Stock Market Analysis

Stock Investment Portfolio-risk and return
Basic statistics in python
Stock covariance and correlation

Inspiration

Demand of stock have become huge with increased in popularity of stock in Digital world. Prediction and analysing stock can benefit people to think before buying or selling stocks. So, A new stock price prediction through machine Learning Algorithms has been analyzed and visualized.

What it does

We have created a stock market analysis app in which we took top companies stocks such as amazon, tesla, apple, microsoft and compared their past stock market exchanges with each other.Installed yfinance which updates us with the current status of stocks.Used regression and classification algorithms to predict the future of these companies.Predicted the accuracy score for top company stocks and every accuracy were above 0.90

How we built it

We built it using streamlit UI and python is the programming language we used.

Challenges we ran into

We rectified many errors while going through this project.We came to know about new algorithms in machine learning.Stock analysis is a method for investors to use (short or long term) and to make buying and selling decisions. By studying and evaluating the past and current historical data,fundamental data or news, investors and traders attempt to gain an winning edge in themarkets by making the best decisions. 📈 📉Same way we can use any company Stock Dataset directly apply this algorithms it will give us the correct prediction.This system successfully runs on any system even on Cloud platforms.

Accomplishments that we're proud of

We were the winners in the daisi hackathon conducted by daisi via hackerearth.We secured a cash prize of $2500.

What we learned

We learned more about stock market analysis and knew the importance of stock market.We came to know about many algorithms in brief.

What's next for Stock Market Analysis

Next we would like to deploy our project as an app since the daisi platform is currently shutdown due to some problems in their company.(The Python code is ready)