Project on Linear Regression_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31c66f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18dd1119",
   "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>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Delivery Time  Sorting Time\n",
       "0          21.00            10\n",
       "1          13.50             4\n",
       "2          19.75             6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the data-set\n",
    "data = pd.read_csv(\"delivery_time.csv\")\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f14ec327",
   "metadata": {},
   "outputs": [],
   "source": [
    "## copy the data\n",
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c3e26f",
   "metadata": {},
   "source": [
    "### Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27764c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total rows in data is :  21\n",
      "total columns in data is :  2\n"
     ]
    }
   ],
   "source": [
    "## shape of the data\n",
    "print(\"total rows in data is : \",data.shape[0])\n",
    "print(\"total columns in data is : \",data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3251cc3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery Time  21 non-null     float64\n",
      " 1   Sorting Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d27263",
   "metadata": {},
   "source": [
    "* There are 21 rows and 2 columns.delivery time is float data type and sorting time is integer data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "368d39e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery Time    0\n",
       "Sorting Time     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bd1956a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery Time    False\n",
       "Sorting Time     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aed7b7e",
   "metadata": {},
   "source": [
    "* There is no null value in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c13fcf4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=21, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a50c4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Delivery Time', 'Sorting Time'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## columns in dataset\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "731c0e04",
   "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>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.790952</td>\n",
       "      <td>6.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.074901</td>\n",
       "      <td>2.542028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.830000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.750000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Delivery Time  Sorting Time\n",
       "count      21.000000     21.000000\n",
       "mean       16.790952      6.190476\n",
       "std         5.074901      2.542028\n",
       "min         8.000000      2.000000\n",
       "25%        13.500000      4.000000\n",
       "50%        17.830000      6.000000\n",
       "75%        19.750000      8.000000\n",
       "max        29.000000     10.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## statistical details of dataset\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b04808",
   "metadata": {},
   "source": [
    "* No more gap in min and max data. so variation is less. STD is also low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f7eca32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery Time    21\n",
       "Sorting Time      9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## number of unique values in columns\n",
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8edce7",
   "metadata": {},
   "source": [
    "### Checking of outliers in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb7478b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Delivery Time'])\n",
    "plt.title(\"Box plot of Delivery Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d939967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['Sorting Time'])\n",
    "plt.title(\"Box plot of Delivery Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c06591e",
   "metadata": {},
   "source": [
    "* No outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1a97b8e",
   "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>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delivery Time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorting Time</th>\n",
       "      <td>0.825997</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Delivery Time  Sorting Time\n",
       "Delivery Time       1.000000      0.825997\n",
       "Sorting Time        0.825997      1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## correlation in dataset\n",
    "corr = data.corr()\n",
    "corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7235f453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr,annot=True,cmap='RdBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f104782f",
   "metadata": {},
   "source": [
    "* Correlation co-eeficient is more, so they have strong correlation(positive correlation)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b1e65e",
   "metadata": {},
   "source": [
    "## EDA(Exploratary data analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37b6ed24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average delivery time is -  17.0\n"
     ]
    }
   ],
   "source": [
    "## Average of delivery time\n",
    "print(\"Average delivery time is - \",round(data['Delivery Time'].mean(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4b858877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum delivery time is -  8.0\n"
     ]
    }
   ],
   "source": [
    "## Minimum of delivery time\n",
    "print(\"Minimum delivery time is - \",round(data['Delivery Time'].min(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "348a4c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum delivery time is -  29.0\n"
     ]
    }
   ],
   "source": [
    "## Maximum of delivery time\n",
    "print(\"Maximum delivery time is - \",round(data['Delivery Time'].max(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bb8db564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average sorting time is -  6.0\n"
     ]
    }
   ],
   "source": [
    "## Average of sorting time time\n",
    "print(\"Average sorting time is - \",round(data['Sorting Time'].mean(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f55d7ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum sorting time is -  2\n"
     ]
    }
   ],
   "source": [
    "## Minimum of sorting time\n",
    "print(\"Minimum sorting time is - \",round(data['Sorting Time'].min(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fa27abbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum sorting time is -  10\n"
     ]
    }
   ],
   "source": [
    "## Maximum of sorting time\n",
    "print(\"Maximum sorting time is - \",round(data['Sorting Time'].max(),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "371a3015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tOeFLRDLEgaMtvFDbwPteM5Xth5p4cMVODPjgomn85YW4t7yQDFTf1Mq/PLzupNAHeOqFg3zoZ6vZ35Ccq4tPucffdetFMyt296ZTvV5E0q8wP5fXzRzNd558uXts9c56Nuw5wl1vnBliZTIQa3bVs3JHfdxlL9Y2snpHPf9wzun3iDrlHr+ZLTSzTcDm4Pn5Zvbd096yiCRNbq7x2MZ9vcbbOpyX9jeGUJEMxo66+Fdfv7o8OfveiUz1fB34B+AQgLuvBS5LytZFJCkam9v7bNmQ7VehZpPiUxzALUpSm+1Er9zd2WOoIylbF5GkKB+Rz8VT+2jZMF0tG4aK8yaMZFhe/FjOMZidpJv9JBL8O83sYsDNrMDMPkkw7SMimaEwP48PLJrWq2VDdYlaNgwlZ1SN4P+97Txy4tw+4YtvPpezxiTnrJ5EPjf8D+AbxG6+vgt4HLg9KVsXkaS5+IxKfvq+C/nDxn1s3NPApdMred1Z1ZyZpLCQ1DMzrj2vhrFlhTy5ZT8b9zRw5pgSrphRzQWTy8mN9xdhMNvp69aLJxRS5e4HkrK1QdKtF0VEBq6vWy8mMtXz32b2uJndpn78IiJDXyL9+KcTa8l8NrDKzB41sxtTXpmIiKREomf1POfuHwcWAHXA0pRWJSKDtvVAI6u2H2ZvfXb3lJfBS6QffynwZuBdwDTgl8T+AIhIBqlrbGXpX7dxz1Mv09LeSVlRPv96zSyuPX8sw/Kyu7+8DEwiZ/WsBX4FfN7d/5rackRksH6zbg/fOOGG6/XH2vjEz9cyqriARWdVh1iZZJpEgn+qn+rUHxEJVV1TC9/+80txl/1m3R4Fv5ykz+A3s6+7+x3AMjPrFfzu/qZUFiYiiWtp76T+WPxbZuw5kpyOjpI9+tvj77q71t3pKEREBq9qxDD+8fwaHlm1u9eyNyahm6Nklz6D391XBt+fSl85IjIYebk53HrxZJ5+4QAHG1/d879gUjmLZmiaR07W31TPeiDe3L4B7u7npawqERmwc8eX8dD7F7Ji22EONrYwoaKI+ZPLGTOyMOzSJMP0N9VzbdqqEJGkmFo1gqlVI8IuQzJcf1M927sem9kkYLq7/9HMCvt7n4iIZLZE7sD1PuBh4D+CofHEzusXEZEhKJGWDbcDlwANAO7+IqCjRSIiQ1Qiwd/i7t2nCZhZHvEP+oqIyBCQyFz9U2Z2F1BoZlcBHwR+k9qyRDLb4aZW1u2q51BTK1Mqi5lVU6p+ODJkJBL8dwK3AeuB9wO/A+5NZVEimWxn3THu/MU6/uvlQ91jn3z9DN576RSGFyj8JfOdMvjdvdPMfgX8Kuw7cYlkgj9urj0p9AHufvx5FkwpZ8GU+Dc8F8kkfc7xW8xnzewgsAV43swOmNmnE1mxmd1vZvvNbMMJY581s91mtib4euPp/yeIpNdv1+2NO/5C7dE0VyIyOP0d3L2D2Nk88919lLtXABcCl5jZxxJY9w+Aq+OMf83dZwdfvxtowSJhm9bHBVIjC/PTXInI4PQX/DcBN7j7K10D7r4VuDFY1i93f5rY3bpkCKhrauWJzbU8vHIXm/YcCbucjHb9nHHk5thJY5NGFTJnYnlIFYkMTH9z/PnufrDnoLsfMLPT2bX5kJndBKwAPuHuh09jXZIEDc1tfG7ZRn69dg8Aw/Jy+MEt81l4RmXIlWWmi6ZW8MNbF/CjZ7exac9Rrj5nDG+dO47x5UVhlyaSkP6CP35z71Mv68/3gC8Quw7gC8C/A/8c74VmthhYDDBx4sRBbk4SsWH3ke7Qh1hv9+8/8wrzp1SQl5vQbZkjxcy4ZHolF06toKW9k+Jh6mAiQ0t//2LPN7OGOOMGDB/Mxty9tnslZt8HHu3ntUuAJQDz5s3TBWMpdKy1o9fYviPNdLirKVM/8nJz9IdRhqT+mrQl/YRkMxvr7l2nRLwZ2NDf6yU9ZtWUUlM2nD31r96p6ZZLpuiCJJEslbIdOjN7AFgEVJrZLuAzwCIzm01sqmcbsQvCJGQ1ZYXce9N8frtuDy/ub+Sa88Zy5Vmjwy5LRFIkZcHv7jfEGb4vVduT0zOrppRZNaVhlyEiaaAJShGRiFHwi4hEjIJfRCRidLaeyCA0trSzcfcR6ppamTSqmBljSnpdzSuSqRT8IgO0t76Zzz+6id9v2AdAbo7x6Wtn8q4FE3UKrAwJmuoRGaAntuzvDn2Ajk7nM8s2sX6XehzJ0KDgFwDqGltZvmkfP1+xk427FWD9WbZ2d9zx5/epLbMMDZrqERqa2/j0sg08GvSZL8jN4T9vnc8latIW17iyIqB3b8ERw/XrJEOD9viF9buPdIc+QGtHJ/f+ZSvtHZ0hVpW53jJnHNbjOG51yTDmTFBbZhkaFPxCc1vvJm21DcfpcPXGi2fhtFHcd/N85k8up6won3fOG8/9t8xn4ii1ZZahQZ9NhVljSxlXVsju+ubusVvVpK1Pebk5XHFWNQunVtDU0kF5cYFO5ZQhRcEvsSZtN8/jt+v28srBRv7h7DFcflZ12GVlvMKCPAoL9CskQ4/+1QoAM8eWMnOsmrSJRIHm+EVEIkbBLyISMQp+EZGIUfCLiESMgl9EJGIU/CIiEaPgFxGJGJ3Hn2Yt7R1s3tNAw/F2zhpTQnXp8LBLEpGIUfCn2c+e28lnlm0EYObYEv7jxguYOKo45KpEJEo01ZNGu+qO8aXfb+5+vnnvUf6+rXd7XxGRVFLwp5NBTo9+vjn6PyAiaabYSaPx5UV86ppZ3b3c50woY96kinCLEpHI0Rx/ml13/lhqyoZzpLmN88aVMqFCPdxFJL0U/Gn20IpdfO7RTQDMGD2CJTfNY5IO7opIGmmqJ4121h3jK3/Y0v38+dpGVujgroikmYI/jXIMcnVwV0RCpthJo3HlRfzrtbPoukvfBRPLmD9ZB3dFJL00x59mb7tgPOeOH8nR4+2cObqEiuKCsEsSkYhR8KdZXm4OZ9eMDLsMEYkwBb8AsPvwMf62tY66plbOHT+SeZPKycvVTKBINlLwC4caW/j4Q2t59pU6AMzg3pvmceXM0SFXJiKpoF06YcOehu7QB3CHJU9vpa29M8SqRCRVFPyCd3qvsbaOTpze4yIy9Cn4hbNrRnJ2TelJY+99zVQK8nJDqkhEUklz/EJV6TC+809z+a+XDnKwsYXZE8u5aKquLxDJVikLfjO7H7gW2O/u5wRjFcCDwGRgG/AOd1fPggwwubKYyZXqGSQSBamc6vkBcHWPsTuBJ9x9OvBE8FxERNIoZcHv7k8DdT2GrwOWBo+XAtenavsiIhJfug/ujnb3vQDB9+o0b19EJPIy9qweM1tsZivMbMWBAwfCLkdEJGukO/hrzWwsQPB9f18vdPcl7j7P3edVVVWlrUARkWyX7uBfBtwcPL4Z+HWatx+6uqZWHtuwlwf/vpPn9x0NuxwRiaBUns75ALAIqDSzXcBngC8DD5nZbcAO4O2p2n4mau/o5Dt/fon7nnkFgNLCPB5avJCzxpae4p0iIsmTsuB39xv6WHRlqraZ6XYdbuYH/72t+3lDcztrdtYr+EUkrTL24G42KirIpbwo/6Sx0sL8Pl4tIpIaCv40qi4dzt1vP59RxQXkGNy8cBIXTlFrBBFJL/XqSbNFM6r5/UdfQ1NLOxMqinSzExFJOwV/CKpLh4ddgohEmHY3RUQiRsEvIhIxCn4RkYhR8IuIRIyCX0QkYhT8IiIRo+AXEYmYrA7++mOt1DYcx93DLkVEJGNkbfCv31XPW7/731x+95P8avVuOjsV/iIikMXB/6O/beflg00ca+3gkw+vY0fdsbBLEhHJCFkb/CXDX+1GMSwvh7xcC7EaEZHMkbW9ev5pwSQON7Wxo66Jj101g/HlRWGXJCKSEbI2+KdVj+Cr75xNZ6eTk6O9fRGRLlk71dNFoS8icrKsD34RETmZgl9EJGIU/CIiEaPgFxGJGAW/iEjEKPhFRCLGhkIDMzM7AGwf5NsrgYNJLCdZVNfAqK6BUV0Dk6l1wenVNsndq3oODongPx1mtsLd54VdR0+qa2BU18CoroHJ1LogNbVpqkdEJGIU/CIiEROF4F8SdgF9UF0Do7oGRnUNTKbWBSmoLevn+EVE5GRR2OMXEZETZG3wm9kEM/uzmW02s41m9tGwawIws+Fm9pyZrQ3q+lzYNZ3IzHLNbLWZPRp2LV3MbJuZrTezNWa2Iux6uphZmZk9bGZbgn9nCzOgphnBz6nrq8HM7gi7LgAz+1jwb36DmT1gZsPDrgnAzD4a1LQxzJ+Vmd1vZvvNbMMJYxVmttzMXgy+lydjW1kb/EA78Al3nwlcBNxuZrNCrgmgBbjC3c8HZgNXm9lF4ZZ0ko8Cm8MuIo7L3X12hp1y9w3gMXc/CzifDPi5ufvzwc9pNnABcAz4ZbhVgZmNAz4CzHP3c4Bc4F3hVgVmdg7wPmABsf+H15rZ9JDK+QFwdY+xO4En3H068ETw/LRlbfC7+153XxU8Pkrsl3JcuFWBxzQGT/ODr4w40GJm44FrgHvDriXTmVkpcBlwH4C7t7p7fahF9XYl8LK7D/bix2TLAwrNLA8oAvaEXA/ATOBv7n7M3duBp4A3h1GIuz8N1PUYvg5YGjxeClyfjG1lbfCfyMwmA3OAZ0MuBeieTlkD7AeWu3tG1AV8HfhfQGfIdfTkwONmttLMFoddTGAqcAD4z2Bq7F4zKw67qB7eBTwQdhEA7r4buBvYAewFjrj74+FWBcAG4DIzG2VmRcAbgQkh13Si0e6+F2I7s0B1Mlaa9cFvZiOAXwB3uHtD2PUAuHtH8FF8PLAg+LgZKjO7Ftjv7ivDriWOS9x9LvAGYlN2l4VdELG917nA99x9DtBEkj6GJ4OZFQBvAn4edi0Awdz0dcAUoAYoNrMbw60K3H0z8BVgOfAYsJbYNHFWy+rgN7N8YqH/E3d/JOx6egqmBp6k97xeGC4B3mRm24CfAVeY2Y/DLSnG3fcE3/cTm69eEG5FAOwCdp3wae1hYn8IMsUbgFXuXht2IYHXAa+4+wF3bwMeAS4OuSYA3P0+d5/r7pcRm2p5MeyaTlBrZmMBgu/7k7HSrA1+MzNi86+b3f2rYdfTxcyqzKwseFxI7BdiS6hFAe7+v919vLtPJjZF8Cd3D32PzMyKzayk6zHwemIfz0Pl7vuAnWY2Ixi6EtgUYkk93UCGTPMEdgAXmVlR8Lt5JRlwMBzAzKqD7xOBt5BZP7dlwM3B45uBXydjpXnJWEmGugR4D7A+mE8HuMvdfxdeSQCMBZaaWS6xP7wPuXvGnDqZgUYDv4xlBXnAT939sXBL6vZh4CfBtMpW4NaQ6wEgmKu+Cnh/2LV0cfdnzexhYBWxqZTVZM7Vsr8ws1FAG3C7ux8OowgzewBYBFSa2S7gM8CXgYfM7DZifzzfnpRt6cpdEZFoydqpHhERiU/BLyISMQp+EZGIUfCLiESMgl9EJGIU/JIVzOz/BN0V1wVdKS8c4PtvMbOaE57fe7pN/YI2AF1dMveZ2e4Tni8ws2+ezvpFBkunc8qQF7RD/iqwyN1bzKwSKOi64jeB9+cS63z4SXdPSdtnM/ss0Ojud6di/SIDoT1+yQZjgYPu3gLg7ge7Qt/MrgyaqK0P+p0PC8a3mdmnzewZYle5ziN2MdYaMys0syfNbF7w2kYz+6LF7qHwNzMbHYxPC57/3cw+b2aN8YqLx8wWWXDPAzP7rJktNbPHg7reYmb/FtT8WNB6BDO7wMyeCprV/aHrUn6RgVLwSzZ4HJhgZi+Y2XfN7LUQu+kNsR7n73T3c4ld+fuBE9533N0vdfcfAyuAdwe97Jt7rL+YWOve84GnifVvh1g//m+4+3xOv8XwNGItsa8Dfgz8Oai5GbgmCP9vAW9z9wuA+4EvnuY2JaIU/DLkBfc3uABYTKxV8oNmdgswg1hjsBeCly4l1kO/y4MJbqIV6GqrsRKYHDxeyKvdL386mNpP8Pugedl6Yjcp6WpLsT7Y3gzgHGB50ILkU8S6u4oMWDb36pEIcfcOYp1OnzSz9cQaWq05xduaElx9m796MKyD1PzedE1TdZrZidvrDLZnwEZ3D/32jjL0aY9fhjyL3Wf2xNvlzQa2E+t6OtnMzgjG30PsDkvxHAVKBrjpvwFvDR6n+jaCzwNVwYFszCzfzM5O8TYlSyn4JRuMINbxdJOZrQNmAZ919+PEOmb+PPgU0Anc08c6fgDc03VwN8Ht3gF83MyeI3aA+chp/Df0y91bgbcBXzGztcQ+zWREP3sZenQ6p8ggBe2Pm93dzexdwA3ufl3YdYmciub4RQbvAuDbwY1F6oF/DrcckcRoj19EJGI0xy8iEjEKfhGRiFHwi4hEjIJfRCRiFPwiIhGj4BcRiZj/DwCjtaWFQNvFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Sorting Time',y='Delivery Time',data=data,marker='o',size='Sorting Time',legend=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6a6bcc",
   "metadata": {},
   "source": [
    "* Sorting time is highly correlated with the delivery time. Delivery time and sorting time is directly propotional.\n",
    "* If delivery time increases then sorting time also increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6bde53c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Sorting Time',data=data,palette='deep')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da7b72a",
   "metadata": {},
   "source": [
    "* Sorting time 7 is occure maximum times ,so maximum number of sorting is at 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535b22f7",
   "metadata": {},
   "source": [
    "## Feature Engineering and Machine Learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a37c61e",
   "metadata": {},
   "source": [
    "* Dataset is having one input to predcit output & linear plot is straight, so we will build simple linear regression  model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d030a999",
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing scikit library\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4a4478c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## set input and output of dataset\n",
    "## x - independent varibale i.e delivery time\n",
    "## y - dependent variable i.e sorting time\n",
    "\n",
    "y=data['Delivery Time']\n",
    "x=data['Sorting Time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "937256c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21.0\n",
       "1    13.5\n",
       "Name: Delivery Time, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed442584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     4\n",
