Kaggle titanic feature engineering

Titanic EDA and Feature Engineering Kaggle

  1. Note that you use the .isnull() method in the code chunk below, which will return True if the passenger doesn't have a cabin and False if that's not the case. However, since you want to store the result in a column 'Has_Cabin', you actually want to flip the result: you want to return True if the passenger has a cabin. That's why you use the tilde ~.
  2. 3. Feature engineering. Feature engineering is an art and one of the most exciting things in the broad field of machine learning. I really enjoy to study the Kaggle subforums to explore all the great ideas and creative approaches. The titanic data set offers a lot of possibilities to try out different methods and to improve your prediction score
  3. The passenger class values are already numerically coded (1,2,3), but we need to make the machine learning model understand that this is a categorical variable. Having numerical entries means an implicit numerical ordering, which is creating patterns where they don’t exist (This is the same reason why we don’t encode the ports of embarkation in numerical values, like we did for ‘Sex’). Thus we use ‘one-hot-encoding’ to create one feature for each passenger class.
  4. Repo: https://github.com/kaggledecal/sp17 Before working, make sure you run `git pull` in your local copy of the Kaggle Decal repo! If you haven't clone the.
  5. With advancements in machine learning and artificial neural networks, the answers to previously unknown questions are surfaced. It is the data and the feature engineering that makes this A.I and ML a great hype of the 21st century. Albeit the algorithm being complex and extraordinary at solving a task there is always need of crunching the numbers right with feature engineering that help model.
  6. Feature engineering is the process of using our knowledge of the data to create features that make machine learning algorithms work. Feature engineering is often considered one of the most challenging parts of Machine Learning as chosing the right features can drastically improve the efficiency of the model

Submit it on Kaggle. You can also try submitting results from other algorithms. Following is the example of Logistic Regression. Note:-1. This article is just to make sure that you understand how to start exploring Data Science Hackathons 2. Feature Engineering is the key 3. Try more algorithms to climb the Leader Board Keep Learning The. [Kaggle] Titanic Survival Prediction — Top 3%. You can also use feature engineering to create new features. The test set should be used to see how well your model performs on unseen data. Feature engineering and ensembled models for the top 10 in Kaggle Housing Prices Competition We detail step by step the procedure to develop a regression model to be in the top 10 of this global competition September 12, 2018 - Data Science, Driverless AI, GPU, NLP - Automatic Feature Engineering for Text Analytics - The Latest Addition to Our Kaggle Grandmasters' Recipes Learn how H2O.ai is lending AI expertise to combating COVID-19 challenges

Based o your interest in R or Python you should get started with any of these two Titanic tutorials: Titanic: Starting with Data Analysis Using R or Titanic: Machine Learning from Disaster in Python. Reason being, you don't need to have much of. # Transform into binary variables data_dum = pd.get_dummies(data, drop_first=True) data_dum.head() .dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; } Pclass Has_Cabin CatAge CatFare Sex_male Embarked_Q Embarked_S Title_Miss Title_Mr Title_Mrs Title_Special 0 3 False 0 0 1 0 1 0 1 0 0 1 1 True 3 3 0 0 0 0 0 1 0 2 3 False 1 1 0 0 1 1 0 0 0 3 1 True 2 3 0 0 1 0 0 1 0 4 3 False 2 1 1 0 1 0 1 0 0 With all of this done, it's time to build your final model!See if you can do some more feature engineering and try some new models out to improve on this score. This notebook, together with the previous two, is posted on GitHub and it would be great to see all of you improve on these models. 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. However; I struggled to complete my first competition submission because of, I would say, improper resources.I went through kernels (read as 'articles') for this competition but all of.

1. Passenger Names (Titles)

Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic: Machine Learning from Disaster Automated feature engineering for Titanic dataset Python notebook using data from Titanic: Machine Learning from Disaster · 4,592 views · 2y ago · tutorial, feature engineering. 46. Copy and Edit Kaggle Titanic challenge solution using python and graphlab create. blog about tags. from graphlab.toolkits.feature_engineering import * binner = graphlab. feature_engineering. create (passengers, FeatureBinner (features = ['Fare'] MachineLearning Classification Python GraphLab Kaggle

Everyone, and I mean everyone, at this point, is familiar with the Kaggle Titanic competition, but, just in case you're not, I'll give you a general introduction. Now, everyone (and this time this is not hyperbole I swear) has seen that movie.Well, it was based on the tragic tale of the RMS Titanic, which sank in 1912, taking with it (her?) 1502 out of 2224 passengers 4. Feature engineering¶ Feature engineering is the process of using domain knowledge of the data to create features (feature vectors) that make machine learning algorithms work. feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of. Predict survival on the Titanic. A comprehensive overview of Machine Learning pipelines. In kaggle challenge, we're asked to complete the analysis of what sorts of people were likely to survive. In particular, we're asked to apply the tools of machine learning to predict which passengers survived the tragedy. Feature engineering is the. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. 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. This sensation.. # View head of 'Name' column data.Name.tail() 413 Spector, Mr. Woolf 414 Oliva y Ocana, Dona. Fermina 415 Saether, Mr. Simon Sivertsen 416 Ware, Mr. Frederick 417 Peter, Master. Michael J Name: Name, dtype: object Suddenly, you see different titles emerging! In other words, this column contains strings or text that contain titles, such as 'Mr', 'Master' and 'Dona'.

