# 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

14m

Data Set Overview 

02m

Encoding 

06m

Stratified Random Sampling 

06m

Selection of Classifiers

02m

Logging for Visual Comparison

05m

Grid Search Mode 

14m

Grid Search for Hyper Parameter Tuning

03m

KNeighbour Classifier Tuning

06m

SVC Classifier Tuning

09m

NSVC Classifier Tuning 

05m

Decision Tree Classifier Tuning

03m

Random Forest Classifier Tuning 

04m

Adapting Boosting Classifier 

04m

Gradient Boosting Classifier

08m

Run all Classifiers

02m

Linear Descriminant Analysis

02m

Gaussian Naive Bayes

00m

Quadratic Discriminant Analysis

00m

Re Run all Classifiers

05m

Recap

00m

Apply Tensor Flow Models

19m

Accuracy and Loss of the Model

04m

Conclusion

06m