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If we were asked to make a prediction about any passenger aboard the RMS Titanic whom we knew nothing about, then the best prediction we could make would be that they did not survive. Using the RMS Titanic data, how accurate would a prediction be that none of the passengers survived?

Hint: Run the code cell below to see the accuracy of this prediction. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. The third parameter indicates which feature we want to plot survival statistics across.

Run the code cell below to plot the survival outcomes of passengers based on their sex. Examining the survival statistics, a large majority of males did not survive the ship sinking. However, a majority of females did survive the ship sinking. Let's build on our previous prediction: If a passenger was female, then we will predict that they survived. Otherwise, we will predict the passenger did not survive. Fill in the missing code below so that the function will make this prediction.

Hint: You can access the values of each feature for a passenger like a dictionary. For example, passenger['Sex'] is the sex of the passenger. How accurate would a prediction be that all female passengers survived and the remaining passengers did not survive? Using just the Sex feature for each passenger, we are able to increase the accuracy of our predictions by a significant margin. Now, let's consider using an additional feature to see if we can further improve our predictions.

For example, consider all of the male passengers aboard the RMS Titanic: Can we find a subset of those passengers that had a higher rate of survival? This time, we'll use a fourth parameter to filter out the data so that only passengers with the Sex 'male' will be included. Run the code cell below to plot the survival outcomes of male passengers based on their age. Examining the survival statistics, the majority of males younger than 10 survived the ship sinking, whereas most males age 10 or older did not survive the ship sinking.

Let's continue to build on our previous prediction: If a passenger was female, then we will predict they survive. If a passenger was male and younger than 10, then we will also predict they survive. Otherwise, we will predict they do not survive.

How accurate would a prediction be that all female passengers and all male passengers younger than 10 survived? Adding the feature Age as a condition in conjunction with Sex improves the accuracy by a small margin more than with simply using the feature Sex alone. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions.

Hint: To use mulitple filter conditions, put each condition in the list passed as the last argument. After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction. Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data?

How accurate are your predictions? Hint: Run the code cell below to see the accuracy of your predictions. After several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic. The technique applied in this project is a manual implementation of a simple machine learning model, the decision tree.

A decision tree splits a set of data into smaller and smaller groups called nodes , by one feature at a time. Each time a subset of the data is split, our predictions become more accurate if each of the resulting subgroups are more homogeneous contain similar labels than before. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above.

This link provides another introduction into machine learning using a decision tree. A decision tree is just one of many models that come from supervised learning. In supervised learning, we attempt to use features of the data to predict or model things with objective outcome labels. That is to say, each of our data points has a known outcome value, such as a categorical, discrete label like 'Survived' , or a numerical, continuous value like predicting the price of a house.

Think of a real-world scenario where supervised learning could be applied. What would be the outcome variable that you are trying to predict? Name two features about the data used in this scenario that might be helpful for making the predictions. Answer : There is the typical spam detection scenario where we predict if an email is a spam or ham not spam.

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