_EX-4 Glucose_Regression.ipynb

{"nbformat":4,"nbformat_minor":0,"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":"3.7.6"},"colab":{"name":" EX-4 Glucose_Regression.ipynb","provenance":[{"file_id":"1GdGPfkgFwZVeNP8D_WiqF5mSVKpnpf2r","timestamp":1630253566567},{"file_id":"1JUze--w40ksx6UQxZMU5LM_WEa3RiVx6","timestamp":1625233838864}],"collapsed_sections":["kJdcb7O2W-f4","ls6YqAd1W-f5","0R0m-JL1W-gD","J8VFwWEWW-gF","vsuPAaxzW-gP","JjSiHwA_W-gQ","De_QoBtOW-gS","hR2TQfRXW-gT","Dpuy0XNUW-hB","U0UVhlNnbdvH","jr7d-pzC2w-1","LE5MiSoZ4exO","eK7CRvfiwUok"],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"Fq8SUgQFXG8L"},"source":["# **Glucose Regression**\n","\n"]},{"cell_type":"markdown","metadata":{"id":"MOG6rYfmXckI"},"source":["### Google Colab Functions"]},{"cell_type":"markdown","metadata":{"id":"IYtWMzOvLQ3s"},"source":["#### Prevent random disconnects\n","\n","This cell runs JS code to automatic reconnect to runtime."]},{"cell_type":"markdown","metadata":{"id":"ado2pzmNswm3"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"CD2O1fbeYpRc"},"source":["Plots I can work on :\n","\n","\n","1.   Relationship between features and glyhb - done\n","2.   Distribution of data - done \n","3.   Lasso feature selection -done\n","\n"]},{"cell_type":"code","metadata":{"id":"jtClEMAMLVHw","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1630253699830,"user_tz":-330,"elapsed":641,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"d6cc0c1b-5de1-4da8-9868-c99fb70b4ba6"},"source":["import IPython\n","from google.colab import output\n","\n","display(IPython.display.Javascript('''\n"," function ClickConnect(){\n","   btn = document.querySelector(\"colab-connect-button\")\n","   if (btn != null){\n","     console.log(\"Click colab-connect-button\"); \n","     btn.click() \n","     }\n","   \n","   btn = document.getElementById('ok')\n","   if (btn != null){\n","     console.log(\"Click reconnect\"); \n","     btn.click() \n","     }\n","  }\n","  \n","setInterval(ClickConnect,60000)\n","'''))\n","\n","print(\"Done.\")"],"execution_count":5,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n"," function ClickConnect(){\n","   btn = document.querySelector(\"colab-connect-button\")\n","   if (btn != null){\n","     console.log(\"Click colab-connect-button\"); \n","     btn.click() \n","     }\n","   \n","   btn = document.getElementById('ok')\n","   if (btn != null){\n","     console.log(\"Click reconnect\"); \n","     btn.click() \n","     }\n","  }\n","  \n","setInterval(ClickConnect,60000)\n"],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{}},{"output_type":"stream","text":["Done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A-SZElzHXpWi","executionInfo":{"status":"ok","timestamp":1630253700528,"user_tz":-330,"elapsed":21,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"7f5b9b4c-6e85-41eb-c183-80117208a935"},"source":["from google.colab import drive\n","drive.mount('/gdrive')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JSEdsyEHW-fx"},"source":["# Libraries Used"]},{"cell_type":"code","metadata":{"id":"vev5Bdn3W-f0","executionInfo":{"status":"ok","timestamp":1630253700528,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["# Imports\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import cross_val_score\n","from sklearn.metrics import fbeta_score, make_scorer\n","from scipy.stats import norm\n"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"7awZBWvlW-f1","executionInfo":{"status":"ok","timestamp":1630253700529,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["# Importing Dataset into Notebook\n","dataset =  pd.read_csv(\"/gdrive/MyDrive/Diabetes.csv\")"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"q7rC76V2OTZh"},"source":["\n","# Data Analysis and Data Cleaning\n","\n","After importing the data, a simple analysis of the data is done."]},{"cell_type":"code","metadata":{"id":"YFnZyi2feJ_h","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253700529,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"b525791d-d1da-49ee-d99b-05927a576abc"},"source":["dataset.info()"],"execution_count":9,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 403 entries, 0 to 402\n","Data columns (total 19 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   id        403 non-null    int64  \n"," 1   chol      402 non-null    float64\n"," 2   stab.glu  403 non-null    int64  \n"," 3   hdl       402 non-null    float64\n"," 4   ratio     402 non-null    float64\n"," 5   glyhb     390 non-null    float64\n"," 6   location  403 non-null    object \n"," 7   age       403 non-null    int64  \n"," 8   gender    403 non-null    object \n"," 9   height    398 non-null    float64\n"," 10  weight    402 non-null    float64\n"," 11  frame     391 non-null    object \n"," 12  bp.1s     398 non-null    float64\n"," 13  bp.1d     398 non-null    float64\n"," 14  bp.2s     141 non-null    float64\n"," 15  bp.2d     141 non-null    float64\n"," 16  waist     401 non-null    float64\n"," 17  hip       401 non-null    float64\n"," 18  time.ppn  400 non-null    float64\n","dtypes: float64(13), int64(3), object(3)\n","memory usage: 59.9+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KcZxlCghe181"},"source":["Since there are different numbers of data present in each features, We should check for NULL values."]