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kidney_app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import base64
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+st.write("""
+# Chronic Kidney Disease
+
+#Context
+This dataset is originally from UCI Machine Learning Repository. The objective of the dataset is to diagnostically predict whether a patient is having chronic kidney disease or not, based on certain diagnostic measurements included in the dataset.
+
+#Content
+The datasets consists of several medical predictor variables and one target variable, Class. Predictor variables includes Blood Pressure(Bp), Albumin(Al), etc.
+
+#Inspiration
+Can you build a machine learning model to accurately predict whether or not the patients in the dataset have chronic kidney disease or not?
+""")
+
+url_dataset = f'<a href="new_model.csv">Download Dataset CSV File</a>'
+st.markdown(url_dataset, unsafe_allow_html=True)
+
+def user_input_features() :
+    Bp = st.sidebar.slider('Bp', 50.000, 180.000)
+    Sg = st.sidebar.slider('Sg', 1.005, 1.025)
+    Al = st.sidebar.slider('Al', 0.000, 5.000)
+    Su = st.sidebar.slider('Su', 0.000, 5.000)
+    Rbc = st.sidebar.slider('Rbc', 0.000, 1.000)
+    Bu = st.sidebar.slider('Bu', 1.500, 391.000)
+    Sc = st.sidebar.slider('Sc', 0.400, 76.000)
+    Sod = st.sidebar.slider('Sod', 4.500, 163.000)
+    Pot = st.sidebar.slider('Pot', 2.500, 47.000)
+    Hemo = st.sidebar.slider('Hemo', 3.100, 17.800)
+    Wbcc = st.sidebar.slider('Wbcc', 2200.000, 26400.000)
+    Rbcc = st.sidebar.slider('Rbcc', 2.100, 8.000)
+    Htn = st.sidebar.slider('Htn', 0.000, 1.000)
+    Class = st.sidebar.slider('Class', 0.000, 1.000)
+
+
+
+    data = {'Bp':[Bp], 
+            'Sg':[Sg],
+            'Al':[Al],
+            'Su':[Su],
+            'Rbc':[Rbc],
+            'Bu':[Bu],
+            'Sc':[Sc],
+            'Sod':[Sod],
+            'Pot':[Pot],
+            'Hemo':[Hemo],
+            'Wbcc':[Wbcc],
+            'Rbcc':[Rbcc],
+            'Htn':[Htn],
+            'Class':[Class]
+            }
+
+    features = pd.DataFrame(data)
+    return features
+
+input_df = user_input_features()
+
+
+kidney_raw = pd.read_csv('new_model.csv')
+kidney_raw.fillna(0, inplace=True)
+kidney = kidney_raw.drop(columns=['Class'])
+df = pd.concat([input_df, kidney], axis=0)
+
+df = df[:1] # Selects only the first row (the user input data)
+df.fillna(0, inplace=True)
+
+features = ['Bp', 'Sg', 'Al',
+'Su',
+'Rbc',
+'Bu',
+'Sc',
+'Sod',
+'Pot',
+'Hemo',
+'Wbcc',
+'Rbcc',
+'Htn'
+]
+
+df = df[features]
+
+
+st.subheader('User Input features')
+st.write(df)
+
+load_clf = pickle.load(open('kidney_clf.pkl', 'rb'))
+prediction = load_clf.predict(df)
+prediction_proba = load_clf.predict_proba(df)
+kidney_labels = np.array(['Normal', 'Chronic Kidney'])
+st.subheader('Prediction')
+st.write(kidney_labels[int(prediction)])
+st.subheader('Prediction Probability')
+df_prob = pd.DataFrame(data = prediction_proba, 
+                    index = ['Probability'], 
+                    columns = kidney_labels)
+st.write(df_prob)

kidney_clf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2da06293dd49baae23891f0444698d210d36bda33d332365f233d8343cf248e
+size 254665

