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#pip install streamlit
#pip install pandas
#pip install sklearn
# IMPORT STATEMENTS
import streamlit as st
import pandas as pd
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import seaborn as sns
df = pd.read_csv(r'/Users/rusiththarindu/Desktop/Final Year Project/diabetes.csv')
# HEADINGS
st.title('Diabetes Checkup')
st.sidebar.header('Patient Data')
st.subheader('Training Data Stats')
st.write(df.describe())
# X AND Y DATA
x = df.drop(['Outcome'], axis = 1)
y = df.iloc[:, -1]
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)
# FUNCTION
def user_report():
pregnancies = st.sidebar.slider('Pregnancies', 0,17, 3 )
glucose = st.sidebar.slider('Glucose', 0,200, 120 )
bp = st.sidebar.slider('Blood Pressure', 0,122, 70 )
skinthickness = st.sidebar.slider('Skin Thickness', 0,100, 20 )
insulin = st.sidebar.slider('Insulin', 0,846, 79 )
bmi = st.sidebar.slider('BMI', 0,67, 20 )
dpf = st.sidebar.slider('Diabetes Pedigree Function', 0.0,2.4, 0.47 )
age = st.sidebar.slider('Age', 21,88, 33 )
user_report_data = {
'pregnancies':pregnancies,
'glucose':glucose,
'bp':bp,
'skinthickness':skinthickness,
'insulin':insulin,
'bmi':bmi,
'dpf':dpf,
'age':age
}
report_data = pd.DataFrame(user_report_data, index=[0])
return report_data
# PATIENT DATA
user_data = user_report()
st.subheader('Patient Data')
st.write(user_data)
# MODEL
rf = RandomForestClassifier()
rf.fit(x_train, y_train)
user_result = rf.predict(user_data)
# VISUALISATIONS
st.title('Visualised Patient Report')
# COLOR FUNCTION
if user_result[0]==0:
color = 'blue'
else:
color = 'red'
# Age vs Pregnancies
st.header('Pregnancy count Graph (Others vs Yours)')
fig_preg = plt.figure()
ax1 = sns.scatterplot(x = 'Age', y = 'Pregnancies', data = df, hue = 'Outcome', palette = 'Greens')
ax2 = sns.scatterplot(x = user_data['age'], y = user_data['pregnancies'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,20,2))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_preg)
# Age vs Glucose
st.header('Glucose Value Graph (Others vs Yours)')
fig_glucose = plt.figure()
ax3 = sns.scatterplot(x = 'Age', y = 'Glucose', data = df, hue = 'Outcome' , palette='magma')
ax4 = sns.scatterplot(x = user_data['age'], y = user_data['glucose'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,220,10))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_glucose)
# Age vs Bp
st.header('Blood Pressure Value Graph (Others vs Yours)')
fig_bp = plt.figure()
ax5 = sns.scatterplot(x = 'Age', y = 'BloodPressure', data = df, hue = 'Outcome', palette='Reds')
ax6 = sns.scatterplot(x = user_data['age'], y = user_data['bp'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,130,10))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_bp)
# Age vs St
st.header('Skin Thickness Value Graph (Others vs Yours)')
fig_st = plt.figure()
ax7 = sns.scatterplot(x = 'Age', y = 'SkinThickness', data = df, hue = 'Outcome', palette='Blues')
ax8 = sns.scatterplot(x = user_data['age'], y = user_data['skinthickness'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,110,10))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_st)
# Age vs Insulin
st.header('Insulin Value Graph (Others vs Yours)')
fig_i = plt.figure()
ax9 = sns.scatterplot(x = 'Age', y = 'Insulin', data = df, hue = 'Outcome', palette='rocket')
ax10 = sns.scatterplot(x = user_data['age'], y = user_data['insulin'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,900,50))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_i)
# Age vs BMI
st.header('BMI Value Graph (Others vs Yours)')
fig_bmi = plt.figure()
ax11 = sns.scatterplot(x = 'Age', y = 'BMI', data = df, hue = 'Outcome', palette='rainbow')
ax12 = sns.scatterplot(x = user_data['age'], y = user_data['bmi'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,70,5))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_bmi)
# Age vs Dpf
st.header('DPF Value Graph (Others vs Yours)')
fig_dpf = plt.figure()
ax13 = sns.scatterplot(x = 'Age', y = 'DiabetesPedigreeFunction', data = df, hue = 'Outcome', palette='YlOrBr')
ax14 = sns.scatterplot(x = user_data['age'], y = user_data['dpf'], s = 150, color = color)
plt.xticks(np.arange(10,100,5))
plt.yticks(np.arange(0,3,0.2))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_dpf)
# OUTPUT
st.subheader('Your Report: ')
output=''
if user_result[0]==0:
output = 'You are not Diabetic'
else:
output = 'You are Diabetic'
st.title(output)
st.subheader('Accuracy: ')
st.write(str(accuracy_score(y_test, rf.predict(x_test))*100)+'%')
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