diff --git a/main.py b/main.py
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+++ b/main.py
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+import streamlit as st
+from datetime import date
+import yfinance as yf
+from plotly import graph_objs as go
+import pandas as pd
+from statsmodels.tsa.arima.model import ARIMA
+
+# Set the date range for data fetching
+START = "2015-01-01"
+TODAY = date.today().strftime("%Y-%m-%d")
+
+# Streamlit App Title
+st.title('Stock Forecast App (ARIMA)')
+
+# Stock selection and prediction period in years
+stocks = ('GOOG', 'AAPL', 'MSFT', 'GME')
+selected_stock = st.selectbox('Select dataset for prediction', stocks)
+n_years = st.slider('Years of prediction:', 1, 4)
+period = n_years * 252  # Number of trading days in a year
+
+# Load data function with caching
+@st.cache_data
+def load_data(ticker):
+    data = yf.download(ticker, START, TODAY)
+    data.reset_index(inplace=True)
+    data['Date'] = pd.to_datetime(data['Date'])
+    return data
+
+data_load_state = st.text('Loading data...')
+data = load_data(selected_stock)
+data_load_state.text('Loading data... done!')
+
+# Display raw data
+st.subheader('Raw data')
+st.write(data.tail())
+
+# Plot raw data function
+def plot_raw_data():
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="stock_open", mode='lines'))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="stock_close", mode='lines'))
+    fig.update_layout(
+        title_text='Time Series Data with Rangeslider',
+        xaxis_rangeslider_visible=True,
+        xaxis_title="Date",
+        yaxis_title="Price"
+    )
+    st.plotly_chart(fig)
+
+plot_raw_data()
+
+# Prepare data for ARIMA model (using only 'Close' prices)
+df_train = data[['Date', 'Close']].copy()
+df_train.set_index('Date', inplace=True)
+
+# Debug: Check df_train contents and data types
+st.write("Historical data (df_train):")
+st.write(df_train.tail())
+st.write("Data types in df_train:", df_train.dtypes)
+
+# Fit ARIMA model
+st.write("Training ARIMA model...")
+model = ARIMA(df_train['Close'], order=(5, 1, 0))  # ARIMA(p,d,q)
+model_fit = model.fit()
+
+# Forecast for the future period
+forecast = model_fit.forecast(steps=period)
+forecast_dates = pd.date_range(df_train.index[-1] + pd.Timedelta(days=1), periods=period, freq='B')
+
+# Prepare DataFrame for plotting forecast
+forecast_df = pd.DataFrame({'Date': forecast_dates, 'Forecast': forecast})
+forecast_df.set_index('Date', inplace=True)
+
+# Debug: Check forecast_df contents and data types
+st.write("Forecast data (forecast_df):")
+st.write(forecast_df.tail())
+st.write("Data types in forecast_df:", forecast_df.dtypes)
+
+# Plot forecast data
+st.subheader('Forecast data')
+st.write(forecast_df.tail())
+
+def plot_forecast_data():
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df_train.index, y=df_train['Close'], name="Historical Data", mode='lines'))
+    fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Forecast'], name="Forecast", mode='lines'))
+    fig.update_layout(
+        title_text=f'Forecast plot for {n_years} years',
+        xaxis_title="Date",
+        yaxis_title="Price",
+        xaxis_rangeslider_visible=True
+    )
+    st.plotly_chart(fig)
+
+plot_forecast_data()