Nyc taxi dataset

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Nyc taxi dataset

I decided to apply machine learning techniques on the data set to try and build some predictive models using Python.

First, on inspecting the Google Big Query tables, we notice that there is one table per year. This gives me an idea. From the data frame, we see that each row is one trip while each column is an attribute related to the trip. I like to start my projects by first building a predictive model purely from intuition. This simple intuitive model will give us our baseline accuracy and we shall try to beat this baseline accuracy using more advanced techniques.

For any predictive model, we need to consider what is the response variable we are trying to predict and what are the feature variables that could have an impact on the said response.

As can be seen from the chart, there are significant outliers for all three columns. Much better! The first function is to split the data so that the training dataset only has data from and the test dataset to only has data from as mentioned before.

nyc taxi dataset

The second function is to calculate some statistics that can be used to evaluate the predictive power of our models. Now we are all set to build our first model purely from intuition. Not bad for our very first attempt! Now, let us try to improve this score.

The method I shall employ here is to add more features to the model by the process of feature engineering. Thinking deeper about the problem. Geohashing is a method to create discrete geographic locations based on absolute latitude and longitudes. You can think of it as a creating a common name for locations belonging to the same neighborhood. I use the pygeohash python package which can be installed using pip install. The new features added are:. This necessitates the use of a regularization technique.

Let us now try hyper-parameter tuning to improve the model RMSE. The parameter we can tune for Lasso Regression is called alpha which is a constant that multiplies the L1 penalty term. The default value of alpha is 1. Smaller alpha will make Lasso regression similar to Linear regression by reducing the penalty term.

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Since we are using Lasso Regression, there are two methods to perform alpha hyperparameter tuning. However, the GridSearchCV method took a whopping seconds to run. Take note we shall use the same alpha search space for LassoCV to make this a true head to head comparison.There are a few sources. I got it from here. There are of course plenty of ways to get the data into shape. I chose whatever I could think of most quickly. There is probably an awk one-liner or more efficient way to do it, but it's not very much data and these steps didn't take long.

There are two sets of files - one for trip data and one for fare data. This site has them broken down into 12 files for each set. No good. I converted them to unix format with dos2unixwhich may not be installed on all linux flavors, but it's easy to install or there are other ways to deal with it. Looking at the files, it turns out that the number of lines match for each numbered trip and fare file. It would be nice to merge these, but we should make sure before merging that the rows match.

We can run a simple awk command to make sure each these match for each row. The code is commented out because we have already verified this so no need to re-run unless you really want to. Everything matches, except some header lines have spaces and therefore don't match. Reading in the raw data to R is as simple as calling drRead.

However, some initial exploration revealed some transformations that would be good to first apply. Second, there are some very large outliers in the pickup and dropoff latitude and longitude that are not plausible and will hinder our analysis.

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We could deal with this later, but might as well take care of it up front. Note that these quantiles have been computed at intervals of 0, 0. So we have some very egregious outliers. We will set any coordinates outside of this bounding box to NA in our initial transformation.

New York Taxi data set analysis

We don't want to remove them altogether as they may contain other interesting information that may be valid.If you don't have an Azure subscription, create a free account before you begin. A SQL pool is created with a defined set of compute resources.

Select Server to create and configure a new server for your new database. Fill out the New server form with the following information:. Select Performance level to specify whether the data warehouse is Gen1 or Gen2, and the number of data warehouse units. For this tutorial, select SQL pool Gen2. The slider is set to DWc by default. Try moving it up and down to see how it works. In the provisioning blade, select a collation for the blank database.

For this tutorial, use the default value.

nyc taxi dataset

For more information about collations, see Collations. Now that you have completed the form, select Create to provision the database. Provisioning takes a few minutes. A firewall at the server-level that prevents external applications and tools from connecting to the server or any databases on the server. To enable connectivity, you can add firewall rules that enable connectivity for specific IP addresses. Follow these steps to create a server-level firewall rule for your client's IP address.

SQL Data Warehouse communicates over port If you are trying to connect from within a corporate network, outbound traffic over port might not be allowed by your network's firewall. The overview page for your database opens, showing you the fully qualified server name such as mynewserver Copy this fully qualified server name for use to connect to your server and its databases in subsequent quick starts.

Then select on the server name to open server settings. Select Show firewall settings. A firewall rule can open port for a single IP address or a range of IP addresses. Select Save. A server-level firewall rule is created for your current IP address opening port on the logical server. When you connect, use the ServerAdmin account you created previously. Get the fully qualified server name for your SQL server in the Azure portal.

