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Browse files- kidney_app.py +102 -0
- kidney_clf.pkl +3 -0
- kidney_model.ipynb +261 -0
- new_model.csv +401 -0
- requirements.txt +6 -0
kidney_app.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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import pickle
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import base64
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import seaborn as sns
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import matplotlib.pyplot as plt
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st.write("""
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# Chronic Kidney Disease
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#Context
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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.
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#Content
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The datasets consists of several medical predictor variables and one target variable, Class. Predictor variables includes Blood Pressure(Bp), Albumin(Al), etc.
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#Inspiration
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Can you build a machine learning model to accurately predict whether or not the patients in the dataset have chronic kidney disease or not?
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""")
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url_dataset = f'<a href="new_model.csv">Download Dataset CSV File</a>'
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st.markdown(url_dataset, unsafe_allow_html=True)
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def user_input_features() :
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Bp = st.sidebar.slider('Bp', 50.000, 180.000)
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Sg = st.sidebar.slider('Sg', 1.005, 1.025)
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Al = st.sidebar.slider('Al', 0.000, 5.000)
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Su = st.sidebar.slider('Su', 0.000, 5.000)
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Rbc = st.sidebar.slider('Rbc', 0.000, 1.000)
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Bu = st.sidebar.slider('Bu', 1.500, 391.000)
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Sc = st.sidebar.slider('Sc', 0.400, 76.000)
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Sod = st.sidebar.slider('Sod', 4.500, 163.000)
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Pot = st.sidebar.slider('Pot', 2.500, 47.000)
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Hemo = st.sidebar.slider('Hemo', 3.100, 17.800)
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Wbcc = st.sidebar.slider('Wbcc', 2200.000, 26400.000)
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Rbcc = st.sidebar.slider('Rbcc', 2.100, 8.000)
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Htn = st.sidebar.slider('Htn', 0.000, 1.000)
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Class = st.sidebar.slider('Class', 0.000, 1.000)
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data = {'Bp':[Bp],
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'Sg':[Sg],
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'Al':[Al],
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'Su':[Su],
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'Rbc':[Rbc],
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'Bu':[Bu],
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'Sc':[Sc],
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'Sod':[Sod],
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'Pot':[Pot],
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'Hemo':[Hemo],
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'Wbcc':[Wbcc],
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'Rbcc':[Rbcc],
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'Htn':[Htn],
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'Class':[Class]
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}
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features = pd.DataFrame(data)
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return features
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input_df = user_input_features()
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kidney_raw = pd.read_csv('new_model.csv')
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kidney_raw.fillna(0, inplace=True)
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kidney = kidney_raw.drop(columns=['Class'])
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df = pd.concat([input_df, kidney], axis=0)
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df = df[:1] # Selects only the first row (the user input data)
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df.fillna(0, inplace=True)
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features = ['Bp', 'Sg', 'Al',
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'Su',
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'Rbc',
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'Bu',
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'Sc',
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'Sod',
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'Pot',
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'Hemo',
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'Wbcc',
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'Rbcc',
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'Htn'
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]
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df = df[features]
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st.subheader('User Input features')
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st.write(df)
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load_clf = pickle.load(open('kidney_clf.pkl', 'rb'))
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prediction = load_clf.predict(df)
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prediction_proba = load_clf.predict_proba(df)
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kidney_labels = np.array(['Normal', 'Chronic Kidney'])
