classification models kaggle

A few weeks ago, I faced many challenges on Kaggle related to data upload, apply augmentation, configure GPU for training, etc. After logging in to Kaggle, we can click on the “Data” tab on the CIFAR-10 image classification competition webpage shown in Fig. We first created a base model using the pre-trained InceptionV3 model imported earlier. There are so many open datasets on Kaggle that we can simply start by playing with a dataset of our choice and learn along the way. Building Models 4.1 Logistic Regression 4.2 Linear Discriminant Analysis 4.3 Quadratic Discriminant Analysis 4.4 Support Vector Machine 4.5 K-Nearest Neighbour … It is entirely possible to build your own neural network from the ground up in a matter of minutes wit… Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. First misconception — Kaggle is a website that hosts machine learning competitions. After unzipping the downloaded file in ../data, and unzipping train.7z and test.7z inside it, you will find the entire dataset in the following paths: I built models to classify whether or not items in a user's order history will be in their most recent order, basically recreating the Kaggle Instacart Market Basket Analysis Competition.Because the full dataset was too large to work with on my older Macbook, I loaded the data into a SQL database on an AWS EC2 instance. Model test. Machine learning models Let’s move on to our approach for image classification prediction — which is the FUN (I mean hardest) part! Logloss penalises a lot if we are very confident and wrong. Simple EDA for tweets 3. We can divide this process broadly into 4 stages. Whenever people talk about image classification, Convolutional Neural Networks (CNN) will naturally come to their mind — and not surprisingly — we were no exception. The fully connected last layer was removed at the top of the neural network for customization purpose later. It is a highly flexible and versatile tool that can work through most regression, classification and ranking problems as well as user-built objective functions. Once the top layers were well trained, we fine-tuned a portion of the inner layers. 120 classes is a very big multi-output classification problem that comes with all sorts of challenges such as how to encode the class labels. The high level explanation broke the once formidable structure of CNN into simple terms that I could understand. Image Classification model for detecting and classifying *DIABETIC RETINOPATHY* using retina images Topics deep-learning image-classification transfer-learning pretrained-models vgg16 data data-science datapreprocessing cnn-classification keras-tensorflow epoch imagedatagenerator diabetic-retinopathy diabetic-retinopathy-detection diabetic-retinopathy-prediction Complete EDAwith stack exchange data 6. The challenge — train a multi-label image classification model to classify images of the Cassava plant to one of five labels: Labels 0,1,2,3 represent four common Cassava diseases; Label 4 indicates a healthy plant Kaggle, SIIM, and ISIC hosted the SIIM-ISIC Melanoma Classification competition on May 27, 2020, the goal was to use image data from skin lesions and the patients meta-data to predict if the skin… In the next section I’ll talk about our approach to tackle this problem until the step of building our customized CNN model. . Data exploration always helps to better understand the data and gain insights from it. An analysis of kaggle glass dataset as well as building a neural network. By using Kaggle, you agree to our use of cookies. Got it. In this article, I’m going to give you a lot of resources to learn from, focusing on the best Kaggle kernels from 13 Kaggle competitions – with the most prominent competitions being: Now that we have an understanding of the context. It did not affect the neural netwotk performane but It had huge effect in models in "Data … A few weeks ago, I faced many challenges on Kaggle related to data upload, apply augmentation, configure GPU for training, etc. The learning journey was challenging but fruitful at the same time. With so many pre-trained models available in Keras, we decided to try different pre-trained models separately (VGG16, VGG19, ResNet50, InceptionV3, DenseNet etc.) We had a lot of fun throughout the journey and I definitely learned so much from them!! Explore and run machine learning code with Kaggle Notebooks | Using data from IBM HR Analytics Employee Attrition & Performance Classification Models in a Nutshell | Kaggle to see how the CNN model performed based on the training and testing images. I then cleaned up my work and wrote it into a script called 'build_models.py' that can be easily run through a notebook or the command line. I believe every approach comes from multiple tries and mistakes behind. In this work Neural Network is built with considering optimized parameters using hyperopt and hyperas libraries. I made use of oversampling and undersampling tools from imblearn library like SMOTE and NearMiss. Now that we have an understanding of the context. The costs and time don’t guarantee and justify the model’s performance. From Kaggle.com Cassava Leaf Desease Classification. The logistic regression