Headbrain case.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:22:30.353181Z",
     "start_time": "2020-11-05T05:22:24.102185Z"
    }
   },
   "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>Age Range</th>\n",
       "      <th>Head Size(cm^3)</th>\n",
       "      <th>Brain Weight(grams)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4512</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3738</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4261</td>\n",
       "      <td>1335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3777</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4177</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
       "0       1          1             4512                 1530\n",
       "1       1          1             3738                 1297\n",
       "2       1          1             4261                 1335\n",
       "3       1          1             3777                 1282\n",
       "4       1          1             4177                 1590"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "## selection of any algorithm always depends on the y variable, if y variable is continuous we use the linear regression\n",
    "## if the Y variable is not continuous you have to use categorical algorithms \n",
    "\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\HP\\Desktop\\dataset\\headbrain.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:22:33.608010Z",
     "start_time": "2020-11-05T05:22:33.585029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 237 entries, 0 to 236\n",
      "Data columns (total 4 columns):\n",
      " #   Column               Non-Null Count  Dtype\n",
      "---  ------               --------------  -----\n",
      " 0   Gender               237 non-null    int64\n",
      " 1   Age Range            237 non-null    int64\n",
      " 2   Head Size(cm^3)      237 non-null    int64\n",
      " 3   Brain Weight(grams)  237 non-null    int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 7.5 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "### https://towardsdatascience.com/top-3-methods-for-handling-skewed-data-1334e0debf4\n",
    "\n",
    "## check the data \n",
    "\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:22:36.478367Z",
     "start_time": "2020-11-05T05:22:36.380427Z"
    }
   },
   "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>Age Range</th>\n",
       "      <th>Head Size(cm^3)</th>\n",
       "      <th>Brain Weight(grams)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.434599</td>\n",
       "      <td>1.535865</td>\n",
       "      <td>3633.991561</td>\n",
       "      <td>1282.873418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.496753</td>\n",
       "      <td>0.499768</td>\n",
       "      <td>365.261422</td>\n",
       "      <td>120.340446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2720.000000</td>\n",
       "      <td>955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>1207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3614.000000</td>\n",
       "      <td>1280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3876.000000</td>\n",
       "      <td>1350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4747.000000</td>\n",
       "      <td>1635.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender   Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
       "count  237.000000  237.000000       237.000000           237.000000\n",
       "mean     1.434599    1.535865      3633.991561          1282.873418\n",
       "std      0.496753    0.499768       365.261422           120.340446\n",
       "min      1.000000    1.000000      2720.000000           955.000000\n",
       "25%      1.000000    1.000000      3389.000000          1207.000000\n",
       "50%      1.000000    2.000000      3614.000000          1280.000000\n",
       "75%      2.000000    2.000000      3876.000000          1350.000000\n",
       "max      2.000000    2.000000      4747.000000          1635.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## used to view some basic statistical details like percentile, mean, std etc. of a data frame or a series of numeric values.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:22:39.703410Z",
     "start_time": "2020-11-05T05:22:39.682421Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " NaN value in the data set column wise :- \n",
