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Supervised Learning in R: Classification

By Salerno on August 19, 2020

Chapter 1 - k-Nearest Neighbors (kNN) 1.1 - Recognizing a road sign with kNN After several trips with a human behind the wheel, it is time for the self-driving car to attempt the test course alone. As it begins to drive away, its camera captures the following image: Figure 1: A caption Can you apply a kNN classifier to help the car recognize this sign? The dataset signs must be loaded in your workspace along with the dataframe next_sign, which holds the observation you want to classify.

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Deploying R Model as API Web Service using Docker and Microsoft Azure

By Bruno Ferrari on April 22, 2020

Objective Our goal here is to create a R Model and put-in into production by deploying it as web service API using Docker to containerize (encapsulate) it and Microsoft Azure to host it. R Model To create the model, we going to use mtcars dataset which one’s is present inside R. head(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.

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Credit Card Fraud Detection

By Bruno Ferrari on March 26, 2020

Objective Our goal is to train a Neural Network to detect fraudulent credit card transactions in a dataset referring to two days transactions by european cardholders. Source: https://www.kaggle.com/mlg-ulb/creditcardfraud/data Data credit = read.csv(path) The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days. As we can see, this dataset consists of thirty explanatory variables, and a response variable which represents whether a transation was a fraud or not.

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Intermediate Importing Data in Python

By Salerno on March 21, 2020

1. Importing flat files from the web: your turn! # Import package from urllib.request import urlretrieve # Import pandas import pandas as pd # Assign url of file: url url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv' # Save file locally urlretrieve(url, 'winequality-red.csv') # Read file into a DataFrame and print its head ## ('winequality-red.csv', <http.client.HTTPMessage object at 0x000000001FBF52C8>) df = pd.read_csv('winequality-red.csv', sep=';') print(df.head()) ## fixed acidity volatile acidity citric acid ... sulphates alcohol quality ## 0 7.

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