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Understanding the problem statement and importing the file
Initializing the libraries and understand it's use
Difference between StratifiedShuffleSplit and Simple Random Sampling
Using info and describe the function and extracting information from the results
Label encoding necessary columns
Importing necessary classifiers; Linear, Non-linear, bagging and boosting
Applying models like Random Forest, KNN, SVC, GradientBoosting and naive Bayes for on-spot checking
Hyperparameter tuning them by defining suitable parameters
Defining evaluation metrics "Log Loss"
Performing LDA(Linear Discriminant Analysis)
Plotting graphs for accuracy and loss versus classifier
Selecting the best model for prediction
Understanding Confusion Matrix and it's importance
Standardization and Normalization of the Dataset
Comparing the final output before and after standardization of the Dataset
Making final predictions and saving the result
The objective of this machine learning project is to use binary leaf images and extracted features, including shape, margin, and texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features. We are going to apply different classification techniques to benchmark the relevance of classifiers in image classification problem.