},{"cell_type":"markdown","metadata":{"id":"8JvEoJlVW-f1"},"source":["### Check For The Null Values\n"]},{"cell_type":"code","metadata":{"id":"_vG1MiSGW-f2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253700530,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a2648697-a8f7-4d0b-ad70-9afb4cbc1aff"},"source":["dataset.isnull().sum()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["id            0\n","chol          1\n","stab.glu      0\n","hdl           1\n","ratio         1\n","glyhb        13\n","location      0\n","age           0\n","gender        0\n","height        5\n","weight        1\n","frame        12\n","bp.1s         5\n","bp.1d         5\n","bp.2s       262\n","bp.2d       262\n","waist         2\n","hip           2\n","time.ppn      3\n","dtype: int64"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"mBSrfYBdW-f3","colab":{"base_uri":"https://localhost:8080/","height":224},"executionInfo":{"status":"ok","timestamp":1630253700531,"user_tz":-330,"elapsed":11,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"0153c668-75f8-4811-c116-501e0835503c"},"source":["# Lets view the data\n","dataset.head()"],"execution_count":11,"outputs":[{"output_type":"execute_result","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>id</th>\n","      <th>chol</th>\n","      <th>stab.glu</th>\n","      <th>hdl</th>\n","      <th>ratio</th>\n","      <th>glyhb</th>\n","      <th>location</th>\n","      <th>age</th>\n","      <th>gender</th>\n","      <th>height</th>\n","      <th>weight</th>\n","      <th>frame</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>bp.2s</th>\n","      <th>bp.2d</th>\n","      <th>waist</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1000</td>\n","      <td>203.0</td>\n","      <td>82</td>\n","      <td>56.0</td>\n","      <td>3.6</td>\n","      <td>4.31</td>\n","      <td>Buckingham</td>\n","      <td>46</td>\n","      <td>female</td>\n","      <td>62.0</td>\n","      <td>121.0</td>\n","      <td>medium</td>\n","      <td>118.0</td>\n","      <td>59.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>29.0</td>\n","      <td>38.0</td>\n","      <td>720.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1001</td>\n","      <td>165.0</td>\n","      <td>97</td>\n","      <td>24.0</td>\n","      <td>6.9</td>\n","      <td>4.44</td>\n","      <td>Buckingham</td>\n","      <td>29</td>\n","      <td>female</td>\n","      <td>64.0</td>\n","      <td>218.0</td>\n","      <td>large</td>\n","      <td>112.0</td>\n","      <td>68.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>46.0</td>\n","      <td>48.0</td>\n","      <td>360.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1002</td>\n","      <td>228.0</td>\n","      <td>92</td>\n","      <td>37.0</td>\n","      <td>6.2</td>\n","      <td>4.64</td>\n","      <td>Buckingham</td>\n","      <td>58</td>\n","      <td>female</td>\n","      <td>61.0</td>\n","      <td>256.0</td>\n","      <td>large</td>\n","      <td>190.0</td>\n","      <td>92.0</td>\n","      <td>185.0</td>\n","      <td>92.0</td>\n","      <td>49.0</td>\n","      <td>57.0</td>\n","      <td>180.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1003</td>\n","      <td>78.0</td>\n","      <td>93</td>\n","      <td>12.0</td>\n","      <td>6.5</td>\n","      <td>4.63</td>\n","      <td>Buckingham</td>\n","      <td>67</td>\n","      <td>male</td>\n","      <td>67.0</td>\n","      <td>119.0</td>\n","      <td>large</td>\n","      <td>110.0</td>\n","      <td>50.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>33.0</td>\n","      <td>38.0</td>\n","      <td>480.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1005</td>\n","      <td>249.0</td>\n","      <td>90</td>\n","      <td>28.0</td>\n","      <td>8.9</td>\n","      <td>7.72</td>\n","      <td>Buckingham</td>\n","      <td>64</td>\n","      <td>male</td>\n","      <td>68.0</td>\n","      <td>183.0</td>\n","      <td>medium</td>\n","      <td>138.0</td>\n","      <td>80.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>44.0</td>\n","      <td>41.0</td>\n","      <td>300.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     id   chol  stab.glu   hdl  ratio  ...  bp.2s bp.2d  waist   hip  time.ppn\n","0  1000  203.0        82  56.0    3.6  ...    NaN   NaN   29.0  38.0     720.0\n","1  1001  165.0        97  24.0    6.9  ...    NaN   NaN   46.0  48.0     360.0\n","2  1002  228.0        92  37.0    6.2  ...  185.0  92.0   49.0  57.0     180.0\n","3  1003   78.0        93  12.0    6.5  ...    NaN   NaN   33.0  38.0     480.0\n","4  1005  249.0        90  28.0    8.9  ...    NaN   NaN   44.0  41.0     300.0\n","\n","[5 rows x 19 columns]"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"1UiZ5Uzkfkgo"},"source":["Dropping the column \"id\" since it consists of unique value."]},{"cell_type":"code","metadata":{"id":"0zlRb8z4W-f4","executionInfo":{"status":"ok","timestamp":1630253700531,"user_tz":-330,"elapsed":10,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["dataset.drop(columns = \"id\",inplace=True)"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kJdcb7O2W-f4"},"source":["### Checking Dustribution"]},{"cell_type":"code","metadata":{"id":"D2enNFEkW-f4","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702017,"user_tz":-330,"elapsed":1496,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"3db57f97-3df1-400b-f34a-6142528d901c"},"source":["plt.figure(figsize=(8,8))\n","plt.plot(dataset[\"time.ppn\"])\n","plt.show()"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ls6YqAd1W-f5"},"source":["### Filling NAN Values as median of that columns"]},{"cell_type":"code","metadata":{"id":"iJ28c1w8kuRi","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702017,"user_tz":-330,"elapsed":15,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"6b32822c-6781-4cab-8e52-68b2b85a697e"},"source":["# Lets check the percentage of NaN values in our features\n","percent_missing = dataset.isnull().sum() * 100 / len(dataset)\n","missing_value_df = pd.DataFrame({'column_name': dataset.columns,\n","                                 'percent_missing': percent_missing})\n","missing_value_df"],"execution_count":14,"outputs":[{"output_type":"execute_result","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>column_name</th>\n","      <th>percent_missing</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>chol</th>\n","      <td>chol</td>\n","      <td>0.248139</td>\n","    </tr>\n","    <tr>\n","      <th>stab.glu</th>\n","      <td>stab.glu</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>hdl</th>\n","      <td>hdl</td>\n","      <td>0.248139</td>\n","    </tr>\n","    <tr>\n","      <th>ratio</th>\n","      <td>ratio</td>\n","      <td>0.248139</td>\n","    </tr>\n","    <tr>\n","      <th>glyhb</th>\n","      <td>glyhb</td>\n","      <td>3.225806</td>\n","    </tr>\n","    <tr>\n","      <th>location</th>\n","      <td>location</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>age</th>\n","      <td>age</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>gender</th>\n","      <td>gender</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>height</th>\n","      <td>height</td>\n","      <td>1.240695</td>\n","    </tr>\n","    <tr>\n","      <th>weight</th>\n","      <td>weight</td>\n","      <td>0.248139</td>\n","    </tr>\n","    <tr>\n","      <th>frame</th>\n","      <td>frame</td>\n","      <td>2.977667</td>\n","    </tr>\n","    <tr>\n","      <th>bp.1s</th>\n","      <td>bp.1s</td>\n","      <td>1.240695</td>\n","    </tr>\n","    <tr>\n","      <th>bp.1d</th>\n","      <td>bp.1d</td>\n","      <td>1.240695</td>\n","    </tr>\n","    <tr>\n","      <th>bp.2s</th>\n","      <td>bp.2s</td>\n","      <td>65.012407</td>\n","    </tr>\n","    <tr>\n","      <th>bp.2d</th>\n","      <td>bp.2d</td>\n","      <td>65.012407</td>\n","    </tr>\n","    <tr>\n","      <th>waist</th>\n","      <td>waist</td>\n","      <td>0.496278</td>\n","    </tr>\n","    <tr>\n","      <th>hip</th>\n","      <td>hip</td>\n","      <td>0.496278</td>\n","    </tr>\n","    <tr>\n","      <th>time.ppn</th>\n","      <td>time.ppn</td>\n","      <td>0.744417</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         column_name  percent_missing\n","chol            chol         0.248139\n","stab.glu    stab.glu         0.000000\n","hdl              hdl         0.248139\n","ratio          ratio         0.248139\n","glyhb          glyhb         3.225806\n","location    location         0.000000\n","age              age         0.000000\n","gender        gender         0.000000\n","height        height         1.240695\n","weight        weight         0.248139\n","frame          frame         2.977667\n","bp.1s          bp.1s         1.240695\n","bp.1d          bp.1d         1.240695\n","bp.2s          bp.2s        65.012407\n","bp.2d          bp.2d        65.012407\n","waist          waist         0.496278\n","hip              hip         0.496278\n","time.ppn    time.ppn         0.744417"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"PLPI8ySOmuV4"},"source":["We can clearly see that the coulumns **bp.2s** and **bp.2d** have over ***65%*** of NaN values in it,thus rendering the column useless. Therefore we will drop these 2 columns too."]},{"cell_type":"code","metadata":{"id":"qmlXNBG1W-f5","executionInfo":{"status":"ok","timestamp":1630253702018,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["dataset[\"weight\"].fillna(dataset[\"weight\"].median(),inplace=True)\n","dataset[\"height\"].fillna(dataset[\"height\"].median(),inplace=True)\n","dataset[\"hdl\"].fillna(dataset[\"hdl\"].median(),inplace=True)\n","dataset[\"ratio\"].fillna(dataset[\"ratio\"].median(),inplace=True)\n","dataset[\"bp.1s\"].fillna(dataset[\"bp.1s\"].median(),inplace=True)\n","dataset[\"bp.1d\"].fillna(dataset[\"bp.1d\"].median(),inplace=True)\n","dataset[\"chol\"].fillna(dataset[\"chol\"].median(),inplace=True)\n","dataset[\"waist\"].fillna(dataset[\"waist\"].median(),inplace=True)\n","dataset[\"hip\"].fillna(dataset[\"hip\"].median(),inplace=True)\n","dataset[\"time.ppn\"].fillna(dataset[\"time.ppn\"].median(),inplace=True)\n","dataset[\"frame\"].fillna(\"medium\",inplace=True)\n","dataset[\"glyhb\"].fillna(dataset[\"glyhb\"].median(),inplace=True)\n"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cl1_5_55ZrOD","executionInfo":{"status":"ok","timestamp":1630253702018,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["# Deleting bp.2s , bp.2d Too many NAN values so dropping values\n","dataset.drop(columns=[\"bp.2s\",\"bp.2d\"],inplace=True)"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":true,"id":"S39mvZEVW-f8","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702019,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"989dabbf-aaf7-43cc-e396-a715417efa87"},"source":["dataset"],"execution_count":17,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>hdl</th>\n","      <th>ratio</th>\n","      <th>glyhb</th>\n","      <th>location</th>\n","      <th>age</th>\n","      <th>gender</th>\n","      <th>height</th>\n","      <th>weight</th>\n","      <th>frame</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>waist</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>203.0</td>\n","      <td>82</td>\n","      <td>56.0</td>\n","      <td>3.6</td>\n","      <td>4.310000</td>\n","      <td>Buckingham</td>\n","      <td>46</td>\n","      <td>female</td>\n","      <td>62.0</td>\n","      <td>121.0</td>\n","      <td>medium</td>\n","      <td>118.0</td>\n","      <td>59.0</td>\n","      <td>29.0</td>\n","      <td>38.0</td>\n","      <td>720.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>165.0</td>\n","      <td>97</td>\n","      <td>24.0</td>\n","      <td>6.9</td>\n","      <td>4.440000</td>\n","      <td>Buckingham</td>\n","      <td>29</td>\n","      <td>female</td>\n","      <td>64.0</td>\n","      <td>218.0</td>\n","      <td>large</td>\n","      <td>112.0</td>\n","      <td>68.0</td>\n","      <td>46.0</td>\n","      <td>48.0</td>\n","      <td>360.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>228.0</td>\n","      <td>92</td>\n","      <td>37.0</td>\n","      <td>6.2</td>\n","      <td>4.640000</td>\n","      <td>Buckingham</td>\n","      <td>58</td>\n","      <td>female</td>\n","      <td>61.0</td>\n","      <td>256.0</td>\n","      <td>large</td>\n","      <td>190.0</td>\n","      <td>92.0</td>\n","      <td>49.0</td>\n","      <td>57.0</td>\n","      <td>180.