using ML to predict who lives and dies in titantic disaster
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May 25, 2024 - Python
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hot-encoding
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Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.
precision svc matplotlib-pyplot standardscaler onehot-encoding simpleimputer recall-precision hot-encoding randomforestclassifier gradientboostingclassifier logisticregression-classifier
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Apr 21, 2025 - Jupyter Notebook
This housing dataset aims to predict land prices according to user preference. The datasets consists of several variables which includes POSTED_BY, UNDER_CONSTRUCTION, RERA, BHK_NO., BHK_OR_RK, SQUARE_FT, READY_TO_MOVE, RESALE, ADDRESS, LONGITUDE, LATITUDE, TARGET(PRICE_IN_LACS).
machine-learning scikit-learn data-visualization seaborn data-analysis feature-engineering feature-scaling matpoltlib hot-encoding
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Jul 5, 2025 - Jupyter Notebook
flask encoding machine-learning pipeline sklearn pandas seaborn matplotlib sckiit-learn ploty joblib onehot-encoding hot-encoding roc-auc-curve
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Dec 26, 2024 - Jupyter Notebook
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