Category: Kaggle airbnb nyc


Super nice and spacious apartment in a vibrant neighborhood. We really enjoyed our stay. Carol and Marc were extremely accommodatinghelpful and friendly. Super comfy bed too! Would highly recommend! I felt like I was at home or staying with relatives.

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The place was as described. Lots of cafes and restaurants within walking distance, very clean. Carol and Marc made great suggestions for breakfast and dinner. They went above and beyond what was expected of them.

The bed was so comfortable.

kaggle airbnb nyc

I wish the weather was nicer so we could have taken advantage of the terrace next time. This is the place you want to stay when visiting New York. Everything you can think of is just outside there door.

Airbnb in New York : How NYC is Killing Airbnb

This is a large, clean and comfortable 3rd floor apartment with keyed elevator. Felt completely safe walking around at night. Very quiet street.

Just 3 blocks to metro and trains taking you uptown. Great coffee shops and lots of cultures and cuisines to explore nearby. The hosts know every nook and cranny of the lower east side, so ask for their recommendations.

Would stay there again.

Predicting a New User's First Travel Destination on AirBnB (Capstone Project)

This is a shared apartment. Marc and Carol live in the back when you are there. It is an amazing place in a great location.

Marc and Carol are kind and respect your space and are also engaging if you are. It is a great way to feel at home away from home in NYC.

We really enjoyed our stay with Marc and Carol. They both are delightful hosts and had so many suggestions for us first timers to NYC. We were given a guide ahead of time of so many things to do, places to see, and places to eat. Marc gave a short tutorial showing us how to get in the building and how to use the elevator.

It was great not to have to navigate stairs with suitcases.Learn R, Python, machine learning and big data in just 12 weeks with our full job support. Upgrade your data science skills with these in-person targeted courses and earn a certificate.

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kaggle airbnb nyc

Data Science Bootcamp. Data Science Bootcamp - Online. Get Inspired. Courses financing available.In this article, I will perform data analysis by using Python and record down every finding and thought during the analysis. After importing modules and csv file, normally my first step is to explore the dataset. This step includes studying the format of the overall dataset, types of all variables, checking and cleaning the dataset.

So there are in total of 16 columns and some columns have missing values. And they also have the same numbers of missing values. Based on the names and numbers of missing values, I suspect that these two columns are related and so have the same numbers of missing values. So they should be handled together. So at this moment, I will just leave it first.

kaggle airbnb nyc

So instead of handling missing values, I will directly remove from the dataset. Also, it can be meaningful if there is any relationship between the number of listing house and price.

Next step is to check numeric variables by using. Using describe can help me understand the range of possible values for each variable. I can know the distribution of values. Also, it is possible to discover any unrealistic records from the dataset.

For example in this dataset, there are some abnormalities. So these records should be removed. I put a threshold as days. Now I can have a look at the dataset. Each column at a time.

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The numbers of Airbnb is not even.Contributed by Amy Yujing Ma. This post is based on her first project - R Shiny.

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Visiting NYC? I have used Airbnb. To better explore its rental listings across New York City, I designed this app to answer some questions: How many of the listings are for an entire home versus a room in an apartment?

How many are controlled by the same host? Should you think twice before trusting a review? Listings 35, locations and 24, hosts ; 2. Most of the listings are in Manhattan. The table shows that most of the super hosts are not local, some of them are not even a person.

For instance, Flatbook is a company to combine hotel and Airbnb together. That's why tax issue is really serious in Airbnb NYC. Yes, be careful with those reviews contains great, nice and recommend! Based on the word cloud of reviews, people use great as frequent as stay!

There are three possible reasons:. Why people tend to leave positive reviews? There are three possible reasons: 1. It's awkward to leave a negative one. Most of the Airbnb experience is really enjoyable. Do not send any emails during weekdays. To hostsI would suggest hosts choose long term rentals in February and November since the fewest people are booking during those months.

And based on the word cloud of reviews, highlight their location with some keywords: such as subway, clean, restaurant and neighborhood. Beyond my question, the app can answer much more.

To answer your questions on Airbnb in NYC, you can play with my app. To gain the basic information about Airbnb Listings in NYC, the first tab would map the whole listings. Click on each circle to find out the basic information about this location. The right panel and map will change based on your input. The third and fourth tab would help you discover the review related to Airbnb listing in NYC.

For instance, changing the number into 7, would show a smoother trend:. Thanks for reading, I hope you found this post and my app interesting. You must be logged in to post a comment. NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry. Bootcamps job support and financing available. In-person Immersive Data Science Bootcamp.

Online Data Science Bootcamp. Get Inspired.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Use the "Data Cleaning. R" Script to inspect the data visualizaiton, cleaning and feature engineering process. Tasked with creating the best predictive model, I had at my disposal 95 possible independent variables across 36, observations. Exploratory Data Analysis I began by filtering out variables which were clearly not relevant such as those only containing URLs or locations which were the same across all the listings since all listings were in New York, country, state, city etc.

Now that I had removed all irrelevant variables, it was time for me to start building a thorough understanding of the data. I began by looking at the distribution of the response variable, price. After removing outliers in price which had values of zero, it was time to start visually analyzing relationships between price and predictors. However, I also noticed that beds had a high multicollinearity with accommodates. With beds no longer in consideration, I strongly felt that the four remaining variables would be used in my final model.

For categorical variables, I utilized box and scatter plots to visually interpret a trend.

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I retained those with a weak level of correlation for further analysis. Lastly, I was able to further drop variables which either had little to no variation or showed no correlation visually. Feature Engineering Before I went any further towards feature selection, I wished to make suitable changes to existing variables as well as engineer new ones. Undoubtedly, this section contained the most amount of missteps but also produced some of my strongest variables.

I began by creating a function which reassigned all values in certain variables beneath a certain threshold to a single value. For instance, I reassigned any property with more than six bedrooms to simply have six bedrooms. Therefore, even if a property had 11 bedrooms, it was counted as six. My reasoning for this followed that after a certain number of bedrooms, there are diminishing returns on the impact on price.

Similarly, I implemented this logic on five other variables. Although I cannot say for certain, I believe this made an impactful enough difference on my predictions. More importantly, this was the first function and for loop I created in R. It gave me confidence moving forward as this skill was going to be invaluable in the hurdles I was going to soon come across.

Unfortunately, this variable did not have enough variance to be significant. Hence, it was not used it in the models to follow.Let's first do EDA to gain some insights from our data.

Let's plot the distribution of sale price target. Size of living area may be an indicator of house price. Figure shows that there are only a few houses are more than 4, square feet. This information may be used to filter out outliers. Also, linear distance of street connected to property may be a useful feature. We group by neighborhood and fill NA using the median of the group's linear distance. Figure shows that there are only a few outliers having a distance of less than 40 feet.

It may be useful to characterize the properties by the months in which they are sold. Figure shows that May to August are the hottest months in terms of number of sales. We also found some numerical features which are highly correlated with the sale price and plot the correlation matrix of these features. This information is useful to determine the correlation of features.

We all know that multicollinearity may make it more difficult to make inferences about the relationships between our independent and dependent variables. These are some basic EDA for our house price data set. In the next section, we are going to perform feature engineering to prepare our train set and test set for machine learning.

We consider numerical and categorical features separately. The numerical features of our data set do not directly lend themselves to a linear model and the features violate some of the necessary assumptions for regression such as linearity, constant variance or normality. For dealing with outliers, we filter out the properties having a living area of more than 4, square feet above grade ground. There are also some minor features considered here. The total number of features is and we have and samples for the training and test sets, respectively.

Now, let's do machine learning. For each models, we perform grid search with cross-validation to find the best parameters for the corresponding models.

For example, for Kernel Ridge. Finally, we use an ensemble model which consists of Lasso, ridge, and XGboost with equal weights as our model. We consider out-of-folder stacking. At the second level, we use the outputs of the models from the first level as the new features and use XGboost as our combiner to train our model. We perform cross-validation for each model to find the best set of parameters.

We should consider using the most important features to train our model. We use a loop to see how the score varies with different number of features included in the training set and set a threshold to determine which features we want to drop from the data set.

In two weeks two people, part-timewe have done EDA, feature engineering, ensembling, stacking, and feature selection. We observed that there is a huge score jump from the score without featuring engineering to the one with feature engineering.

The second score jump is from the score without ensembling to the one with ensembling. Out of folder stacking didn't improve the score too much.

It may be because the models are already statistically equivalent.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

Sinceguests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

This data contains 16 columns, unique values samples. Imported all necessary files and libraries, We removed unnecessary data from the datset like last review, reviews per month and host name as they donot support the data required. We filled the null values with zero constant and did the visualization using seaborn, pyplot, matplotlib.

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Sign up. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Data Sinceguests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Data visualization for Classification. Add files via upload. Oct 7,