# Pokemon Data Analyzing in Python Using Machine Learning

The project aims to see the mean value of Pokemon attack and defense and also plot a graph between attack and defense

Step: 1

First, we will import the necessary libraries then we upload the dataset

```import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
```
```P = pd.read_csv('Pokemon_A.csv')
P```
`P.info()`

Step: 2

Now we see agglomeration values

```Q=P[['Attack','Defence']]
```
```def doAgglomerative(X, nclust=2):
Clust_Labels = Model.fit_predict(X)
return (Clust_Labels)

Clust_Labels = doAgglomerative(Q,20)
agglomerative = pd.DataFrame(Clust_Labels)
Q.insert((Q.shape[1]),'agglomerative',agglomerative)```
```T=Q
T['Name']=P['Name']
T```
```for i in range(20):
names0=T[T['agglomerative']==i]['Name']
print('[ Agglomerative '+str(i)+' ]')
print(list(set(names0)))
print()```

Step: 3

Now we calculate the mean value of

`Q.groupby('agglomerative').mean()`

Step: 4

Now we plot a graph of Agglomerative Clustering between Attack and Defenses

```fig = plt.figure()
scatter = ax.scatter(Q['Attack'],Q['Defence'],c=agglomerative[0],s=50)
ax.set_title('Agglomerative Clustering')
ax.set_xlabel('Attack')
ax.set_ylabel('Defence')
plt.rcParams['figure.figsize'] = (6,5)
plt.colorbar(scatter)```