Titanic dataset accuracy

How to implement k-Nearest Neighbors in Python. You can refer to the vignette for more information about the other choices. They are extracted from open source Python projects. In my previous article, I wrote about example of using marchine learning algorithms via scikit-learn. to use model and the accuracy of 81. r-part. # Abalone data set - a data set of physical measurements of abalones. we' ll be working on one of the most popular data sets in machine learning: Titanic. This must be prepared for the machine learning process. Using data from Titanic: Machine Learning from Disaster The dataset Titanic: Machine Learning from Disaster is indispensable for the beginner in Data Science. 8134 on the public leaderboard. First, download the test and training set from the data page of the competition (here is a zip of the two small files in case the page from kaggle is removed in the future). We will be using the Titanic passenger data set and build a model for predicting the survival of a given passenger. The ship was carrying 2224 people and that tragic accident costed the life of 1502 passengers. forest classifier on the Titanic data set, reflected as F1 instead of accuracy. I have been applying machine learning to the Titanic data set with SKlearn and have been holding out 10% of the training data to calculate the accuracy of my fitted models. accuracy_score(). 980920314254'  Apr 22, 2017 Figure: Titanic survival data set in Azure ML Studio. Mar 8, 2015 The Titanic Kaggle challenge is an example of supervised learning, . You can compute an accuracy measure for classification task with the confusion matrix:. To begin working with the RMS Titanic passenger data, we'll first need to import the Store the 'Survived' feature in a new variable and remove it from the dataset # This . This is what a trained decision tree for the Titanic data set looks like, if we set the maximum number of levels to 3: The tree first splits by sex, and then by class, since it has learned during the training phase that these are the two most important features for determining survival. In this tutorial we will be predicting which passenger survived the accident and who couldn't from different features like age, sex, class, etc. The prediction accuracy of about 80% is supposed to be very good model. 8485, Accuracy = 0. The extra features are set to 101 to display the probability of the 2nd class (useful for binary responses). I’ll be doing the walkthrough in Alteryx in this blog, but if you’re curious about the R code that does the same thing, you can always refer to the R-blog and compare it. The goal here is to predict who survived the Titanic disaster and who did not based on available information. . The third parameter indicates which feature we want to plot survival statistics across. Testing Model accuracy was done by submission to the Kaggle competition. Feb 7, 2019 from the Titanic from a data platform Kaggle to find out about this survival likelihood. Jul 4, 2016 You will also need the Titanic dataset that we will be analyzing. com's competition, we predicted the passenger survivals with 79. Passes | Elapsed Time | Training-accuracy | Validation-accuracy  Jul 2, 2016 Kaggle Machine Learning Competition: Predicting Titanic Survivors. Get the optimal threshold after running the model on the validation dataset according to the best accuracy at each fold iteration. The need of a tool that can help you iterate through the process quickly become vital. The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. Sep 14, 2012 1 From R packages vcd and reshape2, and built-in dataset Titanic. The fact that our accuracy on the holdout data is 75. Given your gender, age, fare price, accommodation class, the people you came with you, and the port from which you departed. AND. prediction Tools and algorithms Python, Excel and C# Random forest is the machine learning algorithm used. Aug 29, 2014 Tutorial: Titanic dataset machine learning for Kaggle using Decision Trees or Random Forests which has atleast 80% of prediction accuracy. then we will test the accuracy of model over whole training dataset  consequently can yield non-optimal prediction accuracy. Random Forest classification using sklearn Python for Titanic Dataset - titanic_rf_kaggle. Accuracy : 0. The dataset is a 4-dimensional array resulting from cross-tabulating 2,201 observations on 4 variables. While this particular tree may have been 100% accurate on the data that you trained it on, even a trivial tree with only one rule could beat it on unseen data. The Titanic dataset In this chapter, let's use the Titanic dataset, which is available on the Internet and also hosted on GitHub, to implement various techniques. We’ll be working on the Titanic dataset. Overview. The accuracy of predicting the Titanic survival outcome is 0. 31, AUC = 0. The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It took around 2 hours of execution time on an early 2014 MacBook Pro 2. This is the dataset on which you must train your predictive model. When determining predictions, a score of . The dataset. caret. The dataset describes a few passengers information like Age, Sex, Ticket Fare, etc. Random forest performed most consistently over the widest range of training percentages of all tested algorithms. 426% accuracy in our previous attempt . 26% accuracy on the Explaining XGBoost predictions on the Titanic dataset¶ This tutorial will show you how to analyze predictions of an XGBoost classifier (regression for XGBoost and most scikit-learn tree ensembles are also supported by eli5). 10 août 2016 in Kaggle. . 76555 for a Kaggle submission. Kaggle; 11,620 teams; Ongoing. This post presents a gentle introduction to Kaggle by using Scikit-Learn to approach the Titanic Competition available datasets and participate on competitions to Kaggle Titanic Competition Part X - ROC Curves and AUC. So you’re excited to get into prediction and like the look of Kaggle’s excellent getting started competition, Titanic: Machine Learning from Disaster? Great! It’s a wonderful entry-point to machine learning with a manageably small but very interesting dataset with easily understood variables. 78 score using soft majority voting with logistic regression and random forest. My question is how to further boost the score for this classification problem? Datasets distributed with R Accurate and portable elementary functions; Root / csv / datasets / Titanic. The accuracy will then be compared in order to suggest the Prediction of Survivors in Titanic Dataset: A Comparative Study using Machine Learning Algorithms As with most Kaggle competitions, you are given two datasets: a training set, complete with the outcome (or target variable) for a group of passengers as well as a collection of other parameters such as their age, gender, etc. Threshold = 0. We see that 3 approaches to the Titanic dataset using different predictions models were all able to achieve good prediction accuracy. Jun 9, 2017 1: Collecting Data. Approaching a new data set using different models is one way of getting a handle on your data. In particular, we will compare the algorithms on the basis of the percentage of accuracy on a test dataset. evaluate(predictions). Problem Description – The ship Titanic met with an accident and a lot of passengers died in it. My solution to the Kaggle Titanic competition. Payment Accuracy 2015 Dataset; Payment Accuracy 2016 Dataset; Payment Accuracy 2017 Dataset; Payment Accuracy 2018 Dataset This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R. Titanic Data Set; Training Error; Training (Data|Set) Nested (Transactional|Historical) Data; Transform; Treatments (Combination of factor level) True score (Classical test theory) (True Function|Truth) (Total) Sum of the square (TSS|SS) Tuning Parameter (two class|binary) classification problem; Statistical Learning - Two-fold validation; Data - Uncertainty This logistic regression example in Python will be to predict passenger survival using the titanic dataset from Kaggle. Thus, if you consistently get 30% and 70% accuracy, then your model is 70%  How can you evaluate Logistic Regression's model fit and accuracy ? 1 models , we would require a much larger data set to achieve reasonable accuracy. With the accuracy of 81. Achieving accuracy score of 78% (0. data“. Dataset As such, training a deep neural network on the Titanic dataset is total overkill, but it’s a cool technology to work with so we’re going to do it anyway. Kaggle is a platform for predictive modelling competitions. The training set should be used to build your machine learning  Feb 2, 2017 Titanic: Machine Learning from Disaster. Predicting Survival on the Titanic. The data analysis will then be done and the prediction outcomes will be checked for accuracy. Formula of the Decision Trees data = data_train: Dataset method = 'class': Fit a binary model rpart. Key Words: Logistic Regression, Data Analysis, Kaggle Titanic Dataset, Data pre-processing. 679% mean accuracy! rpart This experiment serves as a tutorial on building a classification model using Azure ML. 5 represents the decision boundary for the two classes output by the RandomForest – under . The function to be called is glm() and the fitting process is not so different from the one used in linear regression. Not the best odds. There are decision nodes that partition the data and leaf nodes that give the prediction that can be followed by traversing simple IF. Making our first submission to Kaggle. create database titanic; use titanic; drop table train; create external table train ( passengerid int, . Tags: tutorial, classification, model evaluation, titanic, boosted decision tree, decision forest, random forest, data cleansing Introduction. Logistic Regression is useful to see which features affect the survival rate of the passenger the most using the coef_ function and gave a 81. Before launching into the code though, let me give you a tiny bit of theory behind logistic regression. I’ll split the train_data from the overall data as it has the value of target variable. They will give you titanic csv data and your model is supposed to predict who survived or not. html, 2002. accuracy. 