# Euro Cup 2020 Analyzing in Python Using Machine learning

The project aim is to see how many goals are in the tournament, how many fouls are in this tournament and also see how many assists are done in this tournament using machine learning in Python.

Step: 1

First, we upload the necessary libraries and then we upload the dataset

```import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt```
```Euro_Cup = pd.read_csv("EURO_CUP_2020.csv")
Euro_Cup```
`Euro_Cup.columns`
`Euro_Cup.describe()`

Step: 2

Now we see the position

```plt.figure(figsize=(9,7))
Euro_Cup.Position.value_counts().plot(kind='pie', autopct='%.2f',colors=['tomato', 'gold', 'skyblue'])
plt.show()```

Here we see midfielders have a 41.38% position in this tournament

```plt.figure(figsize=(12,10))
Euro_Cup['Avg gpg'].value_counts().plot(kind='pie',autopct='%.2f')
plt.show()```
```plt.figure(figsize=(12,6))
plt.subplot(1,2,1)
sns.boxplot(x='On target',data=Euro_Cup)
plt.subplot(1,2,2)
sns.boxplot(x='Off target',data=Euro_Cup)
plt.show()```

Here we see how many goals are in on the target and off target

Step: 3

Now we see the total goal by a player

```EURO = Euro_Cup[Euro_Cup['Goals']>0]

#Plotting a barplot
plt.figure(figsize=(20,15))
plt.title('Total goals by players', fontsize=20)
sns.barplot(x='Player',y='Goals',data=EURO, order=EURO.sort_values('Goals',ascending = False).Player)
plt.xticks(rotation = 90)
plt.show()```

here we see the Cristiano Ronaldo is the top goal scorer

```plt.figure(figsize=(15,8))
plt.title('Total goals by Countries', fontsize=20)
sns.barplot(x='Country',y='Goals',data=EURO ,estimator=sum, ci=None)
plt.xticks(rotation = 90)
plt.show()```
```plt.figure(figsize=(14,6))
sns.set_theme(style="whitegrid")
plt.subplot(1,2,1)
plt.title("Right foot goals", fontsize=20)
sns.barplot(x='Right foot goals',y='Goals',data=Euro_Cup ,estimator=sum, ci=None)
plt.subplot(1,2,2)
plt.title("Left foot goals", fontsize=20)
sns.barplot(x='Left foot goals',y='Goals',data=Euro_Cup ,estimator=sum, ci=None)
plt.show()```

here we see how many goals from right foot and left foot

```Euro_Cup_Assists = Euro_Cup[Euro_Cup['Assists']>0]

plt.figure(figsize=(10,8))
sns.set_theme(style="whitegrid")
plt.title('Total Assists by players', fontsize=20)
sns.barplot(x='Player',y='Assists',data=Euro_Cup_Assists ,estimator=sum, ci=None,order=Euro_Cup_Assists.sort_values(by=['Assists'], ascending=False).Player)
plt.xticks(rotation = 90)
plt.show()```

Total assists by Players

Step: 4

```plt.figure(figsize=(10,6))
sns.set_theme(style="whitegrid")
sns.barplot(x='Country',y='Yellow cards',data=Euro_Cup,estimator=sum, ci=None)
plt.xticks(rotation = 90)
plt.show()```

here we see Italy have the most number of yellow cards

```plt.figure(figsize=(16,8))
plt.title('Total Fouls committed by players', fontsize=20)
sns.barplot(x='Player',y='Fouls committed',data=Euro_Cup,estimator=sum, ci=None,order=Euro_Cup.sort_values(by=['Fouls committed'], ascending=False).Player)
plt.xticks(rotation = 90)
plt.show()```

Here we see the total number of fouls committed by players