kidney_model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a0c8ad0b",
+   "metadata": {},
+   "source": [
+    "# Dataset\n",
+    "https://www.kaggle.com/datasets/abhia1999/chronic-kidney-disease"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "61347359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b470f34b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Bp     Sg   Al   Su  Rbc    Bu   Sc     Sod   Pot  Hemo    Wbcc  Rbcc  \\\n",
+      "0  80.0  1.020  1.0  0.0  1.0  36.0  1.2  137.53  4.63  15.4  7800.0  5.20   \n",
+      "1  50.0  1.020  4.0  0.0  1.0  18.0  0.8  137.53  4.63  11.3  6000.0  4.71   \n",
+      "2  80.0  1.010  2.0  3.0  1.0  53.0  1.8  137.53  4.63   9.6  7500.0  4.71   \n",
+      "3  70.0  1.005  4.0  0.0  1.0  56.0  3.8  111.00  2.50  11.2  6700.0  3.90   \n",
+      "4  80.0  1.010  2.0  0.0  1.0  26.0  1.4  137.53  4.63  11.6  7300.0  4.60   \n",
+      "\n",
+      "   Htn  Class  \n",
+      "0  1.0      1  \n",
+      "1  0.0      1  \n",
+      "2  0.0      1  \n",
+      "3  1.0      1  \n",
+      "4  0.0      1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', None)\n",
+    "df_kidney = pd.read_csv('new_model.csv')\n",
+    "print(df_kidney.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "412d6119",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
+       "       'Rbcc', 'Htn', 'Class'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_kidney.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "46cacac6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bp         50.000\n",
+       "Sg          1.005\n",
+       "Al          0.000\n",
+       "Su          0.000\n",
+       "Rbc         0.000\n",
+       "Bu          1.500\n",
+       "Sc          0.400\n",
+       "Sod         4.500\n",
+       "Pot         2.500\n",
+       "Hemo        3.100\n",
+       "Wbcc     2200.000\n",
+       "Rbcc        2.100\n",
+       "Htn         0.000\n",
+       "Class       0.000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_kidney.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a4ac47e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bp         180.000\n",
+       "Sg           1.025\n",
+       "Al           5.000\n",
+       "Su           5.000\n",
+       "Rbc          1.000\n",
+       "Bu         391.000\n",
+       "Sc          76.000\n",
+       "Sod        163.000\n",
+       "Pot         47.000\n",
+       "Hemo        17.800\n",
+       "Wbcc     26400.000\n",
+       "Rbcc         8.000\n",
+       "Htn          1.000\n",
+       "Class        1.000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_kidney.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7c835179",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 400 entries, 0 to 399\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   Bp      400 non-null    float64\n",
+      " 1   Sg      400 non-null    float64\n",
+      " 2   Al      400 non-null    float64\n",
+      " 3   Su      400 non-null    float64\n",
+      " 4   Rbc     400 non-null    float64\n",
+      " 5   Bu      400 non-null    float64\n",
+      " 6   Sc      400 non-null    float64\n",
+      " 7   Sod     400 non-null    float64\n",
+      " 8   Pot     400 non-null    float64\n",
+      " 9   Hemo    400 non-null    float64\n",
+      " 10  Wbcc    400 non-null    float64\n",
+      " 11  Rbcc    400 non-null    float64\n",
+      " 12  Htn     400 non-null    float64\n",
+      " 13  Class   400 non-null    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 43.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_kidney.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c5784db2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df_kidney.drop('Class', axis=1)\n",
+    "Y = df_kidney['Class']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b0c5426f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = RandomForestClassifier()\n",
+    "clf.fit(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "db98ab11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(clf, open('kidney_clf.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bbd5901",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

