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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
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diff --git a/Stock-Price-Prediction/README.md b/Stock-Price-Prediction/README.md
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+
+ 📑 Stock Price Prediction
+
+Machine learning has significant applications in the stock price prediction. In this machine learning project, we will be talking about predicting the returns on stocks. This is a very complex task and has uncertainties. We will develop this project into two parts:
+
+First, we will predict stock price using the LSTM neural network.
+Then we will build a dashboard using Plotly dash for stock analysis.
+
+Datasets
+
+To build the stock price prediction model, we will use the NSE TATA GLOBAL dataset. This is a dataset of Tata Beverages from Tata Global Beverages Limited, National Stock Exchange of India: Tata Global Dataset
+To develop the dashboard for stock analysis we will use another stock dataset with multiple stocks like Apple, Microsoft, Facebook: Stocks Dataset
+
+
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diff --git a/Stock-Price-Prediction/Stock_demo - Made with Clipchamp.mp4 b/Stock-Price-Prediction/Stock_demo - Made with Clipchamp.mp4
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diff --git a/Stock-Price-Prediction/stock_app.py b/Stock-Price-Prediction/stock_app.py
new file mode 100644
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+++ b/Stock-Price-Prediction/stock_app.py
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+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+import pandas as pd
+import plotly.graph_objs as go
+from dash.dependencies import Input, Output
+from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+
+
+app = dash.Dash()
+server = app.server
+
+scaler=MinMaxScaler(feature_range=(0,1))
+
+
+
+df_nse = pd.read_csv("./NSE-TATA.csv")
+
+df_nse["Date"]=pd.to_datetime(df_nse.Date,format="%Y-%m-%d")
+df_nse.index=df_nse['Date']
+
+
+data=df_nse.sort_index(ascending=True,axis=0)
+new_data=pd.DataFrame(index=range(0,len(df_nse)),columns=['Date','Close'])
+
+for i in range(0,len(data)):
+ new_data["Date"][i]=data['Date'][i]
+ new_data["Close"][i]=data["Close"][i]
+
+new_data.index=new_data.Date
+new_data.drop("Date",axis=1,inplace=True)
+
+dataset=new_data.values
+
+train=dataset[0:987,:]
+valid=dataset[987:,:]
+
+scaler=MinMaxScaler(feature_range=(0,1))
+scaled_data=scaler.fit_transform(dataset)
+
+x_train,y_train=[],[]
+
+for i in range(60,len(train)):
+ x_train.append(scaled_data[i-60:i,0])
+ y_train.append(scaled_data[i,0])
+
+x_train,y_train=np.array(x_train),np.array(y_train)
+
+x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))
+
+model=load_model("saved_model.h5")
+
+inputs=new_data[len(new_data)-len(valid)-60:].values
+inputs=inputs.reshape(-1,1)
+inputs=scaler.transform(inputs)
+
+X_test=[]
+for i in range(60,inputs.shape[0]):
+ X_test.append(inputs[i-60:i,0])
+X_test=np.array(X_test)
+
+X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
+closing_price=model.predict(X_test)
+closing_price=scaler.inverse_transform(closing_price)
+
+train=new_data[:987]
+valid=new_data[987:]
+valid['Predictions']=closing_price
+
+
+
+df= pd.read_csv("./stock_data.csv")
+
+app.layout = html.Div([
+
+ html.H1("Stock Price Analysis Dashboard", style={"textAlign": "center"}),
+
+ dcc.Tabs(id="tabs", children=[
