ECON30006 - DS/Files/Colab_Regression.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Colab_Regression.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPjgkWdfbwnjOuArouQffEQ"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["***The content of this file is largely from the teaching material of the Data Science unit of University of Bristol***"],"metadata":{"id":"BYCX56D4YTeU"}},{"cell_type":"code","execution_count":20,"metadata":{"id":"zxYRaZvfGiNA","executionInfo":{"status":"ok","timestamp":1641223257570,"user_tz":0,"elapsed":399,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"outputs":[],"source":["# Import tools that we will need:\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","import seaborn as sns; sns.set()\n","import numpy as np\n","import pandas as pd\n","import os\n","from sklearn.linear_model import LinearRegression\n","from sklearn.preprocessing import PolynomialFeatures\n","from sklearn.pipeline import make_pipeline"]},{"cell_type":"code","source":[" # Set working directory\n"," os.chdir(\"/content\")"],"metadata":{"id":"a2XkMTtcGnDM","executionInfo":{"status":"ok","timestamp":1641223258108,"user_tz":0,"elapsed":92,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":["#**Preliminary Cleaning**"],"metadata":{"id":"K7e7zeZCsLWw"}},{"cell_type":"code","source":["# read the csv file uploaded in the woking directory \"/content\" in a panda data frame \n","data=pd.read_csv('dataHealthAgeingOrg.csv')\n","data.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":191},"id":"t408xT2dGrXg","executionInfo":{"status":"ok","timestamp":1641223258111,"user_tz":0,"elapsed":93,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}},"outputId":"c69d2426-bf97-4309-db8b-2bdb462e306b"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-4828d6b8-625f-409b-8599-a8f3c6e72a9f\">\n","    <div class=\"colab-df-container\">\n","      <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>Time</th>\n","      <th>Time Code</th>\n","      <th>Country Name</th>\n","      <th>Country Code</th>\n","      <th>Age dependency ratio, old (% of working-age population) [SP.POP.DPND.OL]</th>\n","      <th>Domestic general government health expenditure per capita, PPP (current international $) [SH.XPD.GHED.PP.CD]</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018</td>\n","      <td>YR2018</td>\n","      <td>Afghanistan</td>\n","      <td>AFG</td>\n","      <td>4.758272778</td>\n","      <td>9.64153671</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018</td>\n","      <td>YR2018</td>\n","      <td>Albania</td>\n","      <td>ALB</td>\n","      <td>20.04121416</td>\n","      <td>376.5013733</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4828d6b8-625f-409b-8599-a8f3c6e72a9f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-4828d6b8-625f-409b-8599-a8f3c6e72a9f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Domestic general government health expenditure per capita, PPP (current international $) [SH.XPD.GHED.PP.CD]\n","0  2018  ...                                         9.64153671                                                          \n","1  2018  ...                                        376.5013733                                                          \n","\n","[2 rows x 6 columns]"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# renaming the columns\n","data.columns = ['year','yearcode','country','countrycode','Dependency','HealthExpPC']"],"metadata":{"id":"RdXIRIlvhB3Q","executionInfo":{"status":"ok","timestamp":1641223258114,"user_tz":0,"elapsed":88,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["# changing the data into numeric values\n","data['HealthExpPC'] = pd.to_numeric(data['HealthExpPC'],errors='coerce')\n","data['Dependency'] = pd.to_numeric(data['Dependency'],errors='coerce')"],"metadata":{"id":"E-iZsD0uGtf1","executionInfo":{"status":"ok","timestamp":1641223258116,"user_tz":0,"elapsed":89,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["# dropping columns and missing values\n","midData = data.drop(columns=['year','yearcode','country','countrycode'])\n","actData = midData.dropna()"],"metadata":{"id":"fMMUgz8UGvPI","executionInfo":{"status":"ok","timestamp":1641223258118,"user_tz":0,"elapsed":89,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":["#**Plotting Dataset and Line**"],"metadata":{"id":"HSsd_14lrow7"}},{"cell_type":"code","source":["# put the column names as the x and y variables\n","y = actData['HealthExpPC']\n","x = actData['Dependency']\n","\n","# change the order of the polynomial regression according to the best fit, in this case, order of 6\n","poly_model = make_pipeline(PolynomialFeatures(6),\n","                           LinearRegression())\n","poly_model.fit(x[:, np.newaxis], y)\n","xfit = np.linspace(min(x), max(x), 1000)\n","yfit = poly_model.predict(xfit[:, np.newaxis])\n","plt.scatter(x, y)\n","plt.plot(xfit, yfit);\n","\n","# name the title of the y and x axis\n","plt.ylabel('')\n","plt.xlabel('Dependency Ratio')\n","\n","# set the domains for y and x\n","plt.xlim([0,50])\n","plt.ylim([0,6500])\n","ax = plt.subplot(111)\n","ax.set_facecolor('white')\n","ax.get_xaxis().tick_bottom()    \n","ax.get_yaxis().tick_left()    \n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360},"id":"pzR6c7NFHlfJ","executionInfo":{"status":"ok","timestamp":1641223258124,"user_tz":0,"elapsed":94,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}},"outputId":"90328334-442b-4534-f45e-090418927a01"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n","  \n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"]},{"output_type":"display_data","data":{"image/png":"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size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","source":["#**Plotting Dataset and Line without Outlier**"],"metadata":{"id":"bPpEj28Fr67D"}},{"cell_type":"code","source":["# dropping the single outlier from the dataset\n","midDataII = midData.drop(midData[midData.Dependency > 40].index) \n","\n","# dropping missing values\n","actDataII = midDataII.dropna()"],"metadata":{"id":"cgE3WlEUG06H","executionInfo":{"status":"ok","timestamp":1641223258127,"user_tz":0,"elapsed":79,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["actDataII"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"KeYxNM9Th2gd","executionInfo":{"status":"ok","timestamp":1641223258677,"user_tz":0,"elapsed":627,"user":{"displayName":"Athirah 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Convert to a numpy array before indexing instead.\n","  if __name__ == '__main__':\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","source":["#**Saving CSV Files**"],"metadata":{"id":"QEz2ZG5or988"}},{"cell_type":"markdown","source":["chart is made in vegalite"],"metadata":{"id":"vAx4r36AskRN"}},{"cell_type":"code","source":[" #create new dataframe from the fitted line\n","fittedLine = pd.DataFrame(data=np.linspace(min(x2), max(x2), 1000), columns = ['xFitted'])\n","\n","# add new columns for the y values of the fitted line\n","fittedLine['yFitted'] = yfit2 "],"metadata":{"id":"PHxmb97sSFKa","executionInfo":{"status":"ok","timestamp":1641223258684,"user_tz":0,"elapsed":114,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["fittedLine"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"sMhPXHamTDTY","executionInfo":{"status":"ok","timestamp":1641223258688,"user_tz":0,"elapsed":116,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}},"outputId":"fac86757-93bd-492c-c27b-5e5e956b1be1"},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-75191d8f-19cc-44cc-9eff-d1a8f5d42b72\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<th>995</th>\n","      <td>35.457067</td>\n","      <td>3051.653180</td>\n","    </tr>\n","    <tr>\n","      <th>996</th>\n","      <td>35.491409</td>\n","      <td>3069.086627</td>\n","    </tr>\n","    <tr>\n","      <th>997</th>\n","      <td>35.525751</td>\n","      <td>3086.952856</td>\n","    </tr>\n","    <tr>\n","      <th>998</th>\n","      <td>35.560093</td>\n","      <td>3105.257684</td>\n","    </tr>\n","    <tr>\n","      <th>999</th>\n","      <td>35.594435</td>\n","      <td>3124.006973</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1000 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75191d8f-19cc-44cc-9eff-d1a8f5d42b72')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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3124.006973\n","\n","[1000 rows x 2 columns]"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["# downloading from panda data frame to csv, for columns xFitted, yFitted\n","fittedLine[['xFitted','yFitted']].to_csv(\"dataHealthAgeingLine.csv\")"],"metadata":{"id":"-9Felp_9XCBT","executionInfo":{"status":"ok","timestamp":1641223258693,"user_tz":0,"elapsed":116,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["actDataII"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"CqQepudYcKy3","executionInfo":{"status":"ok","timestamp":1641223258695,"user_tz":0,"elapsed":117,"user":{"displayName":"Athirah 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  </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>20.041214</td>\n","      <td>376.501373</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>10.021442</td>\n","      <td>633.798828</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>4.347987</td>\n","      <td>69.060318</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>12.731972</td>\n","      <td>780.362488</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>261</th>\n","      <td>5.484690</td>\n","      <td>70.541295</td>\n","    </tr>\n","    <tr>\n","      <th>262</th>\n","      <td>5.484060</td>\n","      <td>70.442734</td>\n","    </tr>\n","    <tr>\n","      <th>263</th>\n","      <td>5.484690</td>\n","      <td>70.541295</td>\n","    </tr>\n","    <tr>\n","      <th>264</th>\n","      <td>14.783966</td>\n","      <td>600.435246</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>13.584800</td>\n","      <td>844.432028</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>224 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d502bb39-7c45-430a-9685-fbb2f06fd51c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 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Convert to a numpy array before indexing instead.\n","  import sys\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":[" #create new dataframe from the fitted line\n","fittedStraight = pd.DataFrame(data=np.linspace(min(x3), max(x3), 1000), columns = ['xStraight'])\n","\n","# add new columns for the y values of the fitted line\n","fittedStraight['yStraight'] = yfit3 "],"metadata":{"id":"LdEaJMKGwVJ5","executionInfo":{"status":"ok","timestamp":1641223259089,"user_tz":0,"elapsed":32,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["# downloading from panda data frame to csv, for columns xFitted, yFitted\n","fittedStraight[['xStraight','yStraight']].to_csv(\"dataHealthAgeingStraight.csv\")"],"metadata":{"id":"XrpaL_E9xF-R","executionInfo":{"status":"ok","timestamp":1641223259094,"user_tz":0,"elapsed":33,"user":{"displayName":"Athirah Ridzal","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgdExTsVjEe5dFgYTdOHUUWeQAyLLl-ueWDH8CRlA=s64","userId":"10095957826784707226"}}},"execution_count":37,"outputs":[]}]}