Week28-2
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Association Rule Learning
Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset. It is based on different rules to discover the interesting relations between variables in the database.
The association rule learning is one of the very important concepts of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. Here market basket analysis is a technique used by the various big retailer to discover the associations between items. We can understand it by taking an example of a supermarket, as in a supermarket, all products that are purchased together are put together.
For example, if a customer buys bread, he most likely can also buy butter, eggs, or milk, so these products are stored within a shelf or mostly nearby. Consider the below diagram:
Association rule learning can be divided into three types of algorithms:
Apriori
Eclat
F-P Growth Algorithm
We will understand these algorithms in later chapters.
How does Association Rule Learning work?
Association rule learning works on the concept of If and Else Statement, such as if A then B.
Here the If element is called antecedent, and then statement is called as Consequent. These types of relationships where we can find out some association or relation between two items is known as single cardinality. It is all about creating rules, and if the number of items increases, then cardinality also increases accordingly. So, to measure the associations between thousands of data items, there are several metrics. These metrics are given below:
Support
Confidence
Lift
Let's understand each of them:
Support
Confidence
Lift
It is the ratio of the observed support measure and expected support if X and Y are independent of each other. It has three possible values:
If Lift= 1: The probability of occurrence of antecedent and consequent is independent of each other.
Lift>1: It determines the degree to which the two itemsets are dependent to each other.
Lift<1: It tells us that one item is a substitute for other items, which means one item has a negative effect on another.
Types of Association Rule Lerning
Association rule learning can be divided into three algorithms:
Apriori Algorithm
This algorithm uses frequent datasets to generate association rules. It is designed to work on the databases that contain transactions. This algorithm uses a breadth-first search and Hash Tree to calculate the itemset efficiently.
It is mainly used for market basket analysis and helps to understand the products that can be bought together. It can also be used in the healthcare field to find drug reactions for patients.
Eclat Algorithm
Eclat algorithm stands for Equivalence Class Transformation. This algorithm uses a depth-first search technique to find frequent itemsets in a transaction database. It performs faster execution than Apriori Algorithm.
F-P Growth Algorithm
The F-P growth algorithm stands for Frequent Pattern, and it is the improved version of the Apriori Algorithm. It represents the database in the form of a tree structure that is known as a frequent pattern or tree. The purpose of this frequent tree is to extract the most frequent patterns.
Applications of Association Rule Learning
It has various applications in machine learning and data mining. Below are some popular applications of association rule learning:
Market Basket Analysis: It is one of the popular examples and applications of association rule mining. This technique is commonly used by big retailers to determine the association between items.
Medical Diagnosis: With the help of association rules, patients can be cured easily, as it helps in identifying the probability of illness for a particular disease.
Protein Sequence: The association rules help in determining the synthesis of artificial Proteins.
It is also used for the Catalog Design and Loss-leader Analysis and many more other applications.
Clustering in Machine Learning
Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as "A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group."
It does it by finding some similar patterns in the unlabelled dataset such as shape, size, color, behavior, etc., and divides them as per the presence and absence of those similar patterns.
It is an unsupervised learning method, hence no supervision is provided to the algorithm, and it deals with the unlabeled dataset.
After applying this clustering technique, each cluster or group is provided with a cluster-ID. ML system can use this id to simplify the processing of large and complex datasets.
The clustering technique is commonly used for statistical data analysis.
Note: Clustering is somewhere similar to the classification algorithm, but the difference is the type of dataset that we are using. In classification, we work with the labeled data set, whereas in clustering, we work with the unlabelled dataset.
Example: Let's understand the clustering technique with the real-world example of Mall: When we visit any shopping mall, we can observe that the things with similar usage are grouped together. Such as the t-shirts are grouped in one section, and trousers are at other sections, similarly, at vegetable sections, apples, bananas, Mangoes, etc., are grouped in separate sections, so that we can easily find out the things. The clustering technique also works in the same way. Other examples of clustering are grouping documents according to the topic.
The clustering technique can be widely used in various tasks. Some most common uses of this technique are:
Market Segmentation
Statistical data analysis
Social network analysis
Image segmentation
Anomaly detection, etc.
Apart from these general usages, it is used by the Amazon in its recommendation system to provide the recommendations as per the past search of products. Netflix also uses this technique to recommend the movies and web-series to its users as per the watch history.
Types of Clustering Methods
The clustering methods are broadly divided into Hard clustering (datapoint belongs to only one group) and Soft Clustering (data points can belong to another group also). But there are also other various approaches of Clustering exist. Below are the main clustering methods used in Machine learning:
Partitioning Clustering
Density-Based Clustering
Distribution Model-Based Clustering
Hierarchical Clustering
Fuzzy Clustering
Partitioning Clustering
It is a type of clustering that divides the data into non-hierarchical groups. It is also known as the centroid-based method. The most common example of partitioning clustering is the K-Means Clustering algorithm.
In this type, the dataset is divided into a set of k groups, where K is used to define the number of pre-defined groups. The cluster center is created in such a way that the distance between the data points of one cluster is minimum as compared to another cluster centroid.
Density-Based Clustering
The density-based clustering method connects the highly-dense areas into clusters, and the arbitrarily shaped distributions are formed as long as the dense region can be connected. This algorithm does it by identifying different clusters in the dataset and connects the areas of high densities into clusters. The dense areas in data space are divided from each other by sparser areas.
Distribution Model-Based Clustering
In the distribution model-based clustering method, the data is divided based on the probability of how a dataset belongs to a particular distribution. The grouping is done by assuming some distributions commonly Gaussian Distribution.
Hierarchical Clustering
Hierarchical clustering can be used as an alternative for the partitioned clustering as there is no requirement of pre-specifying the number of clusters to be created. In this technique, the dataset is divided into clusters to create a tree-like structure, which is also called a dendrogram. The observations or any number of clusters can be selected by cutting the tree at the correct level. The most common example of this method is the Agglomerative Hierarchical algorithm.
Fuzzy Clustering
Fuzzy clustering is a type of soft method in which a data object may belong to more than one group or cluster. Each dataset has a set of membership coefficients, which depend on the degree of membership to be in a cluster. Fuzzy C-means algorithm is the example of this type of clustering; it is sometimes also known as the Fuzzy k-means algorithm.
Clustering Algorithms
The Clustering algorithms can be divided based on their models that are explained above. There are different types of clustering algorithms published, but only a few are commonly used. The clustering algorithm is based on the kind of data that we are using. Such as, some algorithms need to guess the number of clusters in the given dataset, whereas some are required to find the minimum distance between the observation of the dataset.
Here we are discussing mainly popular Clustering algorithms that are widely used in machine learning:
K-Means algorithm: The k-means algorithm is one of the most popular clustering algorithms. It classifies the dataset by dividing the samples into different clusters of equal variances. The number of clusters must be specified in this algorithm. It is fast with fewer computations required, with the linear complexity of O(n).
Mean-shift algorithm: Mean-shift algorithm tries to find the dense areas in the smooth density of data points. It is an example of a centroid-based model, that works on updating the candidates for centroid to be the center of the points within a given region.
DBSCAN Algorithm: It stands for Density-Based Spatial Clustering of Applications with Noise. It is an example of a density-based model similar to the mean-shift, but with some remarkable advantages. In this algorithm, the areas of high density are separated by the areas of low density. Because of this, the clusters can be found in any arbitrary shape.
Expectation-Maximization Clustering using GMM: This algorithm can be used as an alternative for the k-means algorithm or for those cases where K-means can be failed. In GMM, it is assumed that the data points are Gaussian distributed.
Agglomerative Hierarchical algorithm: The Agglomerative hierarchical algorithm performs the bottom-up hierarchical clustering. In this, each data point is treated as a single cluster at the outset and then successively merged. The cluster hierarchy can be represented as a tree-structure.
Affinity Propagation: It is different from other clustering algorithms as it does not require to specify the number of clusters. In this, each data point sends a message between the pair of data points until convergence. It has O(N2T) time complexity, which is the main drawback of this algorithm.
