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Updated 4 years ago
# 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
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
/kaggle/input/titanicdataset-traincsv/train.csv
Types of Transformations
- Standardization and Normalization
- Scaling to Minimum and Maximum values
- Scaling to Median and Quantiles
- Transformations
-
logarithmic transformation
-
reciprocal transformation
-
square root transformation
-
exponential transformation
-
boxcox transformation
Standardization
Centering the variables to zero.
z=(x-x_mean)/std
#taking only selected numeric data
df=pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv',usecols=['Pclass','Age','Fare','Survived'])
df.head()