       "Name: Sorting Time, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1392e938",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "befa9295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16,), (16,))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "becba4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5,), (5,))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "27619c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.values.reshape(-1,1)\n",
    "y_train=y_train.values.reshape(-1,1)\n",
    "x_test=x_test.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dcf2b204",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d2aea23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fit = model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6792b8ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.66384615]\n"
     ]
    }
   ],
   "source": [
    "## intercept of model\n",
    "print(model_fit.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "04d3f8bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.60153846]]\n"
     ]
    }
   ],
   "source": [
    "## coefficient of model\n",
    "print(model_fit.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c1fa6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21.07769231],\n",
       "       [11.46846154],\n",
       "       [16.27307692],\n",
       "       [16.27307692],\n",
       "       [11.46846154]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model_fit.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1b2ee8a6",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.077692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.468462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.273077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.273077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.468462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pred\n",
       "0  21.077692\n",
       "1  11.468462\n",
       "2  16.273077\n",
       "3  16.273077\n",
       "4  11.468462"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y_pred = pd.DataFrame(y_pred,columns=[\"pred\"])\n",
    "data_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a28ead44",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y_pred['actual']=y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0bb766dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        pred  actual\n",
      "0  21.077692   24.00\n",
      "1  11.468462   11.50\n",
      "2  16.273077   19.75\n",
      "3  16.273077   13.75\n",
      "4  11.468462   12.03\n"
     ]
    }
   ],
   "source": [
    "print(data_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "30ccd416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5774636053428878"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(data_y_pred['pred'],data_y_pred['actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ca48d3",
   "metadata": {},
   "source": [
    "* r_square value is 0.57, so built model is towards 1 so it is good model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a1691eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9030769230769227"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(data_y_pred['pred'],data_y_pred['actual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a16571b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.462222721893498"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(data_y_pred['pred'],data_y_pred['actual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f2da2f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "684a10e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3371398592924426"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(mean_squared_error(data_y_pred['pred'],data_y_pred['actual']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0f515f",
   "metadata": {},
   "source": [
    "* Model coefficient of determination is 0.57, so model is good fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c9d359b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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