height-weight-regression-classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# UPVOTE IF YOU LIKE THE TUTORIAL\n",
    "\n",
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "# By the end of this, you will be able to draw basic histograms, KDE and scatter plot and will get to knwo what is univariate and bivariate distributions.\n",
    "# like us on fb: https://www.facebook.com/codemakerz\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['weight-height.csv']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"../input\"))\n",
    "df= pd.read_csv('../input/weight-height.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see what we got in df. head prints by default 5 rows.\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### So finally we have our data in our hand. Now lets do some visualization to understand it even more finely. As we know there are only two columns in our dataset so we will se historgram & kde plot(univariate distribution) and scatter plot(bivariate distribution), What is univariate & bivariate distribution? As name suggest univariate in plotting only a single feature e.g. histogram and kde plot (kernel distribution estimation). Bivariate means plotting 2 two variables e.g. scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    0\n",
       "Height    0\n",
       "Weight    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # As we can see, there is no null values so we dont have to take care of missing values, which is abig relief."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### lets start few visualisation.\n",
    "##### We are plotting histogram for height. We can see there are few params. Lets understand them.\n",
    "<ul>\n",
    "    <li>\n",
    "kind: this mean what kind of plot you want e,g, hist, box and other kind. So in our case its hist for histogram. </li>\n",
    "<li>title: The title of the plot. You can consider it as a name for your plot \\n</li>\n",
    "<li>color: you can choose any color. In my case 'c' stands for cyan</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "# Lets draw a basic histogram. \n",
    "df.Height.plot(kind=\"hist\", title='Univariate: Height Histogram', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# univariate distribution\n",
    "#Above histogram same we can do with matplotlib also. Matplotlib allows you to write more flexible code\n",
    "# title again means you plot name.\n",
    "# xlabel means label on x-axis and ylabel for y-axis\n",
    "# bins will be explained in the next plot\n",
    "plt.hist(x=df.Height, color='c')\n",
    "plt.title(\"Univariate: Height Histogram\")\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Total Counts\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# Lets draw a basic histogram. \n",
    "df.Weight.plot(kind=\"hist\", title='Univariate: Weight Histogram', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "# Weight histogram using matplotlib \n",
    "plt.hist(x=df.Weight, color='c')\n",
    "plt.title(\"Univariate: Weight Histogram\")\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Total Counts\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So with the above histograms you can estimate the number of people having a particular value of height and weight.\n",
    "# e.g. number of people having height of 100 cms is around 250.\n",
    "# Lets say you want increas the bars(thos cyan colored bars). We can increase them by using bins property.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S5qdRhqp+C3h+Vd0NkOTJwOeBz8xmYZKk+WmUo6q2mwiNZv2Iz5MkbYNG6XF8PsllbPjB39F0PQ5J0iI0Ss/h1+l+8HcocBhwXmvbYkn2THJRkn9KcnOSFybZO8nlSW5t93u1ZZPkjCSrk3wtySFbs25J0taZMjiSPC3JC6tzYVW9s6p+FVhHdzXArfFh4G+q6qeA5wA3A6cAV1TVwcAVbRrgVcDB7XYCcNZWrluStBWm63F8GPjeJO0/BP5oS1fYruXxs3RXFaSqflhV9wMr6HoztPvXtccrgPNbgF0N7Jlk3y1dvyRp60wXHAdV1Q3DjVV1LXDQVqzzaXQ72D+W5B+SfDTJ44EnV9W6to51wJPa8vsDdw08f01r20i72NSqJKvWr1+/FeVJkqYzXXDsPM28XbdinTsAhwBnVdXzgH9lw7DUZDJJW23SUHV2VS2rqmVLlizZivIkSdOZLjiuT3LccGOStwL/sBXrXAOsqapr2vRFdEFy98QQVLu/Z2D5wYtJHQCs3Yr1S5K2wnSH454E/FWSN9FdfwNgGbA73X6HLVJV30pyV5JnVNUtwBHATe12LHB6u7+kPeVS4MQkF9Ad1fXAxJCWJGnuTRkc7cv5sCQ/Dzy7NX+wqmbiNxy/CnwiyU7AbcBxdL2fC5McD9wJHNWWvQw4ElgNPNyWlSSNySgXcrocuHwmV9p2ui+bZNYRkyxbwDtmcv2SpC3nqUMkSb0YHJKkXgwOSVIvU+7jSHIfk/xegu53FVVVe89aVZKkeWu6neP7zFkVkqQFY7rDcR8dnE6yN/C4gSZ/hCdJi9Aol479hST/j+4X3Ne0+y/MdmGSpPlplJ3j7wMOB26pqgOBVwArZ7MoSdL8NUpwPFJV64HtkqT9INCLKUnSIjXKpWMfaKc9vwo4P8k9wI9ntyxJ0nw1So/jdcD36U56uBL4JvDqWaxJkjSPjRIc76mqR6vqR1V1TlX9IXDybBcmSZqfRgmOV07S9gszXYgkaWGY7pfjvwK8DfjJJNcPzNodWDXbhUmS5qfpdo5fCFwBfICNL+36YFXdM/lTJEnbuul+OX4fcB9wVJJnAy9us77Mhsu6SpIWmVF+Of4Out7HU9rtwiT/dbYLkyTNT6P8juNXgEOr6iGAJO8H/g74yGwWJkman0YJjgA/Gpj+UWvTNiIrV467BEkLyHRHVe1QVY8AfwZcneTiNuv1wHlzUZwkaf6ZrsdxLXBIVf1ekiuBl9D1NN5WVV+dk+okSfPOdMHx2HBUCwrDQpI0bXAsSTLlqUXaqUckSYvMdMGxPbAb7giXJA2YLjjWVdVpc1aJJGlBmO4HgPY0JEmbmK7HccScVSFpm7Y1vxWq5ctnrA7NjCl7HFV172yuOMn2Sf4hyefa9EFJrklya5JPJ9mpte/cple3+Utnsy5J0vRGuR7HbPk14OaB6Q8CH6qqg+lOrnh8az8euK+qng58qC0nSRqTsQRHkgPoLgb10TYd4KXARW2R8+guWQuwgg2/VL8IOKItL0kag3H1OP4I+A3gx236icD97RQnAGuA/dvj/YG7ANr8B9ryG0lyQpJVSVatX79+NmuXpEVtzoMjyauBe6rqusHmSRatEeZtaKg6u6qWVdWyJUuWzEClkqTJjHJ23Jl2OPDaJEcCjwP2oOuB7DlwYsUDgLVt+TXAgcCaJDsATwBmdce9JGlqc97jqKr3VNUBVbUUOBr4QlW9CbgSeENb7Fjgkvb40jZNm/+FqtqkxyFJmhvjPKpq2LuBk5OsptuHcU5rPwd4Yms/mY2vfy5JmmPjGKp6TFWtBFa2x7cBh06yzPeBo+a0MEnSlOZTj0OStAAYHJKkXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvcx4cSQ5McmWSm5PcmOTXWvveSS5Pcmu736u1J8kZSVYn+VqSQ+a6ZknSBjuMYZ2PAO+qquuT7A5cl+Ry4K3AFVV1epJTgFOAdwOvAg5ut8OAs9q9BmTlynGXIGmRmPMeR1Wtq6rr2+MHgZuB/YEVwHltsfOA17XHK4Dzq3M1sGeSfee4bElSM9Z9HEmWAs8DrgGeXFXroAsX4Eltsf2Buwaetqa1Db/WCUlWJVm1fv362Sxbkha1sQVHkt2Ai4GTquq70y06SVtt0lB1dlUtq6plS5YsmakyJUlDxhIcSXakC41PVNVftua7J4ag2v09rX0NcODA0w8A1s5VrZKkjY3jqKoA5wA3V9UfDsy6FDi2PT4WuGSg/S3t6KoXAA9MDGlJkubeOI6qOhx4M/D1JDe0tvcCpwMXJjkeuBM4qs27DDgSWA08DBw3t+VKGqetOWKwli+fsTq0wZwHR1VdxeT7LQCOmGT5At4xq0VJkkbmL8clSb0YHJKkXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvBockqZcdxl2ANsjKleMuQZI2yx6HJKkXexyStllb04uv5ctnrI5tjT0OSVIvBockqReDQ5LUi8EhSerFneOSNAl3rE9twfQ4krwyyS1JVic5Zdz1SNJitSB6HEm2B/4Y+HlgDfDVJJdW1U3jrWxT/ohP0tZ+D8z3HsuCCA7gUGB1Vd0GkOQCYAUwK8Hhl78kTW2hBMf+wF0D02uAwwYXSHICcEKbfCjJLXNU20KwD/DtcRexALidRuN2Gs0Wb6fMcCE9PHWUhRZKcEy2HWujiaqzgbPnppyFJcmqqlo27jrmO7fTaNxOo9mWt9NC2Tm+BjhwYPoAYO2YapGkRW2hBMdXgYOTHJRkJ+Bo4NIx1yRJi9KCGKqqqkeSnAj8LbA9cG5V3TjmshYSh/BG43YajdtpNNvsdkpVbX4pSZKahTJUJUmaJwwOSVIvBsc2IMm5Se5J8o2Btr2TXJ7k1na/V2tPkjPaqVu+luSQ8VU+t6bYTr+d5JtJbmi3Iwfmvadtp1uSvGI8Vc+tJAcmuTLJzUluTPJrrd3P05BpttU2/5kyOLYNHwdeOdR2CnBFVR0MXNGmAV4FHNxuJwBnzVGN88HH2XQ7AXyoqp7bbpcBJHkW3dF7P92e85F26ptt3SPAu6rqmcALgHe0beHnaVNTbSvYxj9TBsc2oKq+BNw71LwCOK89Pg943UD7+dW5Gtgzyb5zU+l4TbGdprICuKCqflBVtwOr6U59s02rqnVVdX17/CBwM92ZG/w8DZlmW01lm/lMGRzbridX1TroPuDAk1r7ZKdvme7Dvhic2IZZzp0YgsHtRJKlwPOAa/DzNK2hbQXb+GfK4Fh8Nnv6lkXmLOAngOcC64A/aO2Lejsl2Q24GDipqr473aKTtC2a7QSTbqtt/jNlcGy77p4YMmj397R2T98yoKrurqpHq+rHwJ+yYehg0W6nJDvSfRF+oqr+sjX7eZrEZNtqMXymDI5t16XAse3xscAlA+1vaUfDvAB4YGIIYjEaGo9/PTBxxNWlwNFJdk5yEN3O32vnur65liTAOcDNVfWHA7P8PA2Zalsths/UgjjliKaX5FPAcmCfJGuAU4HTgQuTHA/cCRzVFr8MOJJux9zDwHFzXvCYTLGdlid5Lt2QwR3ArwBU1Y1JLqS75ssjwDuq6tFx1D3HDgfeDHw9yQ2t7b34eZrMVNvqmG39M+UpRyRJvThUJUnqxeCQJPVicEiSejE4JEm9GBySpF4MDqmnJA8NTb81yZmbec5rk5yymWWWJ/ncFPNOSrJr/2qlmWdwSHOgqi6tqtO34iVOAgwOzQsGhzSDkixJcnGSr7bb4a39sV5Jkp9IcnWbf9pQD2a3JBcl+ackn2i/yH4nsB9wZZIrx/C2pI34y3Gpv10GfikMsDfd6SQAPkx3LYarkjwF+FvgmUPP/zDw4ar6VJK3Dc17Ht31GtYCXwEOr6ozkpwM/FxVfXum34zUl8Eh9fe9qnruxESStwLL2uTLgGd1pzECYI8kuw89/4VsuJ7FJ4HfH5h3bVWtaa97A7AUuGomi5e2lsEhzaztgBdW1fcGGweCZHN+MPD4Ufw/qnnIfRzSzPo8cOLERDvZ3bCrgV9sj48e8XUfBIZ7LtJYGBzSzHonsKxd/e0mYHgfBnRHSJ2c5FpgX+CBEV73bOCv3Tmu+cCz40pzrP0e43tVVUmOBo6pqhXjrksaleOn0tz7d8CZ7UJA9wP/ecz1SL3Y45Ak9eI+DklSLwaHJKkXg0OS1IvBIUnqxeCQJPXy/wEkGj1w+93CwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=df.Weight, color='c', bins=20)\n",
    "plt.title(\"Univariate: Weight Histogram\")\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Total Counts\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we are done with the histogram try to find out more about histogram.\n",
    "#lets plot a kde. KDE is more like smoother curve. It is very simple just change the kind to kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "# KDE distribution for Height\n",
    "df.Height.plot(kind=\"kde\", title='Univariate: Height KDE', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# KDE distribution for Weight\n",
    "df.Weight.plot(kind=\"kde\", title='Univariate: Weight KDE', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So you can imagine KDE as the outline of histogram.\n",
    "# KDE is more smoother and it gives you a bell shape curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bivariate: Lets plot bivariate using scatter plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# So here we are plotting a  bivariate plot type called scatter plot between height and weight.\n",
