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.
-
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 formgggg_pp
wheregggg
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 formdeck/num/side
, whereside
can be eitherP
for Port orS
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.
-
Machine Learning Models Applied | Accuracy |
---|---|
Light Gradient Boosted Machine (LGBM) | 80.68% |
Extreme Gradient Boosting (XGBoost) | 80.52% |
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.
This project follows the MIT LICENSE.