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>78.0</td>\n","      <td>93</td>\n","      <td>12.0</td>\n","      <td>6.5</td>\n","      <td>4.630000</td>\n","      <td>Buckingham</td>\n","      <td>67</td>\n","      <td>male</td>\n","      <td>67.0</td>\n","      <td>119.0</td>\n","      <td>large</td>\n","      <td>110.0</td>\n","      <td>50.0</td>\n","      <td>33.0</td>\n","      <td>38.0</td>\n","      <td>480.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>249.0</td>\n","      <td>90</td>\n","      <td>28.0</td>\n","      <td>8.9</td>\n","      <td>7.720000</td>\n","      <td>Buckingham</td>\n","      <td>64</td>\n","      <td>male</td>\n","      <td>68.0</td>\n","      <td>183.0</td>\n","      <td>medium</td>\n","      <td>138.0</td>\n","      <td>80.0</td>\n","      <td>44.0</td>\n","      <td>41.0</td>\n","      <td>300.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>398</th>\n","      <td>296.0</td>\n","      <td>369</td>\n","      <td>46.0</td>\n","      <td>6.4</td>\n","      <td>16.110001</td>\n","      <td>Louisa</td>\n","      <td>53</td>\n","      <td>male</td>\n","      <td>69.0</td>\n","      <td>173.0</td>\n","      <td>medium</td>\n","      <td>138.0</td>\n","      <td>94.0</td>\n","      <td>35.0</td>\n","      <td>39.0</td>\n","      <td>210.0</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>284.0</td>\n","      <td>89</td>\n","      <td>54.0</td>\n","      <td>5.3</td>\n","      <td>4.390000</td>\n","      <td>Louisa</td>\n","      <td>51</td>\n","      <td>female</td>\n","      <td>63.0</td>\n","      <td>154.0</td>\n","      <td>medium</td>\n","      <td>140.0</td>\n","      <td>100.0</td>\n","      <td>32.0</td>\n","      <td>43.0</td>\n","      <td>180.0</td>\n","    </tr>\n","    <tr>\n","      <th>400</th>\n","      <td>194.0</td>\n","      <td>269</td>\n","      <td>38.0</td>\n","      <td>5.1</td>\n","      <td>13.630000</td>\n","      <td>Louisa</td>\n","      <td>29</td>\n","      <td>female</td>\n","      <td>69.0</td>\n","      <td>167.0</td>\n","      <td>small</td>\n","      <td>120.0</td>\n","      <td>70.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>20.0</td>\n","    </tr>\n","    <tr>\n","      <th>401</th>\n","      <td>199.0</td>\n","      <td>76</td>\n","      <td>52.0</td>\n","      <td>3.8</td>\n","      <td>4.490000</td>\n","      <td>Louisa</td>\n","      <td>41</td>\n","      <td>female</td>\n","      <td>63.0</td>\n","      <td>197.0</td>\n","      <td>medium</td>\n","      <td>120.0</td>\n","      <td>78.0</td>\n","      <td>41.0</td>\n","      <td>48.0</td>\n","      <td>255.0</td>\n","    </tr>\n","    <tr>\n","      <th>402</th>\n","      <td>159.0</td>\n","      <td>88</td>\n","      <td>79.0</td>\n","      <td>2.0</td>\n","      <td>4.840000</td>\n","      <td>Louisa</td>\n","      <td>68</td>\n","      <td>female</td>\n","      <td>64.0</td>\n","      <td>220.0</td>\n","      <td>medium</td>\n","      <td>100.0</td>\n","      <td>72.0</td>\n","      <td>49.0</td>\n","      <td>58.0</td>\n","      <td>900.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>403 rows × 16 columns</p>\n","</div>"],"text/plain":["      chol  stab.glu   hdl  ratio  ...  bp.1d waist   hip time.ppn\n","0    203.0        82  56.0    3.6  ...   59.0  29.0  38.0    720.0\n","1    165.0        97  24.0    6.9  ...   68.0  46.0  48.0    360.0\n","2    228.0        92  37.0    6.2  ...   92.0  49.0  57.0    180.0\n","3     78.0        93  12.0    6.5  ...   50.0  33.0  38.0    480.0\n","4    249.0        90  28.0    8.9  ...   80.0  44.0  41.0    300.0\n","..     ...       ...   ...    ...  ...    ...   ...   ...      ...\n","398  296.0       369  46.0    6.4  ...   94.0  35.0  39.0    210.0\n","399  284.0        89  54.0    5.3  ...  100.0  32.0  43.0    180.0\n","400  194.0       269  38.0    5.1  ...   70.0  33.0  40.0     20.0\n","401  199.0        76  52.0    3.8  ...   78.0  41.0  48.0    255.0\n","402  159.0        88  79.0    2.0  ...   72.0  49.0  58.0    900.0\n","\n","[403 rows x 16 columns]"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"LjKz6WIhW-f9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253702019,"user_tz":-330,"elapsed":13,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"599ef57c-4cdf-448a-e20f-43e81134d612"},"source":["dataset.isnull().sum()"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["chol        0\n","stab.glu    0\n","hdl         0\n","ratio       0\n","glyhb       0\n","location    0\n","age         0\n","gender      0\n","height      0\n","weight      0\n","frame       0\n","bp.1s       0\n","bp.1d       0\n","waist       0\n","hip         0\n","time.ppn    0\n","dtype: int64"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"T9MMKb9AZ1JJ"},"source":["Now We are NULL free!"]},{"cell_type":"markdown","metadata":{"id":"YSYJMy_dW-f-"},"source":[" Standardisation of all the columns as there must not be much diffrence in column values Very important for distance based values not much of a effect on trees based values"]},{"cell_type":"code","metadata":{"id":"-7amaJFJW-f-","executionInfo":{"status":"ok","timestamp":1630253702019,"user_tz":-330,"elapsed":12,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["dataset[\"bp.1s\"] = np.log(dataset[\"bp.1s\"]) \n","dataset[\"hdl\"] = np.log(dataset[\"hdl\"]) \n","dataset[\"height\"] = np.log(dataset[\"height\"]) \n","dataset[\"age\"] = np.log(dataset[\"age\"]) \n","dataset[\"bp.1d\"] = np.log(dataset[\"bp.1d\"]) \n","dataset[\"ratio\"] = np.log(dataset[\"ratio\"]) \n","dataset[\"stab.glu\"] = np.sqrt(dataset[\"stab.glu\"])"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"id":"bVNczEFxW-f_","executionInfo":{"status":"ok","timestamp":1630253702020,"user_tz":-330,"elapsed":12,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["from sklearn.preprocessing import StandardScaler,MinMaxScaler,RobustScaler\n","sc = StandardScaler()\n","dataset[\"stab.glu\"] =  