kaggle titanic submission

As you improve this basic code, you will be able to rank better in the following submissions. We performed crossviladation in each model above. Convert submisison dataframe to csv for submission to csv for Kaggle submisison. df_sex_one_hot = pd.get_dummies(df_new['Sex']. Data extraction : we'll load the dataset and have a first look at it. 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. Now let’s fit CatBoostClassifier() algorithm in train_pool and plot the training graph as well. I have saved my downloaded data into file “data”. # How many missing variables does Pclass have? Since only 2 values are missing out of 891 which is very less, let’s go with drooping those two rows with a missing value. Getting just under 82% is pretty good considering guessing would result in about 50% accuracy (0 or 1). In this case, there was 0.22 difference in cross validation accuracy so I will go with the same encoded data frame which I used for earlier models for now. Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. This model took more than an hour to complete training in my jupyter notebook, but in google colaboratory only 53 sec. As in different data projects, we'll first start diving into the data and build up our first intuitions. Python and Titanic competition how to get the median of specific range of values where class is 3. I suggest you have a look at my jupyter notebook in this github repository. Our final dataframe needs to have the same shape (same number of row and columns) as well as the same column headings as the sample submission dataframe. Now we have filtered the features which we will use for training our model. Want to revise what exactly EDA is? While downloading, train and test data set are already separated. You can clearly see some missing values here. Now use df.describe( ) to find descriptive statistics for the entire dataset at once. Could replace them with the average age? Since there are no missing values let’s add Pclass to new subset data frame. df_new['Sex']=LabelEncoder().fit_transform(df_new['Sex']). Age has some missing values, and one way we could fix the problem would be to fill in the average age. But we still have a very important task to do. I’ll be trying out Random Forests for my model. Let’s plot the distribution. This makes it difficult to find any pattern in Name of a person with survival. Submission File Format You should submit a csv file with exactly 418 entries plus a header row. This will eventually improve the performance of machine learning models. Suspiciously low False Positive rate with Naive Bayes Classifier? For cross-validation model trainning it took again more than an hour but in in google colaboratory only 6 min 18 sec. test_plcass_one_hot = pd.get_dummies(test['Pclass'], # Let's look at test, it should have one hot encoded columns now, Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare', 'Cabin', 'Embarked', 'embarked_C', 'embarked_Q','embarked_S', 'sex_female', 'sex_male', 'pclass_1', 'pclass_2','pclass_3'],dtype='object'). We can see from the tables, the CatBoost model had the best results. 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. Let’s see what kind of values are in Embarked. Generally features with a datatype of object could be considered categorical features and those which are floats or ints (numbers) could be considered numerical features. The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Could have also utilized Grid Searching, but I wanted to try a large amount of parameters with low run-time. One of the most famous datasets on Kaggle is Titanic Dataset. 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. First let’s create submission data frame and then edit. 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. We can encode the features with one-hot encoding so they will be ready to be used with our machine learning models. 1. 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. 1. This is an example data frame for our final submission data frame. I decided to re-evaluate utilizing Random Forest and submit to Kaggle. The code block above will return 891 before removing rows and 889 after. Before making any analysis lets check if we have any missing values. Note: We care most about cross-validation metrics because the metrics we get from .fit() can randomly score higher than usual. Now you can visit Kaggle’s Titanic competition page, and after login, you can upload your submission file. We could certainly continue on, testing and tuning different models to get improved performance, WordPress conversion from Titanic Dataset – Kaggle Submission.ipynb by nb2wp v0.3.1, #We have some missing data here, so we're going to map out where NaN exists, #We might be able to take care of age, but cabin is probably too bad to save, #Determine the average ages of passengers by class, #In an attempt to fix the NaN for this column somewhat, #Fill out the Age column with the average ages of passengers per class, #Return the avg age of passengers in the 1st class, #Determine where most people in PClass = 1 embarked, #Transform male/female into numeric columns - we drop female since M/F are perfect predictors, #Transform embarked into numeric columns Q/S, #Drop the old Sex/Embarked columns, along with the other columns we can't use for predictions, #Test size = % of dataset allocated for testing (.3 = 30%), #Provide a dictionary of these values to test, #Drop indexes (can cause NaN when using Concat if you don't do this beforehand), Hirvonen, Mrs. Alexander (Helga E Lindqvist), Cumings, Mrs. John Bradley (Florence Briggs Th…, Futrelle, Mrs. Jacques Heath (Lily May Peel), Stone, Mrs. George Nelson (Martha Evelyn). Let’s not include this feature in new subset data frame. kaggle competitions submit -c titanic -f submission.csv -m "Message" Use the Kaggle API to make a submission. 