Applications of Clustering
Below are some commonly known applications of clustering technique in Machine Learning:
In Identification of Cancer Cells: The clustering algorithms are widely used for the identification of cancerous cells. It divides the cancerous and non-cancerous data sets into different groups.
In Search Engines: Search engines also work on the clustering technique. The search result appears based on the closest object to the search query. It does it by grouping similar data objects in one group that is far from the other dissimilar objects. The accurate result of a query depends on the quality of the clustering algorithm used.
Customer Segmentation: It is used in market research to segment the customers based on their choice and preferences.
In Biology: It is used in the biology stream to classify different species of plants and animals using the image recognition technique.
In Land Use: The clustering technique is used in identifying the area of similar lands use in the GIS database. This can be very useful to find that for what purpose the particular land should be used, that means for which purpose it is more suitable.
K-Means Clustering Algorithm
K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.
What is K-Means Algorithm?
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.
It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties.
It allows us to cluster the data into different groups and a convenient way to discover the categories of groups in the unlabeled dataset on its own without the need for any training.
It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters.OOPs Concepts in Java
The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and repeats the process until it does not find the best clusters. The value of k should be predetermined in this algorithm.
The k-means clustering algorithm mainly performs two tasks:
Determines the best value for K center points or centroids by an iterative process.
Assigns each data point to its closest k-center. Those data points which are near to the particular k-center, create a cluster.
Hence each cluster has datapoints with some commonalities, and it is away from other clusters.
How does the K-Means Algorithm Work?
The working of the K-Means algorithm is explained in the below steps:
Step-1: Select the number K to decide the number of clusters.
Step-2: Select random K points or centroids. (It can be other from the input dataset).
Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.
Step-4: Calculate the variance and place a new centroid of each cluster.
Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster.
Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
Step-7: The model is ready.
Let's understand the above steps by considering the visual plots:
Let's take number k of clusters, i.e., K=2, to identify the dataset and to put them into different clusters. It means here we will try to group these datasets into two different clusters.
From the above image, we can see, one yellow point is on the left side of the line, and two blue points are right to the line. So, these three points will be assigned to new centroids.
As reassignment has taken place, so we will again go to the step-4, which is finding new centroids or K-points.
How to choose the value of "K number of clusters" in K-means Clustering?
The performance of the K-means clustering algorithm depends upon highly efficient clusters that it forms. But choosing the optimal number of clusters is a big task. There are some different ways to find the optimal number of clusters, but here we are discussing the most appropriate method to find the number of clusters or value of K. The method is given below:
Elbow Method
The Elbow method is one of the most popular ways to find the optimal number of clusters. This method uses the concept of WCSS value. WCSS stands for Within Cluster Sum of Squares, which defines the total variations within a cluster. The formula to calculate the value of WCSS (for 3 clusters) is given below:WCSS= ∑Pi in Cluster1 distance(Pi C1)2 +∑Pi in Cluster2distance(Pi C2)2+∑Pi in CLuster3 distance(Pi C3)2
In the above formula of WCSS,
∑Pi in Cluster1 distance(Pi C1)2: It is the sum of the square of the distances between each data point and its centroid within a cluster1 and the same for the other two terms.
To measure the distance between data points and centroid, we can use any method such as Euclidean distance or Manhattan distance.
To find the optimal value of clusters, the elbow method follows the below steps:
It executes the K-means clustering on a given dataset for different K values (ranges from 1-10).
For each value of K, calculates the WCSS value.
Plots a curve between calculated WCSS values and the number of clusters K.
The sharp point of bend or a point of the plot looks like an arm, then that point is considered as the best value of K.
Note: We can choose the number of clusters equal to the given data points. If we choose the number of clusters equal to the data points, then the value of WCSS becomes zero, and that will be the endpoint of the plot.
Python Implementation of K-means Clustering Algorithm
In the above section, we have discussed the K-means algorithm, now let's see how it can be implemented using Python.
Before implementation, let's understand what type of problem we will solve here. So, we have a dataset of Mall_Customers, which is the data of customers who visit the mall and spend there.
