_Practice_DATA_CLEANING_AND_LINEAR REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.facebook.com/codemakerz\"><img src=\"https://scontent.ffjr1-4.fna.fbcdn.net/v/t1.0-9/36189148_736466693143793_2172101683281133568_n.png?_nc_cat=107&_nc_eui2=AeHzxv3SUcQBOfijLP-cEnHkX4z9XQXdeau__2MlErWZ1x07aZ1zx1PzJUDDxL6cpr7oPqYiifggXDptgtP8W5iCoDRjcdILDBYZ5Ig40dqi8Q&_nc_oc=AQmMCNXdzelFB2rdtpk8wN8nC410Wm2yKupYfYS1FxHNejTF0Jhr1G3WIZORKRF3TvFpohMB8Puw29Txxan8CW05&_nc_ht=scontent.ffjr1-4.fna&oh=7b13627e991a4d1b508923041bd7bc22&oe=5D8A7B03\" />\n",
    "</a>\n",
    "\n",
    "\n",
    "You can download:\n",
    "> Git Repo for dataset: https://github.com/martandsingh/datasets.git <br/>\n",
    "> Jupyter Notebook(Code): _Practice_DATA_CLEANING_AND_LINEAR REGRESSION.ipynb <br/>\n",
    "\n",
    "\n",
    "In this notebook, we will apply data munging, data cleaning, binning, exploratory data analysis and linear regression\n",
    "prediction and visualization on a height-weight dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/martandsingh/datasets/master/weight-height_old.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "  <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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</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         NaN\n",
       "2   Male        NaN  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # TOP 5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>9995</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.172652</td>\n",
       "      <td>136.777454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.067155</td>\n",
       "      <td>170.867906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.867992</td>\n",
       "      <td>128.475319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.034243</td>\n",
       "      <td>163.852461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.944246</td>\n",
       "      <td>113.649103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "9995  Female  66.172652  136.777454\n",
       "9996  Female  67.067155  170.867906\n",
       "9997  Female  63.867992  128.475319\n",
       "9998  Female  69.034243  163.852461\n",
       "9999  Female  61.944246  113.649103"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5) # Last 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
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       "    <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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Gender  Height  Weight\n",
       "0   False   False   False\n",
       "1   False   False    True\n",
       "2   False    True   False\n",
       "3   False   False   False\n",
       "4   False   False   False"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().head() # Null values will be shown as True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    0\n",
       "Height    6\n",
       "Weight    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # We can see Height has 6 missing values and weight has 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender     object\n",
       "Height    float64\n",
       "Weight    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.3662079651182"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So we can see Gender and Height has float datatype so we can easily replace there values with mean values\n",
    "# Mean Height\n",
    "height_mean = df[\"Height\"].mean(axis = 0)\n",
    "height_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161.43282627899984"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_mean = df[\"Weight\"].mean(axis = 0)\n",
    "weight_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.212156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.399676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213.741169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.186930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.364182</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "    Gender  Height      Weight\n",
       "2     Male     NaN  212.740856\n",
       "5     Male     NaN  152.212156\n",
       "9     Male     NaN  156.399676\n",
       "11    Male     NaN  213.741169\n",
       "15    Male     NaN  172.186930\n",
       "109   Male     NaN  174.364182"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Height\"].isnull()] # So you can select all the 6 rows which contain height as null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.936005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.577202</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.974336</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   Gender     Height  Weight\n",
       "1    Male  68.781904     NaN\n",
       "19   Male  67.936005     NaN\n",
       "34   Male  69.577202     NaN\n",
       "40   Male  67.974336     NaN"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Weight\"].isnull()] # All rows containing weight as Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Height\"].replace(np.nan, height_mean, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gender</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, Height, Weight]\n",
       "Index: []"
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     "execution_count": 98,
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   "source": [
    "df[df[\"Height\"].isnull()] # now if run this, we will not get any rows which contains height as nul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Weight</th>\n",
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       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.366208</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",
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      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904         NaN\n",
       "2   Male  66.366208  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets replace weight\n",
    "df[\"Weight\"].replace(np.nan, weight_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
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       "<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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, Height, Weight]\n",
       "Index: []"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Weight\"].isnull()] # No rows at all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    0\n",
       "Height    0\n",
       "Weight    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So now we have replace our values. You can check isnull() showing 0\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117ae2eb8>"
      ]
     },
     "execution_count": 103,
     "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": [
    "# As datatypes as proper so we dnt have to change it\n",
    "# Here our data is in same scale so we dont need to normalize that\n",
    "plt.scatter(df[\"Height\"], df[\"Weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Height bins')"
      ]
     },
     "execution_count": 104,
     "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": [
    "# We can see Height binning.\n",
    "plt.hist(df[\"Height\"])\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Height bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weight bins')"
      ]
     },
     "execution_count": 105,
     "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": [