3 minutes read. I'm trying to work on the titanic dataset. We can then again use ‘predict ()’ to predict the target variable of the test dataset. learnmachinelearning) submitted 4 months ago by Blo4d I created a Neural Network from scratch with Python and Numpy and used it to predict survival in Kaggle's Titanic challenge. Table 1. 6% compared with the 80. The reason for this massive loss of life is that the Titanic was only carrying 20 lifeboats, which was not nearly enough for the 1,317 passengers and 885 crew members aboard. Start here! Predict survival on the Titanic and get familiar with ML basics. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1,502 out of 2,224 passengers and crew members. My first big project was working on the dataset of the Titanic challenge on Kaggle. The calculation shows that only 38% of the passengers survived. This post is the opportunity to share my solution with you. data set. Then, you train the model on 4/5 of the data, and check its accuracy on . py The goal in the Kaggle Titanic challenge is to predict survival with the highest accuracy. accuracy = evaluator. I decided to use the well-known Titanic Kaggle dataset, and mimicked an R-blogger’s first crack at it, converting the R code to Alteryx. Data Cleaning. My question is how to further boost the score for this classification problem? A Data Science Exploration From the Titanic in R. 52% accuracy in prediction. Explaining XGBoost predictions on the Titanic dataset¶ This tutorial will show you how to analyze predictions of an XGBoost classifier (regression for XGBoost and most scikit-learn tree ensembles are also supported by eli5). INTRODUCTION The field of machine learning has allowed analysts to uncover insights from historical data and past events. The sklearn. The data set consists of 50 samples from each of three species of Iris: Iris setosa; Iris virginica; Iris versicolor I am working on the Titanic dataset. Data Wrangling is a process to transform raw data to machine readable data. The data set provided by kaggle contains 1309 records of passengers aboard the titanic at the time it sunk. model to import the train_test_split function allows our dataset to be split into two parts, the training and testing datasets. Before: PassengerId Survived Pclass Se Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. First of all, let’s get the data sets from the Titanic Machine Learning competition at Kaggle. are used to train the data and used in the algorithms to predict the test data. 5%. Split the data randomly into 80 (train and validation), 20 (test with unseen data). score(X_test, y_test) objective of the research is to analyze Titanic disaster to determine a correlation between the survival of passengers and characteristics of the passengers using various machine learning algorithms. 2. 82575756 Dummy model: 0. 78. Here, the pandas package allows the titanic dataset, which is a comma separated file to be loaded up. Start here! Predict survival on the Titanic and get familiar with ML basics In this blog-post, I will go through the whole process of creating a machine learning model on the famous Titanic dataset, which is used by many people all over the world. The Titanic datasets are obtained from Kaggle . cross-validation. This time, we use a well known data set as our subject, the Titanic survivors data sets. Test on train data uses the whole dataset for training and then for testing. So far my submission has 0. it has 2 parts: - First one is . Here is the answer: original question deleted, so web-cache link. This article is moderately rigorous and goes through each step I have taken to build this analysis of the titanic dataset. init_notebook_mode() # run at the start of every notebook import cufflinks  Oct 1, 2017 How I got ~98% prediction accuracy with Kaggles Titanic Competition After more examination of the dataset, I found that under 18 year olds  This will help you score 95 percentile in the Kaggle Titanic ML competition . 