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stock-prices-prediction's Introduction

Stock Prices Prediction

OBJECTIVE:

The repository is a learning excercise to:

  • Apply the basic machine learning concept to an existing dataset.
  • Train the model and predict the values for future.
  • Creating a notebook and writing all computational records in it.

The analysis is divided into following sections:

  • Importing the libraries and dataset
  • Find number of trading days
  • Create target dataset
  • Splitting the dataset into training and test set
  • Crate the models
  • Predicting the values using the models

Data Used

https://www.kaggle.com/datasets/ankitthebemer/tata-steel-stock-dataset-20152021

Environment and Tools

  • Jupyter Notebook
  • Numpy
  • Pandas
  • Matplotlib
  • Scikit-learn
  • sklearn.model_selection
  • sklearn.ensemble
  • sklearn.linear_model

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