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

import pandas as pd

import seaborn as sns

train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv')
original['id'] = original['Row#']
original.drop(columns=['Row#'], inplace=True)
original.set_index('id', inplace=True)

fig, ax = plt.subplots(1, 1, figsize=(15, 12))
sns.heatmap(train.corr(), annot=True)

fig, ax = plt.subplots(1, 1, figsize=(15, 12))
sns.heatmap(original.corr(), annot=True)

fig, ax = plt.subplots(1, 1, figsize=(15, 12))
sns.heatmap(train.corr() - original.corr(), annot=True)

fig, ax = plt.subplots(1, 3, figsize=(20, 6), sharey=True)
for i, col in enumerate(['fruitset', 'fruitmass', 'seeds']):
    sns.scatterplot(train, x=col, y='yield', ax=ax[i])