Create app.py

app.py

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+# Library importation
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.metrics import mean_squared_error
+
+# Streamlit's cache mechanism to load datasets efficiently
+# @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')
+    return df
+
+# Load your dataset
+data = load_data("./data/cleaned_dataset_with_incomegroup.csv")
+
+# 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', 
+                                           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)
+    fitted_model = model.fit()
+    return fitted_model
+
+# Function to make ARIMA predictions
+def make_arima_predictions(model, start, end):
+    predictions = model.predict(start=start, end=end, typ='levels')
+    return predictions
+
+# Function to evaluate ARIMA model
+def evaluate_arima_model(actual, predicted):
+    mse = mean_squared_error(actual, predicted)
+    rmse = np.sqrt(mse)
+    return rmse
+
+# ARIMA Model Building
+target_column = "economicgap (GDP difference)"
+
+# Filter data for the selected country and elecrate_total range
+selected_country_data = developing_countries[(developing_countries['countryname'] == selected_country) & 
+                                             (developing_countries['elecrate_total'] == selected_elecrate_total)]
+
+
+# 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()
+
+# Fit ARIMA model
+arima_order = (order_p, order_d, order_q)
+arima_model = fit_arima_model(train[target_column], order=arima_order)
+
+# 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)
+
+# Streamlit app
+st.title('ARIMA Time Series Model for Low-Income Countries')
+st.write(f'ARIMA Model Order: {arima_order}')
+st.write(f'Root Mean Squared Error (RMSE): {rmse}')
+
+# Plot actual vs predicted values
+fig, ax = plt.subplots(figsize=(10, 6))
+# ax.plot(test.index, test[target_column], label='Actual')
+ax.plot(test.index, predictions, label='Predicted', color='blue')
+ax.set_title('Economic Gap Prediction')
+ax.set_xlabel('Year')
+ax.set_ylabel('Economic Gap (GDP Differrence)')
+ax.legend()
+st.pyplot(fig)