    "# x = data to show in x-axis(numeric)\n",
    "# y = data to show in y-axis(numeric)\n",
    "# color: color of the dots\n",
    "# title: name for your plot\n",
    "df.plot.scatter(x=\"Height\", y=\"Weight\", color='c', title='Height Vs Weight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# lets do same with matplotlib\n",
    "plt.scatter(x=df[\"Height\"], y=df[\"Weight\"], color='c')\n",
    "plt.title(\"Bivariate: Height Vs Weight Using Matplotplib\")\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.plot(); # this means now plot the visualisation with all the settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So I think it would be helpfull for you. If you like it, please follow us on facebook: https://www.facebook.com/codemakerz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression - Predict Weight Using Height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Train, Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:2].values\n",
    "y = df.iloc[:, 2:3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First lets split our data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a k-fold structure to train our model in better way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fit = regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[163.3254169 ],\n",
       "       [172.4561169 ],\n",
       "       [141.93545547],\n",
       "       ...,\n",
       "       [183.17868191],\n",
       "       [151.35818679],\n",
       "       [140.9103195 ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "plt.scatter(X_test, y_test)\n",
    "plt.plot( X_train, regressor.predict(X_train), color='r')\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.title(\"Heigth Vs Weight Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So you can see our regression line is finely fitting our data points.\n",
    "# blue points are actual values & points on red line are predicted values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To find accuracy of model we have many metries like \n",
    "# R-square, mean-squared-error(MSE), root-mean-squared-error(RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy is: 0.8518184505555159\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model Accuracy is: {regressor.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cool we got an accuracy of 85%, which is not bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8518184505555159"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R-squared Close to 1 is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SO finally you just created your simple linear regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification - Predict Gender By Height & Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Previously we saw how to predict Weigth by Height, which is simple because:\n",
    "# 1. Both were numeric values\n",
    "# 2. There was only one independent variable or feature(Height).\n",
    "# 3. Previously we were finding continues value like Weight but now we are finding discrete values like Male or Female. So\n",
    "# here we will use classification.\n",
    "# Now we wil talk about KNN classification. Here we will use two features(Heigth & Weigth) to predict Gender.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> IT IS A MAJORITY WIN CLASSIFIER.\n",
    "\n",
    "> KNN classification is very simple. It will plot your data point on the graph and select the n number of neighbors(which u define) and related you datapoint to majority.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ml = df.iloc[:, 1:3].values\n",
    "y_ml = df.iloc[:, 0:1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ml.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can you see any problem here? We can see our target variable is in string form, ML model only understands numeric values.\n",