      "  Gender                 0\n",
      "Age Range              0\n",
      "Head Size(cm^3)        0\n",
      "Brain Weight(grams)    0\n",
      "dtype: int64\n",
      " Total NaN value in the data set  :- \n",
      "  0\n"
     ]
    }
   ],
   "source": [
    "## Find the null value in the data set \n",
    "\n",
    "print(\" NaN value in the data set column wise :- \\n \" , df.isnull().sum())\n",
    "print(\" Total NaN value in the data set  :- \\n \" , df.isnull().sum().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:24:42.932462Z",
     "start_time": "2020-11-05T05:24:34.730517Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## check visualization of the null value using the seaborn library\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.heatmap(df.isnull(),annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:26:16.166615Z",
     "start_time": "2020-11-05T05:26:03.411175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:29:54.886942Z",
     "start_time": "2020-11-05T05:29:54.867953Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(237, 4)\n",
      "(236, 4)\n"
     ]
    }
   ],
   "source": [
    "## let's check whether there are outliers present or not \n",
    "\n",
    "from scipy.stats import zscore\n",
    "z = abs(zscore(df))\n",
    "print(df.shape)\n",
    "df_final = df.loc[(z<3).all(axis = 1)]\n",
    "print(df_final.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:29:59.349895Z",
     "start_time": "2020-11-05T05:29:56.879751Z"
    }
   },
   "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": [
    "## lest check that relation between Brain Weight(grams) and Head Size(cm^3)\n",
    "\n",
    "sns.lineplot(x=df[\"Head Size(cm^3)\"] , y=df[\"Brain Weight(grams)\"], data= df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:01.793017Z",
     "start_time": "2020-11-05T05:30:01.135419Z"
    }
   },
   "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 check the what is the average size according to gender\n",
    "\n",
    "sns.barplot(y=df[\"Brain Weight(grams)\"], x=df[\"Gender\"], data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-04T05:01:01.689591Z",
     "start_time": "2020-11-04T05:01:01.038988Z"
    }
   },
   "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 check the what is the average size according to age\n",
    "sns.barplot(y=df[\"Brain Weight(grams)\"], x=df[\"Age Range\"], data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:08.160522Z",
     "start_time": "2020-11-05T05:30:07.653694Z"
    }
   },
   "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 histogram for brain weight\n",
    "plt.hist(x= \"Brain Weight(grams)\" , data = df )\n",
    "plt.show()\n",
    "\n",
    "#we can say it shows almost normal distribution based on below output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:10.053496Z",
     "start_time": "2020-11-05T05:30:10.036504Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                 0.265554\n",
       "Age Range             -0.144748\n",
       "Head Size(cm^3)        0.238807\n",
       "Brain Weight(grams)    0.265478\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets hcek skewnesss of the dataset\n",
    "df.skew()\n",
    "#Below shows acceptable level of skewness (-.55 to +.55 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:12.042463Z",
     "start_time": "2020-11-05T05:30:12.013479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Head Size(cm^3)\n",
      "0             4512\n",
      "1             3738\n",
      "2             4261\n",
      "3             3777\n",
      "4             4177\n",
      "   Brain Weight(grams)\n",
      "0                 1530\n",
      "1                 1297\n",
      "2                 1335\n",
      "3                 1282\n",
      "4                 1590\n"
     ]
    }
   ],
   "source": [
    "#Now let seprate input and output variable to be used for the linear regression problem\n",
    "x=df[[\"Head Size(cm^3)\"]]\n",
    "y=df[[\"Brain Weight(grams)\"]]\n",
    "\n",
    "print(x.head())\n",
    "print(y.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:14.840449Z",
     "start_time": "2020-11-05T05:30:13.835837Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.40886829]\n",
      " [ 0.28535333]\n",
      " [ 1.72023489]\n",
      " [ 0.39235214]\n",
      " [ 1.4897759 ]\n",
      " [-0.13441126]\n",
      " [ 0.41430062]\n",
