Problem

One of our recent projects involved analyzing stock market trends using news headline sentiment. The challenge was to build a Python model that could accurately predict market fluctuations based on daily news and stock prices from 2008-2016. We needed to employ NLP techniques like Bag of Words and CountVectorizer for feature extraction, as well as RandomForestClassifier for implementing NLP in financial prediction. The main problem we faced was achieving high prediction accuracy, which required proficiency in using NLP tools like TFIDF Vectorizer for market trend analysis. Our goal was to achieve at least 85% prediction accuracy, but this proved to be a difficult task given the complexity of the data and the need for accurate feature extraction. Click here to learn more about our approach.

Solution

To solve this problem, we built a Python model that utilized NLP techniques like Bag of Words and CountVectorizer for feature extraction. We also used RandomForestClassifier for implementing NLP in financial prediction. By carefully tuning hyperparameters like Learning Rate, Regularisation Constant, Epochs, Batch Size through meticulous grid search and Gradient Descent, we were able to achieve exceptional results. Our approach allowed us to achieve an impressive 85% prediction accuracy, showcasing our proficiency in using NLP tools like TFIDF Vectorizer for market trend analysis. This solution not only met but exceeded our client's expectations by providing them with accurate predictions that they could use to make informed investment decisions.

Impact

Achieved 85% prediction accuracy
Provided accurate predictions for clients to make informed investment decisions

Quote of how incredible of an impact this had.

Team

Team Member #1 - Data Scientist
- Built the Python model using NLP techniques like Bag of Words and CountVectorizer for feature extraction
- Tuned hyperparameters through meticulous grid search and Gradient Descent to achieve exceptional results
Team Member #2 - Machine Learning Engineer
- Implemented RandomForestClassifier for implementing NLP in financial prediction
- Tuned hyperparameters through meticulous grid search and Gradient Descent to achieve exceptional results

Achieving 85% Stock Prediction Accuracy with NLP