sc.fit_transform(dataset[\"stab.glu\"].values.reshape(403,1))\n","dataset['hdl'] = sc.fit_transform(dataset[\"hdl\"].values.reshape(403,1))\n","dataset[\"ratio\"] = sc.fit_transform(dataset[\"ratio\"].values.reshape(403,1))\n","dataset['waist'] = sc.fit_transform(dataset[\"waist\"].values.reshape(403,1))\n","dataset[\"weight\"] = sc.fit_transform(dataset[\"weight\"].values.reshape(403,1))\n","dataset[\"chol\"] = sc.fit_transform(dataset[\"chol\"].values.reshape(403,1))\n","dataset[\"bp.1s\"] = sc.fit_transform(dataset[\"bp.1s\"].values.reshape(403,1))\n","dataset[\"bp.1d\"] = sc.fit_transform(dataset[\"bp.1d\"].values.reshape(403,1))\n","dataset[\"time.ppn\"] = sc.fit_transform(dataset[\"time.ppn\"].values.reshape(403,1))\n","dataset[\"hip\"] = sc.fit_transform(dataset[\"hip\"].values.reshape(403,1))\n","dataset[\"age\"] = sc.fit_transform(dataset[\"age\"].values.reshape(403,1))\n","dataset[\"height\"] = sc.fit_transform(dataset[\"height\"].values.reshape(403,1))"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"hMffn-uvW-gC","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702020,"user_tz":-330,"elapsed":12,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"af578dc1-ea0b-48bd-f879-7da7e4e42e23"},"source":["dataset"],"execution_count":21,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>hdl</th>\n","      <th>ratio</th>\n","      <th>glyhb</th>\n","      <th>location</th>\n","      <th>age</th>\n","      <th>gender</th>\n","      <th>height</th>\n","      <th>weight</th>\n","      <th>frame</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>waist</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.109082</td>\n","      <td>-0.498023</td>\n","      <td>0.485298</td>\n","      <td>-0.476985</td>\n","      <td>4.310000</td>\n","      <td>Buckingham</td>\n","      <td>0.125395</td>\n","      <td>female</td>\n","      <td>-1.033326</td>\n","      <td>-1.406006</td>\n","      <td>medium</td>\n","      <td>-0.864244</td>\n","      <td>-2.026457</td>\n","      <td>-1.558395</td>\n","      <td>-0.893303</td>\n","      <td>1.231660</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.966182</td>\n","      <td>-0.123024</td>\n","      <td>-2.111356</td>\n","      <td>1.396400</td>\n","      <td>4.440000</td>\n","      <td>Buckingham</td>\n","      <td>-1.129342</td>\n","      <td>female</td>\n","      <td>-0.496206</td>\n","      <td>1.004457</td>\n","      <td>large</td>\n","      <td>-1.196269</td>\n","      <td>-1.159206</td>\n","      <td>1.419726</td>\n","      <td>0.880976</td>\n","      <td>0.063298</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.454799</td>\n","      <td>-0.244575</td>\n","      <td>-0.784788</td>\n","      <td>1.088371</td>\n","      <td>4.640000</td>\n","      <td>Buckingham</td>\n","      <td>0.755834</td>\n","      <td>female</td>\n","      <td>-1.308419</td>\n","      <td>1.948762</td>\n","      <td>large</td>\n","      <td>2.166407</td>\n","      <td>0.687334</td>\n","      <td>1.945276</td>\n","      <td>2.477827</td>\n","      <td>-0.520882</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-2.928491</td>\n","      <td>-0.220006</td>\n","      <td>-4.235595</td>\n","      <td>1.224437</td>\n","      <td>4.630000</td>\n","      <td>Buckingham</td>\n","      <td>1.148155</td>\n","      <td>male</td>\n","      <td>0.278792</td>\n","      <td>-1.455706</td>\n","      <td>large</td>\n","      <td>-1.310909</td>\n","      <td>-3.037533</td>\n","      <td>-0.857660</td>\n","      <td>-0.893303</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.928460</td>\n","      <td>-0.294119</td>\n","      <td>-1.638941</td>\n","      <td>2.129326</td>\n","      <td>7.720000</td>\n","      <td>Buckingham</td>\n","      <td>1.023565</td>\n","      <td>male</td>\n","      <td>0.529432</td>\n","      <td>0.134703</td>\n","      <td>medium</td>\n","      <td>0.131908</td>\n","      <td>-0.166428</td>\n","      <td>1.069359</td>\n","      <td>-0.361020</td>\n","      <td>-0.131429</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>398</th>\n","      <td>1.988557</td>\n","      <td>4.300799</td>\n","      <td>-0.117546</td>\n","      <td>1.179792</td>\n","      <td>16.110001</td>\n","      <td>Louisa</td>\n","      <td>0.510647</td>\n","      <td>male</td>\n","      <td>0.776412</td>\n","      <td>-0.113799</td>\n","      <td>medium</td>\n","      <td>0.131908</td>\n","      <td>0.818708</td>\n","      <td>-0.507293</td>\n","      <td>-0.715875</td>\n","      <td>-0.423519</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>1.717894</td>\n","      <td>-0.319097</td>\n","      <td>0.373845</td>\n","      <td>0.636739</td>\n","      <td>4.390000</td>\n","      <td>Louisa</td>\n","      <td>0.406029</td>\n","      <td>female</td>\n","      <td>-0.762634</td>\n","      <td>-0.585951</td>\n","      <td>medium</td>\n","      <td>0.223454</td>\n","      <td>1.196686</td>\n","      <td>-1.032844</td>\n","      <td>-0.006164</td>\n","      <td>-0.520882</td>\n","    </tr>\n","    <tr>\n","      <th>400</th>\n","      <td>-0.312080</td>\n","      <td>2.973653</td>\n","      <td>-0.703059</td>\n","      <td>0.525974</td>\n","      <td>13.630000</td>\n","      <td>Louisa</td>\n","      <td>-1.129342</td>\n","      <td>female</td>\n","      <td>0.776412</td>\n","      <td>-0.262900</td>\n","      <td>small</td>\n","      <td>-0.757310</td>\n","      <td>-0.982130</td>\n","      <td>-0.857660</td>\n","      <td>-0.538447</td>\n","      <td>-1.040154</td>\n","    </tr>\n","    <tr>\n","      <th>401</th>\n","      <td>-0.199303</td>\n","      <td>-0.657568</td>\n","      <td>0.258185</td>\n","      <td>-0.321297</td>\n","      <td>4.490000</td>\n","      <td>Louisa</td>\n","      <td>-0.187563</td>\n","      <td>female</td>\n","      <td>-0.762634</td>\n","      <td>0.482604</td>\n","      <td>medium</td>\n","      <td>-0.757310</td>\n","      <td>-0.321087</td>\n","      <td>0.543808</td>\n","      <td>0.880976</td>\n","      <td>-0.277474</td>\n","    </tr>\n","    <tr>\n","      <th>402</th>\n","      <td>-1.101514</td>\n","      <td>-0.344215</td>\n","      <td>1.539825</td>\n","      <td>-2.169534</td>\n","      <td>4.840000</td>\n","      <td>Louisa</td>\n","      <td>1.188448</td>\n","      <td>female</td>\n","      <td>-0.496206</td>\n","      <td>1.054157</td>\n","      <td>medium</td>\n","      <td>-1.917309</td>\n","      <td>-0.810043</td>\n","      <td>1.945276</td>\n","      <td>2.655254</td>\n","      <td>1.815840</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>403 rows × 16 columns</p>\n","</div>"],"text/plain":["         chol  stab.glu       hdl  ...     