model_dump.py

import numpy as np
import pandas as pd
import joblib
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler

tf = pd.read_csv("Overview.csv")
# Converting to datetime datatype
tf['DeviceTimeStamp'] = pd.to_datetime(tf['DeviceTimeStamp'],format = '%Y-%m-%d %H:%M:%S')

cv = pd.read_csv("CurrentVoltage.csv")
cv['DeviceTimeStamp'] = pd.to_datetime(cv['DeviceTimeStamp'],format = '%Y-%m-%d %H:%M:%S')
transformer = pd.merge(tf, cv, on='DeviceTimeStamp')

# Train test split
X = transformer.drop(['DeviceTimeStamp','MOG_A'],axis=1)
y = transformer['MOG_A']

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.20,random_state=21)
X.head()


#Normalizing the data
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, y_train)
y_pred_rf = random_forest.predict(X_test)
random_forest.score(X_train, y_train)

random_forest_train = round(random_forest.score(X_train, y_train) * 100, 2)
random_forest_accuracy = round(accuracy_score(y_pred_rf, y_test) * 100, 2)

print(random_forest.predict([X_test[3]]))