Anyone new to machine learning will have probably come across Kaggle's titanic competition. The task involves applying machine learning techniques to predict which passengers survived the tragedy. Whilst not a comprehensive attempt to solve the problem, this tutorial guides you through some simple methods to clean the data, engineer features. Kaggle - Titanic: Machine Learning From Disaster Description. Firstly, each row represents a passenger on the titanic and each column is a feature specific to that passenger. The PassengerId column is a unique reference to each of the passengers. This may come in handy later in feature engineering too Tutorials Titanic: Machine Learning from Disaster - Kaggle Competition Titanic: Machine Learning from Disaster - Kaggle Competition On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew, which translates to 32% survival rate I have written about the Kaggle Titanic Competition before, and that ended up being a series of posts on how to approach and model a simple Binary Classification problem. Feature Engineering. For each entry in the dataset, there are 784 features, i.e. each pixel value is a feature, to learn and predict the target (label) from How I scored in the top 9% of Kaggle's Titanic Machine Learning Challenge. This post will be an abbreviated walk through of some of the data wrangling, feature engineering,.

communityNewsBETAResource CenterTutorialsCheat SheetsOpen CoursesPodcast - DataFramedChatNEWdatacampOfficial BlogSearchLog inCreate Free AccountBack to TutorialsTutorials06767Hugo Bowne-AndersonJanuary 10th, 2018kaggle+4Machine Learning with Kaggle: Feature EngineeringLearn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more!In the two previous Kaggle tutorials, you learned all about how to get your data in a form to build your first machine learning model, using Exploratory Data Analysis and baseline machine learning models. Next, you successfully managed to build your first machine learning model, a decision tree classifier. You submitted all these models to Kaggle and interpreted their accuracy. Kaggle Titanic Competition. The crowdsourcing predicitve modeling competition website Kaggle was used as a platform for this study, providing a means to assess my skills compared to other participants. As of 12/11/15 there were 3946 participants with 2458 scripts. A good description of how Kaggle works is found here Kaggle is the biggest Data Science community with over 2 million users. It provides a whole Data Science ecosystem, ranging from competitions, kernels, discussions to blog and courses. Whatever you need that is connected with Data Science or Machine Learning, you can probably find some clue about it on Kaggle Remember that you can easily spot this by first looking at the total number of entries (1309) and then checking out the number of non-null values in the columns that .info() lists. In this case, you see that 'Age' has 1046 non-null values, so that means that you have 263 missing values. Similarly, 'Fare' only has one missing value and 'Embarked' has two missing values. Polynomial feature engineering¶ Evaluate which are the features with the largest +ve and -ve correlations with TARGET. Extract those features and fill in any np.nan rows by imputing with the median of that column (i.e., sklearn.impute.SimpleImputer) Create new polynomial and interactive features (i.e., sklearn.preprocessing.PolynomialFeatures)

Automated feature engineering for Titanic dataset Kaggle

You begin by splitting the dataset into 5 groups or folds. Then you hold out the first fold as a test set, fit your model on the remaining four folds, predict on the test set and compute the metric of interest. Next, you hold out the second fold as your test set, fit on the remaining data, predict on the test set and compute the metric of interest. Then similarly with the third, fourth and fifth. Please see Github Repository. This repository presents my submission in the Titanic: Machine Learning from Disaster, Kaggle Competition. In this competition, the goal is to perform a 2-label classification problem: predict which passengers survived the tragedy. Kaggle offers two datasets. One training (the labels are known) and one testing (the labels are unknown) You can see that there are several titles in the above plot and there are many that don't occur so often. So, it makes sense to put them in fewer buckets. 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 pl..

4. Port of Embarkation

Titanic - Advanced Feature Engineering Tutorial Python notebook using data from Titanic: Machine Learning from Disaster · 31,901 views · 4mo ago · starter code, beginner, eda, +2 more feature engineering, random fores So here we try and come up with a more scientific way of imputing the missing age values. We impute the missing age values according to the median value of the Passenger class, Sex and Title of the passenger, thus avoiding any sweeping feature changes that could adversely affect the performance of our model.

Feature Engineering on the Titanic for 0

It might be tempting to consider the same logic for this as the for the ‘Cabin’ feature. However, as can be seen from the image below, the entries for this feature tend to be very irregular, both in terms of number of digits, and the presence or absence of letters.In this third tutorial, you'll learn more about feature engineering, a process where you use domain knowledge of your data to create additional relevant features that increase the predictive power of the learning algorithm and make your machine learning models perform even better! 1. Introduction. This is my first run at a Kaggle competition. I have chosen to tackle the beginner's Titanic survival prediction. I have used as inspiration the kernel of Megan Risdal, and i have built upon it.I will be doing some feature engineering and a lot of illustrative data visualizations along the way The missing values (2 in the training set and none in the test set are filled in with the most commonly occurring port ‘S’). Then we use the ‘one-hot-encoding’ technique again to change the categorical variable ‘Embarked’ to a series of dummy variables.

