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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.pipeline import Pipeline |
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from sklearn.metrics import accuracy_score |
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import time |
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import paho.mqtt.client as mqtt |
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df = pd.read_csv("./Churn_Modelling.csv") |
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df.drop(["RowNumber","CustomerId","Surname"], axis=1, inplace=True) |
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df.head() |
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df.Balance.plot(kind="hist", figsize=(10,6)) |
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df.Balance = np.where(df.Balance==0, 0, 1) |
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df.Balance.value_counts() |
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df.Age.plot(kind="hist", figsize=(10,6)) |
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X = df.drop(["Exited","Geography","Gender"], axis=1) |
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y = df["Exited"] |
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) |
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pl = Pipeline([ |
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("scale", StandardScaler()), |
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("logreg", LogisticRegression()) |
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]) |
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pl.fit(X_train, y_train) |
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y_train_pred = pl.predict(X_train) |
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y_test_pred = pl.predict(X_test) |
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def sentence_builder(credit, age, tenure, balance, nb_prods, has_card, active, est_salary): |
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data = [{ |
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"CreditScore": credit, |
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"Age": age, |
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"Tenure": tenure, |
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"Balance": balance, |
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"NumOfProducts": nb_prods, |
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"HasCrCard": has_card, |
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"IsActiveMember": active, |
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"EstimatedSalary": est_salary, |
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}] |
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df = pd.json_normalize(data) |
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res = bool(pl.predict(df)[0]) |
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return 'User will stay' if res else 'User will exit' |
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iface = gr.Interface( |
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sentence_builder, |
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[ |
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gr.inputs.Slider(0, 10000, label='credit'), |
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gr.inputs.Slider(0, 100, label='age'), |
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gr.inputs.Slider(0, 10, label='tenure'), |
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gr.inputs.Slider(0, 10000, label='balance'), |
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gr.inputs.Slider(0, 10, label='number of products'), |
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gr.inputs.Checkbox(label="credit card"), |
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gr.inputs.Checkbox(label="active"), |
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gr.inputs.Slider(0, 200000, label='estimated salary'), |
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], |
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"text", |
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examples=[ |
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[619, 42, 2, 0, 1, 1, 1, 101348], |
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[608, 41, 1, 83807, 1, 0, 1, 112542], |
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], |
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) |
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iface.launch() |
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