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st.subheader('Prediction')
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st.write(kidney_labels[int(prediction)])
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st.subheader('Prediction Probability')
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df_prob = pd.DataFrame(data = prediction_proba,
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index = ['Probability'],
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columns = kidney_labels)
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st.write(df_prob)
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kidney_clf.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:a2da06293dd49baae23891f0444698d210d36bda33d332365f233d8343cf248e
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size 254665
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kidney_model.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a0c8ad0b",
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"metadata": {},
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"source": [
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"# Dataset\n",
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"https://www.kaggle.com/datasets/abhia1999/chronic-kidney-disease"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "61347359",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd \n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"import pickle"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b470f34b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
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"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",
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"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",
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"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",
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"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",
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"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",
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"\n",
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" Htn Class \n",
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"0 1.0 1 \n",
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"1 0.0 1 \n",
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"2 0.0 1 \n",
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"3 1.0 1 \n",
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"4 0.0 1 \n"
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]
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}
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],
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"source": [
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"pd.set_option('display.max_columns', None)\n",
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"pd.set_option('display.max_rows', None)\n",
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"df_kidney = pd.read_csv('new_model.csv')\n",
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"print(df_kidney.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "412d6119",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
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" 'Rbcc', 'Htn', 'Class'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_kidney.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "46cacac6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Bp 50.000\n",
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"Sg 1.005\n",
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"Al 0.000\n",
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"Su 0.000\n",
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"Rbc 0.000\n",
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"Bu 1.500\n",
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"Sc 0.400\n",
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"Sod 4.500\n",
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"Pot 2.500\n",
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"Hemo 3.100\n",
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"Wbcc 2200.000\n",
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"Rbcc 2.100\n",
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"Htn 0.000\n",
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"Class 0.000\n",
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"dtype: float64"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_kidney.min()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a4ac47e3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Bp 180.000\n",
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"Sg 1.025\n",
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"Al 5.000\n",
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"Su 5.000\n",
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"Rbc 1.000\n",
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"Bu 391.000\n",
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"Sc 76.000\n",
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"Sod 163.000\n",
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"Pot 47.000\n",
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"Hemo 17.800\n",
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"Wbcc 26400.000\n",
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"Rbcc 8.000\n",
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"Htn 1.000\n",
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"Class 1.000\n",
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"dtype: float64"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_kidney.max()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "7c835179",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 400 entries, 0 to 399\n",
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"Data columns (total 14 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Bp 400 non-null float64\n",
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" 1 Sg 400 non-null float64\n",
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" 2 Al 400 non-null float64\n",
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" 3 Su 400 non-null float64\n",