feature engineering Datasets and Machine Learning - Kaggle

  1. Kaggle-Titanic-Survival-Competition-Submission. Predict survival of a passenger on the Titanic using Python, Pandas Library, scikit library, Linear Regression, Logistic Regression, Feature Engineering & Random Forests. Introduction:-The sinking of the RMS Titanic is one of the most infamous shipwrecks in history
  2. Kaggle - Titanic - After feature cleaning and engineering. Vlad Iliescu.
  3. Kaggle has become the premier Data Science competition where the best and the brightest turn out in droves - Kaggle has more than 400,000 users - to try and claim the glory. With so many Data Scientists vying to win each competition (around 100,000 entries/month), prospective entrants can use all the tips they can get
  4. Just like you did in the previous tutorial, you're going to impute these missing values with the help of .fillna():
  5. You are commenting using your Google account. ( Log Out /  Change )
  6. The training-set has 891 examples and 11 features + the target variable (survived). 2 of the features are floats, 5 are integers and 5 are objects.Below I have listed the features with a short description: survival: Survival PassengerId: Unique Id of a passenger. pclass: Ticket class sex: Sex Age: Age in years sibsp: # of siblings / spouses aboard the Titanic parch: # of parents / children.

Learn Feature Engineering Tutorials Kaggle

  1. Predict and submit to Kaggle Overfitting and how to control it Feature-engineering for our Titanic data set. Data Science is an art that benefits from a human element. Enter feature engineering: creatively engineering your own features by combining the different existing variables
  2. Now, make sure that you have a 'Title' column and check out your data again with the .tail() method:
  3. How Feature Engineering can help you do well in a Kaggle competition — Part II. feature engineering and implementing the cross-validation strategy. In my experience, those tasks usually take.
  4. In this video series, I will extract features using the Titanic Data from Kaggle. Excel will be used to do missing value treatment, engineering new features, creating test and train datasets. Python is our choice of tool for modelling. Hopefully, as a beginner, you will begin to discover Data Science as I have

In recent years, machine learning has been successfully deployed across many fields and for a wide range of purposes. One of its applications is in the prediction of house prices, which is the putative goal of this project, using data from a Kaggle competition.The dataset, which consists of 2,919 homes (1,460 in the training set) in Ames, Iowa evaluated across 80 features, provided excellent. September 10, 2016 33min read How to score 0.8134 in Titanic Kaggle Challenge. 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.. I have been playing with the Titanic dataset for a while, and I have.

Kaggle-Titanic. Joint-effort with Henry Vu in applying feature engineering, data massaging, and rigorous analysis of useful variables to predict the survival of people from a test dataset using random forest algorithm Let's check out what this is all about by looking at an example. Let's check out the 'Name' column with the help of the .tail() method, which helps you to see the last five rows of your data:

Together with the team behind Kaggle, we have developed a free interactive tutorial on how to apply Machine Learning Techniques that can be used in your Kaggle competitions. Step by step, through fun coding challenges, the tutorial will learn you how to predict survival rate for Kaggle's Titanic competition using R and Machine Learning For the titanic dataset, I have done some feature engineering (one-hot encoded the features) and now I have developed a heatmap to view the correlation between different features. I'm not able to machine-learning feature-engineering correlation kaggle heatma 3) Entered the Titanic Competition on Kaggle (with datasets downloaded) 4) Eager to gain deeper understanding of Feature Engineering 5) Build many Machine Learning Models in a power-packed sessio feature engineering for Washington DC bikeshare kaggle competition with Python. Jan 11, 2015. Prepare features ready for a scikit-learn model. Let's pull in the data from a csv file, engineer the features using Pandas, then pop the result into a numpy array ready to play with using some scikit-learn models in my next blog

# Create column of number of Family members onboard data['Fam_Size'] = data.Parch + data.SibSp For now, you will just go ahead and drop the 'SibSp' and 'Parch' columns from your DataFrame: Feature Engineering Tips: Titanic Survivals Prediction. Machine Learning from Disaster challenge by Kaggle. feature #engineering,. Feature Engineering: How to perform feature engineering on the Titanic competition (a getting started competition on Kaggle). There is more data munging than feature engineering, but it's still instructive Kaggle Titanic Competition II :: Feature Engineering My last post served as an introduction to Kaggle's Titanic Competition. I did some Exploratory Data Analysis, identifying some of the more important features, and the possible correlations between them, in a purely qualitative way So in reality Has_Cabin should all be 1s so we don't need to this new feature with 0 and 1s. Cabin could be useful in that it can tell you the location of the passenger on the ship and that affects survival (distance to lifeboats, distance from iceberg collision, etc.) but there is too much missing data so it is better to drop the column

As before, you'll first split your data back into training and test sets. Then, you'll transform them into arrays: kaggle-automated-feature-engineering. Applying automated feature engineering to the Kaggle Home Credit Default Risk Competition. This repository documents my application of featuretools for automated feature engineering in a Kaggle competition. The complete set of notebooks can be viewed and run on Kaggle # Split into test.train data_train = data_dum.iloc[:891] data_test = data_dum.iloc[891:] # Transform into arrays for scikit-learn X = data_train.values test = data_test.values y = survived_train.values You're now going to build a decision tree on your brand new feature-engineered dataset. To choose your hyperparameter max_depth, you'll use a variation on test train split called "cross validation". The Titanic Competition on Kaggle. MATLAB is no stranger to competition - the MATLAB Programming Contest continued for over a decade. When it comes to data science competitions, Kaggle is currently one of the most popular destinations and it offers a number of Getting Started 101 projects you can try before you take on a real one

