Climate Predition Model

Author: Netcodez

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Load your pre-trained RandomForestRegressor model
+with open("random_forest_model.pkl", "rb") as file:
+    model = pickle.load(file)
+
+# Set the title of the app
+st.title("Predicting Temperature in London")
+
+# header and description
+st.header("Predict Weather Conditions Based on Various Features")
+
+# link to the GitHub repository
+st.markdown("[GitHub Repo](https://github.com/Netcodez/Climate-Prediction-Pipeline)")
+
+st.write(
+    """
+This application uses a Random Forest Regression model to predict the mean temperature in London. 
+Please enter the required features below to get a prediction.
+"""
+)
+feature_names = [
+    "Cloud Cover (oktas)",
+    "Sunshine (hrs)",
+    "Global Radiation (W/m²)",
+    "Max Temp (°C)",
+    "Min Temp (°C)",
+    "Precipitation (mm)",
+    "Pressure (Pa)",
+    "Snow Depth (cm)",
+    "Month",
+]
+
+# feature names with units of measurement and short descriptions
+feature_info = {
+    "Cloud Cover (oktas)": {
+        "range": (0.0, 9.0),
+        "description": "Measurement of cloud cover in oktas",
+    },
+    "Sunshine (hrs)": {
+        "range": (0.0, 24.0),
+        "description": "Measurement of sunshine in hours per day",
+    },
+    "Global Radiation (W/m²)": {
+        "range": (0.0, 500.0),
+        "description": "Measurement of global radiation in Watt per " "square meter",
+    },
+    "Max Temp (°C)": {
+        "range": (-10.0, 40.0),
+        "description": "Maximum temperature recorded in degrees Celsius",
+    },
+    "Min Temp (°C)": {
+        "range": (-10.0, 40.0),
+        "description": "Minimum temperature recorded in degrees Celsius",
+    },
+    "Precipitation (mm)": {
+        "range": (0.0, 100.0),
+        "description": "Measurement of precipitation in millimeters",
+    },
+    "Pressure (Pa)": {
+        "range": (90000.0, 110000.0),
+        "description": "Measurement of pressure in Pascals",
+    },
+    "Snow Depth (cm)": {
+        "range": (0.0, 50.0),
+        "description": "Measurement of snow depth in centimeters",
+    },
+    "Month": {"range": (1, 12), "description": "Month of observation"},
+}
+
+feature_values = []
+for feature_name, info in feature_info.items():
+    min_val, max_val = info["range"]
+    label = f"{feature_name}: {info['description']}"
+    feature_values.append(st.slider(label, min_val, max_val, min_val))
+
+
+# Prediction button
+if st.button("Predict"):
+    input_data = np.array(feature_values).reshape(1, -1)
+    prediction = model.predict(input_data)
+    st.write(f"Mean Temperature in London is predicted to be {prediction[0]}°C")

random_forest_model.pkl

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+streamlit==1.34.0
+mlflow==2.13.0
+cloudpickle==2.2.1
+packaging==24.0
+pathlib==1.0.1
+psutil==5.9.5
+pyyaml==6.0.1
+scikit-learn==1.2.2
+numpy
+pickle-mixin