StudentScore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2996d910",
   "metadata": {},
   "source": [
    "## 5th practice ...  Score of students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87638f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3091eee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"Desktop/score.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4553c06",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "65fea8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "795ed154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of     Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e139e570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b3fc032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxmElEQVR4nO3df3RU9YH//9cF4yShk0lxJUPMJEabHZRgdFcOJbgLroT9KBU4uIpEj4m03ShISWmVUqrFqkmhLQe3uFTsftkoC/Yspyi6RyC6SXpcaokKLb9ONPw2ms3ZNcyEEJKQvL9/ZJk6gsgkk3tnJs/HOXOmec/N8JpDj/Pi3vd9vy1jjBEAAIBNhjkdAAAADC2UDwAAYCvKBwAAsBXlAwAA2IryAQAAbEX5AAAAtqJ8AAAAW1E+AACArS5zOsDn9fb26uOPP5bb7ZZlWU7HAQAAl8AYo7a2NmVmZmrYsIuf24i58vHxxx/L5/M5HQMAAPTDiRMnlJWVddFjYq58uN1uSX3h09LSHE4DAAAuRTAYlM/nC32PX0zMlY9zl1rS0tIoHwAAxJlLmTLBhFMAAGArygcAALAV5QMAANiK8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAA4oQxRkdrj8oY43SUAaF8AAAQJxq3Narq1iod2n7I6SgDQvkAACBOHNh8IOw5XsXc3i4AAKCP6TWqX1uvMyfPSJIObj4oqa98pOemS5KS05M1/uHxsoZ9+Z4qsYLyAQBAjOpq71LtE7Xq+LRDshQqGF2nulTzeI1kpJSRKSp4oEAut8vhtJeOyy4AAMQol9ulst1lyirMkiSZHhP27Cv0qWxPWVwVD4nyAQBATPNke1RaU6qk1KSw8aTUJJXUlsjj8ziUrP8oHwAAxLimXU3qbu8OG+tu71bTriaHEg0M5QMAgBjX8FqDJGnMrDFa2LhQ/pn+vvGtDU7G6jcmnAIAEOP8M/zyFniVPzdflmVpzpY52rdpnzw58XfJRZIsE2PLpAWDQXk8HgUCAaWlpTkdBwAAXIJIvr+57AIAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAAYCvKBwAAsBXlAwAA2IryAQAAbEX5AAAAtqJ8AAAAW1E+AACArSIuH21tbSovL1dOTo5SUlJUWFio+vr60OvGGC1fvlyZmZlKSUnRlClTtH///qiGBgAA8Svi8vGtb31L1dXVeumll7R3715NmzZNU6dOVVNTkyRp5cqVWrVqldasWaP6+np5vV4VFRWpra0t6uEBAED8sYwx5lIP7ujokNvt1quvvqrp06eHxm+88UZ94xvf0FNPPaXMzEyVl5dryZIlkqTOzk5lZGRoxYoVKisr+9I/I5IteQEAQGyI5Ps7ojMfZ8+eVU9Pj5KTk8PGU1JS9Pbbb+vIkSNqbm7WtGnTQq+5XC5NnjxZO3fuvOB7dnZ2KhgMhj0AAEDiiqh8uN1uTZw4UU899ZQ+/vhj9fT0aMOGDfrDH/6gTz75RM3NzZKkjIyMsN/LyMgIvfZ5lZWV8ng8oYfP5+vnRwEAAPEg4jkfL730kowxuuqqq+RyufRP//RPKi4u1vDhw0PHWJYV9jvGmPPGzlm6dKkCgUDoceLEiUgjAQCAOBJx+bj22mtVV1enU6dO6cSJE9q1a5e6u7uVm5srr9crSeed5WhpaTnvbMg5LpdLaWlpYQ8AAJC4+r3Ox4gRIzR69Gi1trZq+/btmjlzZqiAVFdXh47r6upSXV2dCgsLoxIYAADEt8si/YXt27fLGCO/36/GxkY9+uij8vv9evDBB2VZlsrLy1VRUaG8vDzl5eWpoqJCqampKi4uHoz8AAAgAsYYHas7ppzJOV84JWKwRVw+AoGAli5dqo8++kgjR47UXXfdpWeeeUZJSUmSpMcee0wdHR2aP3++WltbNWHCBO3YsUNutzvq4QEAQGQatzVq4x0bdd8b9+lr/+9rjmSIaJ0PO7DOBwAAg+fVb76qPf/fHt30zZs049czova+kXx/R3zmAwAAxA/Ta1S/tl5nTp6RJB3cfFCSdGDzAaXnpkuSktOTNf7h8bKG2XMZhvIBAEAC62rvUu0Tter4tEOyFCoYXae6VPN4jWSklJEpKnigQC63y5ZM7GoLAEACc7ldKttdpqzCLEmS6TFhz75Cn8r2lNlWPCTKBwAACc+T7VFpTamSUpPCxpNSk1RSWyKPz2NrHsoHAABDQNOuJnW3d4eNdbd3q2lXk+1ZKB8AgLhkjNHR2qOKsZs2Y1bDaw2SpDGzxmhh40L5Z/r7xrc22J6FCacAgLgUC+tVxBP/DL+8BV7lz82XZVmas2WO9m3aJ0+OvZdcJMoHACBOHdh8IPRM+fhy2ZOypUl//tmyLI0rHudIFsoHACAuxOJ6FegfygcAIC7E4noV6B8mnAIAYtq5iaWXf+XymFuvAv1D+QAAxLTGbY2qurVKh7Yfirn1KtA/lA8AQEz77MRSKbbWq0D/MOcDABBTvmxi6aHthyRJ/ll+Tfv5NO343g41vNqghq0NfXd0IOZZJsZWZ4lkS14AQOLpbOvUs1c/Gzax1PQYWcMtmV4jGSlpRJIWf7xYyWnJMsaE1qugfDgnku9vLrsAAGLKpWyEtuDgAiWnJUv683oVFI/4QfkAAMQcJpYmNsoHACAmMbE0cVE+AAAxKZY2QkN0cbcLACAmxdJGaIgu7nYBAAADxt0uAAAgZlE+AACArSgfAADAVpQPAABgK8oHAACwFeUDAADYivIBAMAlMMboaO1RxdgKFXGJ8gEAwCVo3NaoqlurdGj7IaejxD3KBwAAl+DA5gNhz+g/llcHAOACTK9R/dp6nTl5RpJ0cPNBSX3lIz03XZKUnJ6s8Q+PlzXMcipmXKJ8AABwAV3tXap9olYdn3ZIlkIFo+tUl2oer5GMlDIyRQUPFMjldjmcNr5EdNnl7Nmz+tGPfqTc3FylpKTommuu0U9+8hP19vaGjjHGaPny5crMzFRKSoqmTJmi/fv3Rz04AACDyeV2qWx3mbIKsyRJpseEPfsKfSrbU0bx6IeIyseKFSv0q1/9SmvWrNHBgwe1cuVK/exnP9Mvf/nL0DErV67UqlWrtGbNGtXX18vr9aqoqEhtbW1RDw8AwGDyZHtUWlOqpNSksPGk1CSV1JbI42OH3f6IqHz8/ve/18yZMzV9+nRdffXV+od/+AdNmzZN7777rqS+sx6rV6/WsmXLNHv2bOXn56uqqkqnT5/Wxo0bB+UDAAAwmJp2Nam7vTtsrLu9W027mhxKFP8iKh+33HKL3nrrLX3wwQeSpD/+8Y96++23dccdd0iSjhw5oubmZk2bNi30Oy6XS5MnT9bOnTsv+J6dnZ0KBoNhDwAAYkXDaw2SpDGzxmhh40L5Z/r7xrc2OBkrrkU04XTJkiUKBAIaM2aMhg8frp6eHj3zzDOaO3euJKm5uVmSlJGREfZ7GRkZOnbs2AXfs7KyUk8++WR/sgMAMOj8M/zyFniVPzdflmVpzpY52rdpnzw5XHLpr4jKx29+8xtt2LBBGzdu1NixY7Vnzx6Vl5crMzNTJSUloeMsK/yWI2PMeWPnLF26VIsXLw79HAwG5fP5IokFAMCgyZ6ULU3688+WZWlc8TjnAiWAiMrHo48+qh/84Ae69957JUnjxo3TsWPHVFlZqZKSEnm9Xkl9Z0BGjx4d+r2Wlpbzzoac43K55HIxUxgAgKEiojkfp0+f1rBh4b8yfPjw0K22ubm58nq9qq6uDr3e1dWluro6FRYWRiEuAACIdxGd+bjzzjv1zDPPKDs7W2PHjtXu3bu1atUqzZs3T1Lfqajy8nJVVFQoLy9PeXl5qqioUGpqqoqLiwflAwAAgPgSUfn45S9/qccff1zz589XS0uLMjMzVVZWpieeeCJ0zGOPPaaOjg7Nnz9fra2tmjBhgnbs2CG32x318AAAIP5YJsb2Bg4Gg/J4PAoEAkpLS3M6DgAAuASRfH+zqy0AALAV5QMAANiK8gEAAGxF+QAAALaifAAAAFtRPgBgiDHG6GjtUcXYzY4YQigfADDENG5rVNWtVTq0/ZDTUTBEUT4AYIg5sPlA2DNgt4hWOAUAxB/Ta1S/tl5nTp6RJB3cfFBSX/lIz02XJCWnJ2v8w+NlDbvwDuRANFE+ACDBdbV3qfaJWnV82iFZChWMrlNdqnm8RjJSysgUFTxQIJebXcYx+LjsAgAJzuV2qWx3mbIKsyRJpseEPfsKfSrbU0bxiCIm9V4c5QMAhgBPtkelNaVKSk0KG09KTVJJbYk8Po9DyRITk3ovjvIBAENE064mdbd3h411t3eraVeTQ4kSF5N6L445HwAwRDS81iBJGjNrjIp+XqQd39uhhlcb1LC1QdmTsh1OF9+Y1BsZy8TYBalItuQFAFy64/91XIFjAeXPzZdlWTLGaN+mffLkeCgfA9TZ1qlnr342bFKv6TGyhlsyvSY0qXfR0UUJO7cmku9vygcAAFEQOB7Q5rmb9dHvP5I++81qSb6JPt318l0JPbcmku9v5nwAABAFTOq9dJQPAACihEm9l4byAQBAlHx2Uu/CxoXyz/T3jW9tcDJWzOFuFwAAosQ/wy9vgTc0qXfOljmhSb34MyacAgCAAWPCKQAAiFmUDwAAYCvKBwAAsBXlAwAA2IryAQAAbEX5AAAAtqJ8AAAAW1E+AACArSgfAADAVpQPAABgK8oHAACwVUTl4+qrr5ZlWec9FixYIEkyxmj58uXKzMxUSkqKpkyZov379w9KcAAAEJ8iKh/19fX65JNPQo/q6mpJ0t133y1JWrlypVatWqU1a9aovr5eXq9XRUVFamtri35yAAAQlyIqH1deeaW8Xm/o8frrr+vaa6/V5MmTZYzR6tWrtWzZMs2ePVv5+fmqqqrS6dOntXHjxsHKDwAA4ky/53x0dXVpw4YNmjdvnizL0pEjR9Tc3Kxp06aFjnG5XJo8ebJ27tz5he/T2dmpYDAY9gAAJA5jjI7WHpUxxukoiBH9Lh+vvPKKTp48qdLSUklSc3OzJCkjIyPsuIyMjNBrF1JZWSmPxxN6+Hy+/kYCAMSgxm2Nqrq1Soe2H3I6CmJEv8vHv/zLv+j2229XZmZm2LhlWWE/G2POG/uspUuXKhAIhB4nTpzobyQAQAw6sPlA2DNwWX9+6dixY3rzzTf129/+NjTm9Xol9Z0BGT16dGi8paXlvLMhn+VyueRyufoTAwAQg0yvUf3aep05eUaSdHDzQUl95SM9N12SlJyerPEPj5c17Iv/cYrE1a/ysX79eo0aNUrTp08PjeXm5srr9aq6ulo33XSTpL55IXV1dVqxYkV00gIAYl5Xe5dqn6hVx6cdkqVQweg61aWax2skI6WMTFHBAwVyufnH51AU8WWX3t5erV+/XiUlJbrssj93F8uyVF5eroqKCm3ZskX79u1TaWmpUlNTVVxcHNXQAIDY5XK7VLa7TFmFWZIk02PCnn2FPpXtKaN4DGERn/l48803dfz4cc2bN++81x577DF1dHRo/vz5am1t1YQJE7Rjxw653e6ohAUAxAdPtkelNaVaMXKFutu7Q+NJqUkqqS3R8KThDqaD0ywTY/c+BYNBeTweBQIBpaWlOR0HANBPx98+rvV/s/688QffflDZk7IdSITBFMn3N3u7AAAGRcNrDZKkMbPGaGHjQvln+vvGtzY4GQsxoF8TTgEAfYwxOlZ3TDmTcy66rMBQ5J/hl7fAq/y5+bIsS3O2zNG+TfvkyfE4HQ0O48wHAAwAC2h9sexJ2RpXPC5UyizL0rjicVxyAeUDAAaCBbSAyHHZBQAiwAJawMBRPgAgAiygBQwcl10AIAIsoAUMHOUDACJ0bgGtpNSksPFzC2h5fNzNAVwM5QMA+qFpV1PYyp2S1N3eraZdTQ4lAuIH5QMA+oEFtID+Y8IpAPQDC2gB/cfeLgAAYMDY2wUAAMQsygcAALAV5QMAANiK8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAAYCvKBwAAsBXlAwAA2IryAQCfY4zR0dqjirGtr4CEQfkAgM9p3NaoqlurdGj7IaejAAmJ8gEAn3Ng84GwZwDRdZnTAQDAaabXqH5tvc6cPCNJOrj5oKS+8pGemy5JSk5P1viHx8saZjkVE0gYlA8AQ15Xe5dqn6hVx6cdkqVQweg61aWax2skI6WMTFHBAwVyuV0OpwXiH5ddAAx5LrdLZbvLlFWYJUkyPSbs2VfoU9meMooHECWUDwCQ5Mn2qLSmVEmpSWHjSalJKqktkcfncSgZkHgoHwDwf5p2Nam7vTtsrLu9W027mhxKBCQmygeAuBXt9TgaXmuQJI2ZNUYLGxfKP9PfN761ISrvD6BPxOWjqalJ999/v6644gqlpqbqxhtv1HvvvRd63Rij5cuXKzMzUykpKZoyZYr2798f1dAAIEV/PQ7/DL9m/9ts3fPbezTy2pGas2WOZv/bbPln+KPy/gD6RFQ+WltbNWnSJCUlJemNN97QgQMH9Itf/ELp6emhY1auXKlVq1ZpzZo1qq+vl9frVVFRkdra2qKdHcAQF+31OLInZWtc8ThZVt/dLpZlaVzxOGVPyo7K+wPoE9GttitWrJDP59P69etDY1dffXXofxtjtHr1ai1btkyzZ8+WJFVVVSkjI0MbN25UWVlZdFIDGJJYjwNIDJaJ4GLp9ddfr7//+7/XRx99pLq6Ol111VWaP3++vv3tb0uSDh8+rGuvvVbvv/++brrpptDvzZw5U+np6aqqqjrvPTs7O9XZ2Rn6ORgMyufzKRAIKC0tbSCfDUCC6Wzr1LNXPxu2HofpMbKGWzK9JrQex6Kji7gtFrBZMBiUx+O5pO/viC67HD58WGvXrlVeXp62b9+uhx56SN/5znf04osvSpKam5slSRkZGWG/l5GREXrt8yorK+XxeEIPn88XSSQAQwjrcQCJIaLy0dvbq