import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # plotly import chart_studio.plotly as py from plotly.offline import init_notebook_mode, iplot init_notebook_mode(connected=True) import plotly…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import matplotlib.p…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np…

# import pandas import pandas as pd # creating a DataFrame pd.DataFrame({'Yes': [50, 31], 'No': [101, 2]}) # another example of creating a dataframe pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland']}) pd.DataFram…

import pandas as pd # Data analysis import numpy as np #Data analysis import seaborn as sns # Data visualization import matplotlib.pyplot as plt # Data Visualization import matplotlib.gridspec as gridspec # subplots and grid from wordcloud import Wor…

import numpy as np import pandas as pd import os import matplotlib.pyplot as pl import seaborn as sns import warnings warnings.filterwarnings('ignore') data = pd.read_csv('F:\\kaggleDataSet\\MedicalCostPersonal\\insurance.csv') data.head() data.isnul…

import os import numpy as np import pandas as pd from datetime import datetime import matplotlib import matplotlib.pyplot as plt import seaborn as sns sns.set_style('white') %matplotlib inline %load_ext autoreload %autoreload 2 def to_utms(ut): retur…

import pandas as pd pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]}) pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']}) pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']},…

#We import libraries for linear algebra, graphs, and evaluation of results import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics impo…