In the given dataset, we have Customer_Id, Gender, Age, Annual Income ($), and Spending Score (which is the calculated value of how much a customer has spent in the mall, the more the value, the more he has spent). From this dataset, we need to calculate some patterns, as it is an unsupervised method, so we don't know what to calculate exactly.
The steps to be followed for the implementation are given below:
Data Pre-processing
Finding the optimal number of clusters using the elbow method
Training the K-means algorithm on the training dataset
Visualizing the clusters
Step-1: Data pre-processing Step
The first step will be the data pre-processing, as we did in our earlier topics of Regression and Classification. But for the clustering problem, it will be different from other models. Let's discuss it:
Importing Libraries As we did in previous topics, firstly, we will import the libraries for our model, which is part of data pre-processing. The code is given below:
# importing libraries
import numpy as nm
import matplotlib.pyplot as mtp
import pandas as pd
In the above code, the numpy we have imported for the performing mathematics calculation, matplotlib is for plotting the graph, and pandas are for managing the dataset.
Importing the Dataset: Next, we will import the dataset that we need to use. So here, we are using the Mall_Customer_data.csv dataset. It can be imported using the below code:
# Importing the dataset
dataset = pd.read_csv('Mall_Customers_data.csv')
From the above dataset, we need to find some patterns in it.
Extracting Independent Variables
Here we don't need any dependent variable for data pre-processing step as it is a clustering problem, and we have no idea about what to determine. So we will just add a line of code for the matrix of features.
x = dataset.iloc[:, [3, 4]].values
As we can see, we are extracting only 3rd and 4th feature. It is because we need a 2d plot to visualize the model, and some features are not required, such as customer_id.
Step-2: Finding the optimal number of clusters using the elbow method
In the second step, we will try to find the optimal number of clusters for our clustering problem. So, as discussed above, here we are going to use the elbow method for this purpose.
As we know, the elbow method uses the WCSS concept to draw the plot by plotting WCSS values on the Y-axis and the number of clusters on the X-axis. So we are going to calculate the value for WCSS for different k values ranging from 1 to 10. Below is the code for it:
#finding optimal number of clusters using the elbow method
from sklearn.cluster import KMeans
wcss_list= [] #Initializing the list for the values of WCSS
#Using for loop for iterations from 1 to 10.
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42)
kmeans.fit(x)
wcss_list.append(kmeans.inertia_)
mtp.plot(range(1, 11), wcss_list)
mtp.title('The Elobw Method Graph')
mtp.xlabel('Number of clusters(k)')
mtp.ylabel('wcss_list')
mtp.show()
As we can see in the above code, we have used the KMeans class of sklearn. cluster library to form the clusters.
Next, we have created the wcss_list variable to initialize an empty list, which is used to contain the value of wcss computed for different values of k ranging from 1 to 10.
After that, we have initialized the for loop for the iteration on a different value of k ranging from 1 to 10; since for loop in Python, exclude the outbound limit, so it is taken as 11 to include 10th value.
The rest part of the code is similar as we did in earlier topics, as we have fitted the model on a matrix of features and then plotted the graph between the number of clusters and WCSS.
Step- 3: Training the K-means algorithm on the training dataset
As we have got the number of clusters, so we can now train the model on the dataset.
To train the model, we will use the same two lines of code as we have used in the above section, but here instead of using i, we will use 5, as we know there are 5 clusters that need to be formed. The code is given below:
#training the K-means model on a dataset
kmeans = KMeans(n_clusters=5, init='k-means++', random_state= 42)
y_predict= kmeans.fit_predict(x)
The first line is the same as above for creating the object of KMeans class.
In the second line of code, we have created the dependent variable y_predict to train the model.
From the above image, we can now relate that the CustomerID 1 belongs to a cluster
3(as index starts from 0, hence 2 will be considered as 3), and 2 belongs to cluster 4, and so on.
Step-4: Visualizing the Clusters
The last step is to visualize the clusters. As we have 5 clusters for our model, so we will visualize each cluster one by one.
To visualize the clusters will use scatter plot using mtp.scatter() function of matplotlib.