      " [-0.2057438 ]\n",
      " [-0.05759159]\n",
      " [ 0.95478182]\n",
      " [-0.52399669]\n",
      " [ 0.98496097]\n",
      " [ 0.01648451]\n",
      " [ 1.57482624]\n",
      " [ 0.54324791]\n",
      " [ 0.66396452]\n",
      " [-0.37584448]\n",
      " [-0.46089482]\n",
      " [-1.47875536]\n",
      " [ 2.16743507]\n",
      " [ 0.66945164]\n",
      " [ 1.13036962]\n",
      " [ 0.46642825]\n",
      " [ 0.20853367]\n",
      " [ 3.05360475]\n",
      " [ 2.16469151]\n",
      " [ 1.10293403]\n",
      " [ 1.06452419]\n",
      " [-0.49381754]\n",
      " [ 1.48428878]\n",
      " [ 0.41978774]\n",
      " [ 0.44447977]\n",
      " [ 1.28675251]\n",
      " [ 1.44587895]\n",
      " [ 1.43764827]\n",
      " [ 0.49386384]\n",
      " [-0.2935377 ]\n",
      " [ 0.31278892]\n",
      " [-0.82304466]\n",
      " [-0.38956228]\n",
      " [ 0.89991063]\n",
      " [-0.35389601]\n",
      " [ 1.8683871 ]\n",
      " [ 0.46642825]\n",
      " [ 0.62829825]\n",
      " [ 1.09744691]\n",
      " [ 1.84918218]\n",
      " [-1.2867062 ]\n",
      " [ 0.01922807]\n",
      " [ 0.02745875]\n",
      " [ 0.70511792]\n",
      " [ 0.4362491 ]\n",
      " [ 1.74492692]\n",
      " [ 1.17701013]\n",
      " [ 1.0370886 ]\n",
      " [-0.4828433 ]\n",
      " [ 0.70237436]\n",
      " [ 1.45959675]\n",
      " [ 0.82583453]\n",
      " [ 0.09604773]\n",
      " [ 0.63652893]\n",
      " [-0.66117466]\n",
      " [ 2.21681914]\n",
      " [ 1.69828641]\n",
      " [ 0.25517418]\n",
      " [-0.83676246]\n",
      " [-0.60081635]\n",
      " [-0.71878941]\n",
      " [ 2.18389643]\n",
      " [ 2.04946202]\n",
      " [ 1.02611436]\n",
      " [ 0.61458046]\n",
      " [ 1.33613657]\n",
      " [ 1.16054877]\n",
      " [ 0.52129944]\n",
      " [-0.6584311 ]\n",
      " [-0.20848736]\n",
      " [-0.746225  ]\n",
      " [ 0.81211673]\n",
      " [ 0.55147859]\n",
      " [ 0.53776079]\n",
      " [ 0.60909334]\n",
      " [-1.05624721]\n",
      " [-0.15635973]\n",
      " [ 0.82034741]\n",
      " [ 0.59263198]\n",
      " [-0.89163365]\n",
      " [-0.62550839]\n",
      " [-0.35115245]\n",
      " [ 0.74901487]\n",
      " [ 1.44313539]\n",
      " [-0.86694161]\n",
      " [ 0.07684282]\n",
      " [ 0.72706639]\n",
      " [ 0.18109807]\n",
      " [ 0.39783926]\n",
      " [-0.44168991]\n",
      " [-0.3950494 ]\n",
      " [ 0.05489434]\n",
      " [-0.42797211]\n",
      " [-0.3813316 ]\n",
      " [ 0.54873503]\n",
      " [ 0.66396452]\n",
      " [ 0.07409926]\n",
      " [-0.0438738 ]\n",
      " [ 0.03843298]\n",
      " [ 1.09195979]\n",
      " [-0.6447133 ]\n",
      " [ 0.7737069 ]\n",
      " [ 2.18389643]\n",
      " [ 0.16738028]\n",
      " [-0.30176838]\n",
      " [-0.17282109]\n",
      " [-0.10971922]\n",
      " [-0.68861025]\n",
      " [-0.37035736]\n",
      " [-0.12343702]\n",
      " [ 0.72980995]\n",
      " [ 1.31693166]\n",
      " [ 0.83132165]\n",
      " [-0.6447133 ]\n",
      " [ 1.55287777]\n",
      " [ 2.34302287]\n",
      " [-0.05484804]\n",
      " [ 1.14408742]\n",
      " [ 0.40606994]\n",
      " [-0.66666178]\n",
      " [-1.39919214]\n",
      " [ 1.14957454]\n",
      " [-0.14264194]\n",
      " [ 0.08781705]\n",
      " [-0.27981991]\n",
      " [ 1.13036962]\n",
      " [ 0.09056061]\n",
      " [-2.13172249]\n",
      " [-0.54320161]\n",
      " [ 0.43076198]\n",
      " [-0.91083856]\n",
      " [-1.45406332]\n",
      " [-1.27024484]\n",
      " [-0.17007753]\n",
      " [-0.28530702]\n",
      " [-1.25927061]\n",
      " [-0.53771449]\n",
      " [ 0.73804063]\n",
      " [ 0.72706639]\n",
      " [-0.63922618]\n",
      " [-1.00686314]\n",
      " [-0.50204822]\n",
      " [-1.49247315]\n",
      " [-0.60630347]\n",
      " [-0.85322382]\n",
      " [ 0.12622688]\n",
      " [-0.53497093]\n",
      " [ 0.60086266]\n",
      " [-1.31139823]\n",
      " [-0.97394043]\n",
      " [ 0.20030299]\n",
      " [ 1.02062724]\n",
      " [-1.00137602]\n",
      " [-1.54460078]\n",
      " [ 0.39783926]\n",
      " [-0.23592295]\n",
      " [-0.93827416]\n",
      " [-0.37584448]\n",
      " [-1.51442163]\n",
      " [-1.05899077]\n",
      " [-0.75719924]\n",
      " [ 0.46368469]\n",
      " [-0.18653889]\n",
      " [-1.34157739]\n",
      " [-0.35938313]\n",
      " [-0.17282109]\n",
      " [ 0.2469435 ]\n",
      " [-0.05210448]\n",
      " [-1.18245094]\n",
      " [-0.06856583]\n",
