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model (3).joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cd88a95d898fdfeaa52814163167680b1069d479b399777f32c33fb800a9e6c2
+size 4550

requirements (1).txt

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+scikit-learn==1.2.2

train.py

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+
+import joblib
+
+from sklearn.datasets import fetch_openml
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import make_column_transformer
+
+from sklearn.pipeline import make_pipeline
+
+from sklearn.model_selection import train_test_split, RandomizedSearchCV
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+df = pd.read_csv("/content/forest_health_data_with_target.csv")
+
+
+
+
+target = 'Health_Status'
+numeric_features = [
+    'Latitude',
+    'Longitude',
+    'DBH',
+    'Tree_Height',
+    'Crown_Width_North_South',
+    'Crown_Width_East_West',
+    'Slope',
+    'Elevation',
+    'Temperature',
+    'Humidity',
+    'Soil_TN',
+    'Soil_TP',
+    'Soil_AP',
+    'Soil_AN',
+    'Menhinick_Index',
+    'Gleason_Index',
+    'Fire_Risk_Index'
+    
+]
+
+
+
+
+print("Creating data subsets")
+
+X = df[numeric_features]
+y = df[target]
+
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+preprocessor = make_column_transformer(
+    (StandardScaler(), numeric_features),    
+)
+
+model_logistic_regression = LogisticRegression(n_jobs=-1)
+
+print("Estimating Best Model Pipeline")
+
+model_pipeline = make_pipeline(
+    preprocessor,
+    model_logistic_regression
+)
+
+param_distribution = {
+    "logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1]
+}
+
+rand_search_cv = RandomizedSearchCV(
+    model_pipeline,
+    param_distribution,
+    n_iter=3,
+    cv=3,
+    random_state=42
+)
+
+rand_search_cv.fit(Xtrain, ytrain)
+
+print("Logging Metrics")
+print(f"Accuracy: {rand_search_cv.best_score_}")
+
+print("Serializing Model")
+
+saved_model_path = "model.joblib"
+
+joblib.dump(rand_search_cv.best_estimator_, saved_model_path)