Build an Image Classifier for Plant Species Identification

Build an Image Classifier for Plant Species Identification

In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
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What will you learn

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

Project Description

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.

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Curriculum For This Mini Project

Problem Statement
Data Set Overview
Stratified Random Sampling
Selection of Classifiers
Logging for Visual Comparison
Grid Search Mode
Grid Search for Hyper Parameter Tuning
KNeighbour Classifier Tuning
SVC Classifier Tuning
NSVC Classifier Tuning
Decision Tree Classifier Tuning
Random Forest Classifier Tuning
Adapting Boosting Classifier
Gradient Boosting Classifier
Run all Classifiers
Linear Descriminant Analysis
Gaussian Naive Bayes
Quadratic Discriminant Analysis
Re Run all Classifiers
Apply Tensor Flow Models
Accuracy and Loss of the Model