GitHub - Akip07/sp-prediction: comparison of ML architectures in predicting S&P index · GitHub

Name		Name	Last commit message	Last commit date
models		models
.gitignore		.gitignore
README.md		README.md
analyze_bias.py		analyze_bias.py
chech_variance.py		chech_variance.py
feature_importance.png		feature_importance.png
feature_importance_rf.png		feature_importance_rf.png
heatmap_features.png		heatmap_features.png
heatmap_features_all.png		heatmap_features_all.png
knn_fixed.ipynb		knn_fixed.ipynb
knn_results.png		knn_results.png
lstm.ipynb		lstm.ipynb
lstm_results.png		lstm_results.png
mlp_fixed.ipynb		mlp_fixed.ipynb
mlp_results.png		mlp_results.png
parse_data.py		parse_data.py
parse_data_corrected.py		parse_data_corrected.py
regression_tree.ipynb		regression_tree.ipynb
regression_tree_fixed.ipynb		regression_tree_fixed.ipynb
regression_tree_results.png		regression_tree_results.png
sp_data.csv		sp_data.csv
sp_data_selected.csv		sp_data_selected.csv
sp_data_selected_test.csv		sp_data_selected_test.csv
sp_data_selected_train.csv		sp_data_selected_train.csv
sp_data_test.csv		sp_data_test.csv
sp_data_train.csv		sp_data_train.csv

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A repository where I compared different ML architectures in the task of predicting the value of the S&P index

Architectures compared:

k-nn
regression tree and random forest
mlp
lstm

Dataset used: https://archive.ics.uci.edu/dataset/554/cnnpred+cnn+based+stock+market+prediction+using+a+diverse+set+of+variables

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comparison of ML architectures in predicting S&P index

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