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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text

Sel.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f875071006b23f5174d755d93b09139562d1c47081b27d7e92fc1ae9e3066269
+size 195742

app.py

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+import pandas as pd
+import streamlit as st
+import joblib
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.compose import ColumnTransformer
+
+# Load data and update column names
+df = pd.read_csv('train.csv')
+
+# Select dependent and independent variables
+x = df.drop(['id', 'FloodProbability'], axis=1)
+y = df[['FloodProbability']]
+
+# Split data into training and testing sets
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+
+# Preprocessing (StandardScaler)
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ['MonsoonIntensity', 'TopographyDrainage', 'RiverManagement', 'Deforestation',
+                                    'Urbanization', 'ClimateChange', 'DamsQuality', 'Siltation',
+                                    'AgriculturalPractices', 'Encroachments', 'IneffectiveDisasterPreparedness',
+                                    'DrainageSystems', 'CoastalVulnerability', 'Landslides', 'Watersheds',
+                                    'DeterioratingInfrastructure', 'PopulationScore', 'WetlandLoss',
+                                    'InadequatePlanning', 'PoliticalFactors'])
+    ]
+)
+
+# Streamlit application
+def sel_pred(MonsoonIntensity, TopographyDrainage, RiverManagement, Deforestation, Urbanization,
+             ClimateChange, DamsQuality, Siltation, AgriculturalPractices, Encroachments,
+             IneffectiveDisasterPreparedness, DrainageSystems, CoastalVulnerability, Landslides,
+             Watersheds, DeterioratingInfrastructure, PopulationScore, WetlandLoss,
+             InadequatePlanning, PoliticalFactors):
+    input_data = pd.DataFrame({
+        'MonsoonIntensity': [MonsoonIntensity],
+        'TopographyDrainage': [TopographyDrainage],
+        'RiverManagement': [RiverManagement],
+        'Deforestation': [Deforestation],
+        'Urbanization': [Urbanization],
+        'ClimateChange': [ClimateChange],
+        'DamsQuality': [DamsQuality],
+        'Siltation': [Siltation],
+        'AgriculturalPractices': [AgriculturalPractices],
+        'Encroachments': [Encroachments],
+        'IneffectiveDisasterPreparedness': [IneffectiveDisasterPreparedness],
+        'DrainageSystems': [DrainageSystems],
+        'CoastalVulnerability': [CoastalVulnerability],
+        'Landslides': [Landslides],
+        'Watersheds': [Watersheds],
+        'DeterioratingInfrastructure': [DeterioratingInfrastructure],
+        'PopulationScore': [PopulationScore],
+        'WetlandLoss': [WetlandLoss],
+        'InadequatePlanning': [InadequatePlanning],
+        'PoliticalFactors': [PoliticalFactors]
+    })
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    model = joblib.load('Sel.pkl')
+
+    prediction = model.predict(input_data_transformed)
+
+    return float(prediction[0])
+    
+st.title("Flood Risk Regression Model")
+st.write("Enter Input Data")
+
+MonsoonIntensity = st.number_input('MonsoonIntensity', min_value=int(df['MonsoonIntensity'].min()), max_value=int(df['MonsoonIntensity'].max()), step=1)
+TopographyDrainage = st.number_input('TopographyDrainage', min_value=int(df['TopographyDrainage'].min()), max_value=int(df['TopographyDrainage'].max()), step=1)
+RiverManagement = st.number_input('RiverManagement', min_value=int(df['RiverManagement'].min()), max_value=int(df['RiverManagement'].max()), step=1)
+Deforestation = st.number_input('Deforestation', min_value=int(df['Deforestation'].min()), max_value=int(df['Deforestation'].max()), step=1)
+Urbanization = st.number_input('Urbanization', min_value=int(df['Urbanization'].min()), max_value=int(df['Urbanization'].max()), step=1)
+ClimateChange = st.number_input('ClimateChange', min_value=int(df['ClimateChange'].min()), max_value=int(df['ClimateChange'].max()), step=1)
+DamsQuality = st.number_input('DamsQuality', min_value=int(df['DamsQuality'].min()), max_value=int(df['DamsQuality'].max()), step=1)
+Siltation = st.number_input('Siltation', min_value=int(df['Siltation'].min()), max_value=int(df['Siltation'].max()), step=1)
+AgriculturalPractices = st.number_input('AgriculturalPractices', min_value=int(df['AgriculturalPractices'].min()), max_value=int(df['AgriculturalPractices'].max()), step=1)
+Encroachments = st.number_input('Encroachments', min_value=int(df['Encroachments'].min()), max_value=int(df['Encroachments'].max()), step=1)
+IneffectiveDisasterPreparedness = st.number_input('IneffectiveDisasterPreparedness', min_value=int(df['IneffectiveDisasterPreparedness'].min()), max_value=int(df['IneffectiveDisasterPreparedness'].max()), step=1)
+DrainageSystems = st.number_input('DrainageSystems', min_value=int(df['DrainageSystems'].min()), max_value=int(df['DrainageSystems'].max()), step=1)
+CoastalVulnerability = st.number_input('CoastalVulnerability', min_value=int(df['CoastalVulnerability'].min()), max_value=int(df['CoastalVulnerability'].max()), step=1)
+Landslides = st.number_input('Landslides', min_value=int(df['Landslides'].min()), max_value=int(df['Landslides'].max()), step=1)
+Watersheds = st.number_input('Watersheds', min_value=int(df['Watersheds'].min()), max_value=int(df['Watersheds'].max()), step=1)
+DeterioratingInfrastructure = st.number_input('DeterioratingInfrastructure', min_value=int(df['DeterioratingInfrastructure'].min()), max_value=int(df['DeterioratingInfrastructure'].max()), step=1)
+PopulationScore = st.number_input('PopulationScore', min_value=int(df['PopulationScore'].min()), max_value=int(df['PopulationScore'].max()), step=1)
+WetlandLoss = st.number_input('WetlandLoss', min_value=int(df['WetlandLoss'].min()), max_value=int(df['WetlandLoss'].max()), step=1)
+InadequatePlanning = st.number_input('InadequatePlanning', min_value=int(df['InadequatePlanning'].min()), max_value=int(df['InadequatePlanning'].max()), step=1)
+PoliticalFactors = st.number_input('PoliticalFactors', min_value=int(df['PoliticalFactors'].min()), max_value=int(df['PoliticalFactors'].max()), step=1)
+
+if st.button('Predict'):
+    sel = sel_pred(MonsoonIntensity, TopographyDrainage, RiverManagement, Deforestation, Urbanization,
+                   ClimateChange, DamsQuality, Siltation, AgriculturalPractices, Encroachments,
+                   IneffectiveDisasterPreparedness, DrainageSystems, CoastalVulnerability, Landslides,
+                   Watersheds, DeterioratingInfrastructure, PopulationScore, WetlandLoss,
+                   InadequatePlanning, PoliticalFactors)
+    st.write(f'The predicted flood probability is: {sel:.2f}')
+

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow

train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c9adeacec0537306a80b957a69e57bf6d389e3c02c1ff33ca0a7dcb5a9b30fb
+size 59127674