DL ICP5 - PardhaSaradhi74/Python GitHub Wiki

NAME

RAMINENI,PARDHA SARADHI

CLASS ID

32

In class programming:

1.Save the model and use the saved model to predict on new text data (ex, “A lot of good things are happening. We are respected again throughout the world, and that's a great thing.@realDonaldTrump”)

2.Apply GridSearchCV on the source code provided in the class

3.Apply the code on spamdata set availablein thesourcecode (text classification on the spam.csvdata set)

Bonus

Visualize Loss and Accuracy on the Tensorboard

SENTIMENTAL ANALYSIS ON TWITTER DATA

For this use case we saved the model and used the saved model to predict the sentiment value of the given tweet.

We applied few processing methods on the input tweet like tokenization and converting the text to sequence vectors.

Applying GridSearch CV

We applied the GridSearchCV on the model with params as batch size of 32,64 and the epochs as 1,2 and provided the model as estimator.

The best score is 0.679220 for the parameters batch_size: 64, epochs: 2

Use Case 2:SPAM DETECTION

We applied the same source code on the Spam Dataset by processing the feature and target columns of the Spam Dataset and evaluated the model. The accuracy of the model is 97.98%.