    "plt.hist(df[\"Weight\"])\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Weight bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets try to bin our data. Binning means converting our continous data into groups. \n",
    "# Lets say we divide our data in three group tall, medium, short\n",
    "bins = np.linspace(min(df[\"Height\"]), max(df[\"Height\"]), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54.26313333, 62.50833633, 70.75353934, 78.99874235])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = [\"short\", \"medium\", \"tall\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Height</th>\n",
       "      <th>height-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.785081</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.348516</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.018950</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71.195382</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64.766329</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>69.283070</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>69.243732</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72.418317</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>63.974326</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.640060</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>67.936005</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>67.915050</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>69.439440</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>66.149132</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>75.205974</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>67.893196</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>68.144033</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>69.089631</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72.800844</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>67.421242</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>68.496415</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>68.481875</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>72.213963</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>68.179533</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>71.981207</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>66.065137</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>66.656164</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>67.599424</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>68.245944</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>64.808621</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>67.492218</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>68.180731</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>69.553385</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>66.402250</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>66.592157</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>71.935887</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>68.287042</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>69.955451</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>71.851129</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>65.755499</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>67.031852</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>76.709835</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>72.571121</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>69.728805</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>72.799224</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>72.539354</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>72.294743</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>67.253325</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>75.944460</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>66.316232</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>71.156053</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Height height-binned\n",
       "0    73.847017          tall\n",
       "1    68.781904        medium\n",
       "2    66.366208        medium\n",
       "3    71.730978          tall\n",
       "4    69.881796        medium\n",
       "5    66.366208        medium\n",
       "6    68.785081        medium\n",
       "7    68.348516        medium\n",
       "8    67.018950        medium\n",
       "9    66.366208        medium\n",
       "10   71.195382          tall\n",
       "11   66.366208        medium\n",
       "12   64.766329        medium\n",
       "13   69.283070        medium\n",
       "14   69.243732        medium\n",
       "15   66.366208        medium\n",
       "16   72.418317          tall\n",
       "17   63.974326        medium\n",
       "18   69.640060        medium\n",
       "19   67.936005        medium\n",
       "20   67.915050        medium\n",
       "21   69.439440        medium\n",
       "22   66.149132        medium\n",
       "23   75.205974          tall\n",
       "24   67.893196        medium\n",
       "25   68.144033        medium\n",
       "26   69.089631        medium\n",
       "27   72.800844          tall\n",
       "28   67.421242        medium\n",
       "29   68.496415        medium\n",
       "..         ...           ...\n",
       "170  68.481875        medium\n",
       "171  72.213963          tall\n",
       "172  68.179533        medium\n",
       "173  71.981207          tall\n",
       "174  66.065137        medium\n",
       "175  66.656164        medium\n",
       "176  67.599424        medium\n",
       "177  68.245944        medium\n",
       "178  64.808621        medium\n",
       "179  67.492218        medium\n",
       "180  68.180731        medium\n",
       "181  69.553385        medium\n",
       "182  66.402250        medium\n",
       "183  66.592157        medium\n",
       "184  71.935887          tall\n",
       "185  68.287042        medium\n",
       "186  69.955451        medium\n",
       "187  71.851129          tall\n",
       "188  65.755499        medium\n",
       "189  67.031852        medium\n",
       "190  76.709835          tall\n",
       "191  72.571121          tall\n",
       "192  69.728805        medium\n",
       "193  72.799224          tall\n",
       "194  72.539354          tall\n",
       "195  72.294743          tall\n",
       "196  67.253325        medium\n",
       "197  75.944460          tall\n",
       "198  66.316232        medium\n",
       "199  71.156053          tall\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['height-binned'] = pd.cut(df['Height'], bins, labels=group_names, include_lowest=True )\n",
    "df[['Height','height-binned']].head(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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",
       "    <tr>\n",
       "      <th>height-binned</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>short</th>\n",
       "      <td>1723</td>\n",
       "      <td>1723</td>\n",
       "      <td>1723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>6868</td>\n",
       "      <td>6868</td>\n",
       "      <td>6868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tall</th>\n",
       "      <td>1409</td>\n",
       "      <td>1409</td>\n",
       "      <td>1409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender  Height  Weight\n",
       "height-binned                        \n",
       "short            1723    1723    1723\n",
