Predicting sale using KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\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 = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting K-NN to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[64,  4],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\n",
    "plt.title('K-NN (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\n",
    "plt.title('K-NN (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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