      " [-0.20025668]\n",
      " [ 0.94655114]\n",
      " [-0.27707635]\n",
      " [ 0.15091892]\n",
      " [-1.30591112]\n",
      " [ 1.01788369]\n",
      " [-1.24280925]\n",
      " [-0.42522855]\n",
      " [ 0.02197163]\n",
      " [-0.00546397]\n",
      " [-1.5500879 ]\n",
      " [-0.6584311 ]\n",
      " [ 0.18932875]\n",
      " [-1.2867062 ]\n",
      " [-0.76817348]\n",
      " [-1.7393935 ]\n",
      " [ 0.1454318 ]\n",
      " [-0.21397448]\n",
      " [-2.36218148]\n",
      " [-1.58026705]\n",
      " [-0.79560907]\n",
      " [-0.38681872]\n",
      " [-0.92455636]\n",
      " [-0.75171212]\n",
      " [-1.11386196]\n",
      " [-0.97942755]\n",
      " [ 0.59537554]\n",
      " [-1.55557502]\n",
      " [ 0.1591496 ]\n",
      " [-0.63648263]\n",
      " [ 0.99044809]\n",
      " [-0.86694161]\n",
      " [-2.50759013]\n",
      " [-1.91223774]\n",
      " [-0.14812905]\n",
      " [-1.90675062]\n",
      " [-1.76957265]\n",
      " [-0.1316677 ]\n",
      " [-1.31139823]\n",
      " [-1.06447789]\n",
      " [-1.2729884 ]\n",
      " [-1.00411958]\n",
      " [-0.6721489 ]\n",
      " [-0.69409737]\n",
      " [-2.11251758]\n",
      " [ 0.29084045]\n",
      " [-0.42522855]\n",
      " [ 0.03568942]\n",
      " [ 0.22499502]\n",
      " [-0.96022263]\n",
      " [ 1.563852  ]\n",
      " [ 0.27712265]\n",
      " [-1.14129755]\n",
      " [ 0.13994468]\n",
      " [ 0.19207231]\n",
      " [-1.15227179]\n",
      " [-0.6584311 ]\n",
      " [-1.10014416]\n",
      " [-0.77366059]\n",
      " [-0.66666178]]\n"
     ]
    }
   ],
   "source": [
    "## #scaling down using standardscaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler() ###Perform standardization by centering and scaling\n",
    "x = sc.fit_transform(x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:20.757837Z",
     "start_time": "2020-11-05T05:30:16.049701Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score corresponding to random state:  0  is:  0.6376364238008984\n",
      "r2_score corresponding to random state:  1  is:  0.5571552826824455\n",
      "r2_score corresponding to random state:  2  is:  0.6793647592908127\n",
      "r2_score corresponding to random state:  3  is:  0.7600797473316105\n",
      "r2_score corresponding to random state:  4  is:  0.6537136105372976\n",
      "r2_score corresponding to random state:  5  is:  0.7548417934153979\n",
      "r2_score corresponding to random state:  6  is:  0.6203314269202966\n",
      "r2_score corresponding to random state:  7  is:  0.6700896298559369\n",
      "r2_score corresponding to random state:  8  is:  0.6351181790533427\n",
      "r2_score corresponding to random state:  9  is:  0.665534730434928\n",
      "r2_score corresponding to random state:  10  is:  0.832745812102125\n",
      "r2_score corresponding to random state:  11  is:  0.4568031562935818\n",
      "r2_score corresponding to random state:  12  is:  0.6424800194863828\n",
      "r2_score corresponding to random state:  13  is:  0.6327692971965183\n",
      "r2_score corresponding to random state:  14  is:  0.5810917193319585\n",
      "r2_score corresponding to random state:  15  is:  0.6100697245025559\n",
      "r2_score corresponding to random state:  16  is:  0.7957002367774237\n",
      "r2_score corresponding to random state:  17  is:  0.642492347625095\n",
      "r2_score corresponding to random state:  18  is:  0.658996492562984\n",
      "r2_score corresponding to random state:  19  is:  0.572994084830188\n",
      "r2_score corresponding to random state:  20  is:  0.614916227102305\n",
      "r2_score corresponding to random state:  21  is:  0.41201863127487126\n",
      "r2_score corresponding to random state:  22  is:  0.5137066355511075\n",
      "r2_score corresponding to random state:  23  is:  0.5348013942307959\n",
      "r2_score corresponding to random state:  24  is:  0.7394815557316852\n",
      "r2_score corresponding to random state:  25  is:  0.7759589177768256\n",
      "r2_score corresponding to random state:  26  is:  0.7415732575349601\n",
      "r2_score corresponding to random state:  27  is:  0.585589124768775\n",
      "r2_score corresponding to random state:  28  is:  0.626338283839841\n",
      "r2_score corresponding to random state:  29  is:  0.5965109967867821\n",
      "r2_score corresponding to random state:  30  is:  0.7179874529859196\n",
