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.ipynb_checkpoints
.ipynb_checkpoints
 
 
Archive
Archive
 
 
Data
Data
 
 
Model_Conclusion
Model_Conclusion
 
 
Week7
Week7
 
 
DeepLearningModel.ipynb
DeepLearningModel.ipynb
 
 
DeepLearningV2.ipynb
DeepLearningV2.ipynb
 
 
Extended_Model_Cross_Validation_.ipynb
Extended_Model_Cross_Validation_.ipynb
 
 
GOOG_GUI.zip
GOOG_GUI.zip
 
 
IL_Corn_Price_1986_2016.csv
IL_Corn_Price_1986_2016.csv
 
 
IL_Corn_Price_Updated_1986_2016.csv
IL_Corn_Price_Updated_1986_2016.csv
 
 
IL_precipitation_statistics.xlsx
IL_precipitation_statistics.xlsx
 
 
Il_precipitation_stats_COUNTY.xlsx
Il_precipitation_stats_COUNTY.xlsx
 
 
LSTM_RNN.ipynb
LSTM_RNN.ipynb
 
 
LSTM_RNN_Prices.ipynb
LSTM_RNN_Prices.ipynb
 
 
Model_Prediction.ipynb
Model_Prediction.ipynb
 
 
Model_Prediction_Returns.ipynb
Model_Prediction_Returns.ipynb
 
 
Model_pre_processing.ipynb
Model_pre_processing.ipynb
 
 
README.md
README.md
 
 
RNN_MNIST.ipynb
RNN_MNIST.ipynb
 
 
Regression_model_time_Lag_analysis.ipynb
Regression_model_time_Lag_analysis.ipynb
 
 
Weighted_Average.ipynb
Weighted_Average.ipynb
 
 
Weighted_Average_Nov_Model.ipynb
Weighted_Average_Nov_Model.ipynb
 
 
Weighted_Average_Oct_Model.ipynb
Weighted_Average_Oct_Model.ipynb
 
 
Weighted_average_models.ipynb
Weighted_average_models.ipynb
 
 
data_preprocessing_template.py
data_preprocessing_template.py
 
 
final_yearly.csv
final_yearly.csv
 
 
input_model.csv
input_model.csv
 
 
input_model_weighted_average.csv
input_model_weighted_average.csv
 
 
lstm_predictior_updated.py
lstm_predictior_updated.py
 
 
model.csv
model.csv
 
 
~$IL_precipitation_statistics.xlsx
~$IL_precipitation_statistics.xlsx
 
 

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Google Practicum

The objective of the practicum is to find associations between weather and financial outcomes using ML and Deep Learning.

Practicum Sponsor

Google, Chicago

Project Supervisor

Rob Schmit https://www.linkedin.com/in/rob-schmit-465b934/

Team Members (alphabetical order)

Jegan Sridharma

Joonha Yoon

Shantanu Bhatt

Siddharth Bhaduri

Udit Anand

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The objective of the practicum is to find associations between weather and financial outcomes using ML and Deep Learning.

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