File size: 1,617 Bytes
0117a59 6eaa5ab 0117a59 d8f3da6 cff1eb0 0117a59 d8f3da6 0117a59 d8f3da6 0117a59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import pandas as pd
import numpy as np
import sklearn as sn
from datasets import Dataset
df=pd.read_csv("https://huggingface.co/spaces/Ralmao/Cart/raw/main/cart2_data.csv", on_bad_lines='skip')
dataset= Dataset.from_pandas(df)
# Select features (columns) for X
X = df.drop(['Cart_Abandoned'], axis = 1)
y = df['Cart_Abandoned']
#Importamos las librerias necesarias para la creacion del modelo
from sklearn.model_selection import train_test_split
#50% para test y 50% para train
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.50, random_state=42)
#Arbol de decision
from sklearn.ensemble import RandomForestClassifier
#Creacion del modelo
random_forest = RandomForestClassifier(n_estimators=10)
#Entrenamiento
random_forest.fit(X_train,y_train)
import gradio as gr
def predict_customer_segment_type(No_Checkout_Confirmed,No_Checkout_Initiated,No_Customer_Login,Session_Activity_Count):
x = np.array([No_Checkout_Confirmed,No_Checkout_Initiated,No_Customer_Login,Session_Activity_Count])
pred = random_forest.predict(x.reshape(1, -1))
return pred[0]
No_Checkout_Confirmed = gr.Number(label='No_Checkout_Confirmed')
No_Checkout_Initiated = gr.Number(label='No_Checkout_Initiated ')
No_Customer_Login = gr.Number(label='No_Customer_Login')
Session_Activity_Count = gr.Number(label='Session_Activity_Count')
output = gr.Textbox(label='Cart_Abandoned')
app = gr.Interface(predict_customer_segment_type, inputs=[No_Checkout_Confirmed,No_Checkout_Initiated,No_Customer_Login,Session_Activity_Count], outputs=output, description= 'This is customer segmented predict')
app.launch() |