Tag: python regex

Python MySQL Create Table

Creating a Table

To create a table in MySQL, use the “CREATE TABLE” statement.

Make sure you define the name of the database when you create the connection

Example

Create a table named “customers”:

import mysql.connector

mydb = mysql.connector.connect(
  host="localhost",
  user="yourusername",
  password="yourpassword",
  database="mydatabase"
)

mycursor = mydb.cursor()

mycursor.execute("CREATE TABLE customers (name VARCHAR(255), address VARCHAR(255))")

If the above code was executed with no errors, you have now successfully created a table. Continue reading Python MySQL Create Table

Python MySQL Create Database

Creating a Database

To create a database in MySQL, use the “CREATE DATABASE” statement:

Example

create a database named “mydatabase”:

import mysql.connector

mydb = mysql.connector.connect(
  host="localhost",
  user="yourusername",
  password="yourpassword"
)

mycursor = mydb.cursor()

mycursor.execute("CREATE DATABASE mydatabase")

If the above code was executed with no errors, you have successfully created a database. Continue reading Python MySQL Create Database

Python MySQL

Python can be used in database applications.

One of the most popular databases is MySQL.

MySQL Database

To be able to experiment with the code examples in this tutorial, you should have MySQL installed on your computer.

You can download a MySQL database at

https://www.mysql.com/downloads/

Continue reading Python MySQL

Machine Learning – K-nearest neighbors (KNN)

KNN

KNN is a simple, supervised machine learning (ML) algorithm that can be used for classification or regression tasks – and is also frequently used in missing value imputation. It is based on the idea that the observations closest to a given data point are the most “similar” observations in a data set, and we can therefore classify unforeseen points based on the values of the closest existing points. By choosing K, the user can select the number of nearby observations to use in the algorithm.

Here, we will show you how to implement the KNN algorithm for classification, and show how different values of K affect the results. Continue reading Machine Learning – K-nearest neighbors (KNN)

Machine Learning – AUC – ROC Curve

AUC – ROC Curve

In classification, there are many different evaluation metrics. The most popular is accuracy, which measures how often the model is correct. This is a great metric because it is easy to understand and getting the most correct guesses is often desired. There are some cases where you might consider using another evaluation metric.

Another common metric is AUC, area under the receiver operating characteristic (ROC) curve. The Reciever operating characteristic curve plots the true positive (TP) rate versus the false positive (FP) rate at different classification thresholds. The thresholds are different probability cutoffs that separate the two classes in binary classification. It uses probability to tell us how well a model separates the classes. Continue reading Machine Learning – AUC – ROC Curve

Machine Learning – Cross Validation

Cross Validation

When adjusting models we are aiming to increase overall model performance on unseen data. Hyperparameter tuning can lead to much better performance on test sets. However, optimizing parameters to the test set can lead information leakage causing the model to preform worse on unseen data. To correct for this we can perform cross validation. Continue reading Machine Learning – Cross Validation

Machine Learning – Bootstrap Aggregation (Bagging)

Bagging

Methods such as Decision Trees, can be prone to overfitting on the training set which can lead to wrong predictions on new data.

Bootstrap Aggregation (bagging) is a ensembling method that attempts to resolve overfitting for classification or regression problems. Bagging aims to improve the accuracy and performance of machine learning algorithms. It does this by taking random subsets of an original dataset, with replacement, and fits either a classifier (for classification) or regressor (for regression) to each subset. The predictions for each subset are then aggregated through majority vote for classification or averaging for regression, increasing prediction accuracy. Continue reading Machine Learning – Bootstrap Aggregation (Bagging)

Machine Learning – K-means

K-means

K-means is an unsupervised learning method for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.

Here, we will show you how to estimate the best value for K using the elbow method, then use K-means clustering to group the data points into clusters.

How does it work?

First, each data point is randomly assigned to one of the K clusters. Then, we compute the centroid (functionally the center) of each cluster, and reassign each data point to the cluster with the closest centroid. We repeat this process until the cluster assignments for each data point are no longer changing.

K-means clustering requires us to select K, the number of clusters we want to group the data into. The elbow method lets us graph the inertia (a distance-based metric) and visualize the point at which it starts decreasing linearly. This point is referred to as the “elbow” and is a good estimate for the best value for K based on our data. Continue reading Machine Learning – K-means

Preprocessing – Categorical Data

Categorical Data

When your data has categories represented by strings, it will be difficult to use them to train machine learning models which often only accepts numeric data.

Instead of ignoring the categorical data and excluding the information from our model, you can tranform the data so it can be used in your models.

Continue reading Preprocessing – Categorical Data

Machine Learning – Grid Search

Grid Search

The majority of machine learning models contain parameters that can be adjusted to vary how the model learns. For example, the logistic regression model, from sklearn, has a parameter C that controls regularization,which affects the complexity of the model.

How do we pick the best value for C? The best value is dependent on the data used to train the model. Continue reading Machine Learning – Grid Search