      "r2_score corresponding to random state:  31  is:  0.53998289163596\n",
      "r2_score corresponding to random state:  32  is:  0.6477271724122664\n",
      "r2_score corresponding to random state:  33  is:  0.722239941287923\n",
      "r2_score corresponding to random state:  34  is:  0.6873935214789475\n",
      "r2_score corresponding to random state:  35  is:  0.5097121298800188\n",
      "r2_score corresponding to random state:  36  is:  0.6506299144609536\n",
      "r2_score corresponding to random state:  37  is:  0.6241285167480145\n",
      "r2_score corresponding to random state:  38  is:  0.4994531057771596\n",
      "r2_score corresponding to random state:  39  is:  0.6680054125246782\n",
      "r2_score corresponding to random state:  40  is:  0.6660179196891471\n",
      "r2_score corresponding to random state:  41  is:  0.6869770791567571\n",
      "r2_score corresponding to random state:  42  is:  0.7149168473012073\n",
      "r2_score corresponding to random state:  43  is:  0.6404165530671617\n",
      "r2_score corresponding to random state:  44  is:  0.6530109688762358\n",
      "r2_score corresponding to random state:  45  is:  0.6939862623941999\n",
      "r2_score corresponding to random state:  46  is:  0.5332650244375814\n",
      "r2_score corresponding to random state:  47  is:  0.6201411206780885\n",
      "r2_score corresponding to random state:  48  is:  0.6851449361873334\n",
      "r2_score corresponding to random state:  49  is:  0.6433491336855988\n",
      "r2_score corresponding to random state:  50  is:  0.7683161771318533\n",
      "r2_score corresponding to random state:  51  is:  0.5935686929145347\n",
      "r2_score corresponding to random state:  52  is:  0.6342022687682458\n",
      "r2_score corresponding to random state:  53  is:  0.5909146568147446\n",
      "r2_score corresponding to random state:  54  is:  0.613033190538937\n",
      "r2_score corresponding to random state:  55  is:  0.5884150240645216\n",
      "r2_score corresponding to random state:  56  is:  0.7399588791116045\n",
      "r2_score corresponding to random state:  57  is:  0.5071803243347979\n",
      "r2_score corresponding to random state:  58  is:  0.6123621922042877\n",
      "r2_score corresponding to random state:  59  is:  0.7318498972427991\n",
      "r2_score corresponding to random state:  60  is:  0.670702983531536\n",
      "r2_score corresponding to random state:  61  is:  0.6107719657611237\n",
      "r2_score corresponding to random state:  62  is:  0.5886035640681522\n",
      "r2_score corresponding to random state:  63  is:  0.6677040991373102\n",
      "r2_score corresponding to random state:  64  is:  0.6296914854979072\n",
      "r2_score corresponding to random state:  65  is:  0.71647493474892\n",
      "r2_score corresponding to random state:  66  is:  0.6203738753194061\n",
      "r2_score corresponding to random state:  67  is:  0.712612390279983\n",
      "r2_score corresponding to random state:  68  is:  0.6576566589195091\n",
      "r2_score corresponding to random state:  69  is:  0.6703630883745615\n",
      "r2_score corresponding to random state:  70  is:  0.5891175986445865\n",
      "r2_score corresponding to random state:  71  is:  0.6417613976759238\n",
      "r2_score corresponding to random state:  72  is:  0.5463136037595998\n",
      "r2_score corresponding to random state:  73  is:  0.5045543063528726\n",
      "r2_score corresponding to random state:  74  is:  0.6908546939589804\n",
      "r2_score corresponding to random state:  75  is:  0.5839986897763758\n",
      "r2_score corresponding to random state:  76  is:  0.5842914878403355\n",
      "r2_score corresponding to random state:  77  is:  0.66166348734816\n",
      "r2_score corresponding to random state:  78  is:  0.6473967116879223\n",
      "r2_score corresponding to random state:  79  is:  0.7378058902454341\n",
      "r2_score corresponding to random state:  80  is:  0.31163981871623503\n",
      "r2_score corresponding to random state:  81  is:  0.6287492712541898\n",
      "r2_score corresponding to random state:  82  is:  0.5498061635931801\n",
      "r2_score corresponding to random state:  83  is:  0.742689268036727\n",
      "r2_score corresponding to random state:  84  is:  0.6841481547920669\n",
      "r2_score corresponding to random state:  85  is:  0.6839611629631983\n",
      "r2_score corresponding to random state:  86  is:  0.5705188355104969\n",
