Analysis Of wine Quality Using Machine Learning Alogrithm(Random Forest)
Analysis Of wine Quality Using Machine Learning Alogrithm(Random Forest). Dataset is taken from UCI Machine Learning Labtorary : http://archive.ics.uci.edu/ml/datasets/Wine+Quality
Here, we'll make a wine quality prediction based on the provided characteristics.The wine quality dataset available on UCI Machine Learning Labtorary : http://archive.ics.uci.edu/ml/datasets/Wine+Quality. This dataset comprises the key features which are responsible for affecting the quality of the wine. By the use of numerous Machine learning models, the quality of the wine is forecasted
Exploratory Data Analysis
EDA is a method of data analysis that employs visual methods. With the use of statistical summaries and graphical representations, it is used to identify trends and patterns as well as to verify assumptions. Checking the null values in each column of the dataset is the first step to outset the process.
- blending.py (A blend of RandomForest, ExtraTress and GradientBoostingClassifier) giving an accuracy of 97%.
- decision_tree.py ( Basic decision tree usage in PYthon via Sci-kit learn)
- gradient_boosting_tree.py ( Basic gradient boosting tree in Python via Sci-kit learn)
- extra_trees.py (Basic extra trees classifier usage in Python via Sci-kit learn)
- random_forest.py( Basic random forest usage in Python via Sci-kit learn, this does not yield 100% accuracy but mostly 95%)
- stratified_k_fold_random_forest.py (Stratified K-fold with random forest)