Exercise_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://replit.com/@VishnuTR/boilerplate-sea-level-predictor#main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import linregress\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>sea</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.952756</td>\n",
       "      <td>0.952756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>-0.732283</td>\n",
       "      <td>1.173228</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.440945</td>\n",
       "      <td>-1.346457</td>\n",
       "      <td>0.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.232283</td>\n",
       "      <td>-1.129921</td>\n",
       "      <td>0.665354</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>-0.283465</td>\n",
       "      <td>1.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year       sea  Lower Error Bound  Upper Error Bound  \\\n",
       "0  1880  0.000000          -0.952756           0.952756   \n",
       "1  1881  0.220472          -0.732283           1.173228   \n",
       "2  1882 -0.440945          -1.346457           0.464567   \n",
       "3  1883 -0.232283          -1.129921           0.665354   \n",
       "4  1884  0.590551          -0.283465           1.464567   \n",
       "\n",
       "   NOAA Adjusted Sea Level  \n",
       "0                      NaN  \n",
       "1                      NaN  \n",
       "2                      NaN  \n",
       "3                      NaN  \n",
       "4                      NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data from file and renaming to \n",
    "df = pd.read_csv(\"epa-sea-level.csv\", float_precision=\"legacy\").rename(\n",
    "        columns={\n",
    "            \"Year\": \"year\",\n",
    "            \"CSIRO Adjusted Sea Level\": \"sea\",\n",
    "        }\n",
    "    )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "#simple scatter plot\n",
    "plt.scatter(df['year'], df['sea'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# c -- color\n",
    "# s -- marker size\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df['year'], df['sea'],c = 'blue',s = 20)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CSIRO Adjusted Sea Level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-- to check the effificiency of two functions using 'timeit module'\n",
    "https://realpython.com/visualizing-python-plt-scatter/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#linear regression with scipy\n",
    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.0630445840121348, intercept=-119.06594196773978, rvalue=0.984757131182585, pvalue=3.7886969791131554e-102, stderr=0.000969211871328715)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a regressor with 'linregress' - as name when imported(scipy.stats)\n",
    "regressor = linregress(df[\"year\"], df[\"sea\"])\n",
    "regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.969747\n"
     ]
    }
   ],
   "source": [
    "#Coefficient of determination (R-squared):\n",
    "print(f\"R-squared: {regressor.rvalue**2:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f-string method\n",
    "\n",
    "https://www.javatpoint.com/f-string-in-python\n",
    "F-string Method\n",
    "It is a new string formatting mechanism introduced by the PEP 498. It is also known as Literal String Interpolation or more commonly as F-strings (f character preceding the string literal). The primary focus of this mechanism is to make the interpolation easier.\n",
    "\n",
    "When we prefix the string with the letter 'F, the string becomes the f-string itself. The f-string can be formatted in much same as the str.format() method. The F-string offers a convenient way to embed Python expression inside string literals for formatting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: {0.969746607414955}\n"
     ]
    }
   ],
   "source": [
    "#different way of printing value (1)\n",
    "print(\"R-squared:\", {regressor.rvalue**2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:0.969746607414955\n"
     ]
    }
   ],
   "source": [
    "#different way of printing value (2)\n",
    "print(\"R-squared:{}\".format(regressor.rvalue**2))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# increase data size for future prediction\n",
    "last_year = df[\"year\"].max()\n",
    "df = df.append([{\"year\": y} for y in range(last_year + 1, 2050)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "appending value to a dataframe, normal case when need to add only few values\n",
    "\n",
    "df = df.append({'Roll': 15, 'Name': \"Wilson\", 'Language': \"Golang\"}, ignore_index=True)\n",
    "\n",
    "https://www.geeksforgeeks.org/python-pandas-dataframe-append/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aacc8e7208>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting with the predicted value\n",
    "plt.plot(\n",
    "        df[\"year\"],\n",
    "        regressor.intercept + regressor.slope * df[\"year\"],\n",
    "        c=\"r\",\n",
    "        label=\"fit all\",\n",
    "        \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>year</th>\n",
       "      <th>sea</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2000</td>\n",
       "      <td>7.062992</td>\n",
       "      <td>6.791339</td>\n",
       "      <td>7.334646</td>\n",
       "      <td>6.920582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2001</td>\n",
       "      <td>7.287402</td>\n",
       "      <td>7.019685</td>\n",
       "      <td>7.555118</td>\n",
       "      <td>7.087460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.381890</td>\n",