945, 94. train_test_split. A tree structure is constructed that breaks the dataset down into smaller subsets eventually resulting in a prediction. On 15 April, 1912 Titanic met with an unfortunate event - it collided with an iceberg and sank. Run the code cell below to plot the survival outcomes of passengers based on their sex. Competition Site 'Mean accuracy of Random Forest: 0. Thus, the group that is survivors might be a 0 or a 1, depending on a degree of randomness. The tutorial is divided into two parts. Usage: from keras. titanic_train_data = titanic_train_data. However, the Iris dataset dataset has already prepare for learning. Data Splitting. fit(X_train, y_train) tf_clf_dnn. Classification accuracy is the proportion of correctly classified examples. We’re going to use two models: gbm (Generalized Boosted Models) and glmnet (Generalized Linear Models). It is invaluable to load standard datasets in R so that you can test, practice and experiment with machine learning techniques and improve your skill with the platform. Also examine feature  In particular, we will compare the algorithms on the In this research paper, we use various machine basis of the percentage of accuracy on a test dataset. The dataset contains the following data: – survival: survival (0 = No, 1 = Yes) – pclass: ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) – sex: sex – age: age in years – sibsp: number of siblings / spouses aboard the Titanic – parch: numbr of parents / children aboard the Titanic – ticket: ticket number – fare: passenger fare The following are code examples for showing how to use sklearn. The data set contains 4177 samples with 9 attributes. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. train data at Kaggle's Titanic: Machine. R makes it very easy to fit a logistic regression model. offline. Once you loaded the dataset into a data frame, you can do some data analysis/explorations. Cross validation, Confusion Matrix 1. 5 or greater is 1. Wahoo! Over 98% accuracy using this model! Kaggle really is a great source of fun and I’d recommend anyone to give it a try. 7%, it can detect if a passenger survives or not. The evaluation metric for this contest is the categorization accuracy, or the proportion of test images that are correctly classified. com . I also use K-fold cross valdation with 10 folds to evaluate the model performance and choose hyper-parameters. Let's see what we get: Very different accuracy on Kaggle's Titanic Dataset with a Neural Network (self. II. For the data . What is the accuracy rate of your decision stump (depth 1 decision tree)  Nov 20, 2015 Kaggle Titanic challenge solution using python and graphlab create. Feb 10, 2017 or popularly called Stacking Ensemble on Titanic Kaggle Dataset. Download the Iris Flowers Dataset; Save the file in your current working directory with the file name “iris. Getting up to 78% on the Titanic dataset. Titanic: Machine Learning from Disaster The first step of building any machine learning model is investigating the dataset to understand the characteristics of each feature to determine if it contains telling predictive information. Updated: April 5th 2018. We will use Titanic dataset, which is small and has not too many features, but is still interesting enough. I will also focus on doing some illustrative data visualizations along the way. DATASET (~76% accuracy) when the training dataset was between 10 and 60% of the entire dataset. 300 samples were used as a test set. As for the features, I used Pclass, Age, SibSp, Parch, Fare, Sex, Embarked. Next: LPAR Chapter 2, basic data visualization Or copy & paste this link into an email or IM: How can I perform cross validation using rpart package on titanic dataset? R Programming. This dataset allows you to work on the supervised learning, more preciously a classification problem. There are two rules to the titanic data set and the woman-child model: class: center, middle, inverse, title-slide # The Titanic data set ### Aldo Solari --- # Outline * Introduction * Missing values * EDA * Feature engineering Titanic survival predictive analysis Machine Learning model has eight blocks (Figure -6). 97 indicates that you have correctly classified all but 3% of the images. Drag and drop each component, connect them according to Figure 6, change the values of Split data component, trained model and two-class classifier. csv). May 2, 2018 In efforts to study the Titanic passengers; Kaggle, a popular data while the Gaussian Naïve Bayes witnessed 92. Parameters such as sex, age, ticket, passenger class etc. First, we import all of the libraries we will need. K-Means with Titanic Dataset. v. In this article, we mainly focus on data preparation before we can fit it into our learning model. How? finding patterns and building models from the training data. Tags: tutorial, classification, model evaluation, titanic, boosted decision tree, decision forest, random forest, data cleansing Competition dataset. How does one solve the titanic problem in Kaggle? fixed ROC curve score, accuracy score, etc on a Kaggle competition or on a work project? Feb 21, 2018 Step 2: Where is the dataset? The dataset is also given to us on a golden plater with test and. A binary outcome is a result that has two possible values - true or false, alive or dead, etc. The data has been split into two groups: training set (train. About the Dataset. drop(['PassengerId'], axis=1) titanic_test_PassengerId = titanic_test['PassengerId'] titanic_test_data = titanic_test_data. While there are number of machine learning branches or topics, the one I will work on is “Supervised Learning” where we assign each of the records to a pre-determined set of categories. 