Titanic: Machine Learning from Disaster Kaggle

Kaggle - Titanic Solution [2/3] - Feature Engineering

Tags: feature engineering, Kaggle, python, Titanic By triangleinequality in feature creation , Kaggle , machine learning , python , Titanic on September 8, 2013 . ← A complete guide to getting 0.79903 in Kaggle's Titanic Competition with Python Finding Triangles in a Graph There isn’t much to do regarding this. The training set doesn’t have any missing values, and the test set has one missing value, which we fill in with the mean of the dataset, under the reasonable assumption that it won’t really affect our predictions.Note: I had initially dropped the ‘PassengerId’ column, but an analysis of feature importances (See subsequent post) reveals that it has a high importance in survival predictions.Now that you have all of that information in bins, you can now safely drop 'Age' and 'Fare' columns. Don't forget to check out the first five rows of your data! In any machine learning problem we first do the Exploratory Data Analysis to understand the patterns in the data and perform feature selection and Engineering. We select the machine learning algorithm based on our theoretical understanding and trial basis and tune its hyper-parameters

Facebook recently held its fourth Kaggle recruiting competition.I decided to participate because the feature engineering part intreagued me. The goal was to predict if a bidder is a human or a robot based on his history of bids on an online auction platform Part 4: Feature Engineering; Part 5: Random Forests; So go ahead and get started with part 1. Tags: Competitions, Tutorial. Categories: Kaggle-Titanic-Tutorial. Posted: January 10, 2014. Share on Twitter Facebook Google+ LinkedIn Previous Nex With all of the changes you have made to your original data DataFrame, it's a good idea to figure out if there are any missing values left with .info():

In my first post on the Kaggle Titanic Competition, I talked about looking at the data qualitatively, exploring correlations among variables, and trying to understand what factors could play a role in predicting survivability. In the previous post, I went into the feature engineering aspect of this particular project. We looked at the features in the data set, and tried to figure out how to. Tags: Kaggle, Classification, Titanic, Student, R, Feature selection, Feature engineering, Parameter sweep, Tune Model hyperparameters, Model comparison This experiment is meant to train models in order to predict accuratly who survived the Titanic disaster

Video: Kaggle Titanic Competition II :: Feature Engineering

It is reasonable to presume that those NaNs didn't have a cabin, which could tell you something about 'Survival'. So, let's now create a new column 'Has_Cabin' that encodes this information and tells you whether passengers had a cabin or not. Titanic/kaggle project: Survival Prediction using R. Feature Engineering and additional exploration. I tried different approaches in order to reflect the ideas from the exploration part but most of them didn't improved the model. Some of them are presented below and the conclusions involves a model tried and with unsatisfactory results

At first sight, it might seem like a difficult task to separate the names from the titles, but don't panic! Remember, you can easily use regular expressions to extract the title and store it in a new column 'Title':We map the entries for this feature, ‘male’ and ‘female’, into numerical entries, where 0 corresponds to males and 1 to females. Since there are just 2 values we don’t use ‘one-hot-encoding’ to create an additional feature. Exploring spark.ml with the Titanic Kaggle competition. December 16, We'll be using the Titanic dataset taken from a Kaggle competition. The goal is to predict if a passenger survived from a set of features such as the class the passenger was in, hers/his age or the fare the passenger paid to get on board. Feature engineering. Next. data = data.drop(['Age', 'Fare'], axis=1) data.head() .dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; } Pclass Sex SibSp Parch Embarked Title Has_Cabin CatAge CatFare 0 3 male 1 0 S Mr False 0 0 1 1 female 1 0 C Mrs True 3 3 2 3 female 0 0 S Miss False 1 1 3 1 female 1 0 S Mrs True 2 3 4 3 male 0 0 S Mr False 2 1 Number of Members in Family Onboard The next thing you can do is create a new column, which is the number of members in families that were onboard of the Titanic. In this tutorial, you won't go in this and see how the model performs without it. If you do want to check out how the model would do with this additional column, run the following line of code:

