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Let's take a look at the numerical attributes of the training data:
Use the describe() function on the training data:
<< your code comes here >>.describe()
What is the mean of the age of the passengers?
What is the maximum fare given?
Note: Kindly copy the complete values you are getting after running the describe command instead of typing it.
describe
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1 Titanic Machine Learning Project - About the dataset
2 Titanic Machine Learning Project - Step 1 - Import Dataset
3 Titanic Machine Learning Project - Download and Import the Titanic dataset
4 Titanic Machine Learning Project - Split into Train and Test Set
5 Titanic Machine Learning Project - Step 2 - Explore the Data
6 Titanic Machine Learning Project - Explore the Training Data
7 Titanic Machine Learning Project - Look at the Numerical Attributes
8 Titanic Machine Learning Project - Find Number of Female Passengers
9 Titanic Machine Learning Project - Step 3 - Create Pipelines
10 Titanic Machine Learning Project - Create Processing Pipeline
11 Titanic Machine Learning Project - Create Pipeline for the Numerical Attributes
12 Titanic Machine Learning Project - Create an Imputer for String Categorical Columns
13 Titanic Machine Learning Project - Build the Pipeline for the Categorical Attributes
14 Titanic Machine Learning Project - Join both the Pipelines
15 Titanic Machine Learning Project - Step 4 - Train SVC Classifier
16 Titanic Machine Learning Project - Train an SVC Classifier
17 Titanic Machine Learning Project - Predict using Test Set
18 Titanic Machine Learning Project - Step 5 - Evaluate SVC Model
19 Titanic Machine Learning Project - Evaluate our SVC Model
20 Titanic Machine Learning Project - Step 6 - Train Random Forest Classifier
21 Titanic Machine Learning Project - Train a RandomForest Classifier
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