Delete train.py

train.py

@@ -1,95 +0,0 @@
-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)