Monsoon 2020: Completed #classroom Weight: 45.0

# DA Project Plant

Classify leaves of the plant

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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

Only with the help of leaf you can talk to a forest. Most of the trees are identified by the type of `leaves` they have. Given a dataset consisting features of leaves, classify these `leaves` as a part of this multi class problem.

## 💾 Dataset

There are three features for each image: `Shape`, `Margin` and `Texture`. For each feature, a `64` element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). Each row has a `64-element` feature vector followed by the target variable `Class` label and it's value lies in the range `1-100` for `100` plants species.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`1279` samples) File that should be used for training. It contains the feature representation and their respective outcomes.
• `test.csv` - (`320` samples) File that will be used for testing. Unlike the training file it contains only the feature representation and not their outcomes.

## 🚀 Submission

• Prepare a python file which should produce a csv file with name "submission.csv" containing header as "class" and predicted value as digit between [1…100] representing one of the 100 possible classes.
• Your submission should read the train and test data (available as environment variables) and should write 'submission.csv' containing the predictions for the test set.
• Sample submission format is available in the resources section of the challenge page as sample_submission.py.                            Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

## 📚 References

• Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

• Author: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.

• Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.

• Image Source