YT_GS_KNN_www.paperlimamenit.com.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./diabetes_latih.csv')\n",
    "\n",
    "X_train = df.values\n",
    "X_train = np.delete(X_train,8,axis=1)\n",
    "\n",
    "y_train = df['Outcome'].values\n",
    "\n",
    "df = pd.read_csv('./diabetes_uji.csv')\n",
    "\n",
    "X_test = df.values\n",
    "X_test = np.delete(X_test,8,axis=1)\n",
    "\n",
    "y_test = df['Outcome'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.682"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN TANPA TUNING\n",
    "\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
    "knn_clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = knn_clf.predict(X_test)\n",
    "\n",
    "round(accuracy_score(y_test,y_pred),3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 9}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_neighbors': np.arange(1,201)}\n",
    "\n",
    "knn_clf = GridSearchCV(KNeighborsClassifier(), param_grid, cv=3, scoring='accuracy')\n",
    "\n",
    "knn_clf.fit(X_train, y_train)\n",
    "knn_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN DENGAN TUNING TETANGGA TERDEKAT 9\n",
    "\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=9)\n",
    "knn_clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = knn_clf.predict(X_test)\n",
    "\n",
    "round(accuracy_score(y_test,y_pred),3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PICKLE\n",
    "\n",
    "import pickle\n",
    "\n",
    "with open('knn_pickle','wb') as r:\n",
    "    pickle.dump(knn_clf,r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('knn_pickle','rb') as r:\n",
    "    knnp = pickle.load(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knnp.predict(X_test)\n",
    "\n",
    "round(accuracy_score(y_test,y_pred),3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_joblib']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#JOBLIB\n",
    "\n",
    "import joblib\n",
    "\n",
    "joblib.dump(knn_clf,'knn_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "knnjl = joblib.load('knn_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knnjl.predict(X_test)\n",
    "\n",
    "round(accuracy_score(y_test,y_pred),3)"
   ]
  }
 ],
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