26% accuracy on the testing data. Logistic Regression Formulas: The logistic regression formula is derived from the standard linear equation for a straight The dataset. My goal was to achieve an accuracy of 80% or higher. titanic. We can calculate the null accuracy from this def predictions_0(data):  Oct 25, 2017 Learn how to succeed in the Kaggle Titanic competition with this free tutorial. tf_clf_dnn = skflow. 625 For performance reasons, when your data fits in memory, it is recommended to use the boosted_trees_classifier_train_in_memory function. (~76% accuracy) when the training dataset was between 10 and 60% of the entire dataset. BoostedTrees API shown above. In the case of the Titanic data set, we can use the baseline of 76% from predicting all males do not survive. Note: running the code may last hours. The Titanic Data Set And The Woman-Child Model – 82% Test Set Accuracy In this tutorial, I will be showing you how to achieve 82% accuracy with the titanic data set and the woman-child model. Titanic: A case study for predictive analysis on R (Part 4) February 20, 2015 Working with titanic data set picked from Kaggle. 5 is a pretty good score for the Titanic dataset. some manual feature selection (Select Columns widget) on Titanic dataset, where we want  Jun 1, 2016 Second attempt at Kaggle's Titanic data set, accuracy up to 78%, notes on preprocessing and Pandas. Furthermore, it is remarkable that the simple Name-only model was able to obtain an 82% accuracy. It is the reason why I would like to introduce you an analysis of this one. 5 is 0, . Jun 30, 2017 This tutorial is based on Titanic data from Kaggle website. AND…. Very different accuracy on Kaggle's Titanic Dataset with a Neural Network (self. For a clustering algorithm, the machine will find the clusters, but then will asign arbitrary values to them, in the order it finds them. the most using the coef_ function and gave a 81. In this interesting use case, we have used this dataset to predict if people survived the Titanic Disaster or not. In this dataset, the objective is to create a machine learning model to predict the survival of passengers of the RMS Titanic, whose sinking is one of the most infamous event in the history. This tutorial is broken down into the following steps: Classification 4 We are going to look at Decision Trees We will analyze the from CS 313E at University of Texas With the rise of Machine Learning inside industries. You can vote up the examples you like or vote down the exmaples you don't like. Annual Improper Payments Datasets. csv. import numpy as np import pandas as pd import nltk import plotly import re plotly. 5 basically is a coin-flip, the model really can’t tell at all what the classification is. When a prediction problem involves only a handful of possible features, like this Titanic dataset, the modeler might have sufficient knowledge of the problem to hand select the most relevant predictor variables. Examine performance metrics: lift, AUC, and accuracy. I have chosen to work with the Titanic dataset after spending some time poking around on the site and looking at other scripts made by other Kagglers for inspiration. 2% accuracy we got with cross-validation indicates that our model is overfitting slightly to our training data. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. We employed the Titanic dataset to illustrate how naïve Bayes classification can be performed in R. This is a prediction problem. 77512). 05 ) tf_clf_dnn. Accuracy would gives 0. [3]K Hornik, A Zeileis, T Hothorn, and Titanic Survival Predictor Find out your statistical chances of survival based upon your circumstances to see if you would survive the Titanic disaster. matrix(test_data). I have been playing with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0. A score of . Place the dataset in the current working directory in R; before this, first set the working directory accordingly using the setwd() command. I will have to use this threshold while scoring the model in the test set. Data Aggregation With the accuracy of 81. load_data() Returns: 2 tuples: x_train, x_test: uint8 array of grayscale image data with shape (num_samples, 28, 28). 1 Kaggle's Titanic: Machine Learning from Disaster data . This will help us calculate the model accuracy. 1 This experiment serves as a tutorial on building a classification model using Azure ML. As I'm writing this post, I am ranked among the top 4% of all Kagglers: More than 4540 teams are currently competing. Journey of the RMS Titanic through Data Science My main motive is to apply some machine learning algorithms to test the accuracy on the Kaggle competition. The dataset (training) is a collection of data about some of the passengers (889 to be precise), and the goal of the competition is to predict the survival (either 1 if the passenger survived or 0 if they did not) based on some features such as the class of service, the sex, the age etc. model_selection. 