Decomposition in Python

Intro When working with time series data, we often want to decompose a time series into several components. We usually want to break out the trend, seasonility, and noise. In this article, we will…

How to Perform a Ljung-Box Test in Python

Intro When working with time series, we deal with autocorrelation often. In our toolkit, we have a statistical test to check if a time series contains an autocorrelation. That test is Ljung-Box. In…

How to Conduct a Ljung-Box Test in R

Intro When working with time series, we deal with autocorrelation often. In our toolkit, we have a statistical test to check if a time series contains an autocorrelation. That test is Ljung-Box. In…

Detrending Time Series in Python

Intro A common task in time series analysis is taking the difference or detrending of a series. This is often used to take a non-stationary time series and make it stationary. In this article, we will…

Detrending Time Series in R

Intro A common task in time series analysis is taking the difference or detrending of a series. This is often used to take a non-stationary time series and make it stationary. In this article, we will…

Python Rolling Mean

Intro When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week…

Moving Average in R

Intro When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week…

Augmented Dickey-Fuller Test in Python

Intro In time series analysis, we often want to check if a time series is stationary. This is because when modeling, most of our techniques rely on stationary time series. One way to check for a…

Augmented Dickey-Fuller Test in R

Intro In time series analysis, we often want to check if a time series is stationary. This is because when modeling, most of our techniques rely on stationary time series. One way to check for a…

Plot ACF Python

Intro The autocorrelation function measures the correlations between an observation and its previous lag in a time series model. These functions are often used to determine which time series model to…

ACF Plot in R

Intro The autocorrelation function measures the correlations between an observation and its previous lag in a time series model. These functions are often used to determine which time series model to…

R Resample Time Series

Intro Resampling is a common task when working with time series dta. Resampling goes in two directions, upsampling and downsampling. Upsampling allows us to go from a lower time frame to a higher, i.e…

Pandas Resample Time Series

Intro Resampling is a common task when working with time series dta. Resampling goes in two directions, upsampling and downsampling. Upsampling allows us to go from a lower time frame to a higher, i.e…

How to Plot a Timeseries in Python

Intro When working with time series models, we would often like to plot the data to see how it changes over time. This is a simply line plot, but the x-axis is always dates. In this article, we will…

How to Plot KMeans Clusters in Python

Intro When modeling clusters with algorithms such as KMeans, it is often helpful to plot the clusters and visualize the groups. This can be done rather simply by filtered our data set and using…

How to Filter and Subset a Time Series in Python

Intro Using time series is a common task in data science with python. We often want to select specific information based on dates or a date range. In this article, we will learn how to index and…

Subsetting a Time Series in R

Intro When working with time series, we often want to access a subset of our data based on a range of dates. When using data frames, we have many ways to index and subset data. With the help of the R…

Subsetting a Time Series in R

Intro When working with time series, we often want to access a subset of our data based on a range of dates. When using data frames, we have many ways to index and subset data. With the help of the R…

Ordinal Encoding in Python

Intro Ordinal Encoding is similar to Label Encoding where we take a list of categories and convert them into integers. However, unlike Label Encoding, we preserve and order. For example, if we are…

Label Encoding in Python

Intro Label Encoding is one of many encoding techniques to convert your categorical variables into numerical variables. This is a requirement for many machine learning algorithms. Label Encoding is…

How to Create a Timeseries in Python

Intro Time series is one of the most common analysis and modeling in Data Science. In this article, we will learn how to create time series in python. Creating a Basic Time Series To create a time…

Ordinal Encoding in R

Intro Ordinal Encoding is similar to Label Encoding where we take a list of categories and convert them into integers. However, unlike Label Encoding, we preserve and order. For example, if we are…

One Hot Encoding in Python

Intro One hot encoding is a method of converting categorical variables into numerical form. It is a preprocessing needed for some machine learning algorithms to improve performance. In this article…

Label Encoding in R

Intro Label Encoding is one of many encoding techniques to convert your categorical variables into numerical variables. This is a requirement for many machine learning algorithms. Label Encoding is…

How to create time series in r

Intro Time series is one of the most common analysis and modeling in Data Science. In this article, we will learn how to create time series in R. Creating a Basic Time Series Let’s say we had a vector…

How to plot time series in R

Intro When working with time series models, we would often like to plot the data to see how it changes over time. This is a simply line plot, but the x-axis is always dates. In this article, we will…

Box Cox in Python

Intro A Box-Cox transformation is a preprocessing technique used to transform a distribution into a normally distributed one. Normal distribution is often a requirement, especially for linear…

Box Cox in R

Intro A Box-Cox transformation is a preprocessing technique used to transform a distribution into a normally distributed one. Normal distribution is often a requirement, especially for linear…

Cubist Regression in R

Intro Cubist is a rule based model that builds regression solutions based on building rules. In this article, we will learn how to use cubist model in r. Data For this tutorial, we will use the Boston…

Boosted Tree Regression in R

Intro Boosted Trees are commonly used in regression. They are an ensemble method similar to bagging, however, instead of building mutliple trees in parallel, they build tress sequentially. They used…

Random Forest in R

Intro Random Forest is a common tree model that uses the bagging technique. Many trees are built up in parallel and used to build a single tree model. In this article, we will learn how to use random…

Decision Tree Regression in R

Intro Decision Trees model regression problems by split data based on different values. This ends by creating a tree structure that you can follow to find the solution. In this article, we will learn…

KNN Regression in R

Intro The KNN model will use the K-closest samples from the training data to predict. KNN is often used in classification, but can also be used in regression. In this article, we will learn how to use…

MARS Regression in R

Intro Multivariate Adaptive Regression Splines or MARS is a regression model that extends linear models to nonlinear. It essentially creates many piecewise functions to model your data. In this…

Pivot Table in R

Intro Pivot tables allow you to summarize groups of data easily. We can simply group data by different categorize and see summaries like totals, mean, etc. In this article, we will learn how to create…

SVM Regression in R

Intro SVM models are a varied model that can work for both regression and classification. They work to find a hyperplance between points and increase the margin. We will leave the math to a different…

One Hot Encoding in R

Intro One hot encoding is a method of converting categorical variables into numerical form. It is a preprocessing needed for some machine learning algorithms to improve performance. In this article…

Partial Least Squares in R

Intro Partial Least Squares is a machine learning model that helps solbe issues with multicollinearity. It has advantages of PCA regression in the sense that it is still easily interpretable and has…

PCA Regression in R

Intro PCA or Principal component regression is the process of using PCA to preprocess the data then running a linear regression model. The PCA process will give us new variables or predictors that we…

Ridge Regression in R

Intro Ridge regression is a modified linear regression model called a penalized regression. It adds a penalty to the linear regression model when optimizing to help with multicollinearity issues. In…

Lasso Regression in R

Intro Lasso regression is a model that builds on linear regression to solve for issues of multicolinearity. The optimization functin in lasso adds a shrinkage parameter which allows for remove…