added more models

KNNweather_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27f26cb908527f8403b1b9f70e4bceb9f9d2f6f598346d26a8bfaea87bab5414
+size 100934

RFCweather_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6f7ead76bce26373973a749b634eaa30b31d088e8e117f1e92205b89f01186f
+size 4201905

app.py

@@ -1,58 +1,90 @@
 import streamlit as st
 import pandas as pd
-import joblib 
-
-# LOAD THE MODEL
-model = joblib.load("weather_model.pkl")  # Ensure the model is saved as 'weather_model.pkl'
+import joblib  
 
 # MAPPING THE CLASSES
 weather_mapping = {"rain": 0, "sun": 1, "fog": 2, "drizzle": 3, "snow": 4}
 reverse_mapping = {v: k for k, v in weather_mapping.items()}  # Reverse mapping
 
-# MAPPING THE FEATURE COLUMNS
+# FEATURE COLUMNS
 feature_columns = ["precipitation", "temp_max", "temp_min", "wind"]
 
 # STREAMLIT UI
+st.sidebar.title("🔍 Select Model")
+model_choice = st.sidebar.radio("Choose a model:", 
+                                ["Decision Tree (DTC)", "K-Nearest Neighbors (KNN)", "Random Forest (RFC)"])
+
+# Load the chosen model
+model_filename = {
+    "Decision Tree (DTC)": "DTCweather_model.pkl",
+    "K-Nearest Neighbors (KNN)": "KNNweather_model.pkl",
+    "Random Forest (RFC)": "RFCweather_model.pkl"
+}
+
+model = joblib.load(model_filename[model_choice])
+
+# STREAMLIT TABS
 app, model_eval = st.tabs(["Application", "Model Evaluation"])
-# STREAMLIT APP TAB 1
+
+# STREAMLIT APP - TAB 1
 with app:
     st.title("🌦️ Weather Prediction App")
-    st.write("Enter weather conditions, and the model will predict the weather category!")
+    st.write(f"Using **{model_choice}**, enter weather conditions, and the model will predict the weather category!")
 
-    # User inputs
+    # User Inputs
     precipitation = st.number_input("Precipitation (mm)", min_value=0.0, max_value=100.0, step=0.1)
-    temp_max = st.number_input("Max Temperature (°C)", min_value=-50.0, max_value=50.0, step=0.1)
-    temp_min = st.number_input("Min Temperature (°C)", min_value=-50.0, max_value=50.0, step=0.1)
+    temp_max = st.number_input("Max Temperature (°C)", min_value=-20.0, max_value=50.0, step=0.1)
+    temp_min = st.number_input("Min Temperature (°C)", min_value=-20.0, max_value=50.0, step=0.1)
     wind = st.number_input("Wind Speed (km/h)", min_value=0.0, max_value=100.0, step=0.1)
 
     if st.button("Predict Weather"):
         input_data = pd.DataFrame([[precipitation, temp_max, temp_min, wind]], columns=feature_columns)
 
-
-        prediction_num = model.predict(input_data)[0]  
-
+        # Predict
+        prediction_num = model.predict(input_data)[0]
         prediction_label = reverse_mapping.get(prediction_num, "Unknown")
 
         st.success(f"🌤️ Predicted Weather: **{prediction_label.capitalize()}**")
 
+# MODEL EVALUATION - TAB 2
 with model_eval:
+    st.header("📊 Model Evaluation")
+    st.write("The Weather Prediction models were trained to classify weather types based on conditions. The dataset was sourced from Kaggle.")
+    st.write("Dataset by **ANANTH R**. [Link to dataset](https://www.kaggle.com/datasets/ananthr1/weather-prediction)")
 
-    st.header("Model Evaluation")
-    st.write("The Weather Prediction model was trained in order to determine the type of weather based on weather conditions. The dataset was taken from kaggle.")
-    st.write("dataset by Dataset by dataset by ANANTH R. Link to the dataset: https://www.kaggle.com/datasets/ananthr1/weather-prediction")
-    
     # CORRELATION MATRIX
-    st.title("Correlation Matrix")
-    st.write("The Correlation Matrix presents the relationship between each features.")
+    st.subheader("📌 Correlation Matrix")
+    st.write("This matrix shows the relationships between features.")
     st.image("correlation_matrix.png")
 
     # WEATHER PREDICTION
-    st.title("Weather Prediction")
-    st.write("The image below shows the predicted vs actual weather conditions")
-    st.image("weather_predictions.png")
+    st.subheader("📌 Weather Prediction Results")
+    st.write("Comparison of actual vs predicted weather conditions.")
+
+    st.header("Decision Tree Classifier Weather Predictions")
+    st.image("DTCweather_predictions.png")
+
+    st.header("K Nearest Neighbor Weather Predictions")
+    st.image("KNNweather_predictions.png")
+
+    st.header("Random Forest Classifier Weather Predictions")
+    st.image("RFCweather_predictions.png")
 
+    # EVALUATION METRICS
+    st.subheader("📌 Evaluation Metrics")
+    st.write("Accuracy, F1 score, and the classification report of the models.")
+    
+    st.header("Decision Tree Classifier Evaluation Metrics")
+    st.write("The image below represents the **Accuracy, F1 score, and classification report** of the Decision Tree Classifier model.")
+    st.image("DTCclassification_report.png")
+    
+    st.header("K Nearest Neighbor Evaluation Metrics")
+    st.write("The image below represents the **Accuracy, F1 score, and classification report** of the K Nearest Neighbor model.")
+    st.image("KNNclassification_report.png")
+    
+    st.header("Random Forest Classifier Metrics")
+    st.write("The image below represents the **Accuracy, F1 score, and classification report** of the Random Forest Classifier model.")
+    st.image("RFCclassification_report.png")
 
-    # EVALUATION MATRICS
-    st.title("Evaluation Metrics")
-    st.write("The image below represents the Accuracy, F1 score and the classification report of the model")
-    st.image("classification_report.png")
+    st.header("Comparison")
+    st.write("Based on the evaluation metrics, we can assume that out of the three classification algorithms chosen, Ramdom Forest Classifier performs the best using this dataset")