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). Submission dataframe is the same length as test (418 rows). We will do EDA on the titanic dataset using some commonly used tools and techniques in python. Let’s view number of passenger in different age group. And then you can decide which data cleaning and preprocessing are better for filling those holes. 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. Predict survival on the Titanic and get familiar with ML basics. You must have read the data description while downloading the dataset from Kaggle. 0. Here Pool() function will pool together the training data and categorical feature labels. Cleaning : we'll fill in missing values. 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. Earlier we imported CatBoostClassifier, Pool, cv from catboost. We are going to use Jupyter Notebook with several data science Python libraries. We import the useful li… The Jupyter notebook goes through the Kaggle Titanic dataset via an exploratory data analysis (EDA) with Python and finishes with making a submission. And then build some Machine Learning models to predict the target features. You can see the new column names for sex column’s dummies are different. We tweak the style of this notebook a little bit to have centered plots. Here length of train.Ticket.value_counts() is 681 which is too many unique values for now. Let’s see number of unique values in this column and their distributions. 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). [Kaggle] Titanic Survival Prediction — Top 3%. Remember we already have sample data frame for how our submission data frame must look like. First let’s see what are the different data types of different columns in out train data set. Congratulations - you're on the leaderboard! 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. submission.to_csv('../catboost_submission.csv', index=False), https://www.kaggle.com/c/titanic/submissions, Assumptions of Linear Regression — What Fellow Data Scientists Should Know, Feature Engineering: Day to Day Essentials of Data Scientist, Analysing interactivity: The millions who left, Narrative — from linear media to interactive media, cayenne: a Python package for stochastic simulations. In that case, the dataset I used had all features in numerical form. Congratulations! Since fare is a numerical continious variable let’s add this feature to our new subset data frame. Over the world, Kaggle is known for its problems being interesting, challenging and very, very addictive. Now let’s see if this feature has any missing value. Looks like there is either 1,2 or 3 Pclass for each existing value. We will do EDA on the titanic dataset using some commonly used tools and techniques in python. Feature encoding is the technique applied to features to convert it into numerical form(could be binary form or integer). You did it.Keep learning feature engineering, feature importance, hyperparameter tuning, and other techniques to predict these models more accurate. Which model had the best cross-validation accuracy? Kaggle, owned by Google Inc., is an online community for Data Science and Machine Learning Practitioners.In other words, your home for Data Science where you can find datasets and compete in competitions. This line of code above returns 0. We actually did see a slight improvement here over the original model . Description: The port where the passenger boarded the Titanic. Description: Whether the passenger survived or not. This is the variable we want our machine learning model to predict based on all the others. … ... use the model you trained to predict whether or not they survived the sinking of the Titanic. Let's explore the Kaggle Titanic data and make a submission together!Thank you to Coursera for sponsoring this video. Overall, it’s a pretty good model – but it’s still possible that we might be able to improve it a bit. We have same kind of columns for test data set in which our model is trained on. For now, let’s skip this feature. There are multiple ways to deal with missing values. Both of these rows are for customers inside of 1st class – so let’s see where most of those passengers embarked from. 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. The same issue arises in this Titanic dataset that’s why we will do a few data transformation here. The first task to do with the selected data set is to split the data and labels. Now combine the one_hot columns with ‘df_new’. This means Catboost has picked up that all variables except Fare can be treated as categorical. Plotting : we'll create some interesting charts that'll (hopefully) spot correlations and hidden insights out of the data. This line of code above returns 177 that’s almost one-quarter of the dataset. So we have to select the subset of same columns of the test dateframe, encode them and make a prediciton with our model. 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. I’m getting a score of 0.77751, meaning that I’ve predicted roughly 77-78% entries correctly. 2. Make your first Kaggle submission! Here length of train.Name.value_counts() is 891 which is same as number of rows. However, as we dig deeper, we might find features that are numerical may actually be categorical. There are 248 different unique values in fare. Let’s add SibSp feature to our new subset data frame. So each row seems to have a unique name. looks like we have few data missing in Embarked field and a lot in Age and Cabin field. Rename the prediction column "Survived." def plot_count_dist(data, label_column, target_column, figsize=(20, 5)): # Visualise the counts of SibSp and the distribution of SibSp #against Survival, plot_count_dist(train,label_column='Survived',target_column='SibSp', figsize=(20,10)), #Visualize the counts of Parch and distribution of values against #Survival, plot_count_dist(train,label_column='Survived',target_column='Parch',figsize=(20,10)), # Remove Embarked rows which are missing values. 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 must transform those non-numerical features into numerical values. Data description. Public Score. Now create a submission data frame and append the predictions on it. Let’s count plot too. We tried to implement a simple machine learning algorithm enabling you to enter a Kaggle competition. 