Inspiration
Yes Bank is a well-known bank in the Indian financial domain. Since 2018, it has been in the news because of the fraud case involving Rana Kapoor. Owing to this fact, it was interesting to see how that impacted the stock prices of the company and whether Time series models or any other predictive models can do justice to such situations. This dataset has monthly stock prices of the bank since its inception and includes closing, starting, highest, and lowest stock prices of every month. The main objective is to predict the stock’s closing price of the month.
What it does
Dataset: Analyzed historical stock data to understand trends and patterns. Features: Explored various factors influencing stock prices, including market trends, trading volumes, and economic indicators. Models: Implemented state-of-the-art machine learning models for accurate predictions. Evaluation: Rigorous testing and evaluation to ensure the reliability and robustness of the models.
How we built it
Data Collection: Gathered comprehensive historical stock data for Yes Bank. Feature Engineering: Engineered relevant features to enhance model performance. Model Training: Utilized advanced machine learning models for accurate predictions. Evaluation: Rigorous evaluation to ensure the model's effectiveness and reliability.
Challenges we ran into
Uncovered meaningful insights into the factors driving Yes Bank stock prices. Explored the dynamic relationship between market conditions and stock performance. Leveraged machine learning for precise and informed predictions.
Accomplishments that we're proud of
Achieved commendable accuracy in predicting Yes Bank stock closing prices. Validated the model's performance through comprehensive testing and validation procedures.
What we learned
Uncovered meaningful insights into the factors driving Yes Bank stock prices. Explored the dynamic relationship between market conditions and stock performance. Leveraged machine learning for precise and informed predictions.
Built With
- jupyternotebook
- python
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