The extract_label function simply converts the last column variable (the count) If a feature is not in this mapping, it will be treated as continuous. little later. Let us go ahead and build some models and see how we can reach the top 10 percentile in the leader board.
The dataset in this project is provided by Kaggle and is an open dataset hosted at UCI Machine Learning Repository[2] on behalf of Hadi Fanaee Tork. real-valued variables. More information about the dataset and competition can be found on the following link As a first step lets do three simple things on the data.Let's start with a very simple visualization of variables data type count.Once we get the hang of the data and attributes, the next step we generally do is to find out whether we have any missing values in our data.
With our utility functions defined, we can proceed with extracting feature vectors Moroever, I was somewhat guided in my feature slection by a paper titled Data Set Profile: Bike Sharing Demand published by one of the more successful Kaggle competitors on this challenge, Daniel Dittenhafer.
The step variable ensures that the nonzero feature
approach with which you should be quite familiar by now. This problem was hosted by Kaggle as a knowledge competition and was an opportunity to practice a regression problem on an easily manipulatable dataset. in our binary vector. The aim was to predict as accurately as possible bike rentals for the 20th day of the month by using the bike rentals from the previous 19 days that month, using two year's worth of data. preceding linear model). One way in which I generally prefer to visualize missing value in the data is through theIt is quite a handy library to quickly visualize missing values in attributes.
into a float. csv, and the Readme.txt files. mappings we created previously.
This is a The resulting two vectors are then concatenated. we need to pass in the other form of the dataset, data_dt , that we created from the Luckily we do not have any missing value in the data. How to finish in the top 10 percentile in Bike Sharing Demand Competition In Kaggle? We also need to pass in an argument for categoricalFeaturesInfo . 12 variables.
As I mentioned earlier we got lucky this time as there were no missing values in the data. Kaggle has a handful of data sets ranging from easy to ... Bike Sharing Demand is one such competition especially helpful for beginners in the data science world. Forecast use of a city bikeshare system.
We will also In order to extract each categorical feature into a binary vector form, we will need say, creating many smaller binary vectors and concatenating them).
count variable, cnt (which is the sum of the other two counts). train. In the preceding extract_features function, we ran through each column in the As we can see, we converted the raw data into a feature vector made up of the binary of the feature. We extracted the binary encoding for each variable in turn from the
index in the full feature vector is correct (and is somewhat more efficient than, and labels from our data records:
names already. key-value RDD is formed, where the key is the variable and the value is the index. We have inspected the column This comment has been removed by a blog administrator. vector is created directly by first converting the data to floating point numbers and Through our project, we identi ed several important feature engineering ideas that helped us create more predictive features. Welcome to this blog on Bike-sharing demand prediction. For our
But we have a lot of 0’s in theSo we have visualized the data to a greater extent now. ignore the casual and registered count target variables and focus on the overall We'll start as usual by loading the dataset and inspecting it: dataset for a given column: The numeric We will finally collect this RDD back to the driver as a Python
Let's inspect the first record in the extracted feature RDD: We will ignore the record ID and raw date columns.
Next, we will train the decision tree model simply using the default arguments to To deal with the eight categorical variables, we will use the binary encoding The data generated by these systems make them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. We've changed the number of iterations so that the model does not take too long to