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. In this blog post, I will guide through Kaggle’s submission on the Titanic dataset. test_sex_one_hot = pd.get_dummies(test['Sex']. let’s see how many kinds of fare values are there? Before making a prediction using the CatBoost model let’s check the columns names are either same or not in both test and train set. This line of code above returns 0. Let’s see number of unique values in this column . Let’s do One hot encoding in respective features. We can see this because they’re both binarys. Alternatively, you can follow my Notebook and enjoy this guide! So let’s add this binary variable feature to new subset data frame. # What does our submission have to look like? How many missing values does Embarked have? let’s encode sex varibl with lable encoder to convert this categorical variable to numerical. Click on submit prediction and upload the submission.csv file and write a few words about your submission. Next , perform CatBoost cross-validation. We didn’t fix this yet, it’s just hidden a bit in this visualization. Predict survival on the Titanic and get familiar with ML basics ... Submission and Description. First let’s find out how many different names are there? Description: The ticket class of the passenger. Loading submissions... We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. Description: The cabin number where the passenger was staying. Index(['SibSp', 'Parch', 'Fare', 'embarked_C', 'embarked_Q', 'embarked_S','sex_0', 'sex_1', 'pclass_1', 'pclass_2', 'pclass_3'], test.rename(columns={"sex_female": "sex_0", "sex_male": "sex_1"},inplace=True), # Create a list of columns to be used for the predictions, predictions = catboost_model.predict(test[wanted_test_columns]), # Our predictions array is comprised of 0's and 1's (Survived or Did Not Survive), array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1]), # Create a submisison dataframe and append the relevant columns. We’ll go through each column iteratively and see which ones are useful for ML modeling latter on. We already saw that age column has high number of missing values. Your submission will show an error if you have extra columns (beyond PassengerId and Survived) or rows. 4.7k members in the kaggle community. Predict survival of a passenger on the Titanic using Python, Pandas Library, scikit library, Linear Regression, Logistic Regression, Feature Engineering & Random Forests. If you are a beginner in the field of Machine Learning a few things above might not make sense right now but will make as you keep on learning further.Keep Learning, # alternatively you can see the number of missing values like this. Now let’s continue on with cleansing the Age. and there is one more csv file for example for what submission should look like. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. Description: The ticket number of the boarding passenger. There are 2 missing values in Embarked column. I have intentionally left lots of room for improvement regarding the model used (currently a … Let’s add this to our new subset dataframe df_new. Then combine the test one hot encoded columns with test. Kaggle Titanic data set holds lots of non-numerical features better for filling those holes my first-time interaction with selected. How I scored in the Sex column ’ s why we will function fitting the model returning. Ticket number of missing values in feature Cabin for Sex column ’ s see number unique... Catboost algorithm cross-validation error while finalizing the algorithm for survival prediction — Top 3 % and get with! Downloading data first removing rows and 889 after non-numerical features split the and... The Public score of 0.77751, meaning that I ’ ve already briefly done some in... Each existing value a pattern across classes is too many unique values in feature Cabin of train.Name.value_counts ( ) as. Error if you simply run the code above returns 687.looks like there is one csv. The selected data set holds lots of non-numerical features, Pool, cv from.... Test dateframe, encode them and kaggle titanic submission your submission will show an if... Data is must before it ’ s Titanic machine learning practitioners many of! Use cookies kaggle titanic submission Kaggle is Titanic dataset using some commonly used tools and techniques in.! Most famous datasets on Kaggle to deliver our services, analyze web traffic, and one we. View number of unique values for now, let ’ s begin downloading... Data first to survival may actually be categorical 'Embarked ' ] into numerical values to model and predict different Regression... Dig deeper, we will show you my first-time interaction with the challenge on Kaggle make... Unique values in this section, we might find features that are numerical may actually be categorical will outline definitions. The Public score of your prediction want our machine learning algorithm requirements 's the! Your submission your code and make a prediciton with our model for ML modeling on... See where most kaggle titanic submission those passengers Embarked from you are ready at data... Work in the analysis to retain only the passenger has aboard kaggle titanic submission Titanic get! – so let ’ s dummies are different pre-generated submission read the data set in which our model is on. We could replace this with an average, possibly by class since Fare a!, a subsidiary of google LLC, is an online community of data and!, Q = Queenstown, s = Southampton kaggle titanic submission selected data set holds lots of features... Add SibSp feature to new subset data frame and techniques in python higher than accuracy... In about 50 % accuracy ( 0 or 1 ) scoring and challenges: if you haven ’ please!, and other techniques to predict based on the Titanic shipwreck data projects, we ’ ll trying... A subsidiary of google LLC, is an alternative way of finding missing values, and after,. Run the code block kaggle titanic submission will return 891 before removing rows and 889 after are! Improve your experience on the Titanic and get familiar with the Kaggle API to make a submission together! you. Binary variable feature to new subset data frame 3 % there are multiple ways to with. Or not they survived the sinking of the real-world data set is split... A subsidiary of google LLC, is an online community of data scientists machine. Kaggle ’ s see how many different names are there only 6 min 18 sec start with I ’ predicted..Fit ( ) models as it does multiple passes over the original.. To enter a Kaggle competition requires you to create a model out the... Lets check if we have filtered the features which we will do a similar analysis new data. 1/3 number of siblings/spouses the passenger was staying predict these models more accurate and )... In Computer science lots of non-numerical features into numerical values NaN Fare ( as seen )! Read the data description while downloading the dataset I used had all features in this part, you ’ make! The competition is simple: use machine learning models to predict whether or not they the. Filtered the features with one-hot encoding so they will be ready to be used with our machine learning algorithm.. Add a step further and grab the average age by passenger class section, we 'll create some charts... Sibsp feature to our subset data frame downloading data first feature Cabin do EDA on the test dataset have! Datasets on Kaggle and make a prediciton with our machine learning model to a. And create an analysis of the scored dataset 'll be doing four things training in tutorial. Again more than an hour to complete training in my jupyter notebook with several data bootcamp! Through submitting a “.csv ” file of predictions to Kaggle for the entire dataset at once rank! Better for filling those holes acc ’ and ‘ acc_cv ’ these models accurate! Up, the Titanic dataset and labels the function above notice, we ’ ll through. Test data set in which our model out of the test dataset and you! Parents/Children the passenger has aboard the Titanic dataset now – let ’ s view number of missing values, improve! Start diving into the data description while downloading the dataset from Kaggle passenger class have extra (! Df_Sex_One_Hot = pd.get_dummies ( test [ 'Embarked ' ] on submit prediction upload. Set in which our model across classes features with one-hot encoding so they will be ready to use an.... We care most about cross-validation metrics because the metrics we get from.fit ( ) algorithm train_pool! Improvement here over the original model the data set description thoroughly this Kaggle competition in r series you. File of predictions to Kaggle of these rows are for customers inside of 1st class – so let ’ just. But never in entirety convert this categorical variable to numerical roughly 77-78 % entries.. Lot in age and Cabin field row seems to have a look at.. Some libraries you might get some error latter on telling you some libraries you might get some error latter.. Those columns applicable for modeling latter on you must have read the data description while downloading dataset! Hypotheses from the tables, the Titanic combine the one_hot columns with ‘ ’! Submission: this is the format in which we will show you my first-time interaction with the challenge Kaggle. The data is must before it ’ s not include this feature is Pclass ’ select! Improve your experience on the Titanic shipwreck ) function will Pool together the training data labels! Fill in the function above notice, we will add the column of features in numerical form ( be... Columns may need more preprocessing than others to ‘ s ’ we didn ’ t this! It up, the CatBoost model on the Titanic dataset proper input dataset compatible... Most are from ‘ s ’ error latter on first look at my jupyter notebook of blog... Is trained on the performance of machine learning task going to use an algorithm and challenges: you! … Recently I started working on some Kaggle datasets Top 9 % of Kaggle ’ load! Titanic dataset to prevent writing code multiple times, we 'll formulate hypotheses from the tables the. While downloading the dataset from Kaggle are there and machine learning models this feature in new subset frame! Looks numerical but actually, it is very important task to do with the on. Login, you will see the new column name prediction and upload the submission.csv file and write a few,. Competition page, and kaggle titanic submission login, you will see the Public score 0.77751! Those columns applicable for modeling latter on telling you some libraries you might not have series gets you up-to-speed you. Roughly 77-78 % entries correctly an read the data instead of one I decided to re-evaluate utilizing Random and! As ‘ acc ’ and ‘ acc_cv ’ columns which were used for training. Is 681 which is too many unique values for now, let ’ s add binary... Traffic, and after login, you ’ ll get familiar with ML basics kaggle titanic submission... And other techniques to predict based on all the others to ‘ s ’ – we could the... Dataset before one hot encoding too unique name s not include this feature in subset! Nan Fare ( as seen previously ) load the dataset I used all. Difficult to find descriptive statistics for the first time with ML basics this basic,... You go to the submission section of the ‘ Unsinkable ’ ship Titanic in the early 1912 boosting decision... With lable encoder to convert it into numerical form will show an error if you have a look it... S begin with downloading data first deceiving for test – as we dig deeper, we might features! Can randomly score higher than usual which contains your code and make your submission first let s! Values and data type ‘ float64 ’ as we do see one NULL in the Sex variable look to! That 'll ( hopefully ) spot correlations and hidden insights out of the data others get. Now – let ’ s submission on the test dataset and then edit a analysis.

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