x_Population_y_Temperature_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": [],
   "source": [
    "dataset = pd.read_csv('Data/combinedclimatedata.csv')"
   ]
  },
  {
   "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['population_millions'].values.reshape(-1,1)\n",
    "y = dataset['LandAndOceanAverageTemperature'].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:   [14.35225079]\n",
      "The slope is:   [[2.1729278e-07]]\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",
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       "        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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.757250</td>\n",
       "      <td>15.756214</td>\n",
       "      <td>-0.001036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.826583</td>\n",
       "      <td>15.738836</td>\n",
       "      <td>-0.087747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.913000</td>\n",
       "      <td>15.937465</td>\n",
       "      <td>0.024465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.827333</td>\n",
       "      <td>15.809405</td>\n",
       "      <td>-0.017929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.854417</td>\n",
       "      <td>15.919058</td>\n",
       "      <td>0.064642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted     Error\n",
       "0  15.757250  15.756214 -0.001036\n",
       "1  15.826583  15.738836 -0.087747\n",
       "2  15.913000  15.937465  0.024465\n",
       "3  15.827333  15.809405 -0.017929\n",
       "4  15.854417  15.919058  0.064642"
      ]
     },
     "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": {
      "image/png": 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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": 10,
   "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.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.039163620922685285\n",
      "Mean Squared Error: 0.002559823884416783\n",
      "Root Mean Squared Error: 0.050594702137840314\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 (Population) 5581597.597999998 7379796.967\n"
     ]
    }
   ],
   "source": [
    "x_min = X.min()\n",
    "x_max = X.max()\n",
    "print(\"Min/Max X values (Population)\", x_min, x_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max Y values (Temperature) 15.466416666666667 16.058583333333335\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 15.466416666666667\n",
      "Calculated Min Value: 15.565091648910741\n",
      "Actual Max Value: 16.058583333333335\n",
      "Calculated Max Value: 15.955827388794997\n"
     ]
    }
   ],
   "source": [
    "#The y intercept is:   [14.35225079]\n",
    "#The slope is:   [[2.1729278e-07]]\n",
    "y_min = 14.35225079 + 2.1729278e-07 * x_min\n",
    "y_max = 14.35225079 + 2.1729278e-07 * x_max\n",
    "print(f\"Actual Min Value: {y_min_actual}\")\n",
    "print(f\"Calculated Min Value: {y_min}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Calculated Max Value: {y_max}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 15.466416666666667\n",
      "Predicted Min Value: [[15.56509165]]\n",
      "Actual Max Value: 16.058583333333335\n",
      "Predicted Max Value: [[15.95582739]]\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 Min Value: {y_min_actual}\")\n",
    "print(f\"Predicted Min Value: {y_min_predicted}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Predicted Max Value: {y_max_predicted}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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(\"Population (Millions)\")\n",
    "plt.ylabel(\"Temperature (Celsius)\")\n",
    "plt.title(\"Global Population to Global Average Temperature\\n\")\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/pop_to_temp.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plugging in the Numbers:\n",
    "#### Average Global Temperature = slope * Population + y-intercept\n",
    "#### slope = 2.1729278e-07\n",
    "#### y-intercept = 14.35225079\n",
    "#### Global Temp. Above 1.5 degrees C from 1850 average global temperature = 16.37\n",
    "#### Population = 16.37 - (14.35225079))/2.1729278e-07\n",
    "#### Population = 9285854 or 9,285,854,000\n",
    "#### Mean Squared Error: 0.002559823884416783"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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