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## Project Description This project analyzes the NIFTY 50 companies in India by performing various calculations and creating new columns in the dataframe. The `nifty.csv` file is used as the dataset, which contains information on the companies' industry, market cap, current value, high and low 52-week values, book value, price-earnings ratio, divi

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machine-learning

Project Description

This project analyzes the NIFTY 50 companies in India by performing various calculations and creating new columns in the dataframe. The nifty.csv file is used as the dataset, which contains information on the companies' industry, market cap, current value, high and low 52-week values, book value, price-earnings ratio, dividend yield, return on capital employed, return on equity, and sales growth over the past three years.

After importing the necessary libraries and loading the dataset, the Unnamed: 0 column is removed. Two new columns are created, price_bookvalue and peg3, which represent the price to book value ratio and the PEG ratio based on a 3-year sales growth rate, respectively.

Libraries Used

  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn

File Descriptions

  • nifty.csv: the main dataset used in this project
  • README.md: this file
  • nifty.ipynb: the Jupyter Notebook containing the code for this project

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## Project Description This project analyzes the NIFTY 50 companies in India by performing various calculations and creating new columns in the dataframe. The `nifty.csv` file is used as the dataset, which contains information on the companies' industry, market cap, current value, high and low 52-week values, book value, price-earnings ratio, divi

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  • Jupyter Notebook 100.0%