{ "cells": [ { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn import preprocessing\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import metrics\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import precision_score\n", "from sklearn.metrics import recall_score\n", "import matplotlib.image as mpimg\n", "from sklearn import tree\n", "from sklearn import preprocessing\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.neural_network import MLPClassifier\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(520, 17)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=pd.read_csv(\"D:/Datasets/diabetes_data_upload.csv\", delimiter=\",\")\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "label_enco = preprocessing.LabelEncoder() #Label encoding\n", "df['Gender']=label_enco.fit_transform(df['Gender'])\n", "df['Polyuria']=label_enco.fit_transform(df['Polyuria'])\n", "df['Polydipsia']=label_enco.fit_transform(df['Polydipsia'])\n", "df['suddenweightloss']=label_enco.fit_transform(df['suddenweightloss'])\n", "df['weakness']=label_enco.fit_transform(df['weakness'])\n", "df['Polyphagia']=label_enco.fit_transform(df['Polyphagia'])\n", "df['Genitalthrush']=label_enco.fit_transform(df['Genitalthrush'])\n", "df['visualblurring']=label_enco.fit_transform(df['visualblurring'])\n", "df['Itching']=label_enco.fit_transform(df['Itching'])\n", "df['Irritability']=label_enco.fit_transform(df['Irritability'])\n", "df['delayedhealing']=label_enco.fit_transform(df['delayedhealing'])\n", "df['partialparesis']=label_enco.fit_transform(df['partialparesis'])\n", "df['musclestiffness']=label_enco.fit_transform(df['musclestiffness'])\n", "df['Alopecia']=label_enco.fit_transform(df['Alopecia'])\n", "df['Obesity']=label_enco.fit_transform(df['Obesity'])\n", "df['Target']=label_enco.fit_transform(df['Target'])" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Age | \n", "Gender | \n", "Polyuria | \n", "Polydipsia | \n", "suddenweightloss | \n", "weakness | \n", "Polyphagia | \n", "Genitalthrush | \n", "visualblurring | \n", "Itching | \n", "Irritability | \n", "delayedhealing | \n", "partialparesis | \n", "musclestiffness | \n", "Alopecia | \n", "Obesity | \n", "Target | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "40 | \n", "1 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "
1 | \n", "58 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "
2 | \n", "41 | \n", "1 | \n", "1 | \n", "0 | \n", "0 | \n", "1 | \n", "1 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "0 | \n", "1 | \n", "1 | \n", "0 | \n", "1 | \n", "