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README.md

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-# diabetes_prediction_streamlit
+# diabetes_prediction
+Preview of the Project
+
+![](images/1.PNG)
+![](images/2.PNG)
+![](images/3.PNG)
+![](images/4.PNG)

app.py

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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('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)+'%')

requirements.txt

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+pandas==1.1.4
+matplotlib==3.3.3
+seaborn==0.11.1
+numpy==1.18.5
+streamlit==0.72.0
+plotly==4.14.1
+Pillow==8.0.1
+scikit_learn==0.24.0