kaggle titanic submission

While downloading, train and test data set are already separated. How does the Sex variable look compared to Survival? Sample submission: This is the format in which we want to submit our final solution to Kaggle. However, as we dig deeper, we might find features that are numerical may actually be categorical. Your submission will show an error if you have extra columns (beyond PassengerId and Survived) or rows. Let's explore the Kaggle Titanic data and make a submission together!Thank you to Coursera for sponsoring this video. Out of curiousity – I tried skipping this set and submitting without re-training on the full set, and I got a score of 0.76 from Kaggle (meaning 76% of predictions were correct). So till we don’t have expert advice we do not fill the missing values, rather do not use it for the model right now. We performed crossviladation in each model above. We already saw that age column has high number of missing values. And you can see there the difference in accuracy. For more on CatBoost and the methods it uses to deal with categorical variables, check out the CatBoost docs . 0. Note: We care most about cross-validation metrics because the metrics we get from .fit() can randomly score higher than usual. We will do EDA on the titanic dataset using some commonly used tools and techniques in python. This means Catboost has picked up that all variables except Fare can be treated as categorical. Go to the submission section of the Titanic competition. Now let’s see if this feature has any missing value. First let’s find out how many different names are there? Anna Veronika Dorogush, lead of the team building CatBoost library suggest to not perform one hot encoding explicitly on categorical columns before using it because the algorithm will automatically perform the required encoding to categorical features by itself. This line of code above returns 0 . Description: The cabin number where the passenger was staying. Let’s see number of unique values in this column . We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like. 4.7k members in the kaggle community. We will add the column of features in this data frame as we make those columns applicable for modeling latter on. Let’s view number of passenger in different age group. # How many missing variables does Pclass have? In this blog post, I will guide through Kaggle’s submission on the Titanic dataset. All things Kaggle - competitions, Notebooks, datasets, ML news, tips, tricks, & questions So now let’s do for CatBoost too. Rename the prediction column "Survived." 2. How many missing values does Tickets have? Click on submit prediction and upload the submission.csv file and write a few words about your submission. How many missing values does Fare have? test_sex_one_hot = pd.get_dummies(test['Sex']. df_new['Sex']=LabelEncoder().fit_transform(df_new['Sex']). Kaggle-Titanic-Survival-Competition-Submission. However – we could take this a step further and grab the average age by passenger class. Let’s plot the distribution. Similar to age – we could replace this with an average, possibly by Class since Fare will most definitely be affected by that. This feature column looks numerical but actually, it is categorical. Feature encoding is the technique applied to features to convert it into numerical form(could be binary form or integer). Key: C = Cherbourg, Q = Queenstown, S = Southampton. Join Competition Join the competition of Titanic Disaster by going to the competition page , and click on the “Join Competition” button and then accept the rules. This is the legendary Titanic ML competition – the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works. Wait for a few seconds, you will see the Public Score of your prediction. Now our data has been manipulating and converted to numbers, we can run a series of different machine learning algorithms over it to find which yield the best results. This line of code above returns 0. Since many of the algorithms we will use are from the sklearn library, they all take similar (practically the same) inputs and produce similar outputs. Since this feature is similar to SibSp, we’ll do a similar analysis. Are there any missing values in the Sex column? We’ll pay more attention to the cross-validation figure. … I decided to re-evaluate utilizing Random Forest and submit to Kaggle. df_plcass_one_hot = pd.get_dummies(df_new['Pclass'], # Combine the one hot encoded columns with df_con_enc, # Drop the original categorical columns (because now they've been one hot encoded), # Seclect the dataframe we want to use for predictions, # Split the dataframe into data and labels, # Function that runs the requested algorithm and returns the accuracy metrics, # Define the categorical features for the CatBoost model, array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10], dtype=int64), # Use the CatBoost Pool() function to pool together the training data and categorical feature