    "# To make our model work, we need to encode it to numeric values. We will use LabelEncoder class for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "y_ml = encoder.fit_transform(y_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ml # so we can see now our string values are encoded. Male as 1 & Female as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ml.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now we can perform KNN classification. Before that we need to split train test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ml, X_test_ml, y_train_ml, y_test_ml = train_test_split(X_ml, y_ml, test_size=0.3, random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3) #n_neighbors is the number of neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train_ml, y_train_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_ml = knn.predict(X_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_ml # So you can see now our model is classifying the gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1315,  157],\n",
       "       [ 138, 1390]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test_ml, y_predict_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So our confusion matrix shows we predicted 1315+1390=2705 values correct and 138+157=295 incorrect. Not bad but can we do it better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets change the n_neighbors values in KNN classifier and see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1330,  142],\n",
       "       [ 135, 1393]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5) #n_neighbors is the number of neighbours\n",
    "knn.fit(X_train_ml, y_train_ml)\n",
    "y_predict_ml = knn.predict(X_test_ml)\n",
    "cm = confusion_matrix(y_test_ml, y_predict_ml)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9076666666666666\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy:\", knn.score(X_test_ml, y_test_ml)) # accuracy of 90% which is a very good number. But let see can we increase this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correct = 2723 & incorrect = 277. So We can see it depend on the number of the knn neighbours. You have to check \n",
    "# multiple value to find the best result. In our case i think 5 seems the good values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improve Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# below is the params dictionary object. Here we can add the parameters which we can experiment with. so now our model will get evaluated with all the possible \n",
    "# combination of below params and let you the best score and param combination.\n",
    "params = {\n",
    "    \"n_neighbors\": [5, 10, 20],\n",
    "    'leaf_size' : [30, 40, 50],\n",
    "    'algorithm': [\"ball_tree\", \"kd_tree\", \"brute\"],\n",
    "    'p': [1, 2]\n",
    "}\n",
    "gs = GridSearchCV(estimator=knn, cv=10, param_grid=params )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsresult = gs.fit(X_train_ml, y_train_ml ) # it may take a while as grid search cv will run your mode with all the possible combinations of given params."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.919\n",
      "{'algorithm': 'ball_tree', 'leaf_size': 30, 'n_neighbors': 20, 'p': 1}\n"
     ]
    }
   ],
   "source": [
    "print(gsresult.best_score_) # So you can see we increased accuracy with more than 1% for training data.\n",
    "print(gsresult.best_params_) # So this is the best possible combination for our model. Lets try with that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=20, p=1,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_best_fit = KNeighborsClassifier(algorithm = \"ball_tree\", leaf_size= 30, n_neighbors = 20, p=1)\n",
    "knn_best_fit.fit(X_train_ml, y_train_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_best = knn_best_fit.predict(X_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1352,  120],\n",
       "       [ 133, 1395]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_best = confusion_matrix(y_test_ml, y_predict_best)\n",
    "cm_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9706666666666667\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy:\", knn.score(X_test_ml, y_predict_best)) # accuracy of our new model is 97% which means 7% more than our old model for unseen data or test data, which is amazing.\n",
    "# So here we will stop with this configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As our data is very small but still GridSearchCV performed amazing. But remember if you have big data it can give you even better results so always try to configure your model with it.\n",
