Predictive Analytics - (LAB PROGRAMS)

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Aim:

☛   Exponential Smoothing - Stock market data

Solution :

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Library Installation:

To install required library files, Open Command Prompt or Terminal and execute the following commands

$ pip install scikit-learn

$ pip install pandas

$ pip install matplotlib

$ pip install statsmodels

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Dataset (stock_data.csv) :

Date,Open,High,Low,Close,Adj Close,Volume
2015-01-02,26.778,27.240,26.564,27.004,23.404,24742002
2015-01-05,27.073,27.487,26.901,27.060,23.452,26967002
2015-01-06,27.118,27.264,26.684,27.029,23.424,23850002
2015-01-07,27.183,27.450,26.959,27.254,23.607,25516002
2015-01-08,27.387,27.717,27.093,27.486,23.815,2877100
2015-01-09,27.500,27.850,27.300,27.700,24.000,3000000
2015-01-12,27.800,28.100,27.600,27.950,24.200,3100000
2015-01-13,28.000,28.300,27.900,28.150,24.350,3200000
2015-01-14,28.200,28.500,28.000,28.400,24.600,3300000
2015-01-15,28.500,28.900,28.300,28.700,24.900,3400000
2015-01-16,28.800,29.000,28.500,28.900,25.100,3500000
2015-01-19,29.000,29.200,28.700,29.100,25.300,3600000
2015-01-20,29.100,29.500,28.900,29.400,25.600,3700000
2015-01-21,29.300,29.700,29.100,29.600,25.800,3800000
2015-01-22,29.500,29.900,29.300,29.900,26.000,3900000

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PROGRAM: (EXPSMOTH.py)

 
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing
# Load stock data
df = pd.read_csv('stock_data.csv', index_col='Date', parse_dates=True)
stock_symbol = 'SampleStock'
# Split train/test
train = df['Close'][:-5]
test = df['Close'][-5:]
# Fit Exponential Smoothing model
model_fit = ExponentialSmoothing(train, trend='add', seasonal=None).fit()
# Make predictions
predictions = model_fit.forecast(steps=len(test))
# Plot actual vs predicted
plt.figure(figsize=(10,6))
plt.plot(test.index, test, label='Actual')
plt.plot(test.index, predictions, label='Predicted', color='red')
plt.title(f'{stock_symbol} Stock Price Forecast using Exponential Smoothing')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.legend()
plt.grid(True)
plt.show()

OUTPUT:

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