Welcome to my Data Science and Machine Learning portfolio! This repository houses my work on the Kaggle Spaceship Titanic competition. Here, I tackle a cosmic mystery by predicting which passengers were transported to an alternate dimension during the Spaceship Titanic's collision with a spacetime anomaly.
Link to my Submission Notebook
Key Insights & Results:
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One of the most critical factors in predicting passenger transportation was the "CryoSleep" feature, showing a strong correlation of over 0.4. Passengers who opted for cryosleep were more likely to be transported to an alternate dimension.
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Through rigorous analysis and modeling, I achieved a commendable score of 0.80, securing a top 28% ranking among 2062 teams. My model's predictions played a vital role in the rescue mission's success.
The Spaceship Titanic competition tasks us with solving a cosmic mystery. By leveraging data science skills, we aim to predict the fate of passengers who encountered a spacetime anomaly during their voyage.
- Goal: Classify passengers as transported or not transported to an alternate dimension.
- Datasets: The competition provides a training dataset with personal records and a test dataset for predictions.
- Evaluation: Submissions are evaluated based on classification accuracy.
Key files related to this project:
- train.csv - Training dataset with personal records and ground truth labels.
- test.csv - Test dataset for predictions.
- sample_submission.csv - Sample submission file with the required format.
- My Notebook or My Kaggle Notebook - My notebook with code and analysis.
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Reading Datasets: I began by loading the provided datasets, both the training and test data.
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Checking Class Distribution: To understand the balance between transported and non-transported passengers, I examined the distribution of classes in the training dataset.
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Handling Missing Values: I addressed missing data in the dataset, ensuring that no valuable information was lost.
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Making the Correlation Heatmap: I created a correlation heatmap to visualize relationships between different features, highlighting the strong correlation between "CryoSleep" and passenger transportation.
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Feature Engineering: To improve model performance, I engineered new features. For example, I extracted additional information from the "Cabin" column, breaking it down into "Deck," "Num," and "Side" components.
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One-Hot Encoding: I prepared the data for modeling by performing one-hot encoding, a necessary step for many machine learning models.
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Handling Missing Data for Test: Given the constraints of the test data, where rows could not be removed, I implemented a specific strategy to handle missing values.
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Extracting (X, y): I separated the feature matrix (X) and the target variable (y) from the training dataset, ensuring that the data was ready for model training.
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Playing with the Models: In this phase, I experimented with multiple machine learning models, including but not limited to:
- RandomForestClassifier
- DNN (Deep Neural Network)
- LogisticRegression
- XGBoost
- LightGBM
- CatBoostClassifier
- AdaBoost
- KNN (K-Nearest Neighbors)
- DecisionTreeClassifier
I performed tuning and tweaks on these models to optimize their performance.
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Ensemble Modeling: To further enhance accuracy, I selected the top-performing models (the best six performers) and created a simple weighted ensemble. This ensemble played a significant role in achieving the impressive accuracy score.
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Making Predictions: Finally, I used the ensemble model to make predictions on the test data and formatted the results according to the competition's requirements. The predictions can be found in the result.csv.
For detailed implementation and analysis, please refer to My Notebook or My Kaggle Notebook.
I'm open to collaboration and eager to learn from the data science community. You can connect with me on LinkedIn or find more of my projects on GitHub.
I want to express my gratitude to Kaggle for hosting this challenging competition. Saving passengers from alternate dimensions is no small feat!
Thank you for visiting my portfolio, and I look forward to sharing more data science projects in the future! 🚀✨