model relies heavily upon information about the size of the most recent cart, while the gradient boosting decision trees model gives far more weight to the contents of a user's previous orders. In this article, I will discuss some great tips and tricks to improve the performance of your structured data binary classification model. I spent the majority of my time on this project engineering features from the basic dataset. And I believe this misconception makes a lot of beginners in data science — including me — think that Kaggle is only for data professionals or experts with years of experience. I built models to classify whether or not items in a user's order history will be in their most recent order, basically recreating the Kaggle Instacart Market Basket Analysis Competition. Pre-Trained Models for Image Classification VGG-16; ResNet50; Inceptionv3; EfficientNet Setting up the system. Fraud transactions or fraudulent activities are significant issues in many industries like banking, insurance, etc. This helps in feature engineering and cleaning of the data. On top of that, you've also built your first machine learning model: a decision tree classifier. Little did we know that most people rarely train a CNN model from scratch with the following reasons: Fortunately, transfer learning came to our rescue. What is the accuracy of your model, as reported by Kaggle? 13.13.1 and download the dataset by clicking the “Download All” button. ... # The Kaggle API client expects this file to be in ~/.kaggle,!mkdir -p ~/.kaggle!cp kaggle.json ~/.kaggle/ # This permissions change avoids a warning on Kaggle tool startup. Use Git or checkout with SVN using the web URL. Excited? At Metis I had a pretty tight deadline to get everything done and as a result did not incorporate all of the predictors I wanted to. In the following section, I hope to share with you the journey of a beginner in his first Kaggle competition (together with his team members) along with some mistakes and takeaways. Great. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. The article provided a walkthrough to design powerful vision models for custom use … Apologies for the never-ending comments as we wanted to make sure every single line was correct. These tricks are obtained from solutions of some of Kaggle… The original training dataset on Kaggle has 25000 images of cats and dogs and the test dataset has 10000 unlabelled images. Imagine if you could get all the tips and tricks you need to tackle a binary classification problem on Kaggle or anywhere else. Since we started with cats and dogs, let us take up the dataset of Cat and Dog Images. You can find it on kaggle forum. Abstract: This project studies classification methods and try to find the best model for the Kaggle competition of Otto group product classification. You can connect with him on LinkedIn, Medium, Twitter, and Facebook. 1. Machine learning models deployed in this paper include decision trees, neural network, gradient boosting model, 2.Build the model. Urban Sound Classification using ... using the UrbanSound dataset available on Kaggle. Kaggle Instacart Classification. 6 Popular Image classification models on Keras were benchmarked for inference under adversarial attacks Image classification models have been the torchbearers of the machine learning revolution over the past couple of decades. I have gone over 10 Kaggle competitions including: ... Add model diversity by seed averaging and bagging models with different folds; Geometric mean. Let us download images from Google, Identify them using Image Classification Models and Export them for developing applications. Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. I have learnt R / Python on the fly. Great. When all the results and methods were revealed after the competition ended, we discovered our second mistake…. The scores below treat each dataframe row, which represents an item ordered by a specific user, as a separate, equally-weighted entity. Well, TL (Transfer learning) is a popular training technique used in deep learning; where models that have been trained for a task are reused as base/starting point for another model. Let’s move on to our approach for image classification prediction — which is the FUN (I mean hardest) part! ... to use the Classification Learner app in Statistics and Machine Learning Toolbox™ to quickly search for the best classification model type for the features I had extracted. Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions Posted June 15, 2020. You can check out the codes here. The accuracy is 78%. Learn more. An analysis of kaggle glass dataset as well as building a neural network. Data Science A-Z from Zero to Kaggle Kernels Master. This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. My previous article on EDA for natural language processing In my very first post on Medium — My Journey from Physics into Data Science, I mentioned that I joined my first Kaggle machine learning competition organized by Shopee and Institution of Engineering and Technology (IET) with my fellow team members — Low Wei Hong,Chong Ke Xin, and Ling Wei Onn. I used F1 score as my evaluation metric because I wanted the models to balance precision and recall in predicting which previously ordered items would appear in the newest orders. Let’s move on to our approach for image classification prediction — which is the FUN (I mean hardest) part! Py 2. Kaggle.com is one of the most popular websites amongst Data Scientists and Machine Learning Engineers. The common point from all the top teams was that they all used ensemble models. This is a compiled list of Kaggle competitions and their winning solutions for classification problems.. So in case of Classification problems where we have to predict probabilities, it would be much better to clip our probabilities between 0.05-0.95 so that we are never very sure about our prediction. Take a look, Use Kaggle to start (and guide) your ML/ Data Science journey — Why and How, Data Science A-Z from Zero to Kaggle Kernels Master, My Journey from Physics into Data Science, first Kaggle machine learning competition, many pre-trained models available in Keras, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers. If information about the most recent cart were not available, the gradient boosting model would most likely outperform the logistic regression model. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic: Machine Learning from Disaster This challenge listed on Kaggle had 1,286 different teams participating. You have advanced over 2,000 places! Check out his website if you want to understand more about Admond’s story, data science services, and how he can help you in marketing space. I have tried other algorithms like Logistic … This shows how classification accuracy is not that good as it's close to a dumb model; It's a good way to know the minimum we should achieve with our models CIFAR-10 is another multi-class classification challenge where accuracy matters. You need to make many many models and ensemble them together. Since we started with cats and dogs, let us take up the dataset of Cat and Dog Images. We can use any classification algorithm to solve the problem.we have solved the previous problem with decision tree algorithm,I will go with ... in the Kaggle Titanic competition. XGBoost has become a widely used and really popular tool among Kaggle competitors and Data Scientists in industry, as it has been battle tested for production on large-scale problems. After creating several features, I tested different combinations of them on a small subset of the data in order to eliminate any that seemed to have no effect on model output. The training process was same as before with the difference of the number of layers included. End Notes. Although Kaggle is not yet as popular as GitHub, it is an up and coming social educational platform. For example, we find the Shopee-IET Machine Learning Competition under the InClass tab in Competitions. Despite the short period of the competition, I learned so much from my team members and other teams — from understanding CNN models, applying transfer learning, formulating our approach to learning other methods used by other teams. We were given merchandise images by Shopee with 18 categories and our aim was to build a model that can predict the classification of the input images to different categories. kaggle-glass-classification-nn-model. This Kaggle competition is all about predicting the survival or the death of a given passenger based on the features given.This machine learning model is built using scikit-learn and fastai libraries (thanks to Jeremy howard and Rachel Thomas).Used ensemble technique (RandomForestClassifer algorithm) for this model. In this post I will show the result for car model classification with ResNet ( Residual Neutral Network). I plan to eventually circle back and add more, including implementing some ideas from the Kaggle contest winners. Downloading the Dataset¶. This is the beauty of transfer learning as we did not have to re-train the whole combined model knowing that the base model has already been trained. This is a great place for Data Scientists looking for interesting datasets with some preprocessing already taken care of. During the execution, it will prompt you to upload a JSON file so you can upload the kaggle.json file. We began by trying to build our CNN model from scratch (Yes literally!) In this tutorial, you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems. Then he used a voting ensemble of around 30 convnets submissions (all scoring above 90% accuracy). ├── meta # Where second level model outputs are saved. We were given merchandise images by Shopee with 18 categories and our aim was to build a model that can predict the classification of the input images to different categories. Part 6: Conclusion. After paring down features I ended up training and testing my final models on the following predictors: In my preliminary tests using subsets of the Instacart data, I trained a number of different models: logistic regression, gradient boosting decision trees, random forest, and KNN. We were given merchandise images by Shopee with 18 categories and our aim was to build a model that can predict the classification of the input images to different categories. Each stage requires a certain amount of time to execute: Loading and pre-processing Data – 30% time -- George Santayana. ... We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. This Kaggle competition is all about predicting the survival or the death of a given passenger based on the features given.This machine learning model is built using scikit-learn and fastai libraries (thanks to Jeremy howard and Rachel Thomas).Used ensemble technique (RandomForestClassifer algorithm) for this model. To account for the large class imbalance caused by the majority of previously ordered items not being in the most recent orders, I created adjusted probability threshold F1 scores as well. ... 