tinvernizzi

import numpy
import sys

numpy.set_printoptions(threshold=numpy.nan)

seq1 ='MGEIGFTEKQEALVKESWEILKQDIPKYSLHFFSQILEIAPAAKGLFSFLRDSDEVPHNNPKLKAHAVKVFKMTCETAIQLREEGKVVVADTTLQYLGSIHLKSGVIDPHFEVVKEALLRTLKEGGEKYNEEVEGAWSQAYDHLALAIKTEMKQEES'

seq2 ='MEKVPGEMEIERRERSEELSEAERKAVQATWARLYANCEDVGVAILVRFFVNFPSAKQYFSQFKHMEEPLEMERSPQLRKHACRVMGALNTVVENLHDPEKVSSVLSLVGKAHALKHKVEPVYFKLSGVILEVIAEEFANDFPPETQRAWAKLRGLIYSHVTAAYKEVGWVQQVPNATTPPATLPSSGP'

seq3 ='MVLSAADKNNVKGIFTKIAGHAEEYGAETLERMFTTYPPTKTYFPHFDLSHGSAQIKGHGKKVVAALIEAANHIDDIAGTLSKLSDLHAHKLRVDPVNFKLLGQCFLVVVAIHHPAALTPEVHASLDKFLCAVGTVLTAKYR'

tinvernizzi

import pandas as pd
from sklearn.linear_model import LinearRegression

features = ['T2M']

train = pd.read_csv('./data/train.csv')
test = pd.read_csv('./data/test.csv')

train = train.dropna(axis=0, how = 'any')

tinvernizzi

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('white')

def plot_correlation_map(df):
    corr = train.corr()
    _, ax = plt.subplots(figsize=(12, 10))
    cmap = sns.diverging_palette(220, 10, as_cmap=True)