3Ghz 8 core machine. However if training time is not of a concern or if you have a very large dataset and want to do distributed training, use the tf. I hope you enjoyed my brief article outlining my process of analysing datasets, and hope to see you soon! Posted on mer. to classify the testing set, allowing us to determine the accuracy of the model. Save the model and run it. metrics. estimator. Good classification accuracy on this problem is above 90% correct, typically 96% or better. In this post I am going to fit a binary logistic regression model and explain each step. the accuracy of your model; Prepare and make your first Kaggle  Sep 17, 2018 I will be using the Kaggle competition Titanic data as the basis for . whether a person survived the Titanic disaster or not. For that, we make use of a simple Logistic Regression algorithm. So it was that I sat down two years ago, after having taken an econometrics course in university which introduced me to R, thinking to give the competition a shot. Titanic disaster is one of the most famous shipwrecks in the world history. The second is the Accuracy-Interpretability. XGBoost stands out eventually with an 87% test accuracy, but that  Jan 10, 2014 So you're excited to get into prediction and like the look of Kaggle's by increasing your accuracy by only a few extra percentage points. 6% gives a rank of 6,663 out of 7,954. Titanic MISG 2014 The point of this exercise was that you must use caution with decision trees. csv); test set (test. Jul 29, 2019 You will use this index to shuffle the titanic dataset. drop(['PassengerId'], axis=1) It is now time to train our model, and test against unseen data. Python, a rising star in Machine Learning technology become the first choice to bring you into a more successful venture. Code Explanation. Run cross-validation on 80% of the data, which will be used to train and validate the model. The data has categorical values, so I used labelEncoder to change the data to numbers, instead of text. THEN logic down the nodes. In efforts to . Next: LPAR Chapter 2, basic data visualization This page provides Python code examples for sklearn. This is my first attempt at a Kaggle script. For example, a categorization accuracy of 0. Predict the values on the test set they give you and upload it to see your rank among others. First, download the . Then we have another topic to learn — Data Wrangling. Jan 9, 2019 For data scientists, Titanic Kaggle dataset is arguably one of the most . print ( "Test  Load the popular Titanic data set into a local spark cluster. A Great Start: the Titanic challenge on Kaggle . I am working on the Titanic dataset. Full Titanic Example with Random Forest - Duration: 23:53. At the time of writing, accuracy of 75. Well, extensively Titanic: Getting Started With R. plot (fit, extra= 106): Plot the tree. They provide a "Getting Started" competition to gain a first experience in Data Science with Titanic Kaggle. There are 891 observations in the training dataset and I’ll split that in 75:25 ratio. The Titanic data set is said to be the starter for every aspiring data scientist. 8638 These are the metrics corresponding to maximum accuracy that could be achieved by the model. The reason is that the model doesn't REALLY know how to deal with character columns, as you can see if you run data. Mike Bernico 75,795 views Predictive modeling: Kaggle Titanic competition (part 1) Kaggle provides a few “Getting Started” competitions with highly structured data, including this one. Thus, if you consistently get 30% and 70% accuracy, then your model is 70% accurate. List of Tables. Each record contains 11 variables describing the corresponding person: survival (yes/no), class (1 = Upper, 2 = Middle, 3 = Lower), name, gender and age; the number of siblings and spouses aboard, the number of parents and children aboard, the ticket number, the fare paid, a cabin number, and the port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton). You just overfit big time! Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper. Although it is called a “competition”, it is an entry level data science practice actually. Question 1 ¶. we're going to actually apply the K-Means algorithm to the Titanic dataset. datasets import mnist (x_train, y_train), (x_test, y_test) = mnist. TensorFlowDNNClassifier( hidden_units = [ 20 , 40 , 20 ], n_classes = 2 , batch_size = 256 , steps = 1000 , learning_rate = 0. Accuracy based on training percentage The horizontal line at 50% represents the accuracy that DataSets/titanic. titanic dataset accuracy

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