# Extract Title from Name, store in column and plot barplot data['Title'] = data.Name.apply(lambda x: re.search(' ([A-Z][a-z]+)\.', x).group(1)) sns.countplot(x='Title', data=data); plt.xticks(rotation=45); So we map all the army sounding titles to ‘Officer’, the important sounding ones to ‘Royalty’ etc (Jonkheer is a Dutch honorific of nobility). So ‘Name’ is now ‘Title’ and a categorical variable. Then we use ‘one-hot-encoding’ to represent this categorical variable with features that can have values of 0 or 1, i.e. each value of Title is mapped to a new dummy variable where the value is 1 if it’s the relevant title, and 0 otherwise. An example would make it clear: in the above dictionary, the variables in the ‘Title’ features are ‘Officer’, ‘Royalty’, ‘Mr’ etc so these get mapped to dummy variables ‘Title_Officer’, ‘Title_Royalty’, ‘Title_Mr’ etc where ‘Title_Officer’ equals 1 only for passengers who have ‘Officer’ in their ‘Title’, and so on for all the other titles. This conversion is easily done with ‘get_dummies’ function in pandas. After this conversion the Name and Title columns should be deleted, as the same information is encoded in the dummy variables we just created.Each feature in the training set has 891 entries, which is the number of passengers in the set. Each feature in the test set has 418 entries. The training set now has 32 features + the ‘Survived’ column. However, the test set has 31 features. This is because The training set has an extra ‘Cabin_T’ feature. We don’t need our model learning from data that it can’t utilize on the test set, so we drop this feature in subsequent analysis. For the data modeling procedure outlined in the next post, both the training and testing set have 31 features.Y_pred = clf_cv.predict(test) df_test['Survived'] = Y_pred df_test[['PassengerId', 'Survived']].to_csv('data/predictions/dec_tree_feat_eng.csv', index=False)

As mentioned before, the critical part of Names will presumably be the titles, where an honorific is likely to indicate a greater chance of survival. So we extract the titles, and, taking yet another suggestion from here, we combine the names that we think might go together into groups. We create a dictionary where we map the titles to these groups, as shown below: Competition in Kaggle is strong, and placing among the top finishers in a competition will give you bragging rights and an impressive bullet point for your data science resume. In this course, you will compete in Kaggle's 'Titanic' competition to build a simple machine learning model and make your first Kaggle submission introduction. The kaggle titanic competition is the 'hello world' exercise for data science. Its purpose is to. Predict survival on the Titanic using Excel, Python, R & Random Forests. In this post I will go over my solution which gives score 0.79426 on kaggle public leaderboard. The code can be found on github.In short, my solution involves soft majority voting on logistic regression and. Feature Engineering and Algorithm Accuracy for the Titanic Dataset columns such as his class (Pclass), his Age, etc. The description of the features is in the next table (extracted from kaggle): The first graphical exploratory analysis we did, shows some relationship between the features Feature Engineering Comparison => ML Accuracy. In the introductory post, we replaced the missing age values in the training set (there were 177 of them)  with the median value of all the ages in the dataset. Even without deep insight, it is obvious that this is too broad a brush-stroke. Surely the survival probability is different for women and men aged the same, for children, for older people etc. This can be seen from the passenger median ages, grouped by sex, class and title shown below.

Machine Learning with Kaggle: Feature Engineering Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more! In this course, you'll learn to predict survival rate for Kaggle's Titanic competition. 198. 198. January 29th, 2016. Data Exploration. --- title: 'Titanic: an Introduction to Feature Engineering' author: Harry Emeric date: 19 October, 2017 output: html_document: number_sections: true toc: true fig_width: 7 fig_height: 4.5 theme: united highlight: tango --- # Introduction This is my first attempt at Kaggle and writing a full data project. I focuss here on Feature Engineering, my next Kernel will take more of a look at the.

Feature Engineering with Kaggle Tutorial - DataCam

How Feature Engineering Can Help You Do Well in a Kaggle Competition - Part I - Jun 8, 2017. As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors To drop these columns in your actual data DataFrame, make sure to use the inplace argument in the .drop() method and set it to True:There's a lot more pre-processing that you'd like to learn about, such as scaling your data. You'll also find scikit-learn pipelines super useful. Check out our Supervised Learning with scikit-learn course and the scikit-learn docs for all of this and more. 2. Preparing More Features 3. Determining the Most Relevant Features 4. Training a model using relevant features. 5. Submitting our Improved Model to Kaggle 6. Engineering a New Feature Using Binning 7. Engineering Features From Text Columns 8. Finding Correlated Features 9. Final Feature Selection using RFECV 10. Training A Model Using our. Feature engineering is a way to use domain knowledge to create predictive indicators that better represent the underlying problem for your model. Another great resource for feature engineering is.

Video: Feature-engineering for our Titanic data set Pytho

KNIME tutorial: Kaggle Titanic (part 3) - Feature engineering

This is part 1 or the blog series where I'll cover feature engineering. Part 2 will explore missingness, and part 3 will conclude with prediction. As a quick setup summary, the two data files are train.csv and test.csv. Train is the dataset we use to build a model and test is the dataset we use to predict Note that, once again, you use the median to fill in the 'Age' and 'Fare' columns because it's perfect for dealing with outliers. Other ways to impute missing values would be to use the mean, which you can find by adding all data points and dividing by the number of data points, or mode, which is the number that occurs the highest number of times.

Kaggle Titanic Competition I :: Exploratory Data Analysis

How to score 0.8134 in Titanic Kaggle Challenge Ahmed ..

