Load & Filter Data for TCS and Reliance

import pandas as pd

Load dataset

df = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip')

Filter for TCS

tcs_data = df[df['Symbol'] == 'TCS'].copy()

Convert 'Date' to datetime and sort

tcs_data['Date'] = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(tcs_data['Date']) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Date', inplace=True) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(drop=True, inplace=True)

Calculate moving averages

tcs_data['MA20'] = tcs_data['Close'].rolling(window=20).mean() tcs_data['MA50'] = tcs_data['Close'].rolling(window=50).mean()

Drop rows with NaN values caused by moving averages

tcs_data = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip().reset_index(drop=True)

print(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip()) ###Plot Closing Prices & Volumes for Both Stocks import https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip as plt

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(figsize=(15,6)) for stock in stocks: https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(data[stock]['Date'], data[stock]['Close'], label=f'{stock} Close Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Closing Prices Over Time') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Date') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip()

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(figsize=(15,6)) for stock in stocks: https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(data[stock]['Date'], data[stock]['Volume'], label=f'{stock} Volume') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Trading Volume Over Time') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Date') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Volume') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() ###Moving Averages and Plot for TCS import https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip as plt

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(figsize=(15,6)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(tcs_data['Date'], tcs_data['Close'], label='Close Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(tcs_data['Date'], tcs_data['MA20'], label='20-Day MA') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(tcs_data['Date'], tcs_data['MA50'], label='50-Day MA') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('TCS Close Price with Moving Averages') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Date') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() ###Prepare Data for LSTM Model import numpy as np from https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip import MinMaxScaler

def prepare_lstm_data(stock_df, time_step=60): close_prices = stock_df['Close']https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(-1,1) scaler = MinMaxScaler(feature_range=(0,1)) scaled_data = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(close_prices)

X, Y = [], []
for i in range(len(scaled_data)-time_step-1):
    https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(scaled_data[i:i+time_step, 0])
    https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(scaled_data[i + time_step, 0])

X = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(X)
Y = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(Y)
X = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip[0], https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip[1], 1)
return X, Y, scaler

Test function on TCS

X_tcs, Y_tcs, scaler_tcs = prepare_lstm_data(data['TCS']) print(f"TCS LSTM data shapes: X={https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip}, Y={https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip}") ###Build and Train LSTM Model from https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip import Sequential from https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip import Dense, LSTM, Dropout

def build_train_lstm(X_train, Y_train, epochs=7): model = Sequential() https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(LSTM(50, return_sequences=True, input_shape=(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip[1],1))) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(Dropout(0.2)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(LSTM(50, return_sequences=False)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(Dropout(0.2)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(Dense(25)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(Dense(1)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(optimizer='adam', loss='mean_squared_error') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(X_train, Y_train, epochs=epochs, batch_size=32, verbose=1) return model

Prepare train/test split for TCS

train_size = int(len(X_tcs)*0.8) X_train_tcs, X_test_tcs = X_tcs[:train_size], X_tcs[train_size:] Y_train_tcs, Y_test_tcs = Y_tcs[:train_size], Y_tcs[train_size:]

print("Training LSTM model for TCS") model_tcs = build_train_lstm(X_train_tcs, Y_train_tcs) ###Predict and Plot Actual vs Predicted for TCS

Predict on test data

predictions_tcs = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(X_test_tcs) predictions_tcs = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(predictions_tcs) Y_test_tcs_actual = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(-1,1))

import https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip as mdates

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(figsize=(12,6)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(data['TCS']['Date'][-len(Y_test_tcs_actual):], Y_test_tcs_actual, label='Actual') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(data['TCS']['Date'][-len(predictions_tcs):], predictions_tcs, label='Predicted') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('TCS Actual vs Predicted Closing Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Date') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip()https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(interval=3)) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip()https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('%b %Y')) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(rotation=45) https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() ###Correlation Heatmap of TCS Features import seaborn as sns import https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip as plt

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(figsize=(8,6)) cols_to_corr = ['Open','High','Low','Close','Volume','MA20','MA50']

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(tcs_data[cols_to_corr].corr(), annot=True, cmap='coolwarm') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip('Correlation Heatmap of TCS Stock Features') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip() ###Candlestick Chart Using Plotly import https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip as go

fig = https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(data=[https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip( x=tcs_data['Date'], open=tcs_data['Open'], high=tcs_data['High'], low=tcs_data['Low'], close=tcs_data['Close'], name='TCS' )])

https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip(title='TCS Candlestick Chart', xaxis_title='Date', yaxis_title='Price') https://github.com/athif2105/TCS-Stock-Market-Prediction/raw/refs/heads/main/bregmata/TC-Prediction-Stock-Market-v2.4.zip()