       "      <td>7.110236</td>\n",
       "      <td>7.653543</td>\n",
       "      <td>7.319698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.759843</td>\n",
       "      <td>7.484252</td>\n",
       "      <td>8.035433</td>\n",
       "      <td>7.488353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.740157</td>\n",
       "      <td>7.464567</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.507933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year       sea  Lower Error Bound  Upper Error Bound  \\\n",
       "120  2000  7.062992           6.791339           7.334646   \n",
       "121  2001  7.287402           7.019685           7.555118   \n",
       "122  2002  7.381890           7.110236           7.653543   \n",
       "123  2003  7.759843           7.484252           8.035433   \n",
       "124  2004  7.740157           7.464567           8.015748   \n",
       "\n",
       "     NOAA Adjusted Sea Level  \n",
       "120                 6.920582  \n",
       "121                 7.087460  \n",
       "122                 7.319698  \n",
       "123                 7.488353  \n",
       "124                 7.507933  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a new recent dataset for creating a new model\n",
    "#consider data from 2000 to 2050\n",
    "df_recent = df.loc[(df[\"year\"] >= 2000) & (df[\"year\"] <= last_year)]\n",
    "df_recent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.16642727333186808, intercept=-325.79346680596467, rvalue=0.976287571614062, pvalue=2.4388064141616947e-09, stderr=0.010652933111541106)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new model\n",
    "regressor_nw = linregress(df_recent[\"year\"], df_recent[\"sea\"])\n",
    "regressor_nw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aaccd33988>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zg2XL/MSg9u3h7LOhoADuvBP22SfqZCKVbq8FPYSwBugCrAbWARtCCB8Uu+xo4PPY9duBDcBuKzXMrK2Z5ZtZfmFhYUWzSzbbvt0PZT79dFi0CF5+GUaPhlq1ok4mEpl4hlyq4R348cBRwAFmdn3xy0r4rWG3F0LoHULIDSHk1qxZszx5RXxsvHFj6NTJx8wXL4bWrf3AZpEsFs+Qy4XApyGEwhDCNmAw0KTYNV8AxwDEhmUOAdYnMqgIW7bAgw9Cbi6sWeNHwg0aBEceGXUykZQQT0FfDZxlZlVj4+LNgSXFrhkG3Bj7+r+BD0MIu3XoIuU2bRqccQb84x9w/fXelf/hD1GnEkkp8Yyhz8QfdM4FFsV+T28ze8zMrohd1heoYWYrgHuATknKK9nmhx98gdC558KmTfD++77qs3r1qJOJpByLqpHOzc0N+fn5kXxvSRMffABt2/r88vbtvTs/6KCoU4lEyszmhBByS3pPK0Ul9Xz7LbRpA7/9Ley/v88pf/55FXORvVBBl9QyeLAv2+/f31d5zp8P55wTdSqRtKDNuSQ1fPkldOjgs1bOOANGjYL69aNOJZJW1KFLtEKAV1/1rvy99/zQiZkzVcxFykEdukRn1Spo184ffp5zDvTt65tqiUi5qEOXyrdzpz/krFvX55d37+6baqmYi1SIOnSpXB9/7MfBTZ0KF18MeXlw3HFRpxLJCOrQpXJs2+bzyOvVgyVLoF8/GDlSxVwkgdShS/LNnQs33+xTEP/nf3y45Ygjok4lknHUoUvybN4M998PZ57p0xIHD/YzPlXMRZJCHbokx5Qp3pUvWwY33QRdukC1alGnEslo6tAlsb7/3hcINW0KW7fCmDE+HVHFXCTpVNAlcUaN8nM9e/SAu+7y4+AuvDDqVCJZQwVdKu6bb6BVK7j0UjjwQJ+S2LUrHHBA1MlEsooKupRfCPDOO75sf8AAeOghmDfPD2sWkUqnh6JSPuvWwR13wLvvQsOGvny/Xr2oU4lkNXXoUjYhwEsvQZ06fnpQ584wY4aKuUgKUIcu8fv0Uz9BaOxYaNYMXnwRTjop6lQiEqMOXfZuxw547jnfTGvmTOjZE8aPVzEXSTHq0GXPFi/2BUIzZvgslrw8OOaYqFOJSAnUoUvJtm6F//s/Pz1o+XJ47TU/gELFXCRlqUOX3eXne1e+cCG0bOnDLYcfHnUqEdkLdejyH5s3w//+LzRuDF9/DUOH+vxyFXORtKAOXdzEiX7wxIoVcOutPh3x0EOjTiUiZaAOPdtt3Ai33w7nn+9Hw40bB717q5iLpCEV9Gw2YoRvptW7N9xzDyxaBBdcEHUqESknFfRs9PXXcP31cNllcMghflDzM89A1apRJxORClBBzyYhwJtv+rL9t9+GRx7x4+EaN446mYgkgB6KZos1a3wzrWHDoFEjP3TitNOiTiUiCaQOPdOF4Huu5OT46UFdusD06SrmIhlIHXom++QTn4I4frzPYnnxRahdO+pUIpIk6tAz0Y4d8Oyz3oXPmeOzWMaNUzEXyXDq0DNNQYEv2581Cy6/3HdGPProqFOJSCVQh54ptm6FRx+FBg1g5Upfsj90qIq5SBZRh54JZs3yrrygAK691jfTOuywqFOJSCVTh57ONm2Ce+/1Q5m//RaGD4fXX1cxF8lS6tDT1fjxvpnWypVw223w5JO+6lNEstZeO3QzO9nM5hf52GhmdxW75nwz21DkmoeTFznLbdgA7dr5niu/+AVMmOAPPlXMRbLeXjv0EMJSoD6Ame0DrAGGlHDp5BDCZYmNJ7sYPty78S+/hPvug7/9TfuviMi/lXUMvTnwSQjhs2SEkVIUFsI118AVV0CNGn5Qc+fOKuYisouyFvSWwIBS3jvbzBaY2SgzO7WkC8ysrZnlm1l+YWFhGb91FgoB3njDN9MaNAgee8yPh8vNjTqZiKSguAu6me0LXAG8U8Lbc4HjQgj1gOeBd0u6RwihdwghN4SQW7NmzfLkzR6ff+4Lg667zld4zpsHDz0E++4bdTIRSVFl6dAvAeaGEL4q/kYIYWMI4YfY1yOBX5qZ5s6Vx86d0KuXHzwxfjx07QpTp/qvRUT2oCzTFq+hlOEWM/s18FUIIZjZmfj/UXyTgHzZZfly30xr4kRo3tz3YDnhhKhTiUiaiKugm1lVoAXQrshrtwGEEPKA/wZuN7PtwGagZQghJD5uhtq+3Tvxhx+G/fbzvcrbtAGzqJOJSBqJq6CHEDYBNYq9llfk6xeAFxIbLUssXOjL9vPz4coroUcPOOqoqFOJSBrS0v+o/PSTd+QNG8Lq1X4k3JAhKuYiUm5a+h+FGTO8K1+8GG64wYdbatTY++8TEdkDdeiV6ccf4e67oUkT+P57GDkS+vVTMReRhFCHXlnGjoW2beHTT6F9e3jiCTjooKhTiUgGUYeebN9958MrLVpAlSowaRK88IKKuYgknAp6Mr37LuTkwKuvQqdOsGABNG0adSoRyVAackmGr76CO++Ed96BevV8l8SGDaNOJSIZTh16IoUA/ft7Vz50KDz+OMyerWIuIpVCHXqirF7tB0+8/77PYunbF045JepUIpJF1KFX1M6d0L27b541eTJ06+afVcxFpJKpQ6+IpUv9XM8pU3wWS+/eUKtW1KlEJEupQy+Pbdv8UOZ69aCgAF5+GUaPVjEXkUipQy+refN8Xvm8efD73/twy69/HXUqERF16HHbsgUefBAaNYK1a2HgQD8WTsVcRFKEOvR4TJ3qXfnSpdC6NTzzDFSvHnUqEZFdqEPfkx9+gI4dfXXnli0+Tv7yyyrmIpKSVNBLM3o01K3r+6506OAPPy+6KOpUIiKlUkEvbv16H1a5+GLYf///zC0/8MCok4mI7JEKelGDBvmy/ddegwcegPnz4Zxzok4lIhIXPRQFWLfOh1UGD4YzzvDl+/XrR51KRKRMsrtDDwFeecW78hEjfLHQrFkq5iKSlrK3Q1+1yk8QGjMGzj0X+vSBk0+OOpWISLllX4e+Y4c/5KxbF6ZP95WeEyeqmItI2suuDn3JEt9Ma9o0n8WSlwfHHRd1KhGRhMiODn3bNj9son59+Phj6NcPRo5UMReRjJL5HfrcuXDTTX6e59VX+3DLEUdEnUpEJOEyt0PfvNkPZj7zTD/jc8gQeOstFXMRyViZ2aFPnuxj5cuW+aZaTz8N1apFnUpEJKkyq0PfuBHat4dmzWDrVp+S2KePirmIZIXMKeijRvlUxJ494a67fDOtCy+MOpWISKVJ/4L+zTfQqhVceqlvoDV1KnTtCgccEHUyEZFKlb4FPQR4+22oUwcGDICHHvJj4c4+O+pkIiKRSM+HomvX+lj5u+9Cw4YwdiycfnrUqUREIpV+BX3kSLj2WvjpJ+jcGe6+G6qk33+GiEiipV8lPOkkH1bp1g1OPDHqNCIiKSP9Cnrt2j6jRUREdrHXh6JmdrKZzS/ysdHM7ip2jZlZNzNbYWYLzaxB8iKLiEhJ9tqhhxCWAvUBzGwfYA0wpNhllwAnxj4aAz1jn0VEpJKUddpic+CTEMJnxV6/EugX3AzgUDM7MiEJRUQkLmUt6C2BASW8fjTweZFffxF7bRdm1tbM8s0sv7CwsIzfWkRE9iTugm5m+wJXAO+U9HYJr4XdXgihdwghN4SQW7NmzfhTiojIXpWlQ78EmBtC+KqE974Ajiny698AaysSTEREyqYsBf0aSh5uARgGtIrNdjkL2BBCWFfhdCIiEre45qGbWVWgBdCuyGu3AYQQ8oCRwKXACmAT0CbhSUVEZI8shN2GuivnG5sVAsVny8TrMODrBMapTMoeDWWPRrpmT+Xcx4UQSnwIGVlBrwgzyw8h5EadozyUPRrKHo10zZ6uudN3+1wREdmFCrqISIZI14LeO+oAFaDs0VD2aKRr9rTMnZZj6CIisrt07dBFRKQYFXQRkQyRMgXdzI4xs/FmtsTMPjKzP8Ver25mY8xseexztdjrpe7BbmY3xq5fbmY3pkNuM6tvZtNj91hoZn9MZu5EZi9yv4PNbI2ZvZBO2c3sWDP7IHavxWZWK42yd47dY0nsmpL2VYoy+ymxn+ufzOzeYve62MyWxv67OqVD7tLukzJCCCnxARwJNIh9fRCwDMgBOgOdYq93Ap6KfX0pMArfGOwsYGbs9erAytjnarGvq6VB7pOAE2NfHwWsAw5Nhz/zIvd7DngDeCFdfl5i700AWsS+PhComg7ZgSbAVGCf2Md04PwUy3440Ah4HLi3yH32AT4BTgD2BRYAOWmQu8T7JPvnPe7/zqgD7OF/gKH4dgNLgSOL/GEujX3dC7imyPVLY+9fA/Qq8vou16Vq7hLus4BYgU+H7EBD4E2gNZVQ0BP485IDTKnsvAnKfjYwB/gVUBXIB+qkUvYi1/2tWGE8Gxhd5Nf3A/eneu7S7hPlz0/Rj5QZcikq9k/eM4CZwBEhttFX7PPhsctK24M9rr3Zk6GCuYve50y8a/kkuYl3+Z61KGd2M/sF8AxwX2XlLaqCf+4nAd+Z2WAzm2dmT5ufzJXy2UMI04Hx+L/m1uEFcknlJI87e2lS/e9pWe+TElKuoJvZgcAg4K4QwsY9XVrCa2EPrydVAnL/fJ8jgf5AmxDCzsSmLCVQxbPfAYwMIXxewvtJlYDsVYCmwL34P7FPwP+VkXQVzW5mtYE6+HbVRwMXmFmzxCctIVD82Uu9RQmvpdLf00q5T6KlVEE3s1/if0ivhxAGx17+Klbkfi52/4q9Xtoe7JW+N3uCcmNmBwMjgL8GP8ov6RKU/Wygg5mtArrgWyk/mSbZvwDmhRBWhhC2A+8CST/kPEHZrwJmhBB+CCH8gI+zn5Vi2UuT6n9Py3qflJAyBT32dL4vsCSE8GyRt4YBP89UuREfs/r59ZL2YB8NXGRm1WJPrC+KvZbSuc1PhBqCn81a0qlQKZs9hHBdCOHYEEItvNPtF0JI9qyFRP28zAaqmdnPu9ddACxOk+yrgfPMrEqsyJwHJHXIpRzZSzMbONHMjo/97LeM3SMpEpV7D/dJDVEP4hd5uHAu/k+uhcD82MelQA1gHLA89rl67HoDuuPjzIuA3CL3ugnfm30FPnSR8rmB64FtRe4xH6ifDtmL3bM1lTPLJZE/Ly1i91kEvALsmw7Z8ZkivfAivhh4NgX/3H+Nd+Mbge9iXx8ce+9SfJbIJ8CD6ZC7tPsk+8893g8t/RcRyRApM+QiIiIVo4IuIpIhVNBFRDKECrqISIZQQRcRyRAq6CIiGUIFXUQkQ/w/m41/PgQh7zgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "        df_recent[\"year\"],\n",
    "        regressor_nw.intercept + regressor_nw.slope * df_recent[\"year\"],\n",
    "        c=\"r\",\n",
    "        label=\"fit all\",\n",
    "        \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recent = df_recent.append(\n",
    "        [{\"year\": y} for y in range(last_year + 1, 2050)]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rise in Sea Level')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "        df_recent[\"year\"],\n",
    "        regressor_nw.intercept + regressor_nw.slope * df_recent[\"year\"],\n",
    "        c=\"b\",\n",
    "        label=\"fit recent\",\n",
    "    )\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Sea Level (inches)\")\n",
    "plt.title(\"Rise in Sea Level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df_recent['year'], df_recent['sea'],c = 'blue',s = 20)\n",
    "plt.plot(\n",
    "        df_recent[\"year\"],\n",
    "        regressor_nw.intercept + regressor_nw.slope * df_recent[\"year\"],\n",
    "        c=\"orange\",\n",
    "        label=\"fit recent\",\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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