x_MinTemp_y_MaxTemp_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Min Avg Temp</th>\n",
       "      <th>Max Avg Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>12.833</td>\n",
       "      <td>16.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>13.245</td>\n",
       "      <td>16.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>13.231</td>\n",
       "      <td>16.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>13.143</td>\n",
       "      <td>16.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>12.983</td>\n",
       "      <td>16.775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Min Avg Temp  Max Avg Temp\n",
       "0  1850        12.833        16.783\n",
       "1  1851        13.245        16.831\n",
       "2  1852        13.231        16.984\n",
       "3  1853        13.143        16.999\n",
       "4  1854        12.983        16.775"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Data/temp_data-AllYears-Min-Max.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let X be world population and y be global average yearly temperature, then Reshape X and y\n",
    "X = dataset['Min Avg Temp'].values.reshape(-1,1)\n",
    "y = dataset['Max Avg Temp'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the train - test data to be 80% train, 20% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the algorithm using a linear regression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The y intercept is:   [8.25909715]\n",
      "The slope is:   [[0.64427629]]\n"
     ]
    }
   ],
   "source": [
    "#To retrieve the intercept:\n",
    "print(\"The y intercept is:  \", regressor.intercept_)\n",
    "#For retrieving the slope:\n",
    "print(\"The slope is:  \", regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.503</td>\n",
       "      <td>17.354345</td>\n",
       "      <td>-0.148655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.972</td>\n",
       "      <td>17.023832</td>\n",
       "      <td>0.051832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.950</td>\n",
       "      <td>16.971001</td>\n",
       "      <td>0.021001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.651</td>\n",
       "      <td>16.766766</td>\n",
       "      <td>0.115766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.218</td>\n",
       "      <td>17.235799</td>\n",
       "      <td>0.017799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.525</td>\n",
       "      <td>16.606985</td>\n",
       "      <td>0.081985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.478</td>\n",
       "      <td>16.616649</td>\n",
       "      <td>0.138649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.685</td>\n",
       "      <td>16.773208</td>\n",
       "      <td>0.088208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.013</td>\n",
       "      <td>17.071508</td>\n",
       "      <td>0.058508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.431</td>\n",
       "      <td>16.575415</td>\n",
       "      <td>0.144415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17.033</td>\n",
       "      <td>16.953606</td>\n",
       "      <td>-0.079394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.150</td>\n",
       "      <td>16.999349</td>\n",
       "      <td>-0.150651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.981</td>\n",
       "      <td>16.835059</td>\n",
       "      <td>-0.145941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.938</td>\n",
       "      <td>16.982598</td>\n",
       "      <td>0.044598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16.718</td>\n",
       "      <td>16.821529</td>\n",
       "      <td>0.103529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17.348</td>\n",
       "      <td>17.188122</td>\n",
       "      <td>-0.159878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.307</td>\n",
       "      <td>17.161707</td>\n",
       "      <td>-0.145293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.864</td>\n",
       "      <td>16.919459</td>\n",
       "      <td>0.055459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.716</td>\n",
       "      <td>16.688808</td>\n",
       "      <td>-0.027192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.106</td>\n",
       "      <td>17.012235</td>\n",
       "      <td>-0.093765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16.877</td>\n",
       "      <td>16.993551</td>\n",
       "      <td>0.116551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16.664</td>\n",
       "      <td>16.702982</td>\n",
       "      <td>0.038982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16.662</td>\n",
       "      <td>16.795758</td>\n",
       "      <td>0.133758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.978</td>\n",
       "      <td>16.826683</td>\n",
       "      <td>-0.151317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.848</td>\n",
       "      <td>16.699117</td>\n",
       "      <td>-0.148883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.423</td>\n",
       "      <td>17.412330</td>\n",
       "      <td>-0.010670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16.830</td>\n",
       "      <td>16.920748</td>\n",
       "      <td>0.090748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16.510</td>\n",
       "      <td>16.777074</td>\n",
       "      <td>0.267074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16.628</td>\n",
       "      <td>16.789315</td>\n",
       "      <td>0.161315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16.630</td>\n",
       "      <td>16.389864</td>\n",
       "      <td>-0.240136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16.687</td>\n",
       "      <td>16.659816</td>\n",
       "      <td>-0.027184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16.944</td>\n",
       "      <td>16.974867</td>\n",
       "      <td>0.030867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17.487</td>\n",
       "      <td>17.360788</td>\n",
       "      <td>-0.126212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16.543</td>\n",
       "      <td>16.644997</td>\n",
       "      <td>0.101997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted     Error\n",
       "0   17.503  17.354345 -0.148655\n",
       "1   16.972  17.023832  0.051832\n",
       "2   16.950  16.971001  0.021001\n",
       "3   16.651  16.766766  0.115766\n",
       "4   17.218  17.235799  0.017799\n",
       "5   16.525  16.606985  0.081985\n",
       "6   16.478  16.616649  0.138649\n",
       "7   16.685  16.773208  0.088208\n",
       "8   17.013  17.071508  0.058508\n",
       "9   16.431  16.575415  0.144415\n",
       "10  17.033  16.953606 -0.079394\n",
       "11  17.150  16.999349 -0.150651\n",
       "12  16.981  16.835059 -0.145941\n",
       "13  16.938  16.982598  0.044598\n",
       "14  16.718  16.821529  0.103529\n",
       "15  17.348  17.188122 -0.159878\n",
       "16  17.307  17.161707 -0.145293\n",
       "17  16.864  16.919459  0.055459\n",
       "18  16.716  16.688808 -0.027192\n",
       "19  17.106  17.012235 -0.093765\n",
       "20  16.877  16.993551  0.116551\n",
       "21  16.664  16.702982  0.038982\n",
       "22  16.662  16.795758  0.133758\n",
       "23  16.978  16.826683 -0.151317\n",
       "24  16.848  16.699117 -0.148883\n",
       "25  17.423  17.412330 -0.010670\n",
       "26  16.830  16.920748  0.090748\n",
       "27  16.510  16.777074  0.267074\n",
       "28  16.628  16.789315  0.161315\n",
       "29  16.630  16.389864 -0.240136\n",
       "30  16.687  16.659816 -0.027184\n",
       "31  16.944  16.974867  0.030867\n",
       "32  17.487  17.360788 -0.126212\n",
       "33  16.543  16.644997  0.101997"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare actual to predicted values\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten(), \"Error\": y_pred.flatten() - y_test.flatten()})\n",
    "# pd.DataFrame({\"Predicted\": predictions, \"Actual\": y, \"Error\": predictions - y})[[\"Predicted\", \"Actual\", \"Error\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart to show comparison between actual and predicted\n",
    "df1 = df.head(23)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "# Plot our straight line with the test data\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.xlabel(\"Minimum Temperature (C)\")\n",
    "plt.ylabel(\"Maximum Temperature (C)\")\n",
    "plt.title(\"Scatter Plot of Test Data Minimum to Maximum Temperature \\n\")\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/MinMax_Temp_scatter_test.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.1034768180141417\n",
      "Mean Squared Error: 0.01437294658402776\n",
      "Root Mean Squared Error: 0.1198872244404205\n"
     ]
    }
   ],
   "source": [
    "# Calculate margin or error\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max X values (Year) 12.475 14.255\n"
     ]
    }
   ],
   "source": [
    "x_min = X.min()\n",
    "x_max = X.max()\n",
    "print(\"Min/Max X values (Year)\", x_min, x_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max Y values (Temperature) 16.430999999999994 17.611\n"
     ]
    }
   ],
   "source": [
    "# Get y (temperature min and max actuals)\n",
    "y_min_actual = y.min()\n",
    "y_max_actual = y.max()\n",
    "print(\"Min/Max Y values (Temperature)\", y_min_actual, y_max_actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Smallest Max Value: 16.430999999999994\n",
      "Calculated Smallest Max Value: 16.29644386775\n",
      "Actual Largest Max Value: 17.611\n",
      "Calculated Largest Max Value: 17.44325566395\n"
     ]
    }
   ],
   "source": [
    "# The y intercept is:   [8.25909715]\n",
    "# The slope is:   [[0.64427629]]\n",
    "y_min = 8.25909715 + 0.64427629 * x_min\n",
    "y_max = 8.25909715 + 0.64427629 * x_max\n",
    "print(f\"Actual Smallest Max Value: {y_min_actual}\")\n",
    "print(f\"Calculated Smallest Max Value: {y_min}\")\n",
    "print(f\"Actual Largest Max Value: {y_max_actual}\")\n",
    "print(f\"Calculated Largest Max Value: {y_max}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Smallest Max Value: 16.430999999999994\n",
      "Predicted Smallest Max Value: [[16.29644383]]\n",
      "Actual Largest Max Value: 17.611\n",
      "Predicted Largest Max Value: [[17.44325562]]\n"
     ]
    }
   ],
   "source": [
    "# Predict calculated values\n",
    "y_min_predicted = regressor.predict([[x_min]])\n",
    "y_max_predicted = regressor.predict([[x_max]])\n",
    "print(f\"Actual Smallest Max Value: {y_min_actual}\")\n",
    "print(f\"Predicted Smallest Max Value: {y_min_predicted}\")\n",
    "print(f\"Actual Largest Max Value: {y_max_actual}\")\n",
    "print(f\"Predicted Largest Max Value: {y_max_predicted}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, c='blue')\n",
    "plt.plot([x_min, x_max], [y_min, y_max], c='red')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Minimum Average Annual Temperature\")\n",
    "plt.ylabel(\"Maximum Average Annual Temperature\")\n",
    "plt.title('Minimum vs. Maximum Average Values (in Celsius)')\n",
    "fig2 = plt.gcf()\n",
    "fig2.savefig('images/Min_vs_Max_Temp.png', bbox_inches='tight')"
   ]
  }
 ],
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