Day-12_SUMMER_TRAINING_AI/ML

Day-12_SUMMER_TRAINING_AIML/day_12(2)_dhruvdhayal_ai_ml.py

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+# -*- coding: utf-8 -*-
+"""Day-12(2)_DHRUVDHAYAL_AI/ML.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1apn7PkA9SqHSzEoTXF-adx5smv8OZBO1
+
+#AUTOMOBILES - GM
+"""
+
+import pandas_datareader as pdr;
+import datetime;
+import pandas as pd;
+
+#Showing the Values of the Date by setting up of the Data.
+start=datetime.datetime(2018,1,1);
+end=datetime.datetime(2024,7,20);
+
+#Printing the Satrt and the end date.
+print("\n 1. Starting Date of the Market: ",start);
+print("\n 2. Ending Date of the Market: ",end);
+
+stock=['GM'];
+data=pdr.DataReader(stock,'stooq',str(start.date()),str(end.date())).stack('Symbols');
+data.head();
+
+#Reset the Values of the Index.
+newData=data.reset_index();
+newData.head();
+
+newData.to_csv('/content/GM_stock_2018_to_2024.csv');
+
+import matplotlib.pyplot as plt
+# plot the close price fo the tesla stock
+# load the csv file
+GM_data = pd.read_csv('/content/GM_stock_2018_to_2024.csv')
+
+# set the date as the index
+GM_data.set_index('Date',inplace=True)
+GM_data.head()
+# and then sperate the close price
+close_price = GM_data['Close']
+# then plot the close price
+close_price.plot(xlabel='Date',ylabel='Price',label='INFY',title='ICICI BANK Stock Price',figsize=(10,6))
+
+import matplotlib.pyplot as plt
+# plot the close price fo the tesla stock
+# load the csv file
+GM_data = pd.read_csv('/content/GM_stock_2018_to_2024.csv')
+
+# set the date as the index
+GM_data.set_index('Date',inplace=True)
+GM_data.sort_index(ascending=True,inplace=True)
+GM_data.head()
+# and then sperate the close price
+close_price = GM_data['Close']
+# then plot the close price
+close_price.plot(xlabel='Date',ylabel='Price',label='INFY',title='ICICI BANK Stock Price',figsize=(10,6))
+
+#We, meed to show the values of the Subplot of data: 'SYMBOLS','CLOSE','HIGH','LOW','OPEN'.
+import matplotlib.pyplot as plt
+import seaborn as sns;
+import numpy as np;
+
+#Now, we have to subplot the values of the graph.
+plt.figure(1,figsize=(6,6));
+
+plt.subplot(3,1,1);
+plt.plot(GM_data['Close'],label='Close Price');
+plt.plot(GM_data['Open'],label='Open Price');
+plt.legend();
+
+plt.subplot(3,1,2);
+plt.plot(GM_data['High'],label='High Price');
+plt.plot(GM_data['Low'],label='Low Price');
+plt.legend();
+
+plt.subplot(3,1,3);
+plt.plot(GM_data['Volume'],label='Volume');
+plt.legend();
+
+"""# >> Showing the Values of the Moving Averages. ON INFOSYS."""
+
+#Showing the Movinf Averages.
+GM_data.head();
+
+#Now, Calculating the Values of the Moving Average.
+# calulate the moving average
+GM_data['SMA_20'] = GM_data['Close'].rolling(20).mean()
+GM_data['SMA_50'] = GM_data['Close'].rolling(50).mean()
+plt.figure(1)
+plt.plot(GM_data['Close'],label='close price',linewidth=0.5)
+plt.plot(GM_data['SMA_20'],label='20-SMA',linestyle='--')
+plt.plot(GM_data['SMA_50'],label='50-SMA',linestyle='--')
+
+plt.title('Moving average analysis')
+plt.legend()
+
+"""#Relative Strength Index."""
+
+!pip install ta;
+
+import ta;
+GM_data['RSI'] = ta.momentum.RSIIndicator(GM_data['Close'], window=14);
+GM_data.head();
+
+# relative strength index
+GM_data['RSI'] = ta.momentum.rsi(GM_data['Close'],window=14)
+
+plt.figure(1,(12,6))
+plt.plot(GM_data['RSI'],label='RSI',linewidth=0.5)
+plt.axhline(70,linestyle='--',color='red',alpha=0.5)
+plt.axhline(30,linestyle='--',color='red',alpha=0.5)
+plt.title('RSI analysis')
+plt.legend();
+GM_data.tail();
+
+# prompt: save the file in  anther file
+
+GM_data.to_csv('/content/GM_stock_2018_to_2024_updated.csv')
+
+"""#Task-2:
+
+--> 1. Consider the 3-Years of Infosys Data.
+
+--> 2. 2018-2019-2020.
+
+--> 3. Try and fit the 'ARIMA' and 'SARIMA'.
+
+--> 4. FORELIST-> 1MONTH DATA.
+
+--> 5. Super in Base of the Actual data.
+"""
+
+#Importing the Inbuilt Libraries.
+from statsmodels.tsa import seasonal,arima_model;
+import pandas as pd;
+import numpy as np;
+
+#Importing the Values of the Data.
+data=pd.read_csv("/content/INFOSYS_stock_2018_to_2024 (1).csv");
+
+#printing the Values of the data.
+print("\n 1. Total Length of the Data: ",data.shape);
+data.head();
+
+#Information of the Data Values.
+data.info();
+
+#Importing the Inbuilt Libraries.
+from statsmodels.tsa import seasonal,arima_model
+import pandas as pd
+import numpy as np
+
+#Importing the Values of the Data.
+data=pd.read_csv("/content/INFOSYS_stock_2018_to_2024 (1).csv")
+
+# Check the column names of your DataFrame
+print(data.columns)
+
+#Convert the Month Column into Text into the DataTime.
+# Adjust the column name below if it's different in your data
+data["Date"] = pd.to_datetime(data["Date"])  # Replace 'Month' with the actual column name if needed
+data.head()
+
+data.head(50);
+
+#Set the Month Column as the Index of the Pandas DataFiles.
+data.set_index("Date",inplace=True);
+data.head();
+
+import matplotlib.pyplot as plt;
+data['Close'].plot();
+
+#Let's Create the ForeCaster.
+import statsmodels.api as st;
+
+# Assuming 'data' is your time series DataFrame
+sarima_model = st.tsa.statespace.SARIMAX(data,order=(1,1,1),seasonal_order=(1,1,1,12))
+
+#Train the Model.
+sarima_model = sarima_model.fit()
+
+# forecaste the value
+value_for= sarima_model.forecast()
+print(value_for)
+