Baseline - EMSPM Educational Challenge

EMSPM
#1

Baseline for EMSPM Educational Challenge on AIcrowd

Author : Ayush Shivani

To open this notebook on Google Computing platform Colab, click below!

Open In Colab

Download Necessary Packages

In [ ]:
import sys
!{sys.executable} -m pip install numpy
!{sys.executable} -m pip install pandas
!{sys.executable} -m pip install scikit-learn

Download data

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

In [ ]:
!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/aicrowd_educational_emspm/data/public/test.csv
!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/aicrowd_educational_emspm/data/public/train.zip
!unzip train.zip

Import packages

In [ ]:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score

Load Data

We use pandas library to load our data. Pandas loads them into dataframes which helps us analyze our data easily. Learn more about it here

In [ ]:
train_data_path = "train.csv" #path where data is stored
In [ ]:
train_data = pd.read_csv(train_data_path,header=None) #load data in dataframe using pandas

Visualize the data

In [ ]:
train_data.head() # Visualize the data

We can see the dataset contains 57 columns, 0-55 defining the features such as average length of uninterrupted sequences of capital letters, total number of capital letters in the e-mail etc and last column contains 1/0 depending whether the email is spam or not.More information about the feature described by the column can be found here.

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

In [ ]:
X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42)

Here we have selected the size of the testing 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.

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

Define the classifier

In [ ]:
classifier = SVC(gamma='auto')

#from sklearn.linear_model import LogisticRegression
# classifier = LogisticRegression()

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 and compare how the performance changes.

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.

Train the classifier

In [ ]:
classifier.fit(X_train, y_train)

Predict on Validation

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

In [ ]:
y_pred = classifier.predict(X_test)

Evaluate the Performance

We use the same metrics as that will be used for the test set.
F1 score and Log Loss are the metrics for this challenge

In [ ]:
precision = precision_score(y_test,y_pred,average='micro')
recall = recall_score(y_test,y_pred,average='micro')
accuracy = accuracy_score(y_test,y_pred)
f1 = f1_score(y_test,y_pred,average='macro')
In [ ]:
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

Load Test Set

Load the test data now

In [ ]:
final_test_path = "test.csv"
final_test = pd.read_csv(final_test_path,header=None)

Predict Test Set

Time for the moment of truth! Predict on test set and time to make the submission.## Predict on evaluation set

In [ ]:
submission = classifier.predict(final_test)

Save the prediction to csv

In [ ]:
submission = pd.DataFrame(submission)
submission.to_csv('/tmp/submission.csv',header=['label'],index=False)

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 collab run the below command

In [ ]:
from google.colab import files
files.download('/tmp/submission.csv')

Go to platform. Participate in the challenge and submit the submission.csv generated.

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