/ML-Price-Prediction

Primary LanguageJupyter Notebook

ML - Price-Prediction

Dependencies

  • Scikit learn
  • Python 3.7
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn

How to run and Instructions

  • The code is in a notebook formant an ipynb file.
  • The first half contains the Exploratory Data Analysis followed. by preprocessing.
  • The third section consists of feature engineering (extracting and selecting the optimum features).
  • The model is trained (Applied Boosting ALgorithms).
  • The final section is of repeating the process on the test set and making predictions.
  • The code is structured in a linked format from start to end.