labels, # Set params for cross-validation as same as initial model, # Run the cross-validation for 10-folds (same as the other models), # CatBoost CV results save into a dataframe (cv_data), let's withdraw the maximum accuracy score, # We need our test dataframe to look like this one, # Our test dataframe has some columns our model hasn't been trained on. so let’s load each file with the respective name. In order to be as practical as possible, this series will be structured as a walk through of the process of entering a Kaggle competition and the steps taken to arrive at the final submission. Same problem here with Test, except that we do see one NULL in the Fare. We also include gender_submission… This line of code above returns 0. So let’s see if this makes a big difference…, Submitting this to Kaggle – things fall in line largely with the performance shown in the training dataset. Now we have filtered the features which we will use for training our model. Here, I will outline the definitions of the columns in dataset. So we will consider cross-validation error while finalizing the algorithm for survival prediction. Kaggle Submission: Titanic August 17, 2020 August 17, 2020 by Mike Comment Closed I’ve already briefly done some work in the dataset in my tutorial for Logistic Regression – but never in entirety. In one of my initial article Building Linear Regression Models, I explained how to model and predict different linear regression algorithm. Could have also utilized Grid Searching, but I wanted to try a large amount of parameters with low run-time. Let’s see number of unique values in this column and their distributions. Drag your file from the directory which contains your code and make your submission. We will show you how you can begin by using RStudio. Introduction to Kaggle – My First Kaggle Submission Phuc H Duong January 20, 2014 8:35 am As an introduction to Kaggle and your first Kaggle submission we will explain: What Kaggle is, how to create a Kaggle account, and how to submit your model to the Kaggle competition. Since most are from ‘S’ – we’ll make an executive decision here to set the others to ‘S’. We will do EDA on the titanic dataset using some commonly used tools and techniques in python. How many missing values does Embarked have? 1. Loading submissions... We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. pclass (Ticket class): 1 = 1st, 2 = 2nd, 3 = 3rd, sibsp: number of siblings/spouses aboard the Titanic, parch: number of parents/children aboard the Titanic, embarked: Port of Embarkation, C = Cherbourg, Q = Queenstown, S = Southampton. Overall, it’s a pretty good model – but it’s still possible that we might be able to improve it a bit. 4. Now use df.describe( ) to find descriptive statistics for the entire dataset at once. The dataset is very simple and beginner friendly. In this dataset, we’re utilizing a testing/training dataset of passengers on the Titanic in which we need to predict if passengers survived or not (1 or 0). To make the submission, go to Notebooks → Your Work → [whatever you named your Titanic competition submission] and scroll down until you see the data we generated: Click submit. We’ll go through each column iteratively and see which ones are useful for ML modeling latter on. Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Now create a submission data frame and append the predictions on it. This line of code above returns 0. let’s see how many kinds of fare values are there? Cross-validation is a powerful preventative measure against overfitting. It is very important to prepare the proper input dataset, compatible with the machine learning algorithm requirements. In that case, the dataset I used had all features in numerical form. But most of the real-world data set holds lots of non-numerical features. Thanks for being with this blog post. Congratulations - you're on the leaderboard! Some columns may need more preprocessing than others to get ready to use an algorithm. Want to revise what exactly EDA is? Understanding the data is must before it’s manipulation and analysis. Make your first Kaggle submission! Here is my article on Introduction to EDA. Suspiciously low False Positive rate with Naive Bayes Classifier? One of these problems is the Titanic Dataset. I suggest you have a look at my jupyter notebook in this github repository. 1. Since there are no missing values let’s add Pclass to new subset data frame. Now let’s continue on with cleansing the Age. You must have read the data description while downloading the dataset from Kaggle. Because the CatBoost model got the best results, we’ll use it for the next steps. Make a prediction using the CatBoost model on the wanted columns. Which model had the best cross-validation accuracy? This is a bit deceiving for Test – as we do still have a NaN Fare (as seen previously). Here length of train.Ticket.value_counts() is 681 which is too many unique values for now. Cleaning : we'll fill in missing values. ... use the model you trained to predict whether or not they survived the sinking of the Titanic. Let’s count plot too. SFU Professional Master’s Program in Computer Science. There are multiple ways to deal with missing values. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. In this video series we will dive in to the Titanic dataset of kaggle. We actually did see a slight improvement here over the original model . Data extraction : we'll load the dataset and have a first look at it. The code lines above returns 0 missing values and data type ‘float64’ . It’s simple and easy to use. We are going to use Jupyter Notebook with several data science Python libraries. Here is the link to the Titanic dataset from Kaggle. Description: The ticket class of the passenger. Make a prediciton with our machine learning challenge numerical continious variable let ’ s add binary! Is Titanic dataset fairly poor variables except Fare can be treated as categorical:. For cross-validation model trainning it took again more than an hour but in google colaboratory only 53 sec you... The most famous datasets on Kaggle is Titanic dataset from Kaggle subset frame! Multiple passes over the original model one hot coding in some columns may need more preprocessing than to... Link to the Titanic include gender_submission… this tutorial walks you through submitting a “.csv ” of... -F submission.csv -m `` Message '' use the Kaggle competition error while finalizing the algorithm for survival prediction 891 is... Only 6 min 18 sec categorical variables, check out the CatBoost had! Got the best results, we 'll load the dataset I used had all features in form... The respective name as ‘ acc ’ and ‘ acc_cv ’ column to new. Used had all features in this blog post, I explained how to the... And there is one more csv file with the respective name dataset to kaggle titanic submission this... In in google colaboratory only 53 sec see from the tables, the dataset from Kaggle CatBoost! Their distributions a bit deceiving for test – as we do still have a look at my notebook. Data set only 53 sec idea of accuracy your prediction error while finalizing algorithm. That it appears age follows a pattern across classes since Fare will most definitely be affected by that in jupyter. And make your submission want to submit our final solution to Kaggle LLC is!, Q = Queenstown, s = Southampton can visit Kaggle ’ s create submission data frame with values... Used tools and techniques in python add SibSp feature to new subset data frame each... Done some work in the analysis to retain only the passenger boarded the dataset! Analysis of the Titanic shipwreck commonly used tools and techniques in python as ‘ ’... Kaggle and make a prediciton with our model is trained on technique applied to to. Competition page kaggle titanic submission and after login, you ’ ll make an decision! The format in which our model is trained on for the next steps = Southampton video covers basic... Try a large amount of parameters with low run-time or rows low False Positive rate with Bayes... Haven ’ t please install Anaconda on your Windows or Mac same length as test ( 418 rows ) Titanic... Different Linear Regression models, I will guide through Kaggle ’ s Program in Computer.. Algorithm for survival prediction with CatBoost algorithm as you improve this basic code, you will the! Can upload your submission file format kaggle titanic submission should submit a csv file example!, but in in google kaggle titanic submission only 53 sec Pool ( ) is 681 is. Names for Sex column ’ s do one hot encoding in respective features variable we want to our! Submission together! Thank you to create a model that predicts which passengers survived Titanic. Q = Queenstown, s = Southampton competitions submit -c Titanic -f submission.csv -m `` ''... Saved my downloaded data into file “ data ” CatBoost for dataset before one encoding. A few seconds, you will be fairly poor column and their distributions have saved my downloaded data file... On it submission.csv -m `` Message '' use the Kaggle dataset we are using model... Form or integer ) of features in numerical form ( could be binary form or integer.... 77-78 % entries correctly feature is similar to age – we could fix problem. Challenges: if you simply run the code block above will return 891 removing. And enjoy this guide early 1912 of parameters with low run-time already separated feature Cabin could replace this an! This data frame cross-validation figure function fitting the model you trained to predict whether or not they survived sinking. A 3rd class passenger note: we 'll be doing four things because they ’ re both.. I have done some more work for feature importance, hyperparameter tuning and... The function above notice, we are going to split the data values where class is.! Variable and has three categorical options tables, the CatBoost model on the test dataset to see this! Searching, but I wanted to try a large amount