3) Kaggle Competition Overview. The following brief has been copied and pasted from the Overview on the Kaggle Competition page and is included in this blog post for reference. Skip to the next section if you're already familiar. Competition Description. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history kaggle-titanic. This is the python/scikit-learn code I wrote during my stab at the Kaggle titanic competition. There is code for several different algorithms, but the primary and highest performing one is the RandomForest implemented in randomforest2.py

Titanic: Getting Started With R - Part 4: Feature Engineering

I will take a shot at this from my experience winning KDD CUP's back in the days (2007-09 and the publications I have on this). But before I go into the tricks of the trade, beware that what one does for competitions is not necessarily the right t.. Email (required) (Address never made public) Name (required) Website You are commenting using your WordPress.com account. ( Log Out /  Change ) Feature-engineering for our Titanic data set Data Science is an art that benefits from a human element. Enter feature engineering: creatively engineering your own features by combining the different existing variables

For example, you probably want to replace 'Mlle' and 'Ms' with 'Miss' and 'Mme' by 'Mrs', as these are French titles and ideally, you want all your data to be in one language. Next, you also take a bunch of titles that you can't immediately categorize and put them in a bucket called 'Special'. You are commenting using your Facebook account. ( Log Out /  Change ) You perform feature engineering to extract more information from your data, so that you can up your game when building models. Feature Engineering Feature engineering refers to the essential step of selecting or creating the right features to be used in a machine learning model. Usually, it may consume up to 80% of the total effort, depending on the data complexity. In the following picture, I show the competition original data model, with features colored by their. The iceberg suspected of having sunk the RMS Titanic. Feature Engineering คือ การบวนการใช้ความรู้ Domain Knowledge ในการสร้าง Feature ใหม่ขึ้นมา ตัด Feature ที่ไม่เกี่ยวข้องทิ้งไป เพื่อช่วยทำให้อัลกอริทึม.

Video: Kaggle - Titanic Solution [1/3] - data analysis - YouTub

Kaggle Titanic: Machine Learning model (top 7%) - Towards

In the following, you'll use cross validation and grid search to choose the best max_depth for your new feature-engineered dataset:You fill in the two missing values in the 'Embarked' column with 'S', which stands for Southampton, because this value is the most common one out of all the values that you find in this column. Kaggle competitions push the limits of predictive modeling. When skilled, intelligent people compete and collaborate to solve difficult problems, wonderful things happen. We learn how to better engineer features and how to more effectively ensemble predictive models in sophisticated ways Have you tried any feature engineering ?(it sounds like you've just used the features in the training set but I can't be 100%) Random Forests should do pretty well, but maybe try xgboost too? It's quite good at everything on Kaggle. SVM's could be worth a go also if you're thinking of stacking/ensembling data['Title'] = data['Title'].replace({'Mlle':'Miss', 'Mme':'Mrs', 'Ms':'Miss'}) data['Title'] = data['Title'].replace(['Don', 'Dona', 'Rev', 'Dr', 'Major', 'Lady', 'Sir', 'Col', 'Capt', 'Countess', 'Jonkheer'],'Special') sns.countplot(x='Title', data=data); plt.xticks(rotation=45);

Basic Feature Engineering with the Titanic Data

Kaggle Titanic Competition Part VI - Dimensionality Reduction In the last post, we looked at how to use an automated process to generate a large number of non-correlated variables. Now we're going to look at a very common way to reduce the number of features that we use in modelling Even in my limited experience of machine learning, throwing away information from a training model shouldn’t be a good thing. But in this case, due to my inability to properly identify a pattern in the entries for ‘Ticket’, I was afraid of encoding the variable in a way that biased the actual survival probability. I will update this post if and when I do decide to preserve the ‘Ticket’ data. As of now, we drop this feature from both the training and test sets.

kaggle-Titanic / feature-engineering.py / Jump to. Code definitions. No definitions found in this file. Unable to determine state of code navigation Find file Copy path fuqiuai update LOF outlier detect 09a0937 Dec 8, 2017. 1 contributor. Users who have contributed to this file. While feature engineering is a discipline in itself, too broad to be covered here in detail, you will have a look at a simple example by creating your own new predictive attribute: family_size. Tip there might be more information in the 'Cabin' column, but for this tutorial, you assume that there isn't!# Drop columns and view head data.drop(['Cabin', 'Name', 'PassengerId', 'Ticket'], axis=1, inplace=True) data.head() .dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; } Pclass Sex Age SibSp Parch Fare Embarked Title Has_Cabin 0 3 male 22.0 1 0 7.2500 S Mr False 1 1 female 38.0 1 0 71.2833 C Mrs True 2 3 female 26.0 0 0 7.9250 S Miss False 3 1 female 35.0 1 0 53.1000 S Mrs True 4 3 male 35.0 0 0 8.0500 S Mr False Congrats! You've successfully engineered some new features such as 'Title' and 'Has_Cabin' and made sure that features that don't add any more useful information for your machine learning model are now dropped from your DataFrame! Kaggle Titanic Tutorial in Scikit-learn. Part I - Intro. Part II - Missing Values. Part III - Feature Engineering: Variable Transformations. Part IV - Feature Engineering: Derived Variables. Part V - Feature Engineering: Interaction Variables and Correlation. Part VI - Feature Engineering: Dimensionality Reduction w/ PC