waist       hip  time.ppn\n","0   -0.109082 -0.498023  0.485298  ... -1.558395 -0.893303  1.231660\n","1   -0.966182 -0.123024 -2.111356  ...  1.419726  0.880976  0.063298\n","2    0.454799 -0.244575 -0.784788  ...  1.945276  2.477827 -0.520882\n","3   -2.928491 -0.220006 -4.235595  ... -0.857660 -0.893303  0.452752\n","4    0.928460 -0.294119 -1.638941  ...  1.069359 -0.361020 -0.131429\n","..        ...       ...       ...  ...       ...       ...       ...\n","398  1.988557  4.300799 -0.117546  ... -0.507293 -0.715875 -0.423519\n","399  1.717894 -0.319097  0.373845  ... -1.032844 -0.006164 -0.520882\n","400 -0.312080  2.973653 -0.703059  ... -0.857660 -0.538447 -1.040154\n","401 -0.199303 -0.657568  0.258185  ...  0.543808  0.880976 -0.277474\n","402 -1.101514 -0.344215  1.539825  ...  1.945276  2.655254  1.815840\n","\n","[403 rows x 16 columns]"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"0R0m-JL1W-gD"},"source":["### Checking Distribution"]},{"cell_type":"code","metadata":{"id":"wKlEdhRpW-gD","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702974,"user_tz":-330,"elapsed":965,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"cd219c62-73de-415c-f876-756d5382aa81"},"source":["fig,ax = plt.subplots()\n","dataset[\"bp.1d\"].hist(bins=50,color='#A9C5D3',edgecolor='black',grid=False)\n","ax.set_title('Ratio', fontsize=12)\n","ax.set_xlabel('Ratio Distribution', fontsize=12)\n","ax.set_ylabel('Frequency', fontsize=12)"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Frequency')"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"iusdOVzCW-gE","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253702974,"user_tz":-330,"elapsed":9,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"99ac89ef-0eba-4676-8fa6-230bb48edc1e"},"source":["fig,ax = plt.subplots()\n","np.log(dataset[\"ratio\"]).hist(bins=10,color='#A9C5D3',edgecolor='black',grid=False)\n","ax.set_title('Ratio Log', fontsize=12)\n","ax.set_xlabel('Ratio Distribution', fontsize=12)\n","ax.set_ylabel('Frequency', fontsize=12)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in log\n","  result = getattr(ufunc, method)(*inputs, **kwargs)\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Frequency')"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Qqi3CGw7W-gE","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253703936,"user_tz":-330,"elapsed":969,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"f9c2a3f2-3a39-49da-e1fc-ceb75caefaa1"},"source":["plt.figure(figsize=(20,10))\n","sns.jointplot(x=dataset[\"ratio\"],y=dataset[\"glyhb\"],kind=\"reg\")\n","plt.show()"],"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x720 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 3 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"8fLENd3y_cU3","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253705322,"user_tz":-330,"elapsed":1391,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"f81b965a-7b08-4a45-a5ff-cfd7091685c4"},"source":["## Plot relation ship between glyhb and stab.glu\n","plt.figure(figsize=(20,10))\n","sns.jointplot(x=dataset[\"stab.glu\"],y=dataset[\"glyhb\"],kind=\"reg\")\n","plt.show()"],"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x720 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 3 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"MTDckfieMMMU","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253705323,"user_tz":-330,"elapsed":26,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a6d71016-59d5-4a91-b5cf-cb84f0dcc746"},"source":["sns.scatterplot(data=dataset, x=dataset['stab.glu'], y=dataset['glyhb'])"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"aeTgArxGATs4","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253705323,"user_tz":-330,"elapsed":24,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a694c546-5426-4dd7-f64c-ab7df8cc1133"},"source":["# Distrubution of stab.glu\n","sns.distplot(dataset['stab.glu']);"],"execution_count":27,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"IJybk-zJAuCO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705324,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"2a84b0c4-b093-49d1-92c3-31832cc94922"},"source":["print(\"Skewness: %f\" % dataset['stab.glu'].skew())\n","print(\"Kurtosis: %f\" % dataset['stab.glu'].kurt())"],"execution_count":28,"outputs":[{"output_type":"stream","text":["Skewness: 2.202467\n","Kurtosis: 5.031123\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oqTN3cgCAjKV"},"source":[""]},{"cell_type":"code","metadata":{"id":"mJ9GhIXfW-gF","executionInfo":{"status":"ok","timestamp":1630253705324,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["# Creating Y \n","y = dataset[\"glyhb\"]\n","dataset.drop(columns=\"glyhb\",inplace=True)"],"execution_count":29,"outputs":[]},{"cell_type":"code","metadata":{"id":"ev0s7HQEO-st","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253705324,"user_tz":-330,"elapsed":22,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"9c95b50a-c994-4550-da77-b3214a1c61de"},"source":["dataset"],"execution_count":30,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>hdl</th>\n","      <th>ratio</th>\n","      <th>location</th>\n","      <th>age</th>\n","      <th>gender</th>\n","      <th>height</th>\n","      <th>weight</th>\n","      <th>frame</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>waist</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.109082</td>\n","      <td>-0.498023</td>\n","      <td>0.485298</td>\n","      <td>-0.476985</td>\n","      <td>Buckingham</td>\n","      <td>0.125395</td>\n","      <td>female</td>\n","      <td>-1.033326</td>\n","      <td>-1.406006</td>\n","      <td>medium</td>\n","      <td>-0.864244</td>\n","      <td>-2.026457</td>\n","      <td>-1.558395</td>\n","      <td>-0.893303</td>\n","      <td>1.231660</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.966182</td>\n","      <td>-0.123024</td>\n","      <td>-2.111356</td>\n","      <td>1.396400</td>\n","      <td>Buckingham</td>\n","      <td>-1.129342</td>\n","      <td>female</td>\n","      <td>-0.496206</td>\n","      <td>1.004457</td>\n","      <td>large</td>\n","      <td>-1.196269</td>\n","      <td>-1.159206</td>\n","      <td>1.419726</td>\n","      <td>0.880976</td>\n","      <td>0.063298</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.454799</td>\n","      <td>-0.244575</td>\n","      <td>-0.784788</td>\n","      <td>1.088371</td>\n","      <td>Buckingham</td>\n","      <td>0.755834</td>\n","      <td>female</td>\n","      <td>-1.308419</td>\n","      <td>1.948762</td>\n","      <td>large</td>\n","      <td>2.166407</td>\n","      <td>0.687334</td>\n","      <td>1.945276</td>\n","      <td>2.477827</td>\n","      <td>-0.520882</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-2.928491</td>\n","      <td>-0.220006</td>\n","      <td>-4.235595</td>\n","      <td>1.224437</td>\n","      <td>Buckingham</td>\n","      <td>1.148155</td>\n","      <td>male</td>\n","      <td>0.278792</td>\n","      <td>-1.455706</td>\n","      <td>large</td>\n","      <td>-1.310909</td>\n","      <td>-3.037533</td>\n","      <td>-0.857660</td>\n","      <td>-0.893303</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.928460</td>\n","      <td>-0.294119</td>\n","      <td>-1.638941</td>\n","      <td>2.129326</td>\n","      <td>Buckingham</td>\n","      <td>1.023565</td>\n","      <td>male</td>\n","      <td>0.529432</td>\n","      <td>0.134703</td>\n","      <td>medium</td>\n","      <td>0.131908</td>\n","      <td>-0.166428</td>\n","      <td>1.069359</td>\n","      <td>-0.361020</td>\n","      <td>-0.131429</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>398</th>\n","      <td>1.988557</td>\n","      <td>4.300799</td>\n","      <td>-0.117546</td>\n","      <td>1.179792</td>\n","      <td>Louisa</td>\n","      <td>0.510647</td>\n","      <td>male</td>\n","      <td>0.776412</td>\n","      <td>-0.113799</td>\n","      <td>medium</td>\n","      <td>0.131908</td>\n","      <td>0.818708</td>\n","      <td>-0.507293</td>\n","      <td>-0.715875</td>\n","      <td>-0.423519</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>1.717894</td>\n","      <td>-0.319097</td>\n","      <td>0.373845</td>\n","      <td>0.636739</td>\n","      <td>Louisa</td>\n","      <td>0.406029</td>\n","      <td>female</td>\n","      <td>-0.762634</td>\n","      <td>-0.585951</td>\n","      <td>medium</td>\n","      <td>0.223454</td>\n","      <td>1.196686</td>\n","      <td>-1.032844</td>\n","      <td>-0.006164</td>\n","      <td>-0.520882</td>\n","    </tr>\n","    <tr>\n","      <th>400</th>\n","      <td>-0.312080</td>\n","      <td>2.973653</td>\n","      <td>-0.703059</td>\n","      <td>0.525974</td>\n","      <td>Louisa</td>\n","      <td>-1.129342</td>\n","      <td>female</td>\n","      <td>0.776412</td>\n","      <td>-0.262900</td>\n","      <td>small</td>\n","      <td>-0.757310</td>\n","      <td>-0.982130</td>\n","      <td>-0.857660</td>\n","      <td>-0.538447</td>\n","      <td>-1.040154</td>\n","    </tr>\n","    <tr>\n","      <th>401</th>\n","      <td>-0.199303</td>\n","      <td>-0.657568</td>\n","      <td>0.258185</td>\n","      <td>-0.321297</td>\n","      <td>Louisa</td>\n","      <td>-0.187563</td>\n","      <td>female</td>\n","      <td>-0.762634</td>\n","      <td>0.482604</td>\n","      <td>medium</td>\n","      <td>-0.757310</td>\n","      <td>-0.321087</td>\n","      <td>0.543808</td>\n","      <td>0.880976</td>\n","      <td>-0.277474</td>\n","    </tr>\n","    <tr>\n","      <th>402</th>\n","      <td>-1.101514</td>\n","      <td>-0.344215</td>\n","      <td>1.539825</td>\n","      <td>-2.169534</td>\n","      <td>Louisa</td>\n","      <td>1.188448</td>\n","      <td>female</td>\n","      <td>-0.496206</td>\n","      <td>1.054157</td>\n","      <td>medium</td>\n","      <td>-1.917309</td>\n","      <td>-0.810043</td>\n","      <td>1.945276</td>\n","      <td>2.655254</td>\n","      <td>1.815840</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>403 rows × 15 columns</p>\n","</div>"],"text/plain":["         chol  stab.glu       hdl  ...     waist       hip  time.ppn\n","0   -0.109082 -0.498023  0.485298  ... -1.558395 -0.893303  1.231660\n","1   -0.966182 -0.123024 -2.111356  ...  1.419726  0.880976  0.063298\n","2    0.454799 -0.244575 -0.784788  ...  1.945276  2.477827 -0.520882\n","3   -2.928491 -0.220006 -4.235595  ... -0.857660 -0.893303  0.452752\n","4    0.928460 -0.294119 -1.638941  ...  1.069359 -0.361020 -0.131429\n","..        ...       ...       ...  ...       ...       ...       ...\n","398  1.988557  4.300799 -0.117546  ... -0.507293 -0.715875 -0.423519\n","399  1.717894 -0.319097  0.373845  ... -1.032844 -0.006164 -0.520882\n","400 -0.312080  2.973653 -0.703059  ... -0.857660 -0.538447 -1.040154\n","401 -0.199303 -0.657568  0.258185  ...  0.543808  0.880976 -0.277474\n","402 -1.101514 -0.344215  1.539825  ...  