       "medium           6868    6868    6868\n",
       "tall             1409    1409    1409"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"height-binned\").count() # total number of bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium    6868\n",
       "short     1723\n",
       "tall      1409\n",
       "Name: height-binned, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"height-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'height bins')"
      ]
     },
     "execution_count": 112,
     "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": [
    "plt.bar(group_names, df[\"height-binned\"].value_counts())\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"height\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"height bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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",
       "      <th>height-binned</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",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>161.432826</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.366208</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>tall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight height-binned\n",
       "0   Male  73.847017  241.893563          tall\n",
       "1   Male  68.781904  161.432826        medium\n",
       "2   Male  66.366208  212.740856        medium\n",
       "3   Male  71.730978  220.042470          tall\n",
       "4   Male  69.881796  206.349801        medium"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # Here you can see we have Gender column as first column, generally we make target column s last\n",
    "# Lets shift it to last columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.concat([df.iloc[:, 1:4], df.iloc[:, 0]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>height-binned</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>tall</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>161.432826</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>tall</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight height-binned Gender\n",
       "0  73.847017  241.893563          tall   Male\n",
       "1  68.781904  161.432826        medium   Male\n",
       "2  66.366208  212.740856        medium   Male\n",
       "3  71.730978  220.042470          tall   Male\n",
       "4  69.881796  206.349801        medium   Male"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head() # So now Gender column is the last one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>short</th>\n",
       "      <th>medium</th>\n",
       "      <th>tall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   short  medium  tall\n",
       "0      0       0     1\n",
       "1      0       1     0\n",
       "2      0       1     0\n",
       "3      0       0     1\n",
       "4      0       1     0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now can you see any problem in the dataset???\n",
    "# As we already in our tutorial discussed that model understand only integer values. So we need to change our\n",
    "# height-binned and Gender column to dummy variables\n",
    "dummy_height = pd.get_dummies(df[\"height-binned\"])\n",
    "dummy_height.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Weight</th>\n",
       "      <th>height-binned</th>\n",
       "      <th>Gender</th>\n",
       "      <th>short</th>\n",
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       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>tall</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>161.432826</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>tall</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight height-binned Gender  short  medium  tall\n",
       "0  73.847017  241.893563          tall   Male      0       0     1\n",
       "1  68.781904  161.432826        medium   Male      0       1     0\n",
       "2  66.366208  212.740856        medium   Male      0       1     0\n",
       "3  71.730978  220.042470          tall   Male      0       0     1\n",
       "4  69.881796  206.349801        medium   Male      0       1     0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Now we need to merge this dummy variable to our dataset\n",
    "df_new = pd.concat([df_new, dummy_height], axis = 1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we already added the dummy variables for height-binned so now we can remove this column\n",
    "df_new.drop(\"height-binned\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Gender</th>\n",
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       "      <td>220.042470</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "      Height      Weight Gender  short  medium  tall\n",
       "0  73.847017  241.893563   Male      0       0     1\n",
       "1  68.781904  161.432826   Male      0       1     0\n",
       "2  66.366208  212.740856   Male      0       1     0\n",
       "3  71.730978  220.042470   Male      0       0     1\n",
       "4  69.881796  206.349801   Male      0       1     0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets do same for Gender\n",
    "dummy_gender = pd.get_dummies(df[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Male</th>\n",
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      "text/plain": [
       "   Female  Male\n",
       "0       0     1\n",
       "1       0     1\n",
       "2       0     1\n",
       "3       0     1\n",
       "4       0     1"
      ]
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     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dummy_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.concat([df_new, dummy_gender], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Gender</th>\n",
       "      <th>short</th>\n",
       "      <th>medium</th>\n",
       "      <th>tall</th>\n",
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       "      <th>Male</th>\n",
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       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>161.432826</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight Gender  short  medium  tall  Female  Male\n",
       "0  73.847017  241.893563   Male      0       0     1       0     1\n",
       "1  68.781904  161.432826   Male      0       1     0       0     1\n",
       "2  66.366208  212.740856   Male      0       1     0       0     1\n",
       "3  71.730978  220.042470   Male      0       0     1       0     1\n",
       "4  69.881796  206.349801   Male      0       1     0       0     1"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now lets delete Gender columns\n",
    "df_new.drop(\"Gender\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight  short  medium  tall  Female  Male\n",
       "0  73.847017  241.893563      0       0     1       0     1\n",
       "1  68.781904  161.432826      0       1     0       0     1\n",
       "2  66.366208  212.740856      0       1     0       0     1\n",
       "3  71.730978  220.042470      0       0     1       0     1\n",
       "4  69.881796  206.349801      0       1     0       0     1"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So finally our dataset is ready. DATA WRANGLING PROCESS DONE !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Now lets do some exploratory dataanalysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>short</th>\n",