#visulaizing the clusters
mtp.scatter(x[y_predict == 0, 0], x[y_predict == 0, 1], s = 100, c = 'blue', label = 'Cluster 1') #for first cluster
mtp.scatter(x[y_predict == 1, 0], x[y_predict == 1, 1], s = 100, c = 'green', label = 'Cluster 2') #for second cluster
mtp.scatter(x[y_predict== 2, 0], x[y_predict == 2, 1], s = 100, c = 'red', label = 'Cluster 3') #for third cluster
mtp.scatter(x[y_predict == 3, 0], x[y_predict == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4') #for fourth cluster
mtp.scatter(x[y_predict == 4, 0], x[y_predict == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5') #for fifth cluster
mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroid')
mtp.title('Clusters of customers')
mtp.xlabel('Annual Income (k$)')
mtp.ylabel('Spending Score (1-100)')
mtp.legend()
mtp.show()
In above lines of code, we have written code for each clusters, ranging from 1 to 5. The first coordinate of the mtp.scatter, i.e., x[y_predict == 0, 0] containing the x value for the showing the matrix of features values, and the y_predict is ranging from 0 to 1.
The output image is clearly showing the five different clusters with different colors. The clusters are formed between two parameters of the dataset; Annual income of customer and Spending. We can change the colors and labels as per the requirement or choice. We can also observe some points from the above patterns, which are given below:
Cluster1 shows the customers with average salary and average spending so we can categorize these customers as
Cluster2 shows the customer has a high income but low spending, so we can categorize them as careful.
Cluster3 shows the low income and also low spending so they can be categorized as sensible.
Cluster4 shows the customers with low income with very high spending so they can be categorized as careless.
Cluster5 shows the customers with high income and high spending so they can be categorized as target, and these customers can be the most profitable customers for the mall owner.
What are some techniques to reduce RAM usage for common ML algorithms?
Sparse Matrix is a wonderful way to optimise the data storage. Let’s say you have 10000 columns and 10 million rows in yours dataset, then you would need 10,000,000 * 10000 bytes for integer storage. That stack for almost 94GB of RAM. Now you might think “Hey how often you get that huge data in ML..I guess it’s not frequent”, then you are absolutely wrong. Try taking the Amazon Food Review Dataset at Kaggle for example. If you are trying to predict the polarity of the review and use a techniques like Bag Of Word or Binary Bag of Words or even a TFIDF bag of Words, the dataset grows so huge that you can't fit it in any place like a normal system. Mostly in text data or image data, the feature set grows so large that you can't store it anywhere. But fortunately most machine learning algorithms can use sparse matrix as an input and hence you can save a hell lot of storage. Mostly it depends on the sparsity of the data but typically a dense matrix of 100GB can be stored into just 700 to 900 MB if using sparse matrix. The biggest shortcoming is that if in your data you have less numbers of repeated values or in fact no repeated values at all, the memory footprint of a sparse and dense matrix becomes almost same and it’s of no use but this scenario itself is very rare.
Partial Fit on the dataset if it is so big to fit in one go. This feature allows you to fit a model on the data partially step by step instead of fitting in one go. So it takes some data fit on that data to get say a weight vector and then it moves to the next part of the data and fit again on the existing weight vector and so on. Needless to say that this decreases the RAM consumption but increases the training time. The biggest shortcoming is not all the algorithms and their implementations use partial fit.
Using right algorithms can save you a lot of hassle. Some algorithms might have large memory requirement as compared to others. This is not during the training time but this is during the runtime. For example, KNN requires to store all the data points to predict during the runtime and even with some of the optimisations like KDTree or BallTree or LSH with KNN, runtime memory requirement is huge while if you are using something say like Naive Bayes the runtime space complexity is only O(d * k)..while d is number of features and k is number of class labels. This is far more lesser than O(n) for n = number of data points most of the times. Logistic regression on the other hand has even better runtime space complexity of O(d). So better use logistic regression against Naive Bayes.
Dimensionality Reduction is not only useful to reduce the training time but also memory consumption during the runtime. Using some techniques like PCA or LDA or Matrix Factorization can reduce the dimensionality dramatically and sometime super useful.