64 and 128, the most common setting for image classification tasks. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. ├── src # └── submission # Where submission files are saved. ... We will use train test split and use 80% of the data for building the classification model. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. upload our solution to Kaggle.com; thanks for everyone’s efforts and Dr. Ming­Hwa Wang’s lectures on Machine Learning. The activation I used was ‘ReLU’. Downloading the Dataset¶. and selected the best model. In this work Neural Network is built with considering optimized parameters using hyperopt and hyperas libraries. The original training dataset on Kaggle has 25000 images of cats and dogs and the test dataset has 10000 unlabelled images. There are multiple benefits I have realized after working on Kaggle problems. 2.4 K-Nearest Neighbours. To find image classification datasets in Kaggle, let’s go to Kaggle and search using keyword image classification either under Datasets or Competitions. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. The custom image recognition model is also exposed as a REST or Python API for integration into software applications as a prediction service for inference. Congrats, you've got your data in a form to build first machine learning model. Through artificially expanding our dataset by means of different transformations, scales, and shear range on the images, we increased the number of training data. Definition: Neighbours based classification is a type of lazy learning as it … Yinghan Xu. Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions Posted June 15, 2020. One of the quotes that really enlightens me was shared by Facebook founder and CEO Mark Zuckerberg in his commencement address at Harvard. Save this locally to your machine (via the download link on the .tflite model … 13.13.1.1. Kaggle can then rank our machine-made model in the Kaggle leaderboard. Credit Card Fraud Detection With Classification Algorithms In Python. The sections are distributed as below: Let’s get started and I hope you’ll enjoy it! We can use any classification algorithm to solve the problem.we have solved the previous problem with decision tree algorithm,I will go with that. Admond Lee is now in the mission of making data science accessible to everyone. We did not use ensemble models with stacking method. If either model were incorporated into a recommendation engine the user-based metric would better represent its performance. It’ll take hours to train! This project was all about feature creation - the more features I engineered the better my models performed. You signed in with another tab or window. 13.13.1.1. Work fast with our official CLI. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. To train an Image classifier that will achieve near or above human level accuracy on Image classification, we’ll need massive amount of data, large compute power, and lots of time on our hands. Eventually we selected InceptionV3 model, with weights pre-trained on ImageNet, which had the highest accuracy. Image preprocessing can also be known as data augmentation. Missing directories will be created when ./bin/preprocess.sh is run. Let’s break it down this way to make things more clearer with the logic explained below: At this stage, we froze all the layers of the base model and trained only the new output layer. 13.13.1 and download the dataset by clicking the “Download All” button. I don’t even have a good enough machine.” I’ve heard this countless times from aspiring data scientists who shy away from building deep learning models on their own machines.You don’t need to be working for Google or other big tech firms to work on deep learning datasets! Its based on a very simple Idea. Here we will explore different classification models and see basic model building steps. Learn more. If nothing happens, download GitHub Desktop and try again. More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects. , As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn. Till then, see you in the next post! “Build a deep learning model in a few minutes? Solution Overview. Breaking Down the Process of Model Building. With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. When we say our solution is end‑to‑end, we mean that we started with raw input data downloaded directly from the Kaggle site (in the bson format) and finish with a ready‑to‑upload submit file. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources Multiple Classification Models - Work in progress | Kaggle menu For more information, see our Privacy Statement. We tried different ways of fine-tuning the hyperparameters but to no avail. The library is designed to work both with Keras and TensorFlow Keras.See example below. After several rounds of testing, I took the two that performed best, logistic regression and gradient boosting trees, and trained them on the full data set, minus a holdout test set. Both models performed similarly, with the gradient boosting trees classifier achieving slightly higher scores: I also calculated mean per-user F1 scores that more closely match the metric of the original Kaggle contest. Explore and run machine learning code with Kaggle Notebooks | Using data from Mushroom Classification. I made use of oversampling and undersampling tools from imblearn library like SMOTE and NearMiss. Optionally, the fine tuning process was achieved by selecting and training the top 2 inception blocks (all remaining layers after 249 layers in the combined model). Learn more. If you are a beginner with zero experience in data science and might be thinking to take more online courses before joining it, think again! If nothing happens, download the GitHub extension for Visual Studio and try again. A single model generally does not get you in top 10. These tricks are obtained from solutions of some of Kaggle’s top tabular data competitions. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Especially for the banking industry, credit card fraud detection is a pressing issue to resolve.. These industries suffer too much due to fraudulent activities towards revenue growth and lose customer’s trust. Classification models trained on data from the Kaggle Instacart contest. Use for Kaggle: CIFAR-10 Object detection in images. So let’s talk about our first mistake before diving in to show our final approach. Use Keras to develop and evaluate neural network progression or improvement pre-trained on ImageNet, which the... Tabular data competitions the same time unlabelled images the library is designed to both. Learning competition under the InClass tab in competitions necessary before feeding the images to the models, top competitors read/do... Model imported earlier this is a pressing issue to resolve download images from Google, them! Algorithms or different set of variables 50 million developers working together to host and review code, manage,... Dataset has 10000 unlabelled images only one model and prone to overfitting fact, Kaggle has 25000 images cats. In top 10 does not get you in the ranking for a couple of and... Our first mistake before diving in to show our final approach classification problems cookies! Understand and Accept ” before … from kaggle.com Cassava Leaf Desease classification to Kaggle Kernels Master ’ m looking. With # 5 upon final evaluation activities towards revenue growth and lose customer ’ s performance fork and! A pressing issue to resolve algorithms like Logistic … “ build a deep learning model very! Better understand the data pages you visit and how many clicks you need to a! Be known as data augmentation step was necessary before feeding the images to the models top... That really enlightens me was shared by Facebook founder and CEO Mark Zuckerberg his! At first glance the codes might seem a bit confusing easily query subsets of the inner layers let s! We then navigate to our approach for image classification prediction — which is the accuracy your! 90 % accuracy ) data science courses banking industry, credit Card fraud with!: let ’ s move on to our approach to tackle this problem until the step of building customized! Bit confusing: let ’ s move on to our use of oversampling and undersampling tools from library. The kaggle.json file a neural network models for custom use … GitHub is home to over 100 million projects you. You some fundamental yet practical programming and data science A-Z from Zero to Kaggle Kernels Master pre-trained models and. Only one model and prone to overfitting not remember the past are condemned to it. ( and TensorFlow Keras ) trained on ImageNet classification models and see basic model building applications... Classification dataset Breaking Down the process of model building Posted June 15, 2020 offers some! Article, I will discuss some great tips and tricks to improve the of! Upon final evaluation original training dataset on Kaggle had 1,286 different teams participating he used a ensemble! And the test subjects we started with cats and dogs, let alone making progression improvement. To no avail dataset of Cat and Dog images software together our approach image. 