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3 years ago
pip install jovian --upgrade
Collecting jovianNote: you may need to restart the kernel to use updated packages. Downloading jovian-0.2.33-py2.py3-none-any.whl (67 kB) Collecting requests Using cached requests-2.25.1-py2.py3-none-any.whl (61 kB) Requirement already satisfied, skipping upgrade: pyyaml in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from jovian) (5.4.1) Collecting click Using cached click-7.1.2-py2.py3-none-any.whl (82 kB) Collecting uuid Downloading uuid-1.30.tar.gz (5.8 kB) Collecting idna<3,>=2.5 Using cached idna-2.10-py2.py3-none-any.whl (58 kB) Collecting chardet<5,>=3.0.2 Using cached chardet-4.0.0-py2.py3-none-any.whl (178 kB) Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from requests->jovian) (2020.11.8) Collecting urllib3<1.27,>=1.21.1 Downloading urllib3-1.26.3-py2.py3-none-any.whl (137 kB) Building wheels for collected packages: uuid Building wheel for uuid (setup.py): started Building wheel for uuid (setup.py): finished with status 'done' Created wheel for uuid: filename=uuid-1.30-py3-none-any.whl size=6505 sha256=4e32993338c2ba098ffea27b5c8174460ea820e791d227633968f3d4d86eb444 Stored in directory: c:\users\asus\appdata\local\pip\cache\wheels\1b\6c\cb\f9aae2bc97333c3d6e060826c1ee9e44e46306a178e5783505 Successfully built uuid Installing collected packages: idna, chardet, urllib3, requests, click, uuid, jovian Successfully installed chardet-4.0.0 click-7.1.2 idna-2.10 jovian-0.2.33 requests-2.25.1 urllib3-1.26.3 uuid-1.30

Import the Dataset

!pip install missingno
Collecting missingno Using cached missingno-0.4.2-py3-none-any.whl (9.7 kB) Requirement already satisfied: matplotlib in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from missingno) (3.3.2) Requirement already satisfied: seaborn in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from missingno) (0.11.0) Requirement already satisfied: scipy in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from missingno) (1.5.0) Requirement already satisfied: numpy in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from missingno) (1.19.2) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (2.4.7) Requirement already satisfied: python-dateutil>=2.1 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (2.8.1) Requirement already satisfied: kiwisolver>=1.0.1 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (1.2.0) Requirement already satisfied: certifi>=2020.06.20 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (2020.11.8) Requirement already satisfied: pillow>=6.2.0 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (8.0.1) Requirement already satisfied: cycler>=0.10 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from matplotlib->missingno) (0.10.0) Requirement already satisfied: pandas>=0.23 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from seaborn->missingno) (1.1.3) Requirement already satisfied: six>=1.5 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from python-dateutil>=2.1->matplotlib->missingno) (1.15.0) Requirement already satisfied: pytz>=2017.2 in e:\machine-learning\kaggle\uber-lift-price-prediction\env\lib\site-packages (from pandas>=0.23->seaborn->missingno) (2020.1) Installing collected packages: missingno Successfully installed missingno-0.4.2 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import jovian
import missingno
dataset = pd.read_csv('data/airplane_crashes_and_fatalities_since_1908.csv')
dataset.head()