A smart technique during cross validation can be performed on the Random Stratified Samples of size m which is far lesser than actual data size n that can give you a decent performing weight vector which might not help you win a Kaggle Championship but it is good for most production environments.
Machine Learning with Kaggle: Feature Engineering
Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more!
In the two previous Kaggle tutorials, you learned all about how to get your data in a form to build your first machine learning model, using Exploratory Data Analysis and baseline machine learning models. Next, you successfully managed to build your first machine learning model, a decision tree classifier. You submitted all these models to Kaggle and interpreted their accuracy.
In this third tutorial, you'll learn more about feature engineering, a process where you use domain knowledge of your data to create additional relevant features that increase the predictive power of the learning algorithm and make your machine learning models perform even better!
More specifically,
You'll first get started by doing all necessary imports and getting the data in your workspace;
Then, you'll see some reasons why you should do feature engineering and start working on engineering your own new features for your data set! You'll create new columns, transform variables into numerical ones, handle missing values, and much more.
Lastly, you'll build a new machine learning model with your new data set and submit it to Kaggle.
Getting Started!
Before you can start off, you're going to do all the imports, just like you did in the previous tutorial, use some IPython magic to make sure the figures are generated inline in the Jupyter Notebook and set the visualization style. Next, you can import your data and make sure that you store the target variable of the training data in a safe place. Afterwards, you merge the train and test data sets (with exception of the 'Survived' column of df_train) and store the result in data.
Remember that you do this because you want to make sure that any preprocessing that you do on the data is reflected in both the train and test sets!
Lastly, you use the .info() method to take a look at your data:
Why Feature Engineer At All?
You perform feature engineering to extract more information from your data, so that you can up your game when building models.
Titanic's Passenger Titles
Let's check out what this is all about by looking at an example. Let's check out the 'Name' column with the help of the .tail() method, which helps you to see the last five rows of your data:
Suddenly, you see different titles emerging! In other words, this column contains strings or text that contain titles, such as 'Mr', 'Master' and 'Dona'.
These titles of course give you information on social status, profession, etc., which in the end could tell you something more about survival.
At first sight, it might seem like a difficult task to separate the names from the titles, but don't panic! Remember, you can easily use regular expressions to extract the title and store it in a new column 'Title':
Note that this new column 'Title' is actually a new feature for your data set!
Tip: to learn more about regular expressions, check out my write up of our last FB Live code along event or check out DataCamp's Python Regular Expressions Tutorial.
You can see that there are several titles in the above plot and there are many that don't occur so often. So, it makes sense to put them in fewer buckets.
For example, you probably want to replace 'Mlle' and 'Ms' with 'Miss' and 'Mme' by 'Mrs', as these are French titles and ideally, you want all your data to be in one language. Next, you also take a bunch of titles that you can't immediately categorize and put them in a bucket called 'Special'.
Tip: play around with this to see how your algorithm performs as a function of it!
Next, you view a barplot of the result with the help of the .countplot() method:
This is what your newly engineered feature 'Title' looks like!
Now, make sure that you have a 'Title' column and check out your data again with the .tail() method:
PassengerId
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
413
1305
3
Spector, Mr. Woolf
male
NaN
0
0
A.5. 3236
8.0500
NaN
S
Mr
414
1306
1
Oliva y Ocana, Dona. Fermina
female
39.0
0
0
PC 17758
108.9000
C105
C
Special
415
1307
3
Saether, Mr. Simon Sivertsen
male
38.5
0
0
SOTON/O.Q. 3101262
7.2500
NaN
S
Mr
416
1308
3
Ware, Mr. Frederick
male
NaN
0
0
359309
8.0500
NaN
S
Mr
417
1309
3
Peter, Master. Michael J
male
NaN
1
1
2668
22.3583
NaN
C
Master
Passenger's Cabins
When you loaded in the data and inspected it, you saw that there are several NaNs or missing values in the 'Cabin' column.