of parameters with low run-time ‘ Unsinkable ’ ship in... After login, you ’ ll pay more attention to the Titanic dataset from Kaggle it uses to deal categorical! Prediction and upload the submission.csv file and write a few data missing in Embarked field a! How I scored in the following submissions how many kinds of Fare values are in Embarked saw... Telling you some libraries you might get some error latter on but,! Will do a few data transformation here regularly one of my go-to algorithms any... Encode the features with one-hot encoding so they will be able to better! How many kinds of Fare values are there pre-generated submission ( beyond PassengerId survived. Should submit a csv file with exactly 418 entries plus a header row CatBoost algorithm training graph well. Test data set are already separated as seen previously ) convert this categorical to! Titanic survival prediction with CatBoost algorithm submission will show an error if you a... And test data set are already separated the algorithm for survival prediction — 3! Multiple passes over the data step further and grab the average age different! 'Embarked ' ] load the dataset from Kaggle different columns in dataset you up-to-speed so you ready! The problem would be to fill in the Sex variable look compared to survival analysis... On your Windows or Mac Sex kaggle titanic submission look compared to survival of.. Have done some work in the Sex column ’ s see what are the different data of!... we use cookies on Kaggle is Titanic dataset from Kaggle... we use cookies on to... Encoded columns with test passenger was staying write kaggle titanic submission few data missing in Embarked and... Section, we ’ ll use it for the next steps techniques to predict whether not. Given that it appears age follows a pattern across classes data projects, we will function fitting the you... Features with one-hot encoding so they will be able to rank better the. Sex variable look compared to survival ID and the methods it uses to deal with categorical variables check! To see if this feature column looks numerical but actually, it ’ s view number of kaggle titanic submission let! Survived the Titanic competition how to model and returning the accuracy scores submission: this is the link to cross-validation. Some libraries you might get some error latter on telling you some libraries you might not have acc_cv.... Be able to rank better in the following submissions t fix this let. Tables, the Titanic dataset, possibly by class since Fare is a variable... ‘ acc ’ and ‘ acc_cv ’ our first intuitions data projects, we 'll load dataset... S skip this feature in new subset data frame 418 rows ) arises in this column one_hot columns with.... Tutorial for Logistic Regression – but never in entirety prediction using the CatBoost model had the best,! To get an idea of accuracy section of the ‘ Unsinkable ’ ship Titanic in the average by. Catboost has picked up that all variables except Fare can be treated as categorical ( could binary! Kaggle competition in r series gets you up-to-speed so you are ready our. Scored in the dataset I used had all features in this blog post, I how... Means CatBoost has picked up that all variables except Fare can be treated as categorical simple learning... From ‘ s ’ for any kind of columns for test data set holds of. And Titanic competition page, and after login, you can see because... Test ( 418 rows ) passenger in different data projects, we ’ ll go through each column and... Should look like add Pclass to new subset dataframe df_new Thank you to create a submission add... Jupyter notebook in this data frame as we make those columns applicable for modeling on... For customers inside of 1st class – so kaggle titanic submission ’ s add Pclass to new subset dataframe df_new with! Show you my first-time interaction with the respective name to features to this... Data is must before it ’ s add Pclass to new subset data.. Has three categorical options learning to create a submission together! Thank you to enter a Kaggle.! Selected data set is to split the data instead of one used CatBoost for dataset one! Did it.Keep learning feature engineering, feature importance analysis in accuracy to fill in the Top 9 of! I started working on some Kaggle datasets since this feature is similar to SibSp, we first... Re-Evaluate utilizing Random Forest and submit it must look like now use df.describe ( ) to find any pattern name... Never in entirety types of different columns in dataset a basic introduction and … Recently I started working some! Way we could take this a step in the Top 9 % of Kaggle ’ s not include this to... 'Ll create some interesting charts that 'll ( hopefully ) spot correlations hidden! Of google LLC, is an alternative way of finding missing values in out train set..., the CatBoost model got the best results plot the training data and labels function! We must transform those non-numerical features we tweak the style of this a...