+
+ dcc.Tab(label='NSE-TATAGLOBAL Stock Data',children=[
+ html.Div([
+ html.H2("Actual closing price",style={"textAlign": "center"}),
+ dcc.Graph(
+ id="Actual Data",
+ figure={
+ "data":[
+ go.Scatter(
+ x=train.index,
+ y=valid["Close"],
+ mode='markers'
+ )
+
+ ],
+ "layout":go.Layout(
+ title='scatter plot',
+ xaxis={'title':'Date'},
+ yaxis={'title':'Closing Rate'}
+ )
+ }
+
+ ),
+ html.H2("LSTM Predicted closing price",style={"textAlign": "center"}),
+ dcc.Graph(
+ id="Predicted Data",
+ figure={
+ "data":[
+ go.Scatter(
+ x=valid.index,
+ y=valid["Predictions"],
+ mode='markers'
+ )
+
+ ],
+ "layout":go.Layout(
+ title='scatter plot',
+ xaxis={'title':'Date'},
+ yaxis={'title':'Closing Rate'}
+ )
+ }
+
+ )
+ ])
+
+
+ ]),
+ dcc.Tab(label='Facebook Stock Data', children=[
+ html.Div([
+ html.H1("Stocks High vs Lows",
+ style={'textAlign': 'center'}),
+
+ dcc.Dropdown(id='my-dropdown',
+ options=[{'label': 'Tesla', 'value': 'TSLA'},
+ {'label': 'Apple','value': 'AAPL'},
+ {'label': 'Facebook', 'value': 'FB'},
+ {'label': 'Microsoft','value': 'MSFT'}],
+ multi=True,value=['FB'],
+ style={"display": "block", "margin-left": "auto",
+ "margin-right": "auto", "width": "60%"}),
+ dcc.Graph(id='highlow'),
+ html.H1("Stocks Market Volume", style={'textAlign': 'center'}),
+
+ dcc.Dropdown(id='my-dropdown2',
+ options=[{'label': 'Tesla', 'value': 'TSLA'},
+ {'label': 'Apple','value': 'AAPL'},
+ {'label': 'Facebook', 'value': 'FB'},
+ {'label': 'Microsoft','value': 'MSFT'}],
+ multi=True,value=['FB'],
+ style={"display": "block", "margin-left": "auto",
+ "margin-right": "auto", "width": "60%"}),
+ dcc.Graph(id='volume')
+ ], className="container"),
+ ])
+
+
+ ])
+])
+
+
+
+
+
+
+
+@app.callback(Output('highlow', 'figure'),
+ [Input('my-dropdown', 'value')])
+def update_graph(selected_dropdown):
+ dropdown = {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",}
+ trace1 = []
+ trace2 = []
+ for stock in selected_dropdown:
+ trace1.append(
+ go.Scatter(x=df[df["Stock"] == stock]["Date"],
+ y=df[df["Stock"] == stock]["High"],
+ mode='lines', opacity=0.7,
+ name=f'High {dropdown[stock]}',textposition='bottom center'))
+ trace2.append(
+ go.Scatter(x=df[df["Stock"] == stock]["Date"],
+ y=df[df["Stock"] == stock]["Low"],
+ mode='lines', opacity=0.6,
+ name=f'Low {dropdown[stock]}',textposition='bottom center'))
+ traces = [trace1, trace2]
+ data = [val for sublist in traces for val in sublist]
+ figure = {'data': data,
+ 'layout': go.Layout(colorway=["#5E0DAC", '#FF4F00', '#375CB1',
+ '#FF7400', '#FFF400', '#FF0056'],
+ height=600,
+ title=f"High and Low Prices for {', '.join(str(dropdown[i]) for i in selected_dropdown)} Over Time",
+ xaxis={"title":"Date",
+ 'rangeselector': {'buttons': list([{'count': 1, 'label': '1M',
+ 'step': 'month',
+ 'stepmode': 'backward'},
+ {'count': 6, 'label': '6M',
+ 'step': 'month',
+ 'stepmode': 'backward'},
+ {'step': 'all'}])},
+ 'rangeslider': {'visible': True}, 'type': 'date'},
+ yaxis={"title":"Price (USD)"})}
+ return figure
+
+
+@app.callback(Output('volume', 'figure'),
+ [Input('my-dropdown2', 'value')])
+def update_graph(selected_dropdown_value):
+ dropdown = {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",}
+ trace1 = []
+ for stock in selected_dropdown_value:
+ trace1.append(
+ go.Scatter(x=df[df["Stock"] == stock]["Date"],
+ y=df[df["Stock"] == stock]["Volume"],
+ mode='lines', opacity=0.7,
+ name=f'Volume {dropdown[stock]}', textposition='bottom center'))