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" 4 Rbc 400 non-null float64\n",
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" 5 Bu 400 non-null float64\n",
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" 6 Sc 400 non-null float64\n",
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" 7 Sod 400 non-null float64\n",
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" 8 Pot 400 non-null float64\n",
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" 9 Hemo 400 non-null float64\n",
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" 10 Wbcc 400 non-null float64\n",
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" 11 Rbcc 400 non-null float64\n",
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" 12 Htn 400 non-null float64\n",
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" 13 Class 400 non-null int64 \n",
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"dtypes: float64(13), int64(1)\n",
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"memory usage: 43.9 KB\n"
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181 |
+
]
|
182 |
+
}
|
183 |
+
],
|
184 |
+
"source": [
|
185 |
+
"df_kidney.info()"
|
186 |
+
]
|
187 |
+
},
|
188 |
+
{
|
189 |
+
"cell_type": "code",
|
190 |
+
"execution_count": 7,
|
191 |
+
"id": "c5784db2",
|
192 |
+
"metadata": {},
|
193 |
+
"outputs": [],
|
194 |
+
"source": [
|
195 |
+
"X = df_kidney.drop('Class', axis=1)\n",
|
196 |
+
"Y = df_kidney['Class']"
|
197 |
+
]
|
198 |
+
},
|
199 |
+
{
|
200 |
+
"cell_type": "code",
|
201 |
+
"execution_count": 8,
|
202 |
+
"id": "b0c5426f",
|
203 |
+
"metadata": {},
|
204 |
+
"outputs": [
|
205 |
+
{
|
206 |
+
"data": {
|
207 |
+
"text/plain": [
|
208 |
+
"RandomForestClassifier()"
|
209 |
+
]
|
210 |
+
},
|
211 |
+
"execution_count": 8,
|
212 |
+
"metadata": {},
|
213 |
+
"output_type": "execute_result"
|
214 |
+
}
|
215 |
+
],
|
216 |
+
"source": [
|
217 |
+
"clf = RandomForestClassifier()\n",
|
218 |
+
"clf.fit(X, Y)"
|
219 |
+
]
|
220 |
+
},
|
221 |
+
{
|
222 |
+
"cell_type": "code",
|
223 |
+
"execution_count": 10,
|
224 |
+
"id": "db98ab11",
|
225 |
+
"metadata": {},
|
226 |
+
"outputs": [],
|
227 |
+
"source": [
|
228 |
+
"pickle.dump(clf, open('kidney_clf.pkl', 'wb'))"
|
229 |
+
]
|
230 |
+
},
|
231 |
+
{
|
232 |
+
"cell_type": "code",
|
233 |
+
"execution_count": null,
|
234 |
+
"id": "3bbd5901",
|
235 |
+
"metadata": {},
|
236 |
+
"outputs": [],
|
237 |
+
"source": []
|
238 |
+
}
|
239 |
+
],
|
240 |
+
"metadata": {
|
241 |
+
"kernelspec": {
|
242 |
+
"display_name": "Python 3 (ipykernel)",
|
243 |
+
"language": "python",
|
244 |
+
"name": "python3"
|
245 |
+
},
|
246 |
+
"language_info": {
|
247 |
+
"codemirror_mode": {
|
248 |
+
"name": "ipython",
|
249 |
+
"version": 3
|
250 |
+
},
|
251 |
+
"file_extension": ".py",
|
252 |
+
"mimetype": "text/x-python",
|
253 |
+
"name": "python",
|
254 |
+
"nbconvert_exporter": "python",
|
255 |
+
"pygments_lexer": "ipython3",
|
256 |
+
"version": "3.9.12"
|
257 |
+
}
|
258 |
+
},
|
259 |
+
"nbformat": 4,
|
260 |
+
"nbformat_minor": 5
|
261 |
+
}
|
new_model.csv
ADDED
@@ -0,0 +1,401 @@
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Bp,Sg,Al,Su,Rbc,Bu,Sc,Sod,Pot,Hemo,Wbcc,Rbcc,Htn,Class
|
2 |
+
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
|
3 |
+
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
|
4 |
+
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
|
5 |
+
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
|
6 |
+
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
|
7 |
+
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
|
8 |
+
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
|
9 |
+
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
|
10 |
+
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
|
11 |
+
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
|
12 |
+
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
|
13 |
+
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
|
14 |
+
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
|
15 |
+
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
|
16 |
+
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
|
17 |
+
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
|
18 |
+
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
|
19 |
+
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
|
20 |
+
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
|
21 |
+
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
|
22 |
+
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
|
23 |
+
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
|
24 |
+
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
|
25 |
+
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
|
26 |
+
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
|
27 |
+
60.0,1.025,0.0,0.0,1.0,75.0,1.9,141.0,5.2,9.9,8400.0,3.7,1.0,1
|
28 |
+
80.0,1.015,0.0,0.0,1.0,45.0,2.4,140.0,3.4,11.6,10300.0,4.0,1.0,1
|
29 |
+
70.0,1.01,3.0,4.0,1.0,87.0,2.7,130.0,4.0,12.5,9600.0,4.1,1.0,1
|
30 |
+
70.0,1.02,1.0,3.0,1.0,31.0,1.4,137.53,4.63,12.53,8406.0,4.71,0.0,1
|
31 |
+
70.0,1.005,1.0,0.0,0.0,28.0,1.4,137.53,4.63,12.9,8406.0,4.71,0.0,1
|
32 |
+
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
|
33 |
+
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
|
34 |
+
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
|
35 |
+
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
|
36 |
+
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
|
37 |
+
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
|
38 |
+
70.0,1.015,1.0,0.0,1.0,29.0,1.8,133.0,3.9,10.3,8406.0,4.71,1.0,1
|
39 |
+
80.0,1.02,1.0,0.0,1.0,65.0,3.4,141.0,4.7,9.7,6900.0,2.5,1.0,1
|
40 |
+
80.0,1.02,3.0,0.0,0.0,103.0,4.1,132.0,5.9,12.5,8406.0,4.71,1.0,1
|
41 |
+
80.0,1.01,2.0,2.0,1.0,70.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
|
42 |
+
90.0,1.01,2.0,0.0,1.0,80.0,2.1,137.53,4.63,11.1,9100.0,4.1,1.0,1
|
43 |
+
70.0,1.01,0.0,0.0,1.0,20.0,0.7,137.53,4.63,12.53,8406.0,4.71,0.0,1
|
44 |
+
100.0,1.01,0.0,0.0,1.0,29.0,1.0,139.0,4.2,9.7,9200.0,4.5,1.0,1
|
45 |
+
80.0,1.01,1.0,0.0,0.0,202.0,10.8,134.0,3.4,7.9,7900.0,3.1,0.0,1
|
46 |
+
80.0,1.01,3.0,0.0,0.0,77.0,6.3,134.0,4.8,9.7,8406.0,4.71,1.0,1
|
47 |
+
80.0,1.02,3.0,0.0,1.0,89.0,5.9,130.0,4.9,9.3,8406.0,4.71,1.0,1