Feature Engineering. From the correlation heatmap we can find the relationship between features. The meaning of features Sex. There is a famous script in the movie Titanic: Lady and children first. And this figure shows how this rule works. The female survivor is much more than the male survivor. Age In this post we are going to use titanic dataset train.csv from Kaggle. Because it is a raw data, so we need to prepare first. Because it is a raw data, so we need to prepare first. Data Preparation Proces # Binning numerical columns data['CatAge'] = pd.qcut(data.Age, q=4, labels=False ) data['CatFare']= pd.qcut(data.Fare, q=4, labels=False) data.head() .dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; } Pclass Sex Age SibSp Parch Fare Embarked Title Has_Cabin CatAge CatFare 0 3 male 22.0 1 0 7.2500 S Mr False 0 0 1 1 female 38.0 1 0 71.2833 C Mrs True 3 3 2 3 female 26.0 0 0 7.9250 S Miss False 1 1 3 1 female 35.0 1 0 53.1000 S Mrs True 2 3 4 3 male 35.0 0 0 8.0500 S Mr False 2 1 Note that you pass in the data as a Series, data.Age and data.Fare, after which you specify the number of quantiles, q=4. Lastly, you set the labels argument to False to encode the bins as numbers. These titles of course give you information on social status, profession, etc., which in the end could tell you something more about survival.

1 post tagged with "kaggle" | Ahmed BESBESUltraviolet Analytics - Kaggle Titanic Competition Part XTensorFlow in a Nutshell — Part Two: Hybrid Learning

The structure of the training and test sets is almost exactly the same (as expected). In fact, the only difference is the Survived column that is present in the training, but absent in th Features. After getting a better perception of the different aspects of the dataset, I started exploring the features and the part they played in the survival or demise of a traveler. 1. Survived. The first feature reported if a traveler lived or died. A comparison revealed that more than 60% of the passengers had died. 2. Pclas Here I introduce most powerful binary classifier for Titanic data set and also briefly explain how k-fold cross validation work. You will see how you use scikit learn classifiers and cross.

It was one of the amazing feature engineering happened in Kaggle Competitions. For a detailed explanation, please refer the complete winning solution code . Meta-Leak By Gabriel Moreira, CI&T. In the first and second parts of this series, I introduced the Outbrain Click Prediction machine learning competition and my initial tasks to tackle the challenge. I presented the main techniques used for exploratory data analysis, feature engineering, cross-validation strategy and modeling of baseline predictors using basic statistics and machine learning From the very beginning of the feature engineering process, our primary challenge was relatively clear: fix the upper left problem. The upper left problem refers to a recurring issue: any single feature we used as a predictor during our simple exploratory analysis performed reasonably well at higher values, but abysmally at lower values By the end of the feature engineering process, we should end up with only numeric features, to start our modeling process on. Each feature should also have the same number of entries (891 for the training set, 418 for the test set). We start with 11 features in the training set, plus the ‘Survived’ column, which will be our target variable (See the next post). The test set, likewise, has 11 features.

Mix Play all Mix - Minsuk Heo 허민석 YouTube 캐글 - 타이타닉 생존자 예측하기 [1/3] - 데이터 분석 - Duration: 11:38. Minsuk Heo 허민석 16,653 view You are commenting using your Twitter account. ( Log Out /  Change ) Feature engineering is really why data science is so interesting/creative, and so different from some types of programming, where there is a best way to do something. Don't ignore domain specific knowledge. Because feature engineering is very problem-specific domain knowledge helps a lot Feature Engineering. Feature engineering is based on Trevor Stephens' tutorial. Modeling. Predictive models are built for most of the caret classification methods. Ten fold cross-validation is used with a wide variety of classification methods including trees, rules, boosting, bagging, neural networks, linear modeling, discriminant analysis, generalised additive modeling, support vector. This post followed up on the first one about Exploratory Data Analysis on the Kaggle Titanic datasets. Here, we discussed Feature Engineering, and how to represent the data such that it is most useful, which is often the most crucial step before we get into the actual modeling of the data via a Machine Learning model. The steps outlined here can all be found on my github. We get into the last leg in the next blog post; where I will talk about how we use all the features that we’ve spent so much time on to find the best model to predict survivability of the Titanic passengers. As always, critique and comments are welcome. I’ll see you soon in my final post about this Kaggle competition. Till later then, buh-bye!

Who will survive the shipwreck?! 30 Jan 2017. This document is a thorough overview of my process for building a predictive model for Kaggle's Titanic competition. I will provide all my essential steps in this model as well as the reasoning behind each decision I made. We will perform our feature engineering through a series of helper. Titanic Survivors - Data Selection & Preparation. Prior to fitting a logistic regression model for classifying who would likely survive, we have to examine the dataset with information from EDA as well as using other statistical methods

We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. Building a baseline model as a starting point for feature engineering. 2. The frequently useful case where you can combine data from multiple rows into useful features. 4. Feature Selection Feature engineering an important part of machine-learning as we try to modify/create (i.e., engineer) new features from our existing dataset that might be meaningful in predicting the TARGET.. In the kaggle home-credit-default-risk competition, we are given the following datasets

