Description

Resources
This week will have 2 minor problem statements.Please refer to these links below for tutorials needed to approach this week’s PS:
 A blog on basic ideas behind sentiment analysis:
https://towardsdatascience.com/a-step-by-step-tutorial-for-conducting-sentiment-analysis-a7190a444366
 LSTM explained: https://youtu.be/QciIcRxJvsM
 LSTM tutorial (Pytorch): https://youtu.be/AvKSPZ7oyVg
 LSTM + Rolling Window algorithm for future prediction (easy to use reference notebook using
Tf.keras):
https://github.com/shubhambhalala/Live_Stock_Market_Forecasting/blob/master/Live_Stock_Predict ion.ipynb

Assignment 1 : Oil Price Prediction
In this problem, a single column of Oil Prices will be provided to you.The task is to predict the oil price for the next 30 days using Univariate LSTM neural network.The evaluation metric for this PS would be RMSE , given price prediction is essentially a regression problem.
Task : Predict future prices for the next 30 days
Evaluation Metric: RMSE

Assignment 2:Sentiment Analysis
In this problem, you will be provided with a text dataset and the aim is to use the NLTK Library to determine the Sentiment of the text in the Sentence column.
Link to the dataset:
https://drive.google.com/drive/folders/17VUgOJ_2dO6vSdVgisXQEc1_OtUf-9XU?usp=share_link Make sure to follow the stepwise order of operations (tokenization,stopwords, lemmatization etc) Evaluation metric: f1_score.