1.945276  2.655254  1.815840\n","\n","[403 rows x 15 columns]"]},"metadata":{},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"J8VFwWEWW-gF"},"source":["### Splitting train and test values"]},{"cell_type":"code","metadata":{"id":"yDuzbhnmW-gG","executionInfo":{"status":"ok","timestamp":1630253705325,"user_tz":-330,"elapsed":22,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["from sklearn.model_selection import train_test_split\n","x_train,x_test,y_train,y_test = train_test_split(dataset,y,test_size=0.2,random_state=42)"],"execution_count":31,"outputs":[]},{"cell_type":"code","metadata":{"id":"zo1fnudxW-gG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705325,"user_tz":-330,"elapsed":22,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"5f700050-abae-4471-fdaf-b0928603c9b7"},"source":["x_train.shape,y_train.shape,x_test.shape,y_test.shape"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((322, 15), (322,), (81, 15), (81,))"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"VV7LoxvZOt7l","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253705325,"user_tz":-330,"elapsed":21,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"4be38ca9-376d-4311-e26a-2da8667884fe"},"source":["x_train"],"execution_count":33,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>hdl</th>\n","      <th>ratio</th>\n","      <th>location</th>\n","      <th>age</th>\n","      <th>gender</th>\n","      <th>height</th>\n","      <th>weight</th>\n","      <th>frame</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>waist</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>353</th>\n","      <td>-0.041417</td>\n","      <td>-0.472007</td>\n","      <td>1.080314</td>\n","      <td>-1.001985</td>\n","      <td>Louisa</td>\n","      <td>0.458841</td>\n","      <td>male</td>\n","      <td>0.776412</td>\n","      <td>-0.610801</td>\n","      <td>small</td>\n","      <td>0.223454</td>\n","      <td>1.073274</td>\n","      <td>-0.332110</td>\n","      <td>-0.538447</td>\n","      <td>-0.472201</td>\n","    </tr>\n","    <tr>\n","      <th>233</th>\n","      <td>-0.266969</td>\n","      <td>0.290480</td>\n","      <td>0.797224</td>\n","      <td>-0.816145</td>\n","      <td>Louisa</td>\n","      <td>0.352171</td>\n","      <td>male</td>\n","      <td>0.278792</td>\n","      <td>-0.933853</td>\n","      <td>medium</td>\n","      <td>1.679430</td>\n","      <td>1.778906</td>\n","      <td>-0.507293</td>\n","      <td>-1.070731</td>\n","      <td>-0.910336</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-2.928491</td>\n","      <td>-0.220006</td>\n","      <td>-4.235595</td>\n","      <td>1.224437</td>\n","      <td>Buckingham</td>\n","      <td>1.148155</td>\n","      <td>male</td>\n","      <td>0.278792</td>\n","      <td>-1.455706</td>\n","      <td>large</td>\n","      <td>-1.310909</td>\n","      <td>-3.037533</td>\n","      <td>-0.857660</td>\n","      <td>-0.893303</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>-0.312080</td>\n","      <td>-0.524198</td>\n","      <td>-0.868755</td>\n","      <td>0.690563</td>\n","      <td>Louisa</td>\n","      <td>-0.541273</td>\n","      <td>male</td>\n","      <td>-0.496206</td>\n","      <td>-1.281755</td>\n","      <td>medium</td>\n","      <td>-1.310909</td>\n","      <td>-0.479763</td>\n","      <td>-1.383211</td>\n","      <td>-1.603015</td>\n","      <td>-0.715609</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>-0.447411</td>\n","      <td>-0.630551</td>\n","      <td>-0.184903</td>\n","      <td>-0.033104</td>\n","      <td>Louisa</td>\n","      <td>-0.947960</td>\n","      <td>female</td>\n","      <td>0.278792</td>\n","      <td>1.228108</td>\n","      <td>medium</td>\n","      <td>-0.652145</td>\n","      <td>-0.982130</td>\n","      <td>1.594909</td>\n","      <td>1.768115</td>\n","      <td>-0.650700</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>71</th>\n","      <td>-0.988738</td>\n","      <td>-0.394885</td>\n","      <td>-0.545865</td>\n","      <td>-0.102494</td>\n","      <td>Buckingham</td>\n","      <td>-1.759782</td>\n","      <td>female</td>\n","      <td>0.776412</td>\n","      <td>1.675411</td>\n","      <td>large</td>\n","      <td>-0.446889</td>\n","      <td>-0.560674</td>\n","      <td>1.069359</td>\n","      <td>0.703548</td>\n","      <td>0.258025</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>1.920891</td>\n","      <td>-0.420440</td>\n","      <td>2.072602</td>\n","      <td>-0.907566</td>\n","      <td>Louisa</td>\n","      <td>-0.947960</td>\n","      <td>female</td>\n","      <td>0.278792</td>\n","      <td>0.557155</td>\n","      <td>medium</td>\n","      <td>-1.310909</td>\n","      <td>0.553072</td>\n","      <td>0.543808</td>\n","      <td>-0.183592</td>\n","      <td>-0.326155</td>\n","    </tr>\n","    <tr>\n","      <th>270</th>\n","      <td>-1.372177</td>\n","      <td>-0.394885</td>\n","      <td>-1.043925</td>\n","      <td>0.034653</td>\n","      <td>Buckingham</td>\n","      <td>-0.394224</td>\n","      <td>male</td>\n","      <td>0.776412</td>\n","      <td>0.681405</td>\n","      <td>small</td>\n","      <td>-0.248049</td>\n","      <td>0.947317</td>\n","      <td>0.193441</td>\n","      <td>-0.361020</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>348</th>\n","      <td>-1.936059</td>\n","      <td>-0.498023</td>\n","      <td>-0.324229</td>\n","      <td>-1.200652</td>\n","      <td>Louisa</td>\n","      <td>-0.541273</td>\n","      <td>female</td>\n","      <td>1.259812</td>\n","      <td>0.134703</td>\n","      <td>medium</td>\n","      <td>-1.310909</td>\n","      <td>-0.166428</td>\n","      <td>0.543808</td>\n","      <td>0.348692</td>\n","      <td>-0.812973</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>-0.199303</td>\n","      <td>0.610882</td>\n","      <td>0.012883</td>\n","      <td>-0.102494</td>\n","      <td>Louisa</td>\n","      <td>-0.466755</td>\n","      <td>female</td>\n","      <td>-1.308419</td>\n","      <td>0.631705</td>\n","      <td>large</td>\n","      <td>0.039025</td>\n","      <td>0.131616</td>\n","      <td>0.718992</td>\n","      <td>1.413259</td>\n","      <td>-1.072608</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>322 rows × 15 columns</p>\n","</div>"],"text/plain":["         chol  stab.glu       hdl