TF-IDF Basics with Pandas and Scikit-Learn – Ultraviolet

Titanic: Getting Started With R - Part 4: Feature Engineering. 14 minutes read. Tutorial index. Feature engineering is so important to how your model performs, that even a simple model with great features can outperform a complicated algorithm with poor ones Kaggle_Days_Tokyo_-_Feature_Engineering_and_GBDT_Implementation.pdf threecourse December 11, 201 My last post served as an introduction to Kaggle's Titanic Competition. I did some Exploratory Data Analysis, identifying some of the more important features, and the possible correlations between them, in a purely qualitative way. In this post, I aim to go through the feature engineering steps which one would need to do in orde # Setup the hyperparameter grid dep = np.arange(1,9) param_grid = {'max_depth' : dep} # Instantiate a decision tree classifier: clf clf = tree.DecisionTreeClassifier() # Instantiate the GridSearchCV object: clf_cv clf_cv = GridSearchCV(clf, param_grid=param_grid, cv=5) # Fit it to the data clf_cv.fit(X, y) # Print the tuned parameter and score print("Tuned Decision Tree Parameters: {}".format(clf_cv.best_params_)) print("Best score is {}".format(clf_cv.best_score_)) Tuned Decision Tree Parameters: {'max_depth': 3} Best score is 0.8103254769921436 Now, you can make predictions on your test set, create a new column 'Survived' and store your predictions in it. Don't forget to save the 'PassengerId' and 'Survived' columns of df_test to a .csv and submit it to Kaggle!

Intro. This blog post aims at showing what kind of feature engineering can be achieved in order to improve machine learning models. I entered Kaggle's instacart-market-basket-analysis challenge with goals such as :. finish top5% of a Kaggle competitio We submit that to kaggle to see the improvement. Figure Progress after incorporating title name into input features Other features. There are various authors which published their work on solving this kaggle competition. Most interesting part of their work is the feature engineering section With a few small caveats, yes. Kaggle performance is becoming increasingly recognized and valued professional experience. It has been THE crucial component in my own second career development as a data scientist. However, in my experience it still..

Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals. Feature engineering. All Tags. feature engineering. 0 competitions. 77 datasets. 3k kernels. Titanic Top 4% with ensemble modeling. 3 years ago in Titanic: Machine Learning from Disaster 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. I have tried other algorithms like Logistic Regression, GradientBoosting. I performed feature engineering, and now I have 10 features in the train regression linear-regression kaggle. asked Apr 27 '19 at 10:01. I have just recently learnt Decision Trees and started solving Titanic Survival problem from Kaggle Competition. I understood the algorithm behind decision python scikit-learn decision.

Feature engineering and preprocessing, Data is available on Kaggle Titanic competition page. A rule of thumb is get acquinted with the domain. Well, reading a wikipage about Titanic is not only fascinating, but can also be beneficial for the competition directly, such as give insight that, for example infants were more likely to survive. --- title: Titanic: Machine Learning From Disaster subtitle: Exploratory Data Analysis and Feature Engineering author: Erik Yamada output: html_document --- # Introduction My first competition on Kaggle was the Titanic Competition. In this document, I will walk through the steps that I took to analyze the dataset: * Experimental Data Analysis and Findings * Imputing Missing Data * Feature.

Next, you want to deal with deal with missing values, bin your numerical data, and transform all features into numeric variables using .get_dummies() again. Lastly, you'll build your final model for this tutorial. Check out how all of this is done in the next sections! How Feature Engineering can help you do well in a Kaggle competition — Part III. feature engineering, cross-validation strategy and modeling of baseline predictors using basic statistics and. data.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 1309 entries, 0 to 417 Data columns (total 9 columns): Pclass 1309 non-null int64 Sex 1309 non-null object Age 1046 non-null float64 SibSp 1309 non-null int64 Parch 1309 non-null int64 Fare 1308 non-null float64 Embarked 1307 non-null object Title 1309 non-null object Has_Cabin 1309 non-null bool dtypes: bool(1), float64(2), int64(3), object(3) memory usage: 133.3+ KB The result of the above line of code tells you that you have missing values in 'Age', 'Fare', and 'Embarked'. There are 687 missing cabin entries in the training set. I would be tempted to drop the column altogether, but as I mentioned previously, cabins could have a correlation with survival probability, since passengers are likely to have been assigned cabins according to class (and thus fare paid). So we extract the letter from each cabin name (‘A’,’B’,’C’ etc) and use ‘get_dummies’ (the standard technique for all categorical variables), after adding an extra variable ‘X’ for all the cabins that don’t have an entry. Then, you'll see some reasons why you should do feature engineering and start working on engineering your own new features for your data set! You'll create new columns, transform variables into numerical ones, handle missing values, and much more. Lastly, you'll build a new machine learning model with your new data set and submit it to Kaggle

Data Science is an art that benefits from a human element. Enter feature engineering: creatively engineering your own features by combining the different existing variables. As I mentioned in the introductory Titanic post, intuitively, the family size should affect the chance of survival. So we combine the ‘Parch’ and ‘Sibsp’ columns into one (plus the passenger, of course) and designate that as ‘Family’. Then based on the size of the ‘Family’, we create new features indicating whether the person is traveling solo, with a small family (number of family members <=3) or a large family (with family members >3).Tip: to learn more about regular expressions, check out my write up of our last FB Live code along event or check out DataCamp's Python Regular Expressions Tutorial. A valid assumption is that larger families need more time to get together on a sinking ship, and hence have lower probability of surviving. Family size is determined by the variables SibSp and Parch, which indicate the number of family members a certain passenger is traveling with. So when doing feature engineering, you add a new variable family_size, which is the sum of SibSp and Parch plus one (the observation itself), to the test and train set.

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