    "# For larger dataset it can take a long time to train model using gridsearchcv."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Confusion Matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3476422400>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Though we can see our confusion matrix but it is always good to plot it in a user friendly way.\n",
    "sns.heatmap(cm, annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f347537c978>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.heatmap(cm_best, annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from above images we can see the difference between our result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though we already got a good accuracy for our model but lets try neural network.\n",
    "My main aim here is to explain how neural network works. It is not necessary that neural networks are always better than other models.\n",
    "We will just see the implementation on Artificial Neural Network and test whether it performs better or worse. Ususally ANN needs huge data but lets apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_dim is no of inputs in ANN which is equal to no of columns in X_train \n",
    "# Dense() adding a layer\n",
    "# Dropout() is to avoid overfit. It drops given percentage of neurons in the next layer.\n",
    "clf = Sequential([\n",
    "    Dense(input_dim=2, units=20, activation='relu'),\n",
    "    #Dropout(0.2),\n",
    "    Dense(units=20, activation='relu'),\n",
    "    Dense(units=2, activation='sigmoid') # final layer is output unit. Here units should be equal to the number of class in you output.\n",
    "])\n",
    "clf.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 20)                60        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                420       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 42        \n",
      "=================================================================\n",
      "Total params: 522\n",
      "Trainable params: 522\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "clf.summary() # you can check your model details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "7000/7000 [==============================] - 6s 914us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 2/50\n",
      "7000/7000 [==============================] - 2s 300us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 3/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 4/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 5/50\n",
      "7000/7000 [==============================] - 2s 299us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 6/50\n",
      "7000/7000 [==============================] - 2s 301us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 7/50\n",
      "7000/7000 [==============================] - 2s 299us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 8/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 9/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 10/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 11/50\n",
      "7000/7000 [==============================] - 2s 294us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 12/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 13/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 14/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 15/50\n",
      "7000/7000 [==============================] - 2s 300us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 16/50\n",
      "7000/7000 [==============================] - 2s 298us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 17/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 18/50\n",
      "7000/7000 [==============================] - 2s 294us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 19/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 20/50\n",
      "7000/7000 [==============================] - 2s 301us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 21/50\n",
      "7000/7000 [==============================] - 2s 304us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 22/50\n",
      "7000/7000 [==============================] - 2s 302us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 23/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 24/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 25/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 26/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 27/50\n",
      "7000/7000 [==============================] - 2s 307us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 28/50\n",