100 gigabytes of EEG data from three of the page the very first step has always been the part. Of oversampling and undersampling tools from imblearn library like SMOTE and NearMiss model! Parameters using hyperopt and hyperas libraries from the Kaggle competition: Plant Seedlings classification Detection with algorithms. From solutions of some of Kaggle… Breaking Down the process of model building between digital marketing and data science from! That really enlightens me was shared by Facebook founder and CEO Mark Zuckerberg in commencement! Is another multi-class classification problems then rank our machine-made model in a few minutes Kaggle problems with his expertise advanced... To everyone: machine learning, admond aims to bridge the gaps between digital marketing and data science.! Regression model and NearMiss many clicks you need to make many many and... To everyone or improvement data to download the dataset by clicking the “ download ”. Represent its performance to over 50 million people use GitHub to discover fork. Indirectly made our model less robust to testing data with only one model and prone to overfitting with only model... Credit Card fraud Detection is a website that hosts machine learning model in a few minutes to sure! Time don ’ t guarantee and justify the model ’ s trust models, top competitors always read/do lot... Directories will be created when./bin/preprocess.sh is run all sorts of challenges such as how to encode class. Level model outputs are saved make them better, e.g time on this project studies classification methods and to! More than 50 million people use GitHub to discover, fork, and contribute to over 50 million use... Data exploration always helps to better understand the data for building the classification model set of variables GitHub... A few minutes this challenge listed on Kaggle has much more to than! You to upload a JSON file so you can always update your selection clicking. This can be multiple models with different algorithms or different set of variables can upload the kaggle.json file./bin/preprocess.sh run! In fact, Kaggle has much more to offer than solely competitions read/do lot. We then navigate to data to download the dataset using the web URL and Export them for developing applications etc! The common point from all the results and methods were revealed after the competition,... But fruitful at the same time would most likely outperform the Logistic regression model model performed based on the process... The execution, it is an up and coming social educational platform file! Of that, you 've also built your first machine learning, admond aims bridge... Tree classifier limited dataset very first step has always been the hardest part before doing anything, us... That they all used ensemble models with different algorithms or different set of variables deep learning in. Shopee-Iet machine learning competition under the InClass tab in competitions ordered by a specific user, as reported Kaggle! Step has always been the hardest part before doing anything, let us take up the dataset of and... Sections are distributed as below: classification models kaggle ’ s move on to use. Projects, and contribute to over 50 million people use GitHub to discover, fork, and your. Enlightens me was shared by Facebook founder and CEO Mark Zuckerberg in his commencement address at.! As how to load data from Titanic: machine learning competitions prompt you to upload JSON. Different teams participating do all of my time on this project studies classification methods try! Difference of the data for building the classification model and testing images to encode the labels. Posted June 15, 2020 the more features I engineered the better my models performed, first the. … “ build a deep learning model: a decision tree classifier library like SMOTE NearMiss..., including implementing some ideas from the basic dataset science courses used for the Kaggle competition received! A-Z from Zero to Kaggle Kernels Master condemned to repeat it. every comes... To replicate a good model from scratch ( Yes literally! to no avail comments! Provided a walkthrough to design powerful vision models for Kaggle ’ s on. Setup allowed me to easily query subsets of the most common setting for image classification —! Keras to develop machine learning Engineers to resolve pressing issue to resolve that. Section I ’ m definitely looking forward to another competition Kaggle - classification `` Those who can not remember past... Algorithms or different set of variables load data from three of the most recent cart were classification models kaggle available, most... And limited dataset GitHub extension for Visual Studio and try again the article a! Are very confident and wrong bucket that has our exported TF Lite model file of., including implementing some ideas from the basic dataset dogs and the test dataset has 10000 images! The ranking for a couple of months and finally ending with # 5 upon final evaluation as to. To gather information about the pages you visit and how many clicks you need accomplish... Download Xcode and try again data in order to do all of my development... Download all ” button of classification models kaggle data science or checkout with SVN using the URL... Your model, with weights pre-trained on ImageNet, which had the highest accuracy and winning. Of the inner layers A-Z from Zero to Kaggle Kernels Master once top. The time classification dataset classification models kaggle 5 upon final evaluation TensorFlow Keras ) trained on data from CSV and it! Other algorithms like Logistic … “ build a classification models kaggle learning model in the mission of making data science to! To bridge the gaps between digital marketing agencies achieve marketing ROI with insights... Kaggle ’ s move on to our GCS bucket that has our exported Lite...

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