|
48 |
+
70.0,1.015,0.0,0.0,1.0,24.0,1.2,142.0,4.2,12.4,6400.0,4.7,0.0,1
|
49 |
+
80.0,1.01,3.0,0.0,1.0,17.0,0.8,137.53,4.63,15.0,8600.0,4.71,0.0,1
|
50 |
+
70.0,1.005,0.0,0.0,1.0,32.0,0.9,125.0,4.0,10.0,18900.0,3.5,1.0,1
|
51 |
+
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
|
52 |
+
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
|
53 |
+
100.0,1.015,3.0,0.0,1.0,66.0,1.6,136.0,4.4,10.3,8406.0,4.71,1.0,1
|
54 |
+
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
|
55 |
+
80.0,1.015,0.0,5.0,1.0,24.0,1.0,137.53,4.63,13.6,8500.0,4.7,1.0,1
|
56 |
+
80.0,1.01,2.0,2.0,1.0,57.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
|
57 |
+
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
|
58 |
+
70.0,1.015,3.0,4.0,1.0,164.0,9.7,131.0,4.4,10.2,11300.0,3.4,1.0,1
|
59 |
+
90.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
|
60 |
+
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
|
61 |
+
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
|
62 |
+
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
|
63 |
+
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
|
64 |
+
60.0,1.02,3.0,0.0,1.0,15.0,0.6,138.0,4.0,11.0,7700.0,3.8,1.0,1
|
65 |
+
70.0,1.015,1.0,0.0,0.0,111.0,6.1,131.0,3.7,7.5,8406.0,4.71,0.0,1
|
66 |
+
80.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,9.8,8406.0,4.71,0.0,1
|
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345 |
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351 |
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353 |
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354 |
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355 |
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357 |
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358 |
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359 |
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360 |
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361 |
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362 |
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363 |
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364 |
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365 |
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366 |
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367 |
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368 |
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369 |
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370 |
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80.0,1.025,0.0,0.0,1.0,42.0,0.7,146.0,5.0,14.9,9400.0,5.9,0.0,0
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371 |
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70.0,1.02,0.0,0.0,1.0,48.0,0.8,144.0,3.5,13.6,10300.0,4.8,0.0,0
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372 |
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373 |
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374 |
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375 |
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70.0,1.025,0.0,0.0,1.0,38.0,1.0,142.0,3.6,13.7,9200.0,4.9,0.0,0
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376 |
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80.0,1.025,0.0,0.0,1.0,44.0,1.2,146.0,3.6,16.3,8000.0,6.4,0.0,0
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377 |
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378 |
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379 |
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380 |
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381 |
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80.0,1.025,0.0,0.0,1.0,45.0,0.6,138.0,3.5,16.1,5400.0,5.7,0.0,0
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382 |
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60.0,1.02,0.0,0.0,1.0,23.0,1.1,139.0,3.5,15.3,6500.0,4.9,0.0,0
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383 |
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384 |
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80.0,1.025,0.0,0.0,1.0,22.0,0.8,137.0,5.0,16.8,6000.0,6.5,0.0,0
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385 |
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80.0,1.025,0.0,0.0,1.0,46.0,0.7,141.0,4.9,13.9,5100.0,5.0,0.0,0
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386 |
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60.0,1.02,0.0,0.0,1.0,18.0,1.1,150.0,4.7,15.4,11000.0,4.5,0.0,0
|
387 |
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70.0,1.02,0.0,0.0,1.0,25.0,0.6,146.0,4.9,16.5,8000.0,5.1,0.0,0
|
388 |
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70.0,1.025,0.0,0.0,1.0,47.0,0.5,142.0,3.5,16.4,5700.0,6.5,0.0,0
|
389 |
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80.0,1.025,0.0,0.0,1.0,17.0,0.9,136.0,3.9,16.7,6200.0,5.2,0.0,0
|
390 |
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80.0,1.02,0.0,0.0,1.0,15.0,1.2,144.0,3.7,15.5,9500.0,6.4,0.0,0
|
391 |
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80.0,1.025,0.0,0.0,1.0,48.0,0.7,140.0,5.0,17.0,7200.0,5.8,0.0,0
|
392 |
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80.0,1.025,0.0,0.0,1.0,25.0,0.8,135.0,3.7,15.0,6300.0,5.3,0.0,0
|
393 |
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80.0,1.025,0.0,0.0,1.0,16.0,1.1,142.0,4.1,15.6,5800.0,6.3,0.0,0
|
394 |
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80.0,1.02,0.0,0.0,1.0,48.0,1.2,147.0,4.3,14.8,6600.0,5.5,0.0,0
|
395 |
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60.0,1.025,0.0,0.0,1.0,45.0,0.7,141.0,4.4,13.0,7400.0,5.4,0.0,0
|
396 |
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80.0,1.02,0.0,0.0,1.0,46.0,0.8,139.0,5.0,14.1,9500.0,4.6,0.0,0
|
397 |
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80.0,1.02,0.0,0.0,1.0,49.0,0.5,150.0,4.9,15.7,6700.0,4.9,0.0,0
|
398 |
+
70.0,1.025,0.0,0.0,1.0,31.0,1.2,141.0,3.5,16.5,7800.0,6.2,0.0,0
|
399 |
+
80.0,1.02,0.0,0.0,1.0,26.0,0.6,137.0,4.4,15.8,6600.0,5.4,0.0,0
|
400 |
+
60.0,1.025,0.0,0.0,1.0,50.0,1.0,135.0,4.9,14.2,7200.0,5.9,0.0,0
|
401 |
+
80.0,1.025,0.0,0.0,1.0,18.0,1.1,141.0,3.5,15.8,6800.0,6.1,0.0,0
|
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
matplotlib==3.5.2
|
2 |
+
numpy==1.23.1
|
3 |
+
pandas==1.4.3
|
4 |
+
streamlit==1.12.0
|
5 |
+
seaborn
|
6 |
+
tensorflow
|