ML--Weather-Prediction

Program to implement the Random Forest Algorithm to predict daily temperature , PM2.5 pollution level and Energy based on environmental sensor data.

Decision Tree Regression for Pollution, Temperature & Energy Prediction

Program:

/*



import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, r2_score

data = pd.read_csv("weather.csv")


X = data[['hum', 'pressure', 'wind_speed', 'illumination', 'co2']]

X = X.fillna(X.mean())

# =========================
# 1. Pollution Prediction (PM2.5)
# =========================
y_pollution = data['pm2_5'].fillna(data['pm2_5'].mean())

X_train, X_test, y_train, y_test = train_test_split(
    X, y_pollution, test_size=0.2, random_state=42
)

pollution_model = DecisionTreeRegressor(random_state=42, max_depth=5)
pollution_model.fit(X_train, y_train)

pollution_pred = pollution_model.predict(X_test)

rmse_pollution = np.sqrt(mean_squared_error(y_test, pollution_pred))
r2_pollution = r2_score(y_test, pollution_pred)
accuracy_pollution = r2_pollution * 100

print("🏭 Pollution Prediction (PM2.5)")
print("Accuracy (%):", accuracy_pollution)

print("R2 Score:", r2_pollution)


print("RMSE:", rmse_pollution)
print("R2 Score:", r2_pollution)


# =========================
# 2. Temperature Prediction
# =========================
y_temp = data['tem'].fillna(data['tem'].mean())

X_train, X_test, y_train, y_test = train_test_split(
    X, y_temp, test_size=0.2, random_state=42
)

temp_model = DecisionTreeRegressor(random_state=42, max_depth=5)
temp_model.fit(X_train, y_train)

temp_pred = temp_model.predict(X_test)

rmse_temp = np.sqrt(mean_squared_error(y_test, temp_pred))
r2_temp = r2_score(y_test, temp_pred)
accuracy_temp = r2_temp * 100
print("\n🌡️ Temperature Prediction")
print("Accuracy (%):", accuracy_temp)
print("RMSE:", rmse_temp)
print("R2 Score:", r2_temp)


# =========================
# 3. Energy Prediction (TSR)
# =========================
y_energy = data['tsr'].fillna(data['tsr'].mean())

X_train, X_test, y_train, y_test = train_test_split(
    X, y_energy, test_size=0.2, random_state=42
)

energy_model = DecisionTreeRegressor(random_state=42, max_depth=5)
energy_model.fit(X_train, y_train)

energy_pred = energy_model.predict(X_test)

rmse_energy = np.sqrt(mean_squared_error(y_test, energy_pred))
r2_energy = r2_score(y_test, energy_pred)
accuracy_energy = r2_energy * 100
print("\n⚡ Energy Prediction (TSR)")
print("Accuracy (%):", accuracy_energy)


print("RMSE:", rmse_energy)
print("R2 Score:", r2_energy)
*/

Output: