text_plain_output_1.png image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

n_features = 300
features = [f'f_{i}' for i in range(n_features)]
train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')

plt.figure(figsize=(25, 30))
plt.title('Pearson Correlation', y=1.05, size=15)
sns.heatmap(train[features].loc[:1000].corr(), annot=True)