Later you will use the fully qualified name when connecting to the server. In the Essentials pane in the Azure portal page for your database, locate and then copy the Server name. In this example, the fully qualified name is mynewserver In Object Explorer, expand Databases. Then expand System databases and master to view the objects in the master database.

Expand mySampleDatabase to view the objects in your new database.This dataset includes trip records from all trips completed in green taxis in NYC in To subscribe to the Taxi dataset via an RSS readeruse one of the following links:.

TLC Trip Record Data

You have unsaved data that will be lost if you leave this page. Please choose whether or not you wish to save this view before you leave; or choose Cancel to return to the page. This change requires a reload. You may Save your changes to view them, or Cancel to stay on this page. You may Update this view or Save a new view to see your changes, or Cancel to stay on this page. Skip to main content Skip to footer links. Based on Taxi. Description This dataset includes trip records from all trips completed in green taxis in NYC in Activity Community Rating Current value: 0 out of 5.

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nyc taxi dataset

To subscribe via email notificationsyou must first sign in. Close Invite Collaborators Your email has been successfully sent. Add More. Close Save view Do you want to save your view? Enter a name for your new view:. Close Choose a Dataset to use.I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides.

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The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. In this project using New York dataset we will predict the fare price of next trip. Given that a ton of open data available, an Analysis on the most relevant features that drive the house prices.

Harvard Data Science Final Project: NYC Taxi Prediction

Predicts the total ride duration of taxi trips in New York City. Examine relationship between NYC weather and taxi data from Written in Clojure. NOTE: I'm just learning clojure and using this set to learn clojure idioms. Add a description, image, and links to the nyc-taxi-dataset topic page so that developers can more easily learn about it.

Curate this topic. To associate your repository with the nyc-taxi-dataset topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 28 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. Updated Nov 13, Jupyter Notebook. Predict NYC taxi travel times Kaggle competition. Updated Nov 3, Jupyter Notebook. Star 4. Updated Mar 22, JavaScript.

Star 3. Updated Apr 10, Scala. DevCon5 Community Meet up. Updated Sep 30, Jupyter Notebook. Updated Nov 16, Jupyter Notebook. Star 2. Updated Sep 21, Jupyter Notebook. Star 1. Updated Apr 2, R. Updated Jun 13, Jupyter Notebook.This dataset contains historical records accumulated from to You can use parameter settings in our SDK to fetch data within a specific time range. This dataset is stored in the East US Azure region. Allocating compute resources in East US is recommended for affinity.

This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.

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Miscellaneous extras and surcharges. The improvement surcharge began being levied in Azure Notebooks. Azure Databricks. Back to datasets. Overview Columns Data access. Volume and Retention This dataset is stored in Parquet format. There are about 1. Access Available in When to use Azure Notebooks Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

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Azure Databricks Use this when you need the scale of an Azure managed Spark cluster to process the dataset. This is a driver-entered value. Cash tips are not included. Does not include cash tips. Select your preferred service: Azure Notebooks Azure Databricks. Package: azureml-opendatasets azure-storage Language: Python Python.

Download Notebook Download Notebook. This is a package in preview.I decided to apply machine learning techniques on the data set to try and build some predictive models using Python.

First, on inspecting the Google Big Query tables, we notice that there is one table per year. This gives me an idea. From the data frame, we see that each row is one trip while each column is an attribute related to the trip. I like to start my projects by first building a predictive model purely from intuition. This simple intuitive model will give us our baseline accuracy and we shall try to beat this baseline accuracy using more advanced techniques.

For any predictive model, we need to consider what is the response variable we are trying to predict and what are the feature variables that could have an impact on the said response. As can be seen from the chart, there are significant outliers for all three columns. Much better! The first function is to split the data so that the training dataset only has data from and the test dataset to only has data from as mentioned before.

The second function is to calculate some statistics that can be used to evaluate the predictive power of our models. Now we are all set to build our first model purely from intuition. Not bad for our very first attempt! Now, let us try to improve this score. The method I shall employ here is to add more features to the model by the process of feature engineering. Thinking deeper about the problem.

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Geohashing is a method to create discrete geographic locations based on absolute latitude and longitudes. You can think of it as a creating a common name for locations belonging to the same neighborhood. I use the pygeohash python package which can be installed using pip install. The new features added are:.

This necessitates the use of a regularization technique.


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