new_model.csv

@@ -0,0 +1,401 @@
+Bp,Sg,Al,Su,Rbc,Bu,Sc,Sod,Pot,Hemo,Wbcc,Rbcc,Htn,Class
+80.0,1.02,1.0,0.0,1.0,36.0,1.2,137.53,4.63,15.4,7800.0,5.2,1.0,1
+50.0,1.02,4.0,0.0,1.0,18.0,0.8,137.53,4.63,11.3,6000.0,4.71,0.0,1
+80.0,1.01,2.0,3.0,1.0,53.0,1.8,137.53,4.63,9.6,7500.0,4.71,0.0,1
+70.0,1.005,4.0,0.0,1.0,56.0,3.8,111.0,2.5,11.2,6700.0,3.9,1.0,1
+80.0,1.01,2.0,0.0,1.0,26.0,1.4,137.53,4.63,11.6,7300.0,4.6,0.0,1
+90.0,1.015,3.0,0.0,1.0,25.0,1.1,142.0,3.2,12.2,7800.0,4.4,1.0,1
+70.0,1.01,0.0,0.0,1.0,54.0,24.0,104.0,4.0,12.4,8406.0,4.71,0.0,1
+76.0,1.015,2.0,4.0,1.0,31.0,1.1,137.53,4.63,12.4,6900.0,5.0,0.0,1
+100.0,1.015,3.0,0.0,1.0,60.0,1.9,137.53,4.63,10.8,9600.0,4.0,1.0,1
+90.0,1.02,2.0,0.0,0.0,107.0,7.2,114.0,3.7,9.5,12100.0,3.7,1.0,1
+60.0,1.01,2.0,4.0,1.0,55.0,4.0,137.53,4.63,9.4,8406.0,4.71,1.0,1
+70.0,1.01,3.0,0.0,0.0,60.0,2.7,131.0,4.2,10.8,4500.0,3.8,1.0,1
+70.0,1.015,3.0,1.0,1.0,72.0,2.1,138.0,5.8,9.7,12200.0,3.4,1.0,1
+70.0,1.02,1.0,0.0,1.0,86.0,4.6,135.0,3.4,9.8,8406.0,4.71,1.0,1
+80.0,1.01,3.0,2.0,1.0,90.0,4.1,130.0,6.4,5.6,11000.0,2.6,1.0,1
+80.0,1.015,3.0,0.0,1.0,162.0,9.6,141.0,4.9,7.6,3800.0,2.8,1.0,1
+70.0,1.015,2.0,0.0,1.0,46.0,2.2,138.0,4.1,12.6,8406.0,4.71,0.0,1
+80.0,1.02,1.0,0.0,1.0,87.0,5.2,139.0,3.7,12.1,8406.0,4.71,1.0,1
+100.0,1.025,0.0,3.0,1.0,27.0,1.3,135.0,4.3,12.7,11400.0,4.3,1.0,1
+60.0,1.015,1.0,0.0,1.0,31.0,1.6,137.53,4.63,10.3,5300.0,3.7,1.0,1
+80.0,1.015,2.0,0.0,0.0,148.0,3.9,135.0,5.2,7.7,9200.0,3.2,1.0,1
+90.0,1.02,1.0,0.0,1.0,180.0,76.0,4.5,4.63,10.9,6200.0,3.6,1.0,1
+80.0,1.025,4.0,0.0,1.0,163.0,7.7,136.0,3.8,9.8,6900.0,3.4,1.0,1
+70.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
+100.0,1.015,4.0,0.0,1.0,50.0,1.4,129.0,4.0,11.1,8300.0,4.6,1.0,1
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+70.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
+90.0,1.015,3.0,0.0,1.0,33.0,1.5,141.0,4.6,10.1,7800.0,4.0,0.0,1
+90.0,1.01,1.0,1.0,1.0,39.0,1.5,133.0,4.9,11.3,9600.0,4.0,1.0,1
+100.0,1.02,2.0,0.0,0.0,55.0,2.5,137.53,4.63,10.1,8406.0,4.71,1.0,1
+70.0,1.01,1.0,0.0,1.0,153.0,5.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
+90.0,1.02,2.0,1.0,0.0,39.0,2.0,137.53,4.63,12.0,9800.0,4.9,1.0,1
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+70.0,1.01,2.0,0.0,1.0,72.0,3.0,137.53,4.63,9.7,21600.0,3.5,1.0,1
+60.0,1.02,1.0,0.0,1.0,114.0,3.25,142.0,4.3,8.6,11000.0,3.8,1.0,1
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+90.0,1.015,0.0,0.0,1.0,38.0,2.2,137.53,4.63,10.9,4300.0,3.7,0.0,1
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+80.0,1.005,3.0,0.0,0.0,57.0,3.07,137.53,4.63,9.5,8406.0,4.71,0.0,1
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+80.0,1.02,2.0,0.0,0.0,142.0,4.6,138.0,5.8,10.5,7200.0,4.3,1.0,1
+100.0,1.02,1.0,0.0,1.0,96.0,6.4,137.53,4.63,6.6,8406.0,4.71,1.0,1
+90.0,1.02,1.0,0.0,1.0,66.0,3.2,138.0,6.6,12.53,8406.0,4.71,1.0,1
+80.0,1.01,1.0,3.0,1.0,391.0,32.0,163.0,39.0,12.53,8406.0,4.71,0.0,1
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requirements.txt

@@ -0,0 +1,6 @@
+matplotlib==3.5.2
+numpy==1.23.1
+pandas==1.4.3
+streamlit==1.12.0
+seaborn
+tensorflow