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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import gradio as gr
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
def main(Age,Sex,BMI,No_of_Children,Smoker,Region):
url="https://raw.githubusercontent.com/ADITHYASNAIR2021/Dataset-cart/main/insurance.csv"
data = pd.read_csv(url)
label_data = data.copy()
s = (data.dtypes =="object")
object_cols = list(s[s].index)
label_encoder = LabelEncoder()
for col in object_cols:
label_data[col] = label_encoder.fit_transform(label_data[col])
X= label_data.drop(["expenses"],axis =1)
y= label_data["expenses"]
X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size = 0.25, random_state = 42)
X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size = 0.5, random_state = 42)
X_train = StandardScaler().fit_transform(X_train)
X_test = StandardScaler().fit_transform(X_test)
Rand_reg=RandomForestRegressor()
Rand_reg.fit(X_train,y_train)
Lin_reg = LinearRegression()
Lin_reg.fit(X_train,y_train)
data = {'age':Age,'sex':Sex,'bmi':BMI,'children':No_of_Children,'smoker':Smoker,'region':Region}
index = [0]
cust_df = pd.DataFrame(data, index)
costpredRand = Rand_reg.predict(cust_df)
costpredLin = Lin_reg.predict(cust_df)
large=[costpredLin,costpredRand]
#large.sort(reverse=True)
if large[0] <= 0 and large[1]<=0:
return 'No values found, 404 error'
if large[0] <= 0 and large[1]>0:
return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
if large[0] >0 and large[1] <= 0:
return f"The amount to be paid at the hospital for the mentioned patient is- {large[0]}"
if large[0] >=0 and large[1] >=0:
return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
iface = gr.Interface(fn = main,
inputs =['number','number','number','number','number','number'],
outputs =['text'],
title=" 🩺 Medical cost prediction ",
description =''' Description
Age: age of the primary beneficiary
Sex: insurance contractor gender, female = 0, male = 1
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height,
objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9
No_of_Children: Number of children covered by health insurance / Number of dependents
Smoker: Smoking (Yes(1)/No(0))
Region: the beneficiary's residential area in the US, northeast =0, northwest =1, southeast = 2, and southwest = 3
''',
article='''
🩺 Medical Cost Prediction
A regression model that predicts medical cost with an accuracy above 85%
''',
examples=[[19,0,27.9,0,1,3],[32,1,28.9,0,0,1]])
iface.launch(debug =True) |