-
Notifications
You must be signed in to change notification settings - Fork 0
/
Preprocessing.py
39 lines (33 loc) · 1.27 KB
/
Preprocessing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn import preprocessing
import seaborn as sns
def preprocess_Data(Songs_df):
songs_data = pd.read_csv('spotify_training.csv')
Songs_df = pd.DataFrame(songs_data)
Songs_df.dropna(how='any', inplace=True)
Songs_df = One_hot_encoder_KEY(Songs_df)
Songs_df = Labeling_Artists(Songs_df)
Correlation(Songs_df)
Songs_df.drop(['release_date', 'name', 'id', 'key'], axis=1, inplace=True)
return Songs_df
def One_hot_encoder_KEY(Songs_df):
art = OneHotEncoder(handle_unknown='ignore')
Artists_df = pd.DataFrame(art.fit_transform(Songs_df[['key']].values).toarray())
Songs_df = Songs_df.join(Artists_df)
return Songs_df
def Labeling_Artists(Songs_df):
labeling = preprocessing.LabelEncoder()
Songs_df['artists'] = (labeling.fit_transform(list(Songs_df['artists'].values)))
return Songs_df
def Correlation(Songs_df):
corr = Songs_df.corr()
top_feature = corr.index[abs(corr['popularity'] > 0.1)]
plt.subplots(figsize=(12, 8))
top_corr = Songs_df[top_feature].corr()
sns.heatmap(top_corr, annot=True)
plt.show()