This project involves building a classification model to distinguish between classes of mice based on their protein expression levels. The work focuses on preprocessing biological data, implementing machine learning models, and optimizing model performance for accurate predictions.
The project explores the relationship between protein expression levels and classification outcomes for mice. Various machine learning algorithms, including decision trees and random forests, were applied to achieve high accuracy in classification.
The objective is to classify mice into predefined categories based on their protein expression levels, focusing on:
- Data preprocessing to handle missing values and outliers.
- Feature selection to identify the most relevant proteins.
- Model building to achieve robust and accurate classification.
- Programming Language: Python
- Libraries Used:
- Data Processing: Pandas, NumPy
- Machine Learning: Scikit-learn
- Visualization: Matplotlib, Seaborn
- Imported dataset containing protein expression levels for multiple samples.
- Handled missing values using imputation techniques.
- Scaled features using standardization for improved model performance.
- Conducted exploratory data analysis (EDA) to understand data distributions and correlations.
- Identified the most important features using correlation analysis and feature selection methods.
- Reduced dimensionality to improve computational efficiency and model interpretability.
- Trained and tested several classification algorithms:
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (optional)
- Evaluated models using metrics such as accuracy, precision, recall, and F1-score.
- Implemented hyperparameter tuning using GridSearchCV for the best-performing models.
- Enhanced model accuracy by optimizing tree depth, number of estimators, and split criteria.
- Comprehensive preprocessing pipeline for biological data.
- Implementation of multiple machine learning models to compare performance.
- Detailed evaluation using confusion matrices and ROC-AUC curves.
- Insights into the most influential protein features driving classification.
- Imbalanced Data: Used techniques like oversampling and weighted loss functions to address class imbalance.
- Feature Correlation: Addressed multicollinearity by removing highly correlated features.
- Model Complexity: Focused on explainable models while maintaining high accuracy.
- Achieved a 15% improvement in model accuracy through advanced feature selection and hyperparameter tuning.
- Identified the most critical proteins contributing to classification.
- Delivered a robust classification model with an accuracy of over 90%.
- Apply deep learning models like neural networks for further performance improvement.
- Extend the analysis to include multi-class classification with more detailed protein data.
- Develop a dashboard for visualizing classification results in real-time.