Iris with Data School Practice .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the names of the four features\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print integers representing the species of each observation\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the encoding schemes for species\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store feature matrix in \"X\"\n",
    "X=iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store response vector in \"y\"\n",
    "y=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a machine learning model with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K-nearest neighbors (KNN) classification\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#value for K = 1\n",
    "knn=KNeighborsClassifier (n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict([[3,5,4,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.9, 3. , 5.1, 1.8])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[149]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target[149]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'virginica'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new=[[3,5,4,2],[5,4,3,2]]\n",
    "knn.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# value for K = 5\n",
    "knn=KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X,y)\n",
    "knn.predict([[3,5,4,2]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dana\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Dana\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict([[3,5,4,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred =logreg.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing machine learning models in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and test on the entire dataset\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier (n_neighbors=5)\n",
    "knn.fit(X,y)\n",
    "y_pred =knn.predict(X)\n",
    "print (metrics.accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier (n_neighbors=1)\n",
    "knn.fit(X,y)\n",
    "y_pred =knn.predict(X)\n",
    "print (metrics.accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train/test split \n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.4, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 4)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dana\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Dana\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 2, 2, 2, 1, 2, 0, 0, 1, 0, 0, 0, 1, 2, 0, 1, 0, 0, 2, 0, 2,\n",
       "       1, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 1, 2, 2, 1, 1, 0, 2, 0, 1, 0,\n",
       "       2, 0, 0, 1, 1, 2, 0, 1, 2, 2, 1, 1, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn =KNeighborsClassifier (n_neighbors =5)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "metrics.accuracy_score(y_pred, y_test)\n",
    "                        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "knn=5 is likely the best model for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try K=1 through K=25 and record testing accuracy\n",
    "k_range= range(1, 26)\n",
    "scores=[]\n",
    "for k in k_range:\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred=knn.predict(X_test)\n",
    "    scores.append(metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c9115200c8>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(k_range, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}