It is reasonable to presume that those NaNs didn't have a cabin, which could tell you something about 'Survival'. So, let's now create a new column 'Has_Cabin' that encodes this information and tells you whether passengers had a cabin or not.
Note that you use the .isnull() method in the code chunk below, which will return True if the passenger doesn't have a cabin and False if that's not the case. However, since you want to store the result in a column 'Has_Cabin', you actually want to flip the result: you want to return True if the passenger has a cabin. That's why you use the tilde ~.
PassengerId
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
Title
Has_Cabin
0
1
3
Braund, Mr. Owen Harris
male
22.0
1
0
A/5 21171
7.2500
NaN
S
Mr
False
1
2
1
Cumings, Mrs. John Bradley (Florence Briggs Th...
female
38.0
1
0
PC 17599
71.2833
C85
C
Mrs
True
2
3
3
Heikkinen, Miss. Laina
female
26.0
0
0
STON/O2. 3101282
7.9250
NaN
S
Miss
False
3
4
1
Futrelle, Mrs. Jacques Heath (Lily May Peel)
female
35.0
1
0
113803
53.1000
C123
S
Mrs
True
4
5
3
Allen, Mr. William Henry
male
35.0
0
0
373450
8.0500
NaN
S
Mr
False
What you want to do now is drop a bunch of columns that contain no more useful information (or that we're not sure what to do with). In this case, you're looking at columns such as ['Cabin', 'Name', 'PassengerId', 'Ticket'], because
You already extracted information on whether or not the passenger had a cabin in your newly added
'Has_Cabin'column;Also, you already extracted the titles from the
'Name'column;You also drop the
'PassengerId'and the'Ticket'columns because these will probably not tell you anything more about the survival of the Titanic passengers.
Tip there might be more information in the 'Cabin' column, but for this tutorial, you assume that there isn't!
To drop these columns in your actual data DataFrame, make sure to use the inplace argument in the .drop() method and set it to True:
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
Has_Cabin
0
3
male
22.0
1
0
7.2500
S
Mr
False
1
1
female
38.0
1
0
71.2833
C
Mrs
True
2
3
female
26.0
0
0
7.9250
S
Miss
False
3
1
female
35.0
1
0
53.1000
S
Mrs
True
4
3
male
35.0
0
0
8.0500
S
Mr
False
Congrats! You've successfully engineered some new features such as 'Title' and 'Has_Cabin' and made sure that features that don't add any more useful information for your machine learning model are now dropped from your DataFrame!
Next, you want to deal with deal with missing values, bin your numerical data, and transform all features into numeric variables using .get_dummies() again. Lastly, you'll build your final model for this tutorial. Check out how all of this is done in the next sections!
Handling Missing Values
With all of the changes you have made to your original data DataFrame, it's a good idea to figure out if there are any missing values left with .info():
The result of the above line of code tells you that you have missing values in 'Age', 'Fare', and 'Embarked'.
Remember that you can easily spot this by first looking at the total number of entries (1309) and then checking out the number of non-null values in the columns that .info() lists. In this case, you see that 'Age' has 1046 non-null values, so that means that you have 263 missing values. Similarly, 'Fare' only has one missing value and 'Embarked' has two missing values.
Just like you did in the previous tutorial, you're going to impute these missing values with the help of .fillna():
Note that, once again, you use the median to fill in the 'Age' and 'Fare' columns because it's perfect for dealing with outliers. Other ways to impute missing values would be to use the mean, which you can find by adding all data points and dividing by the number of data points, or mode, which is the number that occurs the highest number of times.
You fill in the two missing values in the 'Embarked' column with 'S', which stands for Southampton, because this value is the most common one out of all the values that you find in this column.
Tip: you can double check this by doing some more Exploratory Data Analysis!
