1594119480

Soccer (European football) has been one of my favorite sports ever since I was little. I used to bring a ball with me anywhere I went so that I could maximize my opportunity to play soccer.I also like playing soccer games — FIFA. I thought it would be cool to analyze players in FIFA using machine learning.In this tutorial, I am going to use the K-Means clustering algorithm to group soccer players in FIFA 20 with similar skillsets.

Clustering is one of the unsupervised learning techniques (PCA is another one).We can cluster (or group) observations into the same subgroups so that observations within a subgroup are quite similar to each other and observations in different subgroups are quite different from each other.

Clustering Example

The scatter plot above shows us that there are three distinct groups within the dataset.

K-Means clustering is one of the clustering algorithms.The basic algorithm is this:

- Specify K-clusters and initialize random centroidsIterate until the cluster assignments stop changing. The method assigns each observation to exactly one of the K clustersFor each K cluster, calculate the cluster meanProceed through the list of observations and assign an observation to the cluster whose mean is nearest.

The goal is to form the clusters in a way that the observations within the same cluster are as **similar** as possible.K-Means clustering calculates similarity using Squared Euclidean distance.

We are going to use Kaggle’s **FIFA 20 dataset****.**

We are only going to selected numeric values and each player’s name.

```
df = df[['short_name','age', 'height_cm', 'weight_kg', 'overall', 'potential',
'value_eur', 'wage_eur', 'international_reputation', 'weak_foot',
'skill_moves', 'release_clause_eur', 'team_jersey_number',
'contract_valid_until', 'nation_jersey_number', 'pace', 'shooting',
'passing', 'dribbling', 'defending', 'physic', 'gk_diving',
'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
'gk_positioning', 'attacking_crossing', 'attacking_finishing',
'attacking_heading_accuracy', 'attacking_short_passing',
'attacking_volleys', 'skill_dribbling', 'skill_curve',
'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
'movement_reactions', 'movement_balance', 'power_shot_power',
'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'mentality_composure', 'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes']]
```

#k-means-clustering #data-science #fifa-20 #data analysis

1600190040

SciPy is the most efficient open-source library in python. The main purpose is to compute mathematical and scientific problems. There are many sub-packages in SciPy which further increases its functionality. This is a very important package for data interpretation. We can segregate clusters from the data set. We can perform clustering using a single or multi-cluster. Initially, we generate the data set. Then we perform clustering on the data set. Let us learn more SciPy Clusters.

It is a method that can employ to determine clusters and their center. We can use this process on the raw data set. We can define a cluster when the points inside the cluster have the minimum distance when we compare it to points outside the cluster. The k-means method operates in two steps, given an initial set of k-centers,

- We define the cluster data points for the given cluster center. The points are such that they are closer to the cluster center than any other center.
- We then calculate the mean for all the data points. The mean value then becomes the new cluster center.

The process iterates until the center value becomes constant. We then fix and assign the center value. The implementation of this process is very accurate using the SciPy library.

#numpy tutorials #clustering in scipy #k-means clustering in scipy #scipy clusters #numpy

1621443060

This article provides an overview of core data science algorithms used in statistical data analysis, specifically k-means and k-medoids clustering.

Clustering is one of the major techniques used for statistical data analysis.

As the term suggests, “clustering” is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure.

*K-means* clustering is touted as a foundational algorithm every data scientist ought to have in their toolbox. The popularity of the algorithm in the data science industry is due to its extraordinary features:

- Simplicity
- Speed
- Efficiency

#big data #big data analytics #k-means clustering #big data algorithms #k-means #data science algorithms

1601196420

Clustering comes under the data mining topic and there is a lot of research going on in this field and there exist many clustering algorithms.

The following are the main types of clustering algorithms.

*K-Means**Hierarchical clustering**DBSCAN*

Following are some of the applications of clustering

- Customer Segmentation: This is one of the most important use-cases of clustering in the sales and marketing domain. Here the aim is to group people or customers based on some similarities so that they can come up with different action items for the people in different groups. One example could be, amazon giving different offers to different people based on their buying patterns.
- Image Segmentation: Clustering is used in image segmentation where similar image pixels are grouped together. Pixels of different objects in the image are grouped together.

#machine-learning #k-means-clustering #clustering #k-means

1595334123

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

```
Angular demo runner
Online angular editor for building demo.
ng-run.com
<>
```

Let me show what I mean…

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js

1594119480

Soccer (European football) has been one of my favorite sports ever since I was little. I used to bring a ball with me anywhere I went so that I could maximize my opportunity to play soccer.I also like playing soccer games — FIFA. I thought it would be cool to analyze players in FIFA using machine learning.In this tutorial, I am going to use the K-Means clustering algorithm to group soccer players in FIFA 20 with similar skillsets.

Clustering is one of the unsupervised learning techniques (PCA is another one).We can cluster (or group) observations into the same subgroups so that observations within a subgroup are quite similar to each other and observations in different subgroups are quite different from each other.

Clustering Example

The scatter plot above shows us that there are three distinct groups within the dataset.

K-Means clustering is one of the clustering algorithms.The basic algorithm is this:

- Specify K-clusters and initialize random centroidsIterate until the cluster assignments stop changing. The method assigns each observation to exactly one of the K clustersFor each K cluster, calculate the cluster meanProceed through the list of observations and assign an observation to the cluster whose mean is nearest.

The goal is to form the clusters in a way that the observations within the same cluster are as **similar** as possible.K-Means clustering calculates similarity using Squared Euclidean distance.

We are going to use Kaggle’s **FIFA 20 dataset****.**

We are only going to selected numeric values and each player’s name.

```
df = df[['short_name','age', 'height_cm', 'weight_kg', 'overall', 'potential',
'value_eur', 'wage_eur', 'international_reputation', 'weak_foot',
'skill_moves', 'release_clause_eur', 'team_jersey_number',
'contract_valid_until', 'nation_jersey_number', 'pace', 'shooting',
'passing', 'dribbling', 'defending', 'physic', 'gk_diving',
'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
'gk_positioning', 'attacking_crossing', 'attacking_finishing',
'attacking_heading_accuracy', 'attacking_short_passing',
'attacking_volleys', 'skill_dribbling', 'skill_curve',
'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
'movement_reactions', 'movement_balance', 'power_shot_power',
'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
'mentality_aggression', 'mentality_interceptions',
'mentality_positioning', 'mentality_vision', 'mentality_penalties',
'mentality_composure', 'defending_marking', 'defending_standing_tackle',
'defending_sliding_tackle', 'goalkeeping_diving',
'goalkeeping_handling', 'goalkeeping_kicking',
'goalkeeping_positioning', 'goalkeeping_reflexes']]
```

#k-means-clustering #data-science #fifa-20 #data analysis