From 7c0bbd33ad527d6a6625d8f86378d469d8b32371 Mon Sep 17 00:00:00 2001
From: KOLLI KISHORE <kishorekolli7075@gmail.com>
Date: Mon, 14 Oct 2024 18:48:07 +0530
Subject: [PATCH] Create kishore stockpre.py

---
 kishore stockpre.py | 71 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 kishore stockpre.py

diff --git a/kishore stockpre.py b/kishore stockpre.py
new file mode 100644
index 00000000..518adb9e
--- /dev/null
+++ b/kishore stockpre.py	
@@ -0,0 +1,71 @@
+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 keras.models import Sequential
+from keras.layers import LSTM,Dropout,Dense
+
+
+from sklearn.preprocessing import MinMaxScaler
+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')
+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]
+scaler=MinMaxScaler(feature_range=(0,1))
+final_dataset=new_dataset.values
+
+train_data=final_dataset[0:987,:]
+valid_data=final_dataset[987:,:]
+
+new_dataset.index=new_dataset.Date
+new_dataset.drop("Date",axis=1,inplace=True)
+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))
+
+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)
+
+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)
+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))
+predicted_closing_price=lstm_model.predict(X_test)
+predicted_closing_price=scaler.inverse_transform(predicted_closing_price)
+lstm_model.save("saved_model.h5")
+train_data=new_dataset[:987]
+valid_data=new_dataset[987:]
+valid_data['Predictions']=predicted_closing_price
+plt.plot(train_data["Close"])
+plt.plot(valid_data[['Close',"Predictions"]])