Machine Learning with Python and R

Collection by Michael Grogan

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Michael Grogan
Machine Learning Consultant and Educator - TensorFlow and Time Series Specialist Electricity Usage, Electricity Consumption, Normal Values, Weather Data, Time Series, Data Processing, Machine Learning, Python

Predicting Irish electricity consumption with neural networks

In this example, neural networks are used to forecast energy consumption of the Dublin City Council Civic Offices using data between April 2011 – February 2013. The original dataset is available from data.gov.ie, and daily data was created by summing …

“Ever wonder why the coldest days come some weeks after the winter solstice (shortest night of the year)? Here's an example of Irish weather data with ccf Irish Weather, Weather Data, Winter Solstice, Machine Learning, Python

Seasonal Lags

“Ever wonder why the coldest days come some weeks after the winter solstice (shortest night of the year)? Seasonal lag. Here's an example of Irish weather data with ccf #rstats #rlang”

Machine Learning Consultant and Educator - TensorFlow and Time Series Specialist Web Application, Machine Learning, Python

Cumulative Binomial Probability with R and Shiny

Here is how we can generate a cumulative binomial probability distribution in R, and then create a Shiny Web App to visualise probability curves.

A decision tree is a model used to solve classification and regression tasks. Internet Usage, Decision Tree, Data Science, Open Up, Machine Learning, Python

Decision Trees with Python

A decision tree is a model used to solve classification and regression tasks. In this particular example, we will analyse the effect of various explanatory variables (age, gender, web pages viewed…

  Negative Words, Positive Words, Sentiment Analysis, Data Science, Machine Learning, Python, Texts

Sentiment Analysis with twitteR and tidytext

Here is how we can use social media data from Twitter to conduct a sentiment analysis using twitteR and tidytext. In this example, we will see how to conduct a sentiment analysis on the search term "gold prices".

Robust Regressions: Dealing with Outliers in R Logistic Regression, P Value, Linear Regression, Degrees Of Freedom, Internet Usage, Rule Of Thumb, Data Analytics, Data Science

Robust Regressions: Dealing with Outliers

It is often the case that a dataset contains significant outliers – or observations that are significantly out of range from the majority of other observations in our dataset. Let us see how we can use robust regressions to deal …

Machine Learning Consultant and Educator - TensorFlow and Time Series Specialist Kalman Filter, Time Series, Moving Average, Machine Learning, Python, Filters

Kalman Filter: Modelling Time Series Shocks with KFAS in R

A Kalman Filter allows for modelling of time series while taking into account shocks, or sudden changes in a time series trend.

 Here is how to construct this regression with Python. Regression Analysis, Linear Regression, Internet Usage, Variables, Data Science, Machine Learning, Python, Accounting

Huber vs. Ridge Regressions: Accounting for Outliers

Huber regressions work by adjusting weights to give less importance to extreme values within a sample. Here is how to construct this regression with Python.

  Neurons, Data Science, Machine Learning, Python, Cars For Sale, Nerve Cells, Cars For Sell

Keras with R: Predicting car sales

Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. In this particular example, a neural network will be built in Keras to…

ALT Datum Visualizing New York City WiFi Access with K-Means Clustering Data Analytics Map Of New York, New York City, Sum Of Squares, Good Meaning, Information Design, Data Analytics, Data Science, Wifi, Illustration

Visualizing New York City WiFi Access with K-Means Clustering

Here is how a K-Means Clustering algorithm can be used to visualize WiFi zones by density across New York City.

One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, Regression Analysis, Linear Regression, Standard Deviation, Variables, Machine Learning, Python

Multilevel Modelling in R: Analysing Vendor Data

One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, i.e. one that varies at more than one level and allows for variation between different groups or categories. This dataset from data.ok.gov contains information on […]

Cross correlation allows us to determine the strength of a correlation between two time series. Here is how we can determine cross correlation in Python. P Value, Standard Deviation, Time Series, Machine Learning, Python, Texts, Positivity, Captions

Cross Correlation Analysis: Analysing Currency Pairs in Python

Cross correlation allows us to determine the strength of a correlation between two time series. Here is how we can determine cross correlation in Python.

Welcome to Data Science Central. The Community of and for Data Scientist Internet Usage, Data Show, Data Science, Machine Learning, Python, Case Study

K-Nearest Neighbors (KNN): Solving Classification Problems

Here is how we can use the K-Nearest Neighbors (KNN) algorithm in Python to determine the internet usage of consumers based on various attributes.

  Neurons, Machine Learning, Python, Nerve Cells

Keras: Regression-based neural networks

Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. The main competitor to Keras at this point in time is PyTorch, developed by Facebook. While PyTorch has a somewhat higher level of community support, it is a particularly verbose language and I […]

Machine Learning Consultant and Educator - TensorFlow and Time Series Specialist Image Loading, Madrid City, Crop Image, What Is It Called, Facial Recognition, Traffic Light, Machine Learning, Python, Mansions

Image Recognition with Keras: Convolutional Neural Networks

Here is how we can build our own image classifier using Keras. In this example, we will use the VGG16 neural network to identify vehicles.