      "7000/7000 [==============================] - 2s 322us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 29/50\n",
      "7000/7000 [==============================] - 2s 316us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 30/50\n",
      "7000/7000 [==============================] - 2s 310us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 31/50\n",
      "7000/7000 [==============================] - 2s 316us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 32/50\n",
      "7000/7000 [==============================] - 2s 306us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 33/50\n",
      "7000/7000 [==============================] - 2s 305us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 34/50\n",
      "7000/7000 [==============================] - 2s 309us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 35/50\n",
      "7000/7000 [==============================] - 2s 309us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 36/50\n",
      "7000/7000 [==============================] - 2s 303us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 37/50\n",
      "7000/7000 [==============================] - 2s 304us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 38/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 39/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 40/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 41/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 42/50\n",
      "7000/7000 [==============================] - 2s 294us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 43/50\n",
      "7000/7000 [==============================] - 2s 294us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 44/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 45/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 46/50\n",
      "7000/7000 [==============================] - 2s 295us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 47/50\n",
      "7000/7000 [==============================] - 2s 296us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 48/50\n",
      "7000/7000 [==============================] - 2s 297us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 49/50\n",
      "7000/7000 [==============================] - 2s 301us/step - loss: 7.9946 - acc: 0.5040\n",
      "Epoch 50/50\n",
      "7000/7000 [==============================] - 2s 305us/step - loss: 7.9946 - acc: 0.5040\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f34872d2048>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train_ml, y_train_ml, batch_size=10, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7000/7000 [==============================] - 0s 44us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[7.994575355529785, 0.504]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.evaluate(X_train_ml, y_train_ml) # So accuracy of 91% in training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000/3000 [==============================] - 0s 42us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[8.209483375549317, 0.4906666665871938]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.evaluate(X_test_ml, y_test_ml) # So accuracy of 91% in test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we can see neural network did not perform well on data. It may be because we have very less data. But you may try adding more Dense layers.\n",
    "# our main aim was to show the implementation of neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can predict like\n",
    "y_predict_nn = clf.predict_classes(X_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1472,    0],\n",
       "       [1528,    0]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_nn = confusion_matrix(y_test_ml, y_predict_nn)\n",
    "cm_nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we have a very bad model. So finally after evaluation KNN and ANN we found that in our case KNN with optimized values is best model for us.\n",
    "# AGAIN... IT ISNOT NECESSARY THAT NEURAL NETWORK WILL ALWAYS PERFORM GOOD FOR YOUR DATA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now finally you just finished your simple classifier.\n",
    "# I hope you understood the process.\n",
    "# Like us on facebook fo more tutorials :  https://www.facebook.com/codemakerz"
   ]
  }
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