flowchart.txt

1. Importing the packages
2. Exploring the dataset - early AD data with feature value of l handwriting
3. Data processing
	- Pandas Dataframe 
	- keras dataframe
4. Visualization using seaborn & matplotlib
	- Correlation Matrix
	- Plot Features for Cost of Living histogram
5. Label Encoding
	- Encoding the Object data to Int
6. Feature Selection
	- Pandas Dataframe using iloc
7. Building the model
	- Support Vector Machine - SVM
	- Random Forest
	- Decision Tree
	- ANN - MLP
	- Voting Classifier (SVM + DT + RF)
	- CNN
	- CNN + LSTM
	- BiLSTM
	- RC based RNN
	- Kmediods
8. Training the model
9. Building the model with Random Forest Classifier since it gives better accuracy comparing with Other Models 
10. Flask Framework with Sqlite for signup and signin
11. Importing the packages
12. User gives input as Feature Values 
13. The given input is preprocessed for prediction
14. Trained model is used for prediction
15. Final outcome is displayed through frontend

Extension
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In the base paper, the author mentioned few Machine and Deep Learning models (ANN,RC-NN,Kmediods,BiLSTM),in which RC got 85%, 
As an extension we have applied SVM,RF,DT, Voting Classifier and CNN + LSTM, in which Voting Classifier and Random Forest got 87% and 88% 
So with the Random Forest Classifier we build the model used for predicting the result.