Update app.py

app.py

@@ -1,40 +1,41 @@
 # Library importation
 import streamlit as st
+import pytz
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.ticker import FuncFormatter
 from statsmodels.tsa.arima.model import ARIMA
-from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error, mean_squared_error
 
 # Streamlit's cache mechanism to load datasets efficiently
-# @st.cache_resource
+@st.cache_resource
 def load_data(filename):
     df = pd.read_csv(filename)
     df = df.sort_values(by='year').set_index('year')
     df.index = pd.to_datetime(df.index, format='%Y')
+    # df.index = df.index.strftime('%Y')
     return df
 
 # Load your dataset
 data = load_data("./data/cleaned_dataset_with_incomegroup.csv")
+print(data.head())
 
 # Filter data for Low-Income Countries
 developing_countries = data[data['IncomeGroup'] != 'High income']
 
 # Sidebar for ARIMA model configuration
 st.sidebar.header('ARIMA Model Configuration')
-order_p = st.sidebar.slider('Order (p) for ARIMA', min_value=0, max_value=5, value=1)
-order_d = st.sidebar.slider('Order (d) for ARIMA', min_value=0, max_value=5, value=1)
-order_q = st.sidebar.slider('Order (q) for ARIMA', min_value=0, max_value=5, value=1)
 
 # Sidebar for selecting country and elecrate_total range
 selected_country = st.sidebar.selectbox('Select a Country', developing_countries['countryname'].unique())
-selected_elecrate_total = st.sidebar.slider('Select elecrate_total', 
+selected_elecrate_total = st.sidebar.slider('Electrification Level', 
                                            min_value=developing_countries['elecrate_total'].min(), 
                                            max_value=developing_countries['elecrate_total'].max(),
                                            value=developing_countries['elecrate_total'].median())
 # Function to fit ARIMA model
-def fit_arima_model(data, order):
-    model = ARIMA(data, order=order)
+def fit_arima_model(data):
+    model = ARIMA(data, order=(16, 0, 3))
     fitted_model = model.fit()
     return fitted_model
 
@@ -43,11 +44,17 @@ def make_arima_predictions(model, start, end):
     predictions = model.predict(start=start, end=end, typ='levels')
     return predictions
 
+# Function to make ARIMA forecast
+def make_arima_forecast(model, steps):
+    forecast = model.forecast(steps=steps)
+    return forecast
+
 # Function to evaluate ARIMA model
 def evaluate_arima_model(actual, predicted):
+    mae = mean_absolute_error(actual, predicted)
     mse = mean_squared_error(actual, predicted)
     rmse = np.sqrt(mse)
-    return rmse
+    return mae, rmse
 
 # ARIMA Model Building
 target_column = "economicgap (GDP difference)"
@@ -58,34 +65,54 @@ def evaluate_arima_model(actual, predicted):
 
 
 # Split the data into train and test
-train_size = int(len(selected_country_data) * 0.8)
-train, test = selected_country_data[[target_column, 'elecrate_total']][:train_size], selected_country_data[[target_column, 'elecrate_total']][train_size:]
-
-# Drop rows with missing values (if any)
-train = train.dropna()
-test = test.dropna()
+train_size = int(len(developing_countries) * 0.8)
+train, test = developing_countries[[target_column, 'elecrate_total']][:train_size], developing_countries[[target_column, 'elecrate_total']][train_size:]
+# Check if train DataFrame is empty
 
-# Fit ARIMA model
-arima_order = (order_p, order_d, order_q)
-arima_model = fit_arima_model(train[target_column], order=arima_order)
+if train.empty:
+    st.error('Training data is empty.')
+else:
+    # Fit ARIMA model
+    arima_model = fit_arima_model(train[target_column])
 
-# Make predictions
-predictions = make_arima_predictions(arima_model, start=len(train), end=len(train) + len(test) - 1)
+    # Make predictions
+    predictions = make_arima_predictions(arima_model, start=len(train), end=len(train) + len(test) - 1)
 
-# Evaluate the model
-rmse = evaluate_arima_model(test[target_column], predictions)
+    # Evaluate the model
+    mae = evaluate_arima_model(test[target_column], predictions)[0]
+    rmse = evaluate_arima_model(test[target_column], predictions)[1]
 
 # Streamlit app
-st.title('ARIMA Time Series Model for Low-Income Countries')
-st.write(f'ARIMA Model Order: {arima_order}')
+st.title('Forecasting Economic Gap of Developing Countries')
+st.write(f'Mean Absolute Error (MAE): {mae}')
 st.write(f'Root Mean Squared Error (RMSE): {rmse}')
 
+
+# Extend the time index for forecasting
+forecast_years = 20
+forecast_index = pd.date_range(test.index[-1], periods=forecast_years * 12 + 1, freq='M')[1:]
+
+# Make predictions for the forecast period
+# forecast_start = len(train) + len(test)
+# forecast_end = forecast_start + len(forecast_index) - 1
+# forecast = make_arima_predictions(arima_model, start=forecast_start, end=forecast_end)
+forecast = make_arima_forecast(arima_model, steps=forecast_years*12)
+
+# Function to format y-axis values
+def billions(x, pos):
+    'The two args are the value and tick position'
+    return '%1.0f Billion' % (x * 1e-9)
+
+formatter = FuncFormatter(billions)
+
 # Plot actual vs predicted values
 fig, ax = plt.subplots(figsize=(10, 6))
+ax.yaxis.set_major_formatter(formatter)
 # ax.plot(test.index, test[target_column], label='Actual')
-ax.plot(test.index, predictions, label='Predicted', color='blue')
+# ax.plot(test.index, predictions, label='Predicted', color='blue')
+ax.plot(forecast_index, forecast, label='Forecast', linestyle='dashed', color='orange')
 ax.set_title('Economic Gap Prediction')
 ax.set_xlabel('Year')
-ax.set_ylabel('Economic Gap (GDP Differrence)')
+ax.set_ylabel('Economic Gap (GDP Differrence) $')
 ax.legend()
 st.pyplot(fig)