adding files to stock dashboard

app.py

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+import pandas as pd
+import numpy as np
+from matplotlib import pyplot as plt
+import pandas_datareader.data as web
+import sklearn as sk
+import datetime as dt
+import yfinance as yf
+yf.pdr_override()
+from keras.models import load_model
+import streamlit as st
+
+# defining the dates here
+st_dt1 = dt.datetime(2010,1,1)
+end_dt1 = dt.datetime(2019,12,31)
+st_dt2 = dt.datetime(2021,1,1)
+end_dt2 = dt.datetime(2023,12,31)
+
+st.title('Stock Trend Prediction')
+user_input = st.text_input('Enter Stock Tiker','AAPL')
+
+df_stocks1 = web.get_data_yahoo( user_input, st_dt1, end_dt1 )
+df_stocks2 = web.get_data_yahoo(user_input, st_dt2, end_dt2 )
+df_stocks = pd.concat([df_stocks1,df_stocks2])
+
+df = df_stocks.reset_index()
+
+# Desribing the data
+st.subheader('Data from 2010 to 2023 exluding the Covid Year')
+st.write(df.describe())
+
+# visualization
+st.subheader('The Closing price vs Time Chart')
+fig = plt.figure(figsize=(12,6))
+plt.plot(df.close)
+st.pyplot(fig)
+
+# with Moving Averages
+st.subheader('The Closing price vs Time Chart with MA')
+ma100 = df.Close.rolling(100).mean()
+ma200 = df.Close.rolling(200).mean()
+fig = plt.figure(figsize=(12,6))
+plt.plot(df.close,'b')
+plt.plot(ma100,'r')
+plt.plot(ma200,'g')
+st.pyplot(fig)
+
+# train-test split
+df_train = pd.DataFrame(df['Close'][0:int(df.shape[0]*0.7)])
+df_test = pd.DataFrame(df['Close'][int(df.shape[0]*0.7):int(df.shape[0])])
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0,1))
+df_train_array = scaler.fit_transform(df_train)
+
+# need to fix the step size of 100
+x_train = []
+y_train = []
+
+for i in range(100,df_train_array.shape[0]):
+  x_train.append(df_train_array[i-100:i])
+  y_train.append(df_train_array[i,0])
+
+x_train,y_train = np.array(x_train), np.array(y_train)
+
+model = load_model("keras_model.h5")
+
+past_100_days = df_train.tail(100)
+df_final = past_100_days.append(df_test,ignore_index=True)
+
+df_final = scaler.fit_transform(df_final)
+x_test = []
+y_test = []
+
+for i in range(100,df_final.shape[0]):
+  x_test.append(df_final[i-100:i])
+  y_test.append(df_final[i,0])
+x_test, y_test = np.array(x_test), np.array(y_test)  
+
+y_pred = model.predict(x_test)
+
+scaler_val = scaler.scale_
+scale_factor = 1/0.00615148
+y_pred = y_pred*scale_factor
+y_test = y_test*scale_factor
+
+#plotting
+st.subheader("Trends vs Original Prie")
+fig2 = plt.figure(figsize=(12,6))
+plt.plot(y_test,'b',label='Original Price')
+plt.plot(y_pred,'r',label='Predicted Price')
+plt.xlabel('Time')
+plt.ylabel('Price')
+plt.legend()
+st.pyplot(fig2)
+

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:745734736901b9186c5f50e2b1b79a7fbbd2084b81a3b32a38737420e00d3b2b
+size 1679912

requirements.txt

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+pandas
+streamlit
+keras
+matplotlib
+numpy
+sklearn
+datetime
+yfinane
+tensorflow