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
Has_Cabin
0
3
male
22.0
1
0
7.2500
S
Mr
False
1
1
female
38.0
1
0
71.2833
C
Mrs
True
2
3
female
26.0
0
0
7.9250
S
Miss
False
3
1
female
35.0
1
0
53.1000
S
Mrs
True
4
3
male
35.0
0
0
8.0500
S
Mr
False
Bin numerical data
Next, you want to bin the numerical data, because you have a range of ages and fares. However, there might be fluctuations in those numbers that don't reflect patterns in the data, which might be noise. That's why you'll put people that are within a certain range of age or fare in the same bin. You can do this by using the pandas function qcut() to bin your numerical data:
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
Has_Cabin
CatAge
CatFare
0
3
male
22.0
1
0
7.2500
S
Mr
False
0
0
1
1
female
38.0
1
0
71.2833
C
Mrs
True
3
3
2
3
female
26.0
0
0
7.9250
S
Miss
False
1
1
3
1
female
35.0
1
0
53.1000
S
Mrs
True
2
3
4
3
male
35.0
0
0
8.0500
S
Mr
False
2
1
Note that you pass in the data as a Series, data.Age and data.Fare, after which you specify the number of quantiles, q=4. Lastly, you set the labels argument to False to encode the bins as numbers.
Now that you have all of that information in bins, you can now safely drop 'Age' and 'Fare' columns. Don't forget to check out the first five rows of your data!
Pclass
Sex
SibSp
Parch
Embarked
Title
Has_Cabin
CatAge
CatFare
0
3
male
1
0
S
Mr
False
0
0
1
1
female
1
0
C
Mrs
True
3
3
2
3
female
0
0
S
Miss
False
1
1
3
1
female
1
0
S
Mrs
True
2
3
4
3
male
0
0
S
Mr
False
2
1
Number of Members in Family Onboard
The next thing you can do is create a new column, which is the number of members in families that were onboard of the Titanic. In this tutorial, you won't go in this and see how the model performs without it. If you do want to check out how the model would do with this additional column, run the following line of code:
For now, you will just go ahead and drop the 'SibSp' and 'Parch' columns from your DataFrame:
Pclass
Sex
Embarked
Title
Has_Cabin
CatAge
CatFare
0
3
male
S
Mr
False
0
0
1
1
female
C
Mrs
True
3
3
2
3
female
S
Miss
False
1
1
3
1
female
S
Mrs
True
2
3
4
3
male
S
Mr
False
2
1
Transform Variables into Numerical Variables
Now that you have engineered some more features, such as 'Title' and 'Has_Cabin', and you have dealt with missing values, binned your numerical data, it's time to transform all variables into numeric ones. You do this because machine learning models generally take numeric input.
As you have done previously, you will use .get_dummies() to do this:
Pclass
Has_Cabin
CatAge
CatFare
Sex_male
Embarked_Q
Embarked_S
Title_Miss
Title_Mr
Title_Mrs
Title_Special
0
3
False
0
0
1
0
1
0
1
0
0
1
1
True
3
3
0
0
0
0
0
1
0
2
3
False
1
1
0
0
1
1
0
0
0
3
1
True
2
3
0
0
1
0
0
1
0
4
3
False
2
1
1
0
1
0
1
0
0
With all of this done, it's time to build your final model!
Building models with Your New Data Set!
As before, you'll first split your data back into training and test sets. Then, you'll transform them into arrays:
You're now going to build a decision tree on your brand new feature-engineered dataset. To choose your hyperparameter max_depth, you'll use a variation on test train split called "cross validation".
You begin by splitting the dataset into 5 groups or folds. Then you hold out the first fold as a test set, fit your model on the remaining four folds, predict on the test set and compute the metric of interest. Next, you hold out the second fold as your test set, fit on the remaining data, predict on the test set and compute the metric of interest. Then similarly with the third, fourth and fifth.
As a result, you get five values of accuracy, from which you can compute statistics of interest, such as the median and/or mean and 95% confidence intervals.
You do this for each value of each hyperparameter that you're tuning and choose the set of hyperparameters that performs the best. This is called grid search.
Enough about that for now, let's get it!
In the following, you'll use cross validation and grid search to choose the best max_depth for your new feature-engineered dataset:
Now, you can make predictions on your test set, create a new column 'Survived' and store your predictions in it. Don't forget to save the 'PassengerId' and 'Survived' columns of df_test to a .csv and submit it to Kaggle!
The accuracy of your submission is 78.9.
