/Bank-Negara-Indonesia-Stock-Prediction

Stock Prediction of Bank of Negara Indonesia

Primary LanguageJupyter Notebook

BANK Negara Stock Prediction

GOAL

To predict a the closing price of the bank stock prices from the dataset.

DATASET

https://www.kaggle.com/datasets/caesarmario/bank-negara-indonesia-stock-historical-price

DESCRIPTION

The main aim of the project is to make a model that helps to predict the closing price of the bank.

Visualization and EDA of different attributes:

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MODELS USED

Model MAE_test MSE_test R2_test
Random Forest Regression 19.47 866.65 0.99
XG Boost Regression 24.92 1154.11 0.99
Ridge Regression 20.81 701.38 0.99
Decision Tree Regression 22.86 1374.32 0.99
Lasso 22.24 793.25 0.99
SVR 788.22 882792.81 0.014

WORK DONE

  • Analyzed the data and found insights such as correlation, missing values etc.
  • Selected the columns that have high correlation than other columns to be used as features. (Refer : eda-banknote-dataset)
  • Next trained model with algorithms with default parameters:
    • Linear SVR
    • Lasso
    • Ridge
    • Decision Tree
    • Random Forest
    • XGBoost
  • In this Linear SVR and performed the best with 90% accuracy.

MODELS USED

  1. Logistic Regression : Logistic regression is easier to implement, interpret, and very efficient to train. It is very fast at classifying unknown records.
  2. Linear SVM : SVM performs well on classification problems when size of dataset is not too large.
  3. Random Forest : It provides higher accuracy through cross validation. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data. If there are more trees, it won't allow over-fitting trees in the model.
  4. XGBoost : XGBoost is a library for developing fast and high performance gradient boosting tree models. XGBoost achieves the best performance on a range of difficult machine learning tasks.
  5. LightGBM : Light GBM is prefixed as Light because of its high speed. Light GBM can handle the large size of data and takes lower memory to run. it is so popular is because it focuses on accuracy of results.

LIBRARIES NEEDED

  • Numpy
  • Pandas
  • Matplotlib
  • scikit-learn
  • xgboost
  • seaborn

CONCLUSION

We investigated the data, checking for data unbalancing, visualizing the features, and understanding the relationship between different features. We then investigated two predictive models. The data was split into two parts, a train set, a test set. For the first five base models, we only used the train and test set.

We started with SVR, Decision Tree, Lasso, Ridge, Random Forrest Regressor and XGBoost Regressor for which we obtained an highest accuracy of 90%, when predicting the target for the test set.

CONTRIBUTION BY

Pawas Pandey