MLOps Day16

Binary Classification :-

• If you want to predict something and output of it is to be happen or not(0/1) this kind of problem solved under Binary classification. For this we use an algorithms/models is Sigmoid.
• To solve binary classification problems we use sklearn, sklearn call logistic regression and logistic regression internally use Sigmoid function.
• Hypothesis - Creating a model is also known a hypothesis. Today I am going to analysis 'Titanic' passenger data set, and try to create a model and try predict something so that what we can do in future to avoid such casualties.
• Any data which has category is categorical data, doesn't matter if it contains integer or string.

import pandas as pd

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

dataset.head()

dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

dataset.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

import seaborn as sns

sns.set()

sns.countplot(dataset['Survived'])

<matplotlib.axes._subplots.AxesSubplot at 0xca7c788>

sns.countplot(dataset['Survived'], hue='Sex', data=dataset)

<matplotlib.axes._subplots.AxesSubplot at 0xe6bac88>

sns.countplot(dataset['Survived'], hue='Pclass', data=dataset)

<matplotlib.axes._subplots.AxesSubplot at 0xe36aec8>

sns.heatmap(dataset.isnull(), cbar=False, yticklabels=False, cmap='viridis')

<matplotlib.axes._subplots.AxesSubplot at 0xfe72248>

age = dataset['Age']

sns.distplot(age.dropna())

<matplotlib.axes._subplots.AxesSubplot at 0xffb9d88>

sns.countplot(dataset['SibSp'], data=dataset, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x100d7448>

• If have have null values in a column and that column you want to use as a feature, because it has very much weightage then we have to feature engineering on it and this type of feature engieering is known as Imputation.
• Imputation is the process of replacing values into substitute values.
• We can find out mean using boxplot like below

sns.boxplot(data=dataset, y='Age',x='Pclass')

<matplotlib.axes._subplots.AxesSubplot at 0x10453f48>

def n_age(cols):

    age = cols[0]

    Pclass = cols[1]

    if pd.isnull(age):

        if Pclass == 1:

            return 38

        elif Pclass == 2:

            return 30

        elif Pclass == 3:

            return 25

        else:

            return 30

    else:

        return age

dataset['Age'] = dataset[['Age', 'Pclass']].apply(n_age,axis=1)

dataset['Age']

sns.heatmap(dataset.isnull(), cbar=False, yticklabels=False, cmap='viridis')

<matplotlib.axes._subplots.AxesSubplot at 0x10a5d448>

dataset.drop('Cabin', axis=1, inplace=True)

sns.heatmap(dataset.isnull(), cbar=False, yticklabels=False, cmap='viridis')

<matplotlib.axes._subplots.AxesSubplot at 0x10446d88>

• We have removed all the Null values, this process is known as data cleaning.
• Please check next post for further practical of model creation......