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On this page there are 6 sections

Data Science in VS Code tutorial

This tutorial demonstrates using Visual Studio Code and the Microsoft Python extension with common data science libraries to explore a basic data science scenario. Specifically, using passenger data from the Titanic, you will learn how to set up a data science environment, import and clean data, create a machine learning model for predicting survival on the Titanic, and evaluate the accuracy of the generated model.

Prerequisites

The following installations are required for the completion of this tutorial. Make sure to install them if you haven't already.

Visual Studio Code
The Python extension for VS Code and Jupyter extension for VS Code from the Visual Studio Marketplace. For more details on installing extensions, see Extension Marketplace. Both extensions are published by Microsoft.
Miniconda with latest Python

Note: If you already have the full Anaconda distribution installed, you don't need to install Miniconda. Alternatively, if you'd prefer not to use Anaconda or Miniconda, you can create a Python virtual environment and install the packages needed for the tutorial using pip. If you go this route, you will need to install the following packages: pandas, jupyter, seaborn, scikit-learn, keras, and tensorflow.
import pandas as pd import numpy as np data = pd.read_csv('titanic3.csv')
Now, run the cell using the Run cell icon or the Shift+Enter shortcut.
After the cell finishes running, you can view the data that was loaded using the Variables Explorer and Data Viewer. First select the Variables icon in the notebook's upper toolbar.
A JUPYTER: VARIABLES pane will open at the bottom of VS Code. It contains a list of the variables defined so far in your running kernel.
To view the data in the Pandas DataFrame previously loaded, select the Data Viewer icon to the left of the data variable.
Use the Data Viewer to view, sort, and filter the rows of data. After reviewing the data, it can then be helpful to graph some aspects of it to help visualize the relationships between the different variables.

Alternatively, you can use the data viewing experience offered by other extensions like Data Wrangler. The Data Wrangler extension offers a rich user interface to show insights about your data and helps you perform data profiling, quality checks, transformations, and more. Learn more about the Data Wrangler extension in our docs.
Before the data can be graphed, you need to make sure that there aren't any issues with it. If you look at the Titanic csv file, one thing you'll notice is that a question mark ("?") was used to identify cells where data wasn't available.

While Pandas can read this value into a DataFrame, the result for a column like age is that its data type will be set to object instead of a numeric data type, which is problematic for graphing.

This problem can be corrected by replacing the question mark with a missing value that pandas is able to understand. Add the following code to the next cell in your notebook to replace the question marks in the age and fare columns with the numpy NaN value. Notice that we also need to update the column's data type after replacing the values.

Tip: To add a new cell you can use the insert cell icon that's in the bottom left corner of an existing cell. Alternatively, you can also use the Esc to enter command mode, followed by the B key.
import seaborn as sns import matplotlib.pyplot as plt fig, axs = plt.subplots(ncols=5, figsize=(30,5)) sns.violinplot(x="survived", y="age", hue="sex", data=data, ax=axs[0]) sns.pointplot(x="sibsp", y="survived", hue="sex", data=data, ax=axs[1]) sns.pointplot(x="parch", y="survived", hue="sex", data=data, ax=axs[2]) sns.pointplot(x="pclass", y="survived", hue="sex", data=data, ax=axs[3]) sns.violinplot(x="survived", y="fare", hue="sex", data=data, ax=axs[4])

Tip: To quickly copy your graph, you can hover over the upper right corner of your graph and click on the Copy to Clipboard button that appears. You can also better view details of your graph by clicking the Expand image button.
These graphs are helpful in seeing some of the relationships between survival and the input variables of the data, but it's also possible to use pandas to calculate correlations. To do so, all the variables used need to be numeric for the correlation calculation and currently gender is stored as a string. To convert those string values to integers, add and run the following code.
data.corr(numeric_only=True).abs()[["survived"]]
Looking at the correlation results, you'll notice that some variables like gender have a fairly high correlation to survival, while others like relatives (sibsp = siblings or spouse, parch = parents or children) seem to have little correlation.

Let's hypothesize that sibsp and parch are related in how they affect survivability, and group them into a new column called "relatives" to see whether the combination of them has a higher correlation to survivability. To do this, you will check if for a given passenger, the number of sibsp and parch is greater than 0 and, if so, you can then say that they had a relative on board.

Use the following code to create a new variable and column in the dataset called relatives and check the correlation again.
data = data[['sex', 'pclass','age','relatives','fare','survived']].dropna()
Note: Although age had a low direct correlation, it was kept because it seems reasonable that it might still have correlation in conjunction with other inputs.

Train and evaluate a model

With the dataset ready, you can now begin creating a model. For this section, you'll use the scikit-learn library (as it offers some useful helper functions) to do pre-processing of the dataset, train a classification model to determine survivability on the Titanic, and then use that model with test data to determine its accuracy.

A common first step to training a model is to divide up the dataset into training and validation data. This allows you to use a portion of the data to train the model and a portion of the data to test the model. If you used all your data to train the model, you wouldn't have a way to estimate how well it would actually perform against data the model hasn't yet seen. A benefit of the scikit-learn library is that it provides a method specifically for splitting a dataset into training and test data.

Add and run a cell with the following code to the notebook to split up the data.
from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(x_train) X_test = sc.transform(x_test)
There are many different machine learning algorithms that you could choose from to model the data. The scikit-learn library also provides support for many of them and a chart to help select the one that's right for your scenario. For now, use the Naïve Bayes algorithm, a common algorithm for classification problems. Add a cell with the following code to create and train the algorithm.
from sklearn import metrics predict_test = model.predict(X_test) print(metrics.accuracy_score(y_test, predict_test))

Looking at the result of the test data, you'll see that the trained algorithm had a ~75% success rate at estimating survival.

(Optional) Use a neural network

A neural network is a model that uses weights and activation functions, modeling aspects of human neurons, to determine an outcome based on provided inputs. Unlike the machine learning algorithm you looked at previously, neural networks are a form of deep learning wherein you don't need to know an ideal algorithm for your problem set ahead of time. It can be used for many different scenarios and classification is one of them. For this section, you'll use the Keras library with TensorFlow to construct the neural network, and explore how it handles the Titanic dataset.