Live Code, Decision Trees

Live Code, Decision Trees Weather Data Classification using Decision Trees In this notebook, we will use scikit-learn to perform a decision tree-based classification of metadata curated from sensor data of a weather station located in San Diego, California. The weather station captures weather-related measurements such as air temperature, air pressure, and relative humidity. The data was collected over a period of three years from September 2011 to September 2014, ensuring that sufficient data for different seasons and weather conditions are captured. Importing Necessary Libraries We first import the required libraries before beginning the analysis. We will use the decision tree classifier from the scikit-learn library in this example. We import decision tree classifier, sklearn.tree, pandas as a pd, sklearn metrics accuracy score, and sklearn, which is a scikit-learn model selection train test split. While you can import these libraries at any point in your notebook, it is a good practice to list them all on top of the notebook. This ensures that the users of your notebook understand what you will be using when they open the notebook. Ingesting the CSV File We will be importing the CSV file, dailyweather.csv, into a Pandas DataFrame using the read_csv function. This file is given to you for this week in the folder called "Weather". The DataFrame contains columns such as air pressure at 9 am, air temperature at 9 am, and so on. You can read through the markdown cell below to understand what each of these columns are and why they are named as such. Cleaning the DataFrame After looking at the DataFrame, we see that there are some NaN values. We need to clean the DataFrame before we start with the analysis. This dataset was curated a little bit, so there is not much to clean, but we need to remove the number column since it is just a unique number ID for each row. Performing the Decision Tree-Based Classification Now, we are ready to perform the decision tree-based classification. We split the data into training and testing sets using the train_test_split function from scikit-learn. We will use 80% of the data for training and 20% for testing. After splitting the data, we fit the decision tree classifier on the training set. Then, we use the fitted classifier to predict the labels for the testing set. Finally, we calculate the accuracy of the classifier using the accuracy_score function from scikit-learn. Visualizing the Decision Tree We can also visualize the decision tree using the plot_tree function from scikit-learn. The plot_tree function takes the fitted decision tree classifier as an argument and outputs a plot of the decision tree. In this notebook, we used scikit-learn to perform a decision tree-based classification of metadata curated from sensor data of a weather station located in San Diego, California. We imported the necessary libraries, ingested the CSV file, cleaned the DataFrame, performed the decision tree-based classification, and visualized the decision tree. This notebook can be useful for anyone interested in working with sensor data and performing decision tree-based classification. Understanding the Train Test Split Function in Python for Machine Learning As machine learning continues to advance, developers are constantly looking for ways to improve the accuracy and efficiency of their algorithms. One of the key steps in the machine learning process is splitting the data into training and testing sets. In this article, we will explore the train test split function in Python and how it can be used to optimize the performance of machine learning models. Okay, let's look at what this function has as a summary. Train test split here takes two DataFrames and returns four DataFrames, right? It's the x train, x test, y train, and y test. Now, let's dive into the details. What is Train Test Split? In machine learning, we typically have a dataset that we want to use to train our algorithm. However, it is important to test the model to see how well it performs on new, unseen data. This is where train test split comes in. Train test split is a technique that allows us to divide the dataset into two separate sets: one for training the algorithm and the other for testing its accuracy. The function randomly splits the data into two parts, typically with a 70-30 or 80-20 split, with the majority of the data used for training and the remainder for testing. The goal of this process is to evaluate the performance of the algorithm on data that it has not yet seen, in order to gauge its accuracy on new data. How Does Train Test Split Work? The train test split function is a built-in feature in Python's scikit-learn library. To use it, we need to first import the library and the function. Here's how to do it: import pandas as pd from sklearn.model_selection import train_test_split Once the library and function have been imported, we can use them to divide our data into training and testing sets. Here is an illustration of train test split in action: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) In this example, we have four variables: X_train, X_test, y_train, and y_test. The 'X' variables represent the input data, while the 'y' variables represent the output or labels. The train test split function takes four arguments. The first two arguments are the input and output data, which are represented by X and y respectively. The third argument is test_size, which determines the proportion of the dataset that will be allocated to the test set. In this example, we have specified a test size of 0.3, which means that 30% of the dataset will be used for testing. The fourth argument is random_state, which sets the seed for the random number generator used by the function. This ensures that the same random splits are generated each time the function is run, making the results more reproducible. After running the train test split function, we will have four datasets: X_train, X_test, y_train, and y_test. These datasets are used to train the machine learning model and evaluate its performance. Using the Decision Tree Classifier We can now train a machine learning model using the training and testing sets we have. In this instance, we'll construct our model using a decision tree classifier. To do this, we first need to create a decision tree classifier object, which we will call the 'humidity classifier'. We will set the maximum number of leaf nodes to 10 and the random state to 0. Here's how to create the classifier: from sklearn.tree import DecisionTreeClassifier humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random