text_plain_output_1.png
from sklearn.model_selection import train_test_split

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')

df.isnull().sum().sum()

catg = []
for i in df.columns:
    if df[i].dtype == 'O':
        catg.append(i)

import seaborn as sns
correl = df.corr()

x = df.drop(['company_size'], axis=1)
y = df['company_size']
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)

k = np.sqrt(df.shape[0])
k