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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import pickle
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
def soil_model():
data = pd.read_csv("Cr3.csv")
import re
obj_columns = data.select_dtypes("object")
for col in obj_columns:
data[col] = data[col].apply(lambda x: re.sub(r'[^a-zA-Z0-9]', '', x.lower())).astype("str")
data.head()
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data["Plant"] = le.fit_transform(data["Plant"])
# Assuming 'data' is your DataFrame
# If 'data' is not defined, make sure to load or create your dataset
X = data.drop('Plant', axis=1)
y = data['Plant']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Standardize the training and testing sets using StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Hyperparameter search for RandomForestClassifier
param_grid = {
'n_estimators': [50, 100, 200],
'max_depth': [None, 10, 20],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4]
}
rf_classifier = RandomForestClassifier(random_state=42)
grid_search =GridSearchCV(rf_classifier, param_grid, cv=5, scoring='accuracy', n_jobs=-1)
grid_search.fit(X_train_scaled, y_train)
# Get the best parameters and the best estimator
best_params = grid_search.best_params_
best_rf_classifier = grid_search.best_estimator_
# Fit the final model with the best parameters on the entire dataset
final_rf_classifier = RandomForestClassifier(**best_params, random_state=42)
final_rf_classifier.fit(X, y)
pickle.dump(final_rf_classifier,open('Soli_to_recommandation_model_Raghuu.pkl','wb'))
# return final_rf_classifier
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