7/6q79SRUWFbrrpJpWVlenb3/621q5dG3bcueul5xhjzhs7Z+nSpQoEAqHHiRMnIvwIAIYS1uMA4l9E5WP06NG6/vrrw8auu+46HT9+XJLk9Xol6byzHC0tLeedDTnH5XIpLS0t7AEAF8N6HEB8i6h8TJo0SQ0N4fe7f/DBB8rJyZEk5ebmyuv1qrq6OvR6V1eX6urqVFhYGIW4AMB6HEC8i+hul+9+97sqLCxURUWF7rnnHu3atUvr1q3TunXrJPVdbikvL1dFRYXy8vKUl5eniooKpaamqri4eFA+AIChxz/DL2+BV/lz82VZluZsmaN9m/bJk8MlFyAeRHS3iyS9/vrrWrp0qT788EPl5uZq8eLFobtdpL75HU8++aSef/55tba2asKECXruueeUn59/Se8fyWxZAAAQGyL5/o64fAw2ygcAAPFn0G61BQAAGCjKBwAAsBXlAwAA2IryAQAAbEX5AAAAtqJ8AAAAW1E+AACArSgfAADAVpQPAABgK8oHAACwFeUDAADYivIBAABsRfkAAAC2onwAAABbUT4AXJQxRkdrj8oY43QUAAmC8gHgohq3Narq1iod2n7I6SgAEgTlA8BFHdh8IOwZAAbqMqcDAIgtpteofm29zpw8I0k6uPmgpL7ykZ6bLklKTk/W+IfHyxpmORUTQByjfAAI09XepdonatXxaYdkKVQwuk51qebxGslIKSNTVPBAgVxul8NpAcQjLrsACONyu1S2u0xZhVmSJNNjwp59hT6V7SmjeADoN8oHgPN4sj0qrSlVUmpS2HhSapJKakvk8XkcSgYgEVA+AFxQ064mdbd3h411t3eraVeTQ4kAJArKB4ALanitQZI0ZtYYLWxcKP9Mf9/41gYnYwFIAEw4BXBB/hl+eQu8yp+bL8uyNGfLHO3btE+eHC65ABgYy8TYsoXBYFAej0eBQEBpaWlOxwEAAJcgku9vLrsAAABbUT4AAICtKB8AAMBWlA8AAGArygcAALAV5QMAANiK8gEAAGwVUflYvny5LMsKe3i93tDrxhgtX75cmZmZSklJ0ZQpU7R///6ohwYAAPEr4jMfY8eO1SeffBJ67N27N/TaypUrtWrVKq1Zs0b19fXyer0qKipSW1tbVEMDAID4FXH5uOyyy+T1ekOPK6+8UlLfWY/Vq1dr2bJlmj17tvLz81VVVaXTp09r48aNUQ8OAADiU8Tl48MPP1RmZqZyc3N177336vDhw5KkI0eOqLm5WdOmTQsd63K5NHnyZO3cufML36+zs1PBYDDsAQAAEldE5WPChAl68cUXtX37dr3wwgtqbm5WYWGh/vd//1fNzc2SpIyMjLDfycjICL12IZWVlfJ4PKGHz+frx8cAAADxIqLycfvtt+uuu+7SuHHjNHXqVP3Hf/yHJKmqqip0jGVZYb9jjDlv7LOWLl2qQCAQepw4cSKSSAAAIM4M6FbbESNGaNy4cfrwww9Dd718/ixHS0vLeWdDPsvlciktLS3sAQAAEteAykdnZ6cOHjyo0aNHKzc3V16vV9XV1aHXu7q6VFdXp8LCwgEHBQAAieGySA7+/ve/rzvvvFPZ2dlqaWnR008/rWAwqJKSElmWpfLyclVUVCgvL095eXmqqKhQamqqiouLBys/AACIMxGVj48++khz587V//zP/+jKK6/U17/+db3zzjvKycmRJD322GPq6OjQ/Pnz1draqgkTJmjHjh1yu92DEh4AAMQfyxhjnA7xWcFgUB6PR4FAgPkfAADEiUi+v9nbBQAA2IryAWDQGWN0tPaoYuxEKwCHUD4ADLrGbY2qurVKh7YfcjoKgBhA+QAw6A5sPhD2DGBoi+huFwC4FKbXqH5tvc6cPCNJOrj5oKS+8pGemy5JSk5P1viHx8sa9sUrIANITJQPAFHX1d6l2idq1fFph2QpVDC6TnWp5vEayUgpI1NU8ECBXG6Xw2kB2I3LLgCizuV2qWx3mbIKsyRJpseEPfsKfSrbU0bxAIYoygeAQeHJ9qi0plRJqUlh40mpSSqpLZHH53EoGQCnUT4ADJqmXU3qbu8OG+tu71bTriaHEgGIBZQPAIOm4bUGSdKYWWO0sHGh/DP9feNbG5yMBcBhTDgFMGj8M/zyFniVPzdflmVpzpY52rdpnzw5XHIBhjL2dgEAAAPG3i4AACBmUT4AAICtKB8AAMBWlA8AAGArygcAALAV5QMAANiK8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDyQcY4yO1h5VjG1bBAD4P5QPJJzGbY2qurVKh7YfcjoKAOACKB9IOAc2Hwh7BgDElsucDgAMlOk1ql9brzMnz0iSDm4+KKmvfKTnpkuSktOTNf7h8bKGWU7FBAD8H8oH4l5Xe5dqn6hVx6cdkqVQweg61aWax2skI6WMTFHBAwVyuV0Op7WXMUbH6o4pZ3KOLIviBSA2cNkFcc/ldqlsd5myCrMkSabHhD37Cn0q21M25IqHxPwXALGJ8oGE4Mn2qLSmVEmpSWHjSalJKqktkcfncSiZs5j/AiAWcdkFCaNpV5O627vDxrrbu9W0q0nZk7IdSmUv5r8AiAeUDySMhtcaJEljZo1R0c+LtON7O9TwaoMatjYMmfLB/BcA8WBAl10qKytlWZbKy8tDY8YYLV++XJmZmUpJSdGUKVO0f//+geYEvpR/hl+z/2227vntPRp57UjN2TJHs/9ttvwz/E5Hsw3zXwDEg36Xj/r6eq1bt0433HBD2PjKlSu1atUqrVmzRvX19fJ6vSoqKlJbW9uAwwIXkz0pW+OKx4Xu6rAsS+OKxw2Zsx7nMP8FQKzrV/k4deqU7rvvPr3wwgv66le/Gho3xmj16tVatmyZZs+erfz8fFVVVen06dPauHFj1EIDuLiLzX8BAKf1q3wsWLBA06dP19SpU8PGjxw5oubmZk2bNi005nK5NHnyZO3cufOC79XZ2algMBj2ADAwn53/srBxofwz+y49NWxtcDIWAEjqx4TTl19+We+//77q6+vPe625uVmSlJGRETaekZGhY8eOXfD9Kisr9eSTT0YaA8BF+Gf45S3wKn9uvizL0pwtc7Rv0z55crjkAsB5EZWPEydOaNGiRdqxY4eSk5O/8LjPr6RojPnC1RWXLl2qxYsXh34OBoPy+XyRxALwOdmTsqVJf/753PwXAIgFEZWP9957Ty0tLfrrv/7r0FhPT49+97vfac2aNWpo6Dul29zcrNGjR4eOaWlpOe9syDkul0suFzPvAQAYKiKa83Hbbbdp79692rNnT+hx880367777tOePXt0zTXXyOv1qrq6OvQ7XV1dqqurU2FhYdTDAwCA+BPRmQ+32638/PywsREjRuiKK64IjZeXl6uiokJ5eXnKy8tTRUWFUlNTVVxcHL3UAAAgbkV9hdPHHntMHR0dmj9/vlpbWzVhwgTt2LFDbrc72n8UAACIQ5Yxxjgd4rOCwaA8Ho8CgYDS0tKcjgMAAC5BJN/f7GoLAABsRfkAAAC2onwAAABbUT4AAICtKB8AAMBWlA8AAGArygcAALAV5QMAANiK8gEAAGxF+QAAALaifAADZIzR0dqjirGdCgAgZlE+gAFq3NaoqlurdGj7IaejAEBcoHwAA3Rg84GwZwDAxV3mdAAg3pheo/q19Tpz8owk6eDmg5L6ykd6brokKTk9WeMfHi9rmOVUTACIWZQPIEJd7V2qfaJWHZ92SJZCBaPrVJdqHq+RjJQyMkUFDxTI5XY5nBYAYg+XXYAIudwule0uU1ZhliTJ9JiwZ1+hT2V7yigeAPAFKB9AP3iyPSqtKVVSalLYeFJqkkpqS+TxeRxKBgCxj/IB9FPTriZ1t3eHjXW3d6tpV5NDiQAgPlA+gH5qeK1BkjRm1hgtbFwo/0x/3/jWBidjAUDMY8Ip0E/+GX55C7zKn5svy7I0Z8sc7du0T54cLrkAwMVYJsaWZQwGg/J4PAoEAkpLS3M6DgAAuASRfH9z2QUAANiK8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAAYCvKBwAAsBXlAwAA2IryAQAAbEX5wJcyxuho7VFdykr8kRwbK+IxMwDEs4jKx9q1a3XDDTcoLS1NaWlpmjhxot54443Q68YYLV++XJmZmUpJSdGUKVO0f//+qIeGvRq3Narq1iod2n4oqsfGinjMDADxLKLykZWVpZ/+9Kd699139e677+rv/u7vNHPmzFDBWLlypVatWqU1a9aovr5eXq9XRUVFamtrG5TwsMeBzQfCnqN1bKyIx8wAEM8GvKvtyJEj9bOf/Uzz5s1TZmamysvLtWTJEklSZ2enMjIytGLFCpWVlV3S+7GrrfNMr1H92nqdOXlGkrRz5U51Bjvl8rhU+GihJCk5PVnjHx4vSZd8rDXMcuDTnC+SzxcrmQEg1kXy/d3v8tHT06N///d/V0lJiXbv3q3k5GRde+21ev/993XTTTeFjps5c6bS09NVVVV1wffp7OxUZ2dnWHifz0f5cFBnW6eevfpZdXzaIVmSNcyS6TGyhlsyvUYyUsrIFC06ukiSLvlYl9vl8CfrE8nni5XMABDrIikfEU843bt3r77yla/I5XLpoYce0pYtW3T99derublZkpSRkRF2fEZGRui1C6msrJTH4wk9fD5fpJEQZS63S2W7y5RVmCVJMj0m7NlX6FPZnjK53K6Ijo0V8ZgZABJJxOXD7/drz549euedd/Twww+rpKREBw78+Vq5ZYWfpjbGnDf2WUuXLlUgEAg9Tpw4EWkkDAJPtkelNaVKSk0KG09KTVJJbYk8Pk+/jo0V8ZgZABJFxOXj8ssv19e+9jXdfPPNqqysVEFBgZ599ll5vV5JOu8sR0tLy3lnQz7L5XKF7p4590BsaNrVpO727rCx7vZuNe1qGtCxsSIeMwNAIhjwOh/GGHV2dio3N1der1fV1dWh17q6ulRXV6fCwsKB/jFwQMNrDZKkMbPGaGHjQvln+vvGtzYM6NhYEY+ZASARXBbJwT/84Q91++23y+fzqa2tTS+//LJqa2u1bds2WZal8vJyVVRUKC8vT3l5eaqoqFBqaqqKi4sHKz8GkX+GX94Cr/Ln5suyLM3ZMkf7Nu2TJ+f8SxKRHBsr4jEzACSCiO52+eY3v6m33npLn3zyiTwej2644QYtWbJERUVFkvrOgjz55JN6/vnn1draqgkTJui5555Tfn7+JQfiVlsAAOKPLbfaDhbKBwAA8WdQb7UFAAAYCMoH4hqbwgFA/KF8IK6xKRwAxB/KB+Iam8IBQPyJ6FZbwGmf3xTu4OaDkvrKR3puuiQ2hQOAWEf5QFzpau9S7RO1YZvCSVLXqS7VPF4T2hSu4IEC9mYBgBjFZRfEFTaFA4D4R/lA3GFTOACIb5QPxCU2hQOA+EX5QFxiUzgAiF9MOEVUGWN0rO6YcibnyLIG724TNoUDgPjFmQ9ElV2LfmVPyta44nGhgmNZlsYVj1P2pOxB/XMBAANH+UBUsegXAODLcNkFA8KiXwCASFE+Ypxdcyj6i0W/AACR4rJLjIv1jdNY9AsAECnKR4yLhzkULPoFAIgEl11iTLzOobjYol/cgQIA+CzKR4yJ1zkUn130q+jnRdrxvR1qeLVBDVsbKB8AgDCWMcY4HeKzgsGgPB6PAoGA0tLSnI7jiMDxgDbP3ayPfv+R9Nm/HUvyTfTprpfvirlLGcf/67gCxwKhRb+MMaFFvygfAJD4Ivn+pnzEqJ6uHq0YuSLsUkbSiCQtaV2i4UnDHUwGAMD5Ivn+ZsJpjGLjNABAoqJ8xCg2TgMAJComnMYoNk4DACQq5nwAAIABY84HAACIWZQPAABgK8oHvpQxRkdrjyrGrtABAOIU5QNfKtY3twMAxBfKB75UPGxuBwCIH9xqi/PE6+Z2AID4QPnAeeJ1czsAQHyI6LJLZWWlxo8fL7fbrVGjRmnWrFlqaAhfcdMYo+XLlyszM1MpKSmaMmWK9u/fH9XQsSbRJmS63C6V7S5TVmGWJMn0mLBnX6FPZXvKKB4AgH6JqHzU1dVpwYIFeuedd1RdXa2zZ89q2rRpam9vDx2zcuVKrVq1SmvWrFF9fb28Xq+KiorU1tYW9fCxIhEnZHqyPSqtKVVSalLYeFJqkkpqS2JuV10AQPyIqHxs27ZNpaWlGjt2rAoKCrR+/XodP35c7733nqS+MwCrV6/WsmXLNHv2bOXn56uqqkqnT5/Wxo0bB+UDxIJEnZDJ5nYAgMEwoDkfgUBAkjRy5EhJ0pEjR9Tc3Kxp06aFjnG5XJo8ebJ27typsrKygfxxMWOoTMj87OZ2RT8v0o7v7VDDqw1q2Nqg7EnZDqcDAMSrfpcPY4wWL16sW265Rfn5+ZKk5uZmSVJGRkbYsRkZGTp27NgF36ezs1OdnZ2hn4PBYH8j2WaoTMhkczsAwGDo9zofjzzyiP70pz9p06ZN571mWeH/2jfGnDd2TmVlpTweT+jh8/n6G8k2Q2VCZvakbI0rHhf6u7MsS+OKx3HWAwAwIP0qHwsXLtTWrVtVU1OjrKys0LjX65X05zMg57S0tJx3NuScpUuXKhAIhB4nTpzoTyTbMSETAID+iah8GGP0yCOP6Le//a3+8z//U7m5uWGv5+bmyuv1qrq6OjTW1dWluro6FRYWXvA9XS6X0tLSwh7xggmZAABELqLysWDBAm3YsEEbN26U2+1Wc3Ozmpub1dHRIanvtHx5ebkqKiq0ZcsW7du3T6WlpUpNTVVxcfGgfAAnfXZC5sLGhfLP9PeNb2242K8BADCkWSaClbG+aN7G+vXrVVpaKqnv7MiTTz6p559/Xq2trZowYYKee+650KTULxMMBuXxeBQIBGL+LMjx/zquwLFAaEKmMSY0IZN5EQCAoSSS7++Iyocd4ql8AACAPpF8f7OrLQAAsNWQKh+JtgcLAADxaEiVj0TcgwUAgHgzpMpHou7BAgBAPBnQ3i6xbqjswQIAQDxJ6PIxVPZgAQAgniT0ZZehsgcLAADxJKHLh8QeLAAAxJqELx8Se7AAABBLhkT5YA8WAABiR0JPOD3HP8Mvb4E3tAfLnC1zQnuwAAAAe7G3CwAAGDD2dgEAADGL8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAAYCvKBwAAsBXlAwAA2IryAQAAbBVze7ucW+09GAw6nAQAAFyqc9/bl7JrS8yVj7a2NkmSz+dzOAkAAIhUW1ubPJ6Lb9wacxvL9fb26uOPP5bb7ZZlWf16j2AwKJ/PpxMnTiTk5nR8vviVyJ9N4vPFs0T+bBKfzw7GGLW1tSkzM1PDhl18VkfMnfkYNmyYsrKyovJeaWlpCfl/snP4fPErkT+bxOeLZ4n82SQ+32D7sjMe5zDhFAAA2IryAQAAbJWQ5cPlcunHP/6xXC6X01EGBZ8vfiXyZ5P4fPEskT+bxOeLNTE34RQAACS2hDzzAQAAYhflAwAA2IryAQAAbEX5AAAAtkq48vG73/1Od955pzIzM2VZll555RWnI0VNZWWlxo8fL7fbrVGjRmnWrFlqaGhwOlZUrF27VjfccENogZyJEyfqjTfecDrWoKmsrJRlWSovL3c6SlQsX75clmWFPbxer9OxoqapqUn333+/rrjiCqWmpurGG2/Ue++953SsqLj66qvP+7uzLEsLFixwOlpUnD17Vj/60Y+Um5urlJQUXXPNNfrJT36i3t5ep6NFRVtbm8rLy5WTk6OUlBQVFhaqvr7e6VhfKuZWOB2o9vZ2FRQU6MEHH9Rdd93ldJyoqqur04IFCzR+/HidPXtWy5Yt07Rp03TgwAGNGDHC6XgDkpWVpZ/+9Kf62te+JkmqqqrSzJkztXv3bo0dO9bhdNFVX1+vdevW6YYbbnA6SlSNHTtWb775Zujn4cOHO5gmelpbWzVp0iTdeuuteuONNzRq1CgdOnRI6enpTkeLivr6evX09IR+3rdvn4qKinT33Xc7mCp6VqxYoV/96leqqqrS2LFj9e677+rBBx+Ux+PRokWLnI43YN/61re0b98+vfTSS8rMzNSGDRs0depUHThwQFdddZXT8b6YSWCSzJYtW5yOMWhaWlqMJFNXV+d0lEHx1a9+1fz61792OkZUtbW1mby8PFNdXW0mT55sFi1a5HSkqPjxj39sCgoKnI4xKJYsWWJuueUWp2PYZtGiRebaa681vb29TkeJiunTp5t58+aFjc2ePdvcf//9DiWKntOnT5vhw4eb119/PWy8oKDALFu2zKFUlybhLrsMJYFAQJI0cuRIh5NEV09Pj15++WW1t7dr4sSJTseJqgULFmj69OmaOnWq01Gi7sMPP1RmZqZyc3N177336vDhw05HioqtW7fq5ptv1t13361Ro0bppptu0gsvvOB0rEHR1dWlDRs2aN68ef3e2DPW3HLLLXrrrbf0wQcfSJL++Mc/6u2339Ydd9zhcLKBO3v2rHp6epScnBw2npKSorffftuhVJcm4S67DBXGGC1evFi33HKL8vPznY4TFXv37tXEiRN15swZfeUrX9GWLVt0/fXXOx0ral5++WW9//77cXE9NlITJkzQiy++qL/8y7/Uf//3f+vpp59WYWGh9u/fryuuuMLpeANy+PBhrV27VosXL9YPf/hD7dq1S9/5znfkcrn0wAMPOB0vql555RWdPHlSpaWlTkeJmiVLligQCGjMmDEaPny4enp69Mwzz2ju3LlORxswt9utiRMn6qmnntJ1112njIwMbdq0SX/4wx+Ul5fndLyLc/rUy2BSAl92mT9/vsnJyTEnTpxwOkrUdHZ2mg8//NDU19ebH/zgB+Yv/uIvzP79+52OFRXHjx83o0aNMnv27AmNJdJll887deqUycjIML/4xS+cjjJgSUlJZuLEiWFjCxcuNF//+tcdSjR4pk2bZr7xjW84HSOqNm3aZLKyssymTZvMn/70J/Piiy+akSNHmn/91391OlpUNDY2mr/92781kszw4cPN+PHjzX333Weuu+46p6NdFOUjDj3yyCMmKyvLHD582Okog+q2224z//iP/+h0jKjYsmVL6D8O5x6SjGVZZvjw4ebs2bNOR4y6qVOnmoceesjpGAOWnZ1tvvnNb4aN/fM//7PJzMx0KNHgOHr0qBk2bJh55ZVXnI4SVVlZWWbNmjVhY0899ZTx+/0OJRocp06dMh9//LExxph77rnH3HHHHQ4nujguu8QRY4wWLlyoLVu2qLa2Vrm5uU5HGlTGGHV2djodIypuu+027d27N2zswQcf1JgxY7RkyZKEuTPknM7OTh08eFB/8zd/43SUAZs0adJ5t7R/8MEHysnJcSjR4Fi/fr1GjRql6dOnOx0lqk6fPq1hw8KnNw4fPjxhbrU9Z8SIERoxYoRaW1u1fft2rVy50ulIF5Vw5ePUqVNqbGwM/XzkyBHt2bNHI0eOVHZ2toPJBm7BggXauHGjXn31VbndbjU3N0uSPB6PUlJSHE43MD/84Q91++23y+fzqa2tTS+//LJqa2u1bds2p6NFhdvtPm9uzogRI3TFFVckxJyd73//+7rzzjuVnZ2tlpYWPf300woGgyopKXE62oB997vfVWFhoSoqKnTPPfdo165dWrdundatW+d0tKjp7e3V+vXrVVJSossuS6yvhTvvvFPPPPOMsrOzNXbsWO3evVurVq3SvHnznI4WFdu3b5cxRn6/X42NjXr00Ufl9/v14IMPOh3t4hw+8xJ1NTU1RtJ5j5KSEqejDdiFPpcks379eqejDdi8efNMTk6Oufzyy82VV15pbrvtNrNjxw6nYw2qRJrzMWfOHDN69GiTlJRkMjMzzezZsxNmvo4xxrz22msmPz/fuFwuM2bMGLNu3TqnI0XV9u3bjSTT0NDgdJSoCwaDZtGiRSY7O9skJyeba665xixbtsx0dnY6HS0qfvOb35hrrrnGXH755cbr9ZoFCxaYkydPOh3rS1nGGONM7QEAAEMR63wAAABbUT4AAICtKB8AAMBWlA8AAGArygcAALAV5QMAANiK8gEAAGxF+QAAALaifAAAAFtRPgAAgK0oHwAAwFaUDwAAYKv/Hx5tD1KAIZNKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = train.Hours\n",
    "Y = train.Scores\n",
    "fig = plt.scatter(X , Y , marker = \"*\" , c = \"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d35bf8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = sns.heatmap(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49be9166",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e68e45b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a68891ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75746f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , Y_train , Y_test = train_test_split(X , Y , random_state = 2 , test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "095e00e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.values.reshape(-1 ,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e9f5ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = Y_train.values.reshape(-1 ,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5bc2c6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train.reshape(-1 ,1) , Y_train.reshape(-1 ,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d51acddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prediction = model.predict(X_test.values.reshape(-1 ,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2ed2eb4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2879ff3ac50>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = train.Hours\n",
    "Y = train.Scores\n",
    "plt.scatter(X , Y , marker = \"*\" , c = \"purple\")\n",
    "plt.plot(X_test.values.reshape(-1 ,1) , model.predict(X_test.values.reshape(-1 ,1)) , c = \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0b7ecd27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9735538080811826"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test.values.reshape(-1 ,1) , Y_test.values.reshape(-1 ,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e4aed2",
   "metadata": {},
   "source": [
    "# predict new user :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1c001d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = [[12]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5e288344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[120.7952922]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "22b29540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[90.97344677]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2 = [[9]]\n",
    "model.predict(new2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88637fde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}