       "      <th>medium</th>\n",
       "      <th>tall</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>161.432826</td>\n",
       "      <td>0.17230</td>\n",
       "      <td>0.686800</td>\n",
       "      <td>0.140900</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.846226</td>\n",
       "      <td>32.104730</td>\n",
       "      <td>0.37766</td>\n",
       "      <td>0.463818</td>\n",
       "      <td>0.347936</td>\n",
       "      <td>0.500025</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.509087</td>\n",
       "      <td>135.818051</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.319161</td>\n",
       "      <td>161.212928</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.169753</td>\n",
       "      <td>187.146757</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989699</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height        Weight        short        medium          tall  \\\n",
       "count  10000.000000  10000.000000  10000.00000  10000.000000  10000.000000   \n",
       "mean      66.366208    161.432826      0.17230      0.686800      0.140900   \n",
       "std        3.846226     32.104730      0.37766      0.463818      0.347936   \n",
       "min       54.263133     64.700127      0.00000      0.000000      0.000000   \n",
       "25%       63.509087    135.818051      0.00000      0.000000      0.000000   \n",
       "50%       66.319161    161.212928      0.00000      1.000000      0.000000   \n",
       "75%       69.169753    187.146757      0.00000      1.000000      0.000000   \n",
       "max       78.998742    269.989699      1.00000      1.000000      1.000000   \n",
       "\n",
       "             Female          Male  \n",
       "count  10000.000000  10000.000000  \n",
       "mean       0.500000      0.500000  \n",
       "std        0.500025      0.500025  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.500000      0.500000  \n",
       "75%        1.000000      1.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.describe() # it will show the general statistical summary. but short,medium,tall, female, male \n",
    "# are our dummy variables. So we need to exclude them befor this summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.366208</td>\n",
       "      <td>161.432826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.846226</td>\n",
       "      <td>32.104730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.509087</td>\n",
       "      <td>135.818051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.319161</td>\n",
       "      <td>161.212928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.169753</td>\n",
       "      <td>187.146757</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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      ],
      "text/plain": [
       "             Height        Weight\n",
       "count  10000.000000  10000.000000\n",
       "mean      66.366208    161.432826\n",
       "std        3.846226     32.104730\n",
       "min       54.263133     64.700127\n",
       "25%       63.509087    135.818051\n",
       "50%       66.319161    161.212928\n",
       "75%       69.169753    187.146757\n",
       "max       78.998742    269.989699"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[[\"Height\", \"Weight\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.924448</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "          Height    Weight\n",
       "Height  1.000000  0.924448\n",
       "Weight  0.924448  1.000000"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[[\"Height\", \"Weight\"]].corr() # using correlation we can see height and weight as strongly related.\n",
    "# but how they are related?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1a0811d0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will plot a visualization between height and weight to check what kind of relation they have \n",
    "# e.g. linear or non linear\n",
    "\n",
    "plt.scatter(df[\"Height\"], df[\"Weight\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Now lets do some regression </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we know these two varibles(height, weight) as linearly related to eachother. So linear regression model\n",
    "# will be a good choice for the model. To create a linear regression model, lets import new library sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"Height\"]]\n",
    "y = df[\"Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here lets break out data into two categories training and test. usually test should be only 25-30% of total data\n",
    "# We are doing this so that we can compare the values of or predicted values to existing values\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Height    6666\n",
      "dtype: int64\n",
      "Height    3334\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# So you can see our data is successully splited into test and train set.\n",
    "print(X_train.count())\n",
    "print(X_test.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So now we will first train our model with train data\n",
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.72022984])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-350.93811231904664"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in.a equation of line Y = mx+c\n",
    "# coef_ = m & intercept = c\n",
    "# New equation will be\n",
    "# y = 7.72022984 * x + (350.93811231904664)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets predict the value of all the given values\n",
    "y_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 280.04612756597584)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets first draw visualizaiton between training set. We will plot scatter for real values and a regression line\n",
    "# for predicted values for training set only(Not for test data).\n",
    "\n",
    "width = 12 # width of the visualization\n",
    "height = 10 # height of the visualization\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.scatter(X_train, y_train, color='red')\n",
    "plt.plot(X_train, lm.predict(X_train), color='blue')\n",
    "plt.title('Height Vs Weight (Train Data)') # Visualization title\n",
    "plt.xlabel('Height') # x-axis label\n",
    "plt.ylabel('Weight') # y-axis label\n",
    "plt.ylim(0,) # settin the minimum value of y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So in the above example you can see, the red dots are actuall values which is present in our dataset.\n",
    "# Blue line is the predicted values. So you can see values are uniformly scattered around line. So out line is good."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Now lets plot test data\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.scatter(X_test, y_test, color='red')\n",
    "plt.plot(X_train, lm.predict(X_train), color='blue') # We dont have to change this as our model is trained with this\n",
    "plt.title('Height Vs Weight (Test Data)') # Visualization title\n",
    "plt.xlabel('Height') # x-axis label\n",
    "plt.ylabel('Weight') # y-axis label\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now finally we have plotted our regression. Blue line is prediction and red are actual values. "
   ]
  }
 ],
 "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"
  }
 },
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}