# Baseline for MSHRM Challenge on AIcrowd¶

#### Author : Shubham Sharma¶

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import sys
!pip install numpy
!pip install pandas
!pip install scikit-learn


The first step is to download out train test data. We will be training a classifier on the train data and make predictions on test data. We submit our predictions

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!rm -rf data
!mkdir data
!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/mshrm/v0.1/test.csv
!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/mshrm/v0.1/train.csv
!mv train.csv data/train.csv
!mv test.csv data/test.csv


## Import packages¶

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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score


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all_data_path = "data/train.csv" #path where data is stored

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all_data = pd.read_csv(all_data_path,header=None) #load data in dataframe using pandas


## Visualize data¶

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all_data.head()


You can see the columns goes from 0 to 22, where column 0 called class consists of label 0 for poisonous classes of mushrooms and label 1 for edible classes. The rest 22 attributes describes mushrooms in terms of their physical characteristics.

## Split Data into Train and Validation¶

Now we want to see how well our classifier is performing, but we dont have the test data labels with us to check. What do we do ? So we split our dataset into train and validation. The idea is that we test our classifier on validation set in order to get an idea of how well our classifier works. This way we can also ensure that we dont overfit on the train dataset. There are many ways to do validation like k-fold,leave one out, etc

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X_train, X_val= train_test_split(all_data, test_size=0.2, random_state=42)


Here we have selected the size of the validation data to be 20% of the total data. You can change it and see what effect it has on the accuracies. To learn more about the train_test_split function click here.

Now, since we have our data splitted into train and validation sets, we need to get the label separated from the data.

Check which column contains the variable that needs to be predicted. Here it is the first column.

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X_train,y_train = X_train.iloc[:,1:],X_train.iloc[:,0]
X_val,y_val = X_val.iloc[:,1:],X_val.iloc[:,0]


## Define the Classifier¶

We have fixed our data and now we train a classifier. The classifier will learn the function by looking at the inputs and corresponding outputs. There are a ton of classifiers to choose from some being Logistic Regression, SVM, Random Forests, Decision Trees, etc.
Tip: A good model doesnt depend solely on the classifier but on the features(columns) you choose. So make sure to play with your data and keep only whats important.

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classifier = LogisticRegression(solver = 'lbfgs',multi_class='auto',max_iter=10)


We have used Support Vector Machines as a classifier here and set few of the parameteres. But one can set more parameters and increase the performance. To see the list of parameters visit here.

We can also use other classifiers. To read more about sklean classifiers visit here. Try and use other classifiers to see how the performance of your model changes. Try using Logistic Regression or MLP and compare how the performance changes.

## Train the classifier¶

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classifier.fit(X_train, y_train)


Got a warning! Dont worry, its just beacuse the number of iteration is very less(defined in the classifier in the above cell).Increase the number of iterations and see if the warning vanishes and also see how the performance changes.Do remember increasing iterations also increases the running time.( Hint: max_iter=500)

## Predict on Validation¶

Now we predict our trained classifier on the validation set and evaluate our model

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y_pred = classifier.predict(X_val)


## Evaluate the Performance¶

We use the same metrics as that will be used for the test set.
F1 score is the metrics for this challenge## Find the scores

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precision = precision_score(y_val,y_pred,average='micro')
recall = recall_score(y_val,y_pred,average='micro')
accuracy = accuracy_score(y_test,y_val)
f1 = f1_score(y_test,y_val,average='macro')

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print("Accuracy of the model is :" ,accuracy)
print("Recall of the model is :" ,recall)
print("Precision of the model is :" ,precision)
print("F1 score of the model is :" ,f1)


# Prediction on Evaluation Set¶

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final_test_path = "test.csv"


## Predict on test set¶

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submission = classifier.predict(final_test)


## Save the prediction to csv¶

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submission = pd.DataFrame(submission)


Note: Do take a look at the submission format.The submission file should contain a header.For eg here it is "label".

## To download the generated csv in colab run the below command¶

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try: