Code/plot_confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating a confusion matrix using only pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperating independent and dependent variables\n",
    "\n",
    "x = data.drop(['Survived'], axis=1)\n",
    "y = data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "x = ss.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(x,y, random_state = 42, stratify=y)\n",
    "\n",
    "\n",
    "# Creating instance of KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "clf = KNN(n_neighbors = 3)\n",
    "\n",
    "# Fitting the model\n",
    "clf.fit(train_x, train_y)\n",
    "\n",
    "# Predicting over the Train Set and test set\n",
    "train_predict = clf.predict(train_x)\n",
    "test_predict = clf.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "#Titanic dataset\n",
    "disp = metrics.plot_confusion_matrix(clf, test_x, test_y,\n",
    "                                     values_format = 'n',\n",
    "                                     display_labels = ['Survived', \"Didn't Survive\"]\n",
    "                                    )"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}