+ traces = [trace1]
+ data = [val for sublist in traces for val in sublist]
+ figure = {'data': data,
+ 'layout': go.Layout(colorway=["#5E0DAC", '#FF4F00', '#375CB1',
+ '#FF7400', '#FFF400', '#FF0056'],
+ height=600,
+ title=f"Market Volume for {', '.join(str(dropdown[i]) for i in selected_dropdown_value)} Over Time",
+ xaxis={"title":"Date",
+ 'rangeselector': {'buttons': list([{'count': 1, 'label': '1M',
+ 'step': 'month',
+ 'stepmode': 'backward'},
+ {'count': 6, 'label': '6M',
+ 'step': 'month',
+ 'stepmode': 'backward'},
+ {'step': 'all'}])},
+ 'rangeslider': {'visible': True}, 'type': 'date'},
+ yaxis={"title":"Transactions Volume"})}
+ return figure
+
+
+
+if __name__=='__main__':
+ app.run_server(debug=True)
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diff --git a/Stock-Price-Prediction/stock_pred.py b/Stock-Price-Prediction/stock_pred.py
new file mode 100644
index 00000000..187eb3c0
--- /dev/null
+++ b/Stock-Price-Prediction/stock_pred.py
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+import pandas as pd
+import numpy as np
+
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+from matplotlib.pylab import rcParams
+rcParams['figure.figsize']=20,10
+
+from sklearn.preprocessing import MinMaxScaler
+scaler=MinMaxScaler(feature_range=(0,1))
+
+df=pd.read_csv("NSE-TATA.csv")
+df.head()
+
+df["Date"]=pd.to_datetime(df.Date,format="%Y-%m-%d")
+df.index=df['Date']
+
+plt.figure(figsize=(16,8))
+plt.plot(df["Close"],label='Close Price history')
+
+from keras.models import Sequential
+from keras.layers import LSTM,Dropout,Dense
+
+data=df.sort_index(ascending=True,axis=0)
+new_dataset=pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])
+
+for i in range(0,len(data)):
+ new_dataset["Date"][i]=data['Date'][i]
+ new_dataset["Close"][i]=data["Close"][i]
+
+
+new_dataset.index=new_dataset.Date
+new_dataset.drop("Date",axis=1,inplace=True)
+
+final_dataset=new_dataset.values
+
+train_data=final_dataset[0:987,:]
+valid_data=final_dataset[987:,:]
+
+scaler=MinMaxScaler(feature_range=(0,1))
+scaled_data=scaler.fit_transform(final_dataset)
+
+x_train_data,y_train_data=[],[]
+
+for i in range(60,len(train_data)):
+ x_train_data.append(scaled_data[i-60:i,0])
+ y_train_data.append(scaled_data[i,0])
+
+x_train_data,y_train_data=np.array(x_train_data),np.array(y_train_data)
+
+x_train_data=np.reshape(x_train_data,(x_train_data.shape[0],x_train_data.shape[1],1))
+
+lstm_model=Sequential()
+lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(x_train_data.shape[1],1)))
+lstm_model.add(LSTM(units=50))
+lstm_model.add(Dense(1))
+
+
+
+
+lstm_model.compile(loss='mean_squared_error',optimizer='adam')
+lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)
+
+inputs_data=new_dataset[len(new_dataset)-len(valid_data)-60:].values
+inputs_data=inputs_data.reshape(-1,1)
+inputs_data=scaler.transform(inputs_data)
+
+
+X_test=[]
+for i in range(60,inputs_data.shape[0]):
+ X_test.append(inputs_data[i-60:i,0])
+X_test=np.array(X_test)
+
+X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
+closing_price=model.predict(X_test)
+closing_price=scaler.inverse_transform(closing_price)
+
+lstm_model.save("saved_lstm_model.h5")
+
+train_data=new_dataset[:987]
+valid_data=new_dataset[987:]
+valid_data['Predictions']=prediction_closing
+plt.plot(train_data["Close"])
+plt.plot(valid_data[['Close',"Predictions"]])
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