MohdAzamSayeed/KNN.ipynb

## KNN.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(iris.feature_names)\n",
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(iris.data))\n",
    "print(type(iris.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data_np = np.array(iris.data)\n",
    "target_np =np.array(iris.target)\n",
    "combined = np.insert(data_np, 4, target_np, axis=1)\n",
    "data = pd.DataFrame(combined,columns=['sepal length','sepal width','petal length','petal width','species'])\n",
    "data['species'] =data['species'].astype(int)\n",
    "\n",
    "#seperating features and target\n",
    "X=data.iloc[:,:4]\n",
    "Y=data.iloc[:,-1]\n",
    "\n",
    "#building training and test set using modelselection in 70-30 proportions\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "K_range =range(1,26)\n",
    "scores={}\n",
    "score_list=[]\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[k]=metrics.accuracy_score(y_test,y_pred)\n",
    "    score_list.append(metrics.accuracy_score(y_test,y_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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",
    "%matplotlib inline\n",
    "plt.plot(K_range,score_list)\n",
    "plt.xlabel('value of k in KNN')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=knn.predict(x_test.iloc[[1]])\n",
    "y_pred"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"from sklearn.datasets import load_iris"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"iris = load_iris()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"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]])"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"iris.data[0:5]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
	"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
	" 2 2]\n"
	]
	}
	],
	"source": [
	"print(iris.feature_names)\n",
	"print(iris.target)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"<class 'numpy.ndarray'>\n",
	"<class 'numpy.ndarray'>\n"
	]
	}
	],
	"source": [
	"print(type(iris.data))\n",
	"print(type(iris.target))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"import pandas as pd\n",
	"import numpy as np\n",
	"data_np = np.array(iris.data)\n",
	"target_np =np.array(iris.target)\n",
	"combined = np.insert(data_np, 4, target_np, axis=1)\n",
	"data = pd.DataFrame(combined,columns=['sepal length','sepal width','petal length','petal width','species'])\n",
	"data['species'] =data['species'].astype(int)\n",
	"\n",
	"#seperating features and target\n",
	"X=data.iloc[:,:4]\n",
	"Y=data.iloc[:,-1]\n",
	"\n",
	"#building training and test set using modelselection in 70-30 proportions\n",
	"from sklearn.model_selection import train_test_split\n",
	"x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=5)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [],
	"source": [
	"from sklearn.neighbors import KNeighborsClassifier\n",
	"from sklearn import metrics\n",
	"\n",
	"K_range =range(1,26)\n",
	"scores={}\n",
	"score_list=[]\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[k]=metrics.accuracy_score(y_test,y_pred)\n",
	" score_list.append(metrics.accuracy_score(y_test,y_pred))\n",
	" "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 15,
	"metadata": {},
	"outputs": [
	{
	"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",
	"%matplotlib inline\n",
	"plt.plot(K_range,score_list)\n",
	"plt.xlabel('value of k in KNN')\n",
	"plt.ylabel('Accuracy')\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 33,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([2])"
	]
	},
	"execution_count": 33,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"y_pred=knn.predict(x_test.iloc[[1]])\n",
	"y_pred"
	]
	}
	],
	"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": "3.7.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}