{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean and transform the temperatures and load into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Temperature</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1895</td>\n",
       "      <td>33.2</td>\n",
       "      <td>-3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1896</td>\n",
       "      <td>33.4</td>\n",
       "      <td>-3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1897</td>\n",
       "      <td>34.3</td>\n",
       "      <td>-2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Temperature  Anomaly\n",
       "0  1895         33.2     -3.8\n",
       "1  1896         33.4     -3.6\n",
       "2  1897         34.3     -2.7"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "nyc = pd.read_csv('nyc_avg_jan_1895-2021.csv')\n",
    "nyc.Date = nyc.Date.floordiv(100)\n",
    "nyc.columns = ['Year', 'Temperature', 'Anomaly']\n",
    "\n",
    "nyc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nyc.Year.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905,\n",
       "       1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916,\n",
       "       1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927,\n",
       "       1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938,\n",
       "       1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949,\n",
       "       1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
       "       1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
       "       1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "       1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
       "       1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
       "       2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
       "       2016, 2017, 2018, 2019, 2020, 2021])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc.Year.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1895],\n",
       "       [1896],\n",
       "       [1897],\n",
       "       [1898],\n",
       "       [1899],\n",
       "       [1900],\n",
       "       [1901],\n",
       "       [1902],\n",
       "       [1903],\n",
       "       [1904],\n",
       "       [1905],\n",
       "       [1906],\n",
       "       [1907],\n",
       "       [1908],\n",
       "       [1909],\n",
       "       [1910],\n",
       "       [1911],\n",
       "       [1912],\n",
       "       [1913],\n",
       "       [1914],\n",
       "       [1915],\n",
       "       [1916],\n",
       "       [1917],\n",
       "       [1918],\n",
       "       [1919],\n",
       "       [1920],\n",
       "       [1921],\n",
       "       [1922],\n",
       "       [1923],\n",
       "       [1924],\n",
       "       [1925],\n",
       "       [1926],\n",
       "       [1927],\n",
       "       [1928],\n",
       "       [1929],\n",
       "       [1930],\n",
       "       [1931],\n",
       "       [1932],\n",
       "       [1933],\n",
       "       [1934],\n",
       "       [1935],\n",
       "       [1936],\n",
       "       [1937],\n",
       "       [1938],\n",
       "       [1939],\n",
       "       [1940],\n",
       "       [1941],\n",
       "       [1942],\n",
       "       [1943],\n",
       "       [1944],\n",
       "       [1945],\n",
       "       [1946],\n",
       "       [1947],\n",
       "       [1948],\n",
       "       [1949],\n",
       "       [1950],\n",
       "       [1951],\n",
       "       [1952],\n",
       "       [1953],\n",
       "       [1954],\n",
       "       [1955],\n",
       "       [1956],\n",
       "       [1957],\n",
       "       [1958],\n",
       "       [1959],\n",
       "       [1960],\n",
       "       [1961],\n",
       "       [1962],\n",
       "       [1963],\n",
       "       [1964],\n",
       "       [1965],\n",
       "       [1966],\n",
       "       [1967],\n",
       "       [1968],\n",
       "       [1969],\n",
       "       [1970],\n",
       "       [1971],\n",
       "       [1972],\n",
       "       [1973],\n",
       "       [1974],\n",
       "       [1975],\n",
       "       [1976],\n",
       "       [1977],\n",
       "       [1978],\n",
       "       [1979],\n",
       "       [1980],\n",
       "       [1981],\n",
       "       [1982],\n",
       "       [1983],\n",
       "       [1984],\n",
       "       [1985],\n",
       "       [1986],\n",
       "       [1987],\n",
       "       [1988],\n",
       "       [1989],\n",
       "       [1990],\n",
       "       [1991],\n",
       "       [1992],\n",
       "       [1993],\n",
       "       [1994],\n",
       "       [1995],\n",
       "       [1996],\n",
       "       [1997],\n",
       "       [1998],\n",
       "       [1999],\n",
       "       [2000],\n",
       "       [2001],\n",
       "       [2002],\n",
       "       [2003],\n",
       "       [2004],\n",
       "       [2005],\n",
       "       [2006],\n",
       "       [2007],\n",
       "       [2008],\n",
       "       [2009],\n",
       "       [2010],\n",
       "       [2011],\n",
       "       [2012],\n",
       "       [2013],\n",
       "       [2014],\n",
       "       [2015],\n",
       "       [2016],\n",
       "       [2017],\n",
       "       [2018],\n",
       "       [2019],\n",
       "       [2020],\n",
       "       [2021]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc.Year.values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(nyc.Year.values.reshape(-1, 1), \n",
    "                     nyc.Temperature.values, \n",
    "                     random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set = (95, 1)\n",
      "Shape of testing  set = (32, 1)\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape of training set = {X_train.shape}')\n",
    "print(f'Shape of testing  set = {X_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regression coefficient (slope) = [0.03061817]\n",
      "                     intercept = -23.15950296932693\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X=X_train, y=y_train)\n",
    "\n",
    "print(f'regression coefficient (slope) = {linear_regression.coef_}')\n",
    "print(f'                     intercept = {linear_regression.intercept_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: 36.42, expected: 39.30, difference: -2.88\n",
      "predicted: 34.92, expected: 34.30, difference:  0.62\n",
      "predicted: 37.25, expected: 39.60, difference: -2.35\n",
      "predicted: 35.14, expected: 28.10, difference:  7.04\n",
      "predicted: 35.35, expected: 40.00, difference: -4.65\n",
      "predicted: 37.22, expected: 39.50, difference: -2.28\n",
      "predicted: 34.86, expected: 33.20, difference:  1.66\n",
      "predicted: 38.02, expected: 45.80, difference: -7.78\n",
      "predicted: 38.41, expected: 34.80, difference:  3.61\n",
      "predicted: 36.64, expected: 41.20, difference: -4.56\n",
      "predicted: 35.75, expected: 38.10, difference: -2.35\n",
      "predicted: 37.89, expected: 31.50, difference:  6.39\n",
      "predicted: 36.52, expected: 42.30, difference: -5.78\n",
      "predicted: 38.11, expected: 38.80, difference: -0.69\n",
      "predicted: 35.05, expected: 36.40, difference: -1.35\n",
      "predicted: 36.76, expected: 33.00, difference:  3.76\n",
      "predicted: 38.32, expected: 42.40, difference: -4.08\n",
      "predicted: 36.70, expected: 34.90, difference:  1.80\n",
      "predicted: 37.16, expected: 29.00, difference:  8.16\n",
      "predicted: 37.40, expected: 32.20, difference:  5.20\n",
      "predicted: 37.98, expected: 39.20, difference: -1.22\n",
      "predicted: 35.78, expected: 32.60, difference:  3.18\n",
      "predicted: 35.93, expected: 37.30, difference: -1.37\n",
      "predicted: 36.88, expected: 31.00, difference:  5.88\n",
      "predicted: 35.32, expected: 35.50, difference: -0.18\n",
      "predicted: 38.69, expected: 45.20, difference: -6.51\n",
      "predicted: 38.66, expected: 39.00, difference: -0.34\n",
      "predicted: 36.15, expected: 44.40, difference: -8.25\n",
      "predicted: 35.54, expected: 37.10, difference: -1.56\n",
      "predicted: 35.50, expected: 41.00, difference: -5.50\n",
      "predicted: 35.66, expected: 38.50, difference: -2.84\n",
      "predicted: 36.06, expected: 38.60, difference: -2.54\n"
     ]
    }
   ],
   "source": [
    "predicted = linear_regression.predict(X_test)\n",
    "expected  = y_test\n",
    "\n",
    "for p, e in zip(predicted, expected):\n",
    "    print(f'predicted: {p:.2f}, expected: {e:.2f}, difference: {p - e:5.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict future temperatures and estimate past temperatures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_temperature(linear_regression, year):\n",
    "    m = linear_regression.coef_\n",
    "    b = linear_regression.intercept_\n",
    "    x = year\n",
    "    \n",
    "    y_hat = m*x + b\n",
    "    \n",
    "    return y_hat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated temperature in year 1890 is 34.71\n"
     ]
    }
   ],
   "source": [
    "year = 1890\n",
    "temp = predict_temperature(linear_regression, year)\n",
    "\n",
    "print(f'The estimated temperature in year {year} is {temp:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted temperature in year 2022 is 38.75\n"
     ]
    }
   ],
   "source": [
    "year = 2022\n",
    "temp = predict_temperature(linear_regression, year)\n",
    "\n",
    "print(f'The predicted temperature in year {year} is {temp:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot with regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "axes = sns.scatterplot(data=nyc, x='Year', y='Temperature',\n",
    "                       hue='Temperature', palette='winter', \n",
    "                       legend=False)\n",
    "axes.set_ylim(25, 50)\n",
    "\n",
    "x = np.array([min(nyc.Year.values), max(nyc.Year.values)])\n",
    "y = x*linear_regression.coef_ + linear_regression.intercept_\n",
    "\n",
    "line = plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################\n",
    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
    "#                                                                        #\n",
    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
    "# best efforts in preparing the book. These efforts include the          #\n",
    "# development, research, and testing of the theories and programs        #\n",
    "# to determine their effectiveness. The authors and publisher make       #\n",
    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
    "# programs or to the documentation contained in these books. The authors #\n",
    "# and publisher shall not be liable in any event for incidental or       #\n",
    "# consequential damages in connection with, or arising out of, the       #\n",
    "# furnishing, performance, or use of these programs.                     #\n",
    "##########################################################################"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}