      "r2_score corresponding to random state:  87  is:  0.5235044952071041\n",
      "r2_score corresponding to random state:  88  is:  0.521218236230014\n",
      "r2_score corresponding to random state:  89  is:  0.6647722042837871\n",
      "r2_score corresponding to random state:  90  is:  0.692591428371049\n",
      "r2_score corresponding to random state:  91  is:  0.7602597914098644\n",
      "r2_score corresponding to random state:  92  is:  0.7279273279367925\n",
      "r2_score corresponding to random state:  93  is:  0.6673454912138065\n",
      "r2_score corresponding to random state:  94  is:  0.6801980030607431\n",
      "r2_score corresponding to random state:  95  is:  0.44961695878564867\n",
      "r2_score corresponding to random state:  96  is:  0.6854497779131984\n",
      "r2_score corresponding to random state:  97  is:  0.5090577072170102\n",
      "r2_score corresponding to random state:  98  is:  0.4944064453867716\n",
      "r2_score corresponding to random state:  99  is:  0.5312933151260452\n",
      "r2_score corresponding to random state:  100  is:  0.6887976995086573\n",
      "\n",
      "\n",
      "max r2 score corresponding to  10  is  0.832745812102125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import linear_model\n",
    "max_r_score = 0\n",
    "for r_state in range (0 , 101):\n",
    "    x_train,x_test,y_train,y_test = train_test_split(x , y , random_state = r_state , test_size = 0.20 ) ## split the data set \n",
    "    regr = linear_model.LinearRegression() #  use the regression model \n",
    "    regr.fit(x_train,y_train) # train and test data set fit in the model \n",
    "    y_pred = regr.predict(x_test)\n",
    "    r2_scr = r2_score(y_test,y_pred)\n",
    "    print(\"r2_score corresponding to random state: \",r_state,\" is: \",r2_scr)\n",
    "    if r2_scr>max_r_score:\n",
    "        max_r_score=r2_scr\n",
    "        final_r_state=r_state\n",
    "print()\n",
    "print()\n",
    "print(\"max r2 score corresponding to \",final_r_state,\" is \",max_r_score)\n",
    "#The belwo output shows that we are getting acceptable level of r2 score in most of the cases and max value is also shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:24.122980Z",
     "start_time": "2020-11-05T05:30:23.942096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4936984443261272"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets do the cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(linear_model.LinearRegression(), x, y, cv=5,scoring=\"r2\").mean()\n",
    "#the Below cross validation score is less and it shows that we further need to improve our model using more data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:30.512611Z",
     "start_time": "2020-11-05T05:30:30.488628Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "#since maximum is comming around random state 10  will use that to create our final model\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y,random_state =10,test_size=0.20)\n",
    "lr=linear_model.LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "y_pred=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=y_test , y=y_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T05:30:32.899736Z",
     "start_time": "2020-11-05T05:30:32.871753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score is:  0.832745812102125\n",
      "RMSE is :  54.3187676163564\n"
     ]
    }
   ],
   "source": [
    "#Lets evaluate RMSE and r2 score using sklearn library\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import numpy as np\n",
    "print(\"r2 score is: \",r2_score(y_test,y_pred))\n",
    "print(\"RMSE is : \",np.sqrt(mean_squared_error(y_test,y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-05T17:22:20.591813Z",
     "start_time": "2020-11-05T17:22:20.559827Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'logreg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-2d9467ca062a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0macc_log\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"acc_log\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0macc_log\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'logreg' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "from sklearn.model_selection import cross_val_predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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