Spaceship Titanic

Predict which passengers are transported to an alternate dimension

📌 Dataset Description

The task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help us make these predictions, we are given a set of personal records recovered from the ship's damaged computer system.

📌 File and Data Field Descriptions

• train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.

  • PassengerId - A unique Id for each passenger. Each Id takes the form gggg_pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.

  • HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.

  • CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.

  • Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.

  • Destination - The planet the passenger will be debarking to.

  • Age - The age of the passenger.

  • VIP - Whether the passenger has paid for special VIP service during the voyage.

  • RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.

  • Name - The first and last names of the passenger.

  • Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.

• test.csv - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data.

  • The task is to predict the value of Transported for the passengers in this set.

• sample_submission.csv - A submission file in the correct format.

  • PassengerId - Id for each passenger in the test set.

  • Transported - The target. For each passenger, predict either True or False.

👀 Screenshots

📓 Overview

Machine Learning Models Applied	Accuracy
Light Gradient Boosted Machine (LGBM)	80.68%
Extreme Gradient Boosting (XGBoost)	80.52%

👉 Application

Predict the probability of user clicking the ad which is shown to them on the partner websites for the next 7 days based on historical view log data, ad impression data and user data. Since every individual may have a different view of your brand, stories or slogans can resonate with everyone differently. Through target marketing, you can better understand each customer's needs and create a marketing campaign that targets a specific audience, so you can meet their expectations.

✍️ Authors

• Himanshu Agarwal

🔗 Links

🪪 License

This project follows the MIT LICENSE.

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