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This web page is supposed to provide the Titanic Survival Prediction Dataset from Kaggle, but it crashes due to a SyntaxError. The error message indicates a problem with the app.js file on Kaggle's static assets.
- Titanic
Start here! Predict survival on the Titanic and get familiar...
- Titanic
A Kaggle competition to predict survival on the Titanic using machine learning. Learn ML basics, explore the data, and compete with other participants.
- Importing The Data
- Preprocessing
- Training
- Getting The Validation Set Predictions
- Getting The Test Set Predictions
- Generating The Output Dataframe
- Generating The Output File
- Conclusion
- GeneratedCaptionsTabForHeroSec
You can use bash commands in jupyter notebooks by placing an exclamation mark in front of the cell. We will be checking the location of the uploaded dataset files using pwd and ls commands. /work/files/workspace notebook.ipynb test.csv train.csv Pandas is a Python library and it is used for data manipulation and data analysis processes. We have now...
We should convert the type of the values to float, which will be used as features in the model. This conversion will allow us to perform some numerical operations when normalizing the data. We need to feed the model using numerical values. In the code piece below the strings are converted into numerical values Below, all the features are normalized...
Support vector machines are supervised learning models that are used for classification and regression analysis. This model was chosen with the parameters below. You should be trying other models with different parameter combinations. There is no need to start and stop the code every time you want to modify a parameter or to try another model, as y...
In this part, we will be feeding the model validation set features and getting the predictions. Then we will be comparing them with the ground truth values. The Sklearn's metrics.accuracy_scoremethod was used to make the accuracy calculation easier. The validation set accuracy is calculated as 0.799, which is a good start. Accuracy: 0.7988826815642...
Now we proceed to get the predictions for the actual test set. Because there are no ground truth values for the test set, we can only know the accuracy after the predictions file is uploaded to Kaggle.
The PassengerId column is fetched from the dataframe named test_df_matcher. When it is directly returned, the type of the return value will be Series. For this reason, we will be casting it to Pandas DataFrame type. Then we will be inserting the test set predictions as the second column of the dataframe.
Finally, the output format is ready. As the last step, the index values will be removed by passing False to the indexparameter. Now you can download the generated file and upload it to Kaggle. This way, you will learn the test set accuracy, after which you will be placed on the leaderboard. We will now return to the competition page and click on "S...
In this tutorial, you have learned how to analyze a dataset and submit your results to a Kaggle competition. If you enjoy doing data science competitions you can learn more about DataCamp Competitions here.
Learn how to use DataLab and Pandas to prepare and analyze the Titanic dataset for a Kaggle competition. This tutorial covers data import, preprocessing, visualization, and submission.
Jun 22, 2019 · Over the world, Kaggle is known for its problems being interesting, challenging and very, very addictive. One of these problems is the Titanic Dataset. So summing it up, the Titanic Problem is based on the sinking of the ‘Unsinkable’ ship Titanic in the early 1912.
Learn how to use Python and PyData tools to analyze the Titanic disaster data and predict survival outcomes. This notebook provides examples of data handling, visualization, and machine learning techniques for Kaggle's Titanic: Machine Learning from Disaster competition.
Solution. In a form of a jupyter notebook, my solution goes through the basic steps of a data science pipeline: Exploratory data analysis with visualizations. Data cleaning. Feature engineering. Modeling. Modelfine-tuning. Note that I have included a script with stacking for information only as it achive lower score.
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About. Kaggle Competition | Titanic Machine Learning from Disaster. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden...
