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Hackernoon logoImplementation of Data Preprocessing on Titanic Dataset by@afroz-chakure

Implementation of Data Preprocessing on Titanic Dataset

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@afroz-chakureAfroz Chakure

A book fanatic who loves coding in python

What is Required ?

  1. Python, Numpy, Pandas
  2. Kaggle titanic dataset : https://www.kaggle.com/c/titanic-gettingStarted/data

Goal

The machine learning model is supposed to predict who survived during the titanic shipwreck.

Here I will show you how to apply preprocessing techniques on the Titanic dataset.

Why do we need Preprocessing ?

For machine learning algorithms to work, it is necessary to convert the raw data into a clean data set and dataset must be converted to numeric data. You have to encode all the categorical lables to column vectors with binary values.  Missing values or NaNs in the dataset is an annoying problem. You have to either drop the missing rows or fill them up with a mean or interpolated  values..

Note: Kaggle provides 2 datasets: train and results data separately. Both must have same dimensions for the model.

Loading data in pandas

To work on the data, you can either load the CSV in excel software or in pandas. Lets load the csv data in pandas.

df = pd.read_csv('train.csv')

Lets take a look at the data format below

>>> df.info()
 <class 'pandas.core.frame.DataFrame'>
 Int64Index: 891 entries, 0 to 890
 Data columns (total 12 columns):
 PassengerId 891 non-null int64
 Survived 891 non-null int64
 Pclass 891 non-null int64
 Name 891 non-null object
 Sex 891 non-null object
 Age 714 non-null float64
 SibSp 891 non-null int64
 Parch 891 non-null int64
 Ticket 891 non-null object
 Fare 891 non-null float64
 Cabin 204 non-null object
 Embarked 889 non-null object

If you carefully observe the above summary of pandas, there are total 891 rows, Age shows only 714 (means missing), Embarked (2 missing) and Cabin missing a lot as well. Object data types are non-numeric so we have to find a way to encode them to numerical values.

Dropping Columns which are not useful

Lets try to drop some of the columns which many not contribute much to our machine learning model such as Name, Ticket, Cabin etc.

cols = ['Name', 'Ticket', 'Cabin']
df = df.drop(cols, axis=1)

We dropped 3 columns:

>>>df.info()
 PassengerId 891 non-null int64
 Survived 891 non-null int64
 Pclass 891 non-null int64
 Sex 891 non-null object
 Age 714 non-null float64
 SibSp 891 non-null int64
 Parch 891 non-null int64
 Fare 891 non-null float64
 Embarked 889 non-null object

Dropping rows having missing values

Next if we want we can drop all rows in the data that has missing values (NaN).  You can do it like

df = df.dropna()
>>>df.info()
 Int64Index: 712 entries, 0 to 890
 Data columns (total 9 columns):
 PassengerId 712 non-null int64
 Survived 712 non-null int64
 Pclass 712 non-null int64
 Sex 712 non-null object
 Age 712 non-null float64
 SibSp 712 non-null int64
 Parch 712 non-null int64
 Fare 712 non-null float64
 Embarked 712 non-null object

Problem with dropping rows having missing values

After dropping rows with missing values we find that the dataset is reduced to 712 rows from 891, which means we are wasting data. Machine learning models need data for training to perform well. So we preserve the data and make use of it as much as we can. We will see it later.

Creating Dummy Variables

Now we convert the Pclass, Sex, Embarked to columns in pandas and drop them after conversion.

dummies = []
cols = ['Pclass', 'Sex', 'Embarked']
for col in cols:
    dummies.append(pd.get_dummies(df[col]))

then

titanic_dummies = pd.concat(dummies, axis=<span class="s1">1</span>)

We have 8 columns transformed to columns. 1,2,3 represents passenger class.

finally we concatenate to the original dataframe columnwise

df = pd.concat((df,titanic_dummies), axis=1)

Now that we converted Pclass, Sex, Embarked values into columns, we drop the redundant same columns from the dataframe

df = df.drop(['Pclass', 'Sex', 'Embarked'], axis=1)

Lets take a look on the new dataframe

>>>df.info()
 PassengerId 891 non-null int64
 Survived 891 non-null int64
 Age 714 non-null float64
 SibSp 891 non-null int64
 Parch 891 non-null int64
 Fare 891 non-null float64
 1 891 non-null float64
 2 891 non-null float64
 3 891 non-null float64
 female 891 non-null float64
 male 891 non-null float64
 C 891 non-null float64
 Q 891 non-null float64
 S 891 non-null float64

Taking Care of Missing Data

All is good, except age which has lots of missing values. Lets compute a median or interpolate() all the ages and fill those missing age values. Pandas has a interpolate() function that will replace all the missing NaNs to interpolated values.

df['Age'] = df['Age'].interpolate()

Now lets observe the data columns. Notice age which is interpolated now with imputed new values.

>>>df.info()
 Data columns (total 14 columns):
 PassengerId 891 non-null int64
 Survived 891 non-null int64
 Age 891 non-null float64
 SibSp 891 non-null int64
 Parch 891 non-null int64
 Fare 891 non-null float64
 1 891 non-null float64
 2 891 non-null float64
 3 891 non-null float64
 female 891 non-null float64
 male 891 non-null float64
 C 891 non-null float64
 Q 891 non-null float64

Converting the dataframe to numpy

Now that we have converted all the data to numeric, its time for preparing the data for machine learning models. This is where scikit and numpy come into play:

X = Input set with 14 attributes
y =  Small y Output, in this case ‘Survived’

Now we convert our dataframe from pandas to numpy and we assign input and output

X = df.values
y = df['Survived'].values

X has still Survived values in it, which should not be there. So we drop in numpy column which is the 1st column.

X = np.delete(X, 1, axis=1)

Dividing data set into training set and test set

Now that we are ready with X and y, lets split the dataset for 70% Training and  30% test set using scikit model_selection

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

And That's about it folks.

You have learned how to preprocess data in the titanic dataset. So go on, try it for yourself and start making your own predictions.

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