anshul1004 / MachineLearningClassificationModels

Implementation of various machine learning models in scikit-learn

MachineLearningClassificationModels

We learned that parameter tuning is one of the most important parts in a machine learning project. Rather than having to manually try every combination of parameters, scikit-learn provides tools that can help automate this process. In this project, GridSearchCV (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) was used for automatic parameter tuning for some of the machine learning classication algorithms.

List of Classification Algorithms Used

1. Decision Tree
2. Neural Net
3. Support Vector Machine
4. Gaussian Naive Bayes
5. Logistic Regression
6. k-Nearest Neighbors
7. Bagging
8. Random Forest
9. AdaBoost Classifier
10. Gradient Boosting Classifier
11. XGBoost

Dataset Used

Breast Cancer (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic))

Results

See "Report.pdf".