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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Predicting NYC AirBnB Rental Prices with TensorFlow

Tim
Tim Last Updated : 11 Jan, 2021
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Airbnb is an online marketplace that lets people rent out their properties or spare rooms to guests. Airbnb takes 3% commission of every booking from hosts, and between 6% and 12% from guests.

Since the company launched in 2009, it’s grown from helping 21,000 guests a year find a place to stay to helping six million a year go on holiday, and currently lists a staggering 800,000 properties in 34,000 cities across 90 different countries.

In this article, I will use the Kaggle New York City Airbnb Open Data dataset and try to build a neural network model with TensorFlow for prediction.

The goal is to build a suitable machine learning model that will be able to predict prices of further accommodation data.

Throughout this article, I will be showing insights from the Jupyter notebook I created. You can find it on GitHub.

 

Loading the Data

For starters, let’s look at loading the data. We’re pulling the data straight off the Kaggle website using wget. Note the -o flag indicating the filename.

The dataset should look like this. It has 48,895 rows and 16 columns in total. It’s got everything we need.

 

Data Analysis and Preprocessing

Seaborn has a very neat API for plotting all sorts of graphs for all sorts of data. If you’re not comfortable with the syntax, feel free to check this article.

After using the correlation on the pandas dataframe, we passed it to a heatmap function. This is the outcome:

Since we have both the longitude and the longitude as well as the neighbourhood data, let’s create a scatterplot:

Furthermore, I removed the duplicates and some unnecessary columns and filled the column ‘reviews_per_month’ because it had too many missing values. The data looks like this. It has 10 columns and no zero values:

Great, right?

First of all, computers do numbers. That’s why we have to convert categorical columns into one-hot encoded vectors. This is done using the pandas’ factorize method. You could have used a lot of other tools for this:

For the sake of keeping the loss function in stable limits, let’s normalize some of the data so that the mean is 0 and the standard deviation is 1.

 

Feature Cross

There is one change that we have to make and this is an essential one. In order for the longitude and latitude to be correlated to the model output, we have to create a feature cross. The links below should provide you with enough context to get a proper feel for feature crosses:

Our goal here is to feature cross longitude and latitude, which is one of the oldest tricks in the book. If we merely put the two columns as values to the model, it will assume those values are progressively related to the output.

Instead, we’ll be using a feature cross, meaning we will split the longitude*langitude map into a grid. Quite a delicate little problem. Lucky for us, TensorFlow makes it easy.

I’m making a grid of equally spread frames by iterating from the minimum to the maximum value with an iteration of (max-min)/100.

I’m using a 100×100 grid:

Essentially, what we’re doing here, is defining a bucketed column with boundaries defined earlier and creating a DenseFeatures layer, which will be passed to the Sequential API later.

If you’re not familiar with the Tensorflow syntax, do check the docs:
https://www.tensorflow.org/api_docs/python/tf/feature_column/

Now, finally, we are ready for model training. Except for splitting the data part, that is.

Obviously, we have to create two datasets, one containing all the data and the other the predicted score. Since data sizes don’t match and that could represent issues to our model, I’ve decided to truncate the one that’s too long.

 

Creating a Model

Finally, the creation of the the Keras Sequential model.

We’re compiling the model using the Adam optimizer, mean squared error loss and two metrics. Keep track of these while the model trains.

Additionally, we are using two callbacks:

  • EarlyStopping, which is self-explanatory, but check the docs
  • Reduce the learning rate on plateau. Definitely check the docs on this one

After training for 50 epochs with a batch size of 64, our model was quite successful.

 

End Notes

We have used the AirBnB data for the city of New York to build a dense neural network for predicting further prices. Pandas and seaborn made it super easy to visualize and inspect the data. We introduced the idea of a feature cross between longitude and latitude as a feature to our model. Thanks to Kaggle’s open datasets, we have derived a fully-operational machine learning model.

Tim
Tim

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