In this project, we developed and implemented robust machine learning models for the accurate classification of vector-borne diseases. We utilized a tabular dataset containing diverse features to achieve this goal.
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Data Preprocessing and Analysis: Conducted extensive data preprocessing, analysis, and exploratory data analysis (EDA) to gain insights into the dataset.
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Feature Engineering: Applied advanced feature engineering techniques to enhance model performance and extract valuable information from the dataset.
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Machine Learning Algorithm: Employed the Support Vector Machine (SVC) and Neural Network (NN) classifier as the primary machine learning algorithm for disease classification.
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Model Training and Optimization: Conducted rigorous model training, hyperparameter tuning, and cross-validation to optimize model performance.
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Performance Evaluation: Evaluated model performance using the mean average precision at k (MAP@K) metric, showcasing the ability to assess the model's predictive accuracy.
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Generalization: Demonstrated the capacity to build models that generalize effectively to unseen data.
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Kaggle Competition: Achieved competitive results in the Kaggle competition by consistently ranking among the top participants.
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Collaboration: Collaborated with fellow data scientists, actively participating in forums and discussions, and contributing to the broader data science community.
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Prediction: Successfully applied the trained SVC model to predict the top three likely vector-borne diseases on an unseen dataset.
- Data preprocessing and analysis.
- Feature engineering techniques.
- Machine learning model selection and implementation, with a focus on Support Vector Machines (SVM) Neural Network (NN) classifier.
- Hyperparameter tuning and cross-validation.
- Evaluation metrics, specifically MAP@K.
- Collaboration and knowledge sharing in a competitive data science environment.
- Demonstrated proficiency in developing accurate machine learning models for disease classification.
- Established the ability to work effectively in a competitive, data-driven environment.
- Showcased strong analytical, problem-solving, and teamwork skills.