Episode 4.3/Simple Linear Regression.ipynb

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     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature(C)</th>\n",
       "      <th>Humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.472222</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.355556</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.288889</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.755556</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.222222</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.733333</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.772222</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.822222</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.772222</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.016667</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.144444</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.800000</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17.333333</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.877778</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.911111</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15.388889</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.550000</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.255556</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.144444</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11.550000</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11.183333</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.116667</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10.422222</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.911111</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11.183333</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.155556</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6.111111</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6.788889</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.261111</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9.872222</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.222222</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>15.094444</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17.355556</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19.005556</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>20.044444</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>21.050000</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>21.183333</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20.116667</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20.216667</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>17.800000</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16.061111</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>15.022222</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14.422222</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>14.255556</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>13.772222</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13.283333</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature(C)  Humidity\n",
       "0         9.472222      0.89\n",
       "1         9.355556      0.86\n",
       "2         9.377778      0.89\n",
       "3         8.288889      0.83\n",
       "4         8.755556      0.83\n",
       "5         9.222222      0.85\n",
       "6         7.733333      0.95\n",
       "7         8.772222      0.89\n",
       "8        10.822222      0.82\n",
       "9        13.772222      0.72\n",
       "10       16.016667      0.67\n",
       "11       17.144444      0.54\n",
       "12       17.800000      0.55\n",
       "13       17.333333      0.51\n",
       "14       18.877778      0.47\n",
       "15       18.911111      0.46\n",
       "16       15.388889      0.60\n",
       "17       15.550000      0.63\n",
       "18       14.255556      0.69\n",
       "19       13.144444      0.70\n",
       "20       11.550000      0.77\n",
       "21       11.183333      0.76\n",
       "22       10.116667      0.79\n",
       "23       10.200000      0.77\n",
       "24       10.422222      0.62\n",
       "25        9.911111      0.66\n",
       "26       11.183333      0.80\n",
       "27        7.155556      0.79\n",
       "28        6.111111      0.82\n",
       "29        6.788889      0.83\n",
       "30        7.261111      0.85\n",
       "31        7.800000      0.83\n",
       "32        9.872222      0.78\n",
       "33       12.222222      0.72\n",
       "34       15.094444      0.61\n",
       "35       17.355556      0.52\n",
       "36       19.005556      0.46\n",
       "37       20.044444      0.40\n",
       "38       21.050000      0.40\n",
       "39       21.183333      0.37\n",
       "40       20.116667      0.40\n",
       "41       20.216667      0.36\n",
       "42       20.000000      0.43\n",
       "43       17.800000      0.50\n",
       "44       16.061111      0.53\n",
       "45       15.022222      0.55\n",
       "46       14.422222      0.58\n",
       "47       14.255556      0.59\n",
       "48       13.772222      0.60\n",
       "49       13.283333      0.63"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Pandas Library, used for data manipulation\n",
    "# Import matplotlib, used to plot our data\n",
    "# Import nump for mathemtical operations\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Import our WeatherData and store it in the variable weather_data\n",
    "weather_data = pd.read_csv(\"D:\\ProjectData\\WeatherData.csv\") \n",
    "# Display the data in the notebook\n",
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-d8d1c662d6ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Plot Humidity against Temperature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;31m# Add labels to the graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Temperature(C)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# Set x to our input Temperature\n",
    "x = weather_data['Temperature(C)']\n",
    "# Set y to our output humidity, this is the value we trying to predict\n",
    "y = weather_data['Humidity']\n",
    "\n",
    "# Plot Humidity against Temperature\n",
    "plt.scatter(x, y)\n",
    "# Add labels to the graph\n",
    "plt.xlabel('Temperature(C)')\n",
    "plt.ylabel('Humidity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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       "[<matplotlib.lines.Line2D at 0x1b25aad0080>]"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "x = weather_data['Temperature(C)'].values.reshape(-1,1)\n",
    "y = weather_data['Humidity']\n",
    "\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(x, y)\n",
    "y_pred = lr_model.predict(x)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('Temperature(C)')\n",
    "plt.ylabel('Humidity')\n",
    "\n",
    "plt.plot(x, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1336757355537121, array([-0.03522051]))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_0 = lr_model.intercept_\n",
    "theta_1 = lr_model.coef_\n",
    "theta_0, theta_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00661941])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr_model.predict(np.array([32]).reshape(1, 1))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}