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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Title of the Streamlit app
st.title("Load Forecasting Application")
# File upload section
uploaded_file = st.file_uploader("Upload a CSV file containing historical load data", type=["csv"])
if uploaded_file is not None:
# Load the dataset
data = pd.read_csv(uploaded_file)
st.write("Preview of the uploaded data:")
st.write(data.head())
# Ensure the date column is in datetime format
if 'date' in data.columns:
data['date'] = pd.to_datetime(data['date'])
# Extract useful features from the date column
data['year'] = data['date'].dt.year
data['month'] = data['date'].dt.month
data['day'] = data['date'].dt.day
data['day_of_week'] = data['date'].dt.dayofweek
# Drop the original date column
data = data.drop(columns=['date'])
# Check for missing values
if data.isnull().sum().sum() > 0:
st.write("The dataset contains missing values. They will be filled with the mean.")
data = data.fillna(data.mean())
# Define features and target variable
X = data.drop(columns=['load']) # Features (exclude the target 'load')
y = data['load'] # Target variable
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Model training
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Model prediction
y_pred = model.predict(X_test)
# Calculate and display performance metrics
mse = mean_squared_error(y_test, y_pred)
st.write(f"Mean Squared Error (MSE): {mse:.2f}")
# Feature importance
feature_importance = pd.DataFrame({
'Feature': X.columns,
'Importance': model.feature_importances_
}).sort_values(by='Importance', ascending=False)
st.write("Feature Importance:")
st.write(feature_importance)
# Future prediction
st.write("## Predict Future Load")
user_input = {}
for feature in X.columns:
user_input[feature] = st.number_input(f"Enter value for {feature}")
if st.button("Predict"):
input_data = np.array([list(user_input.values())]).reshape(1, -1)
prediction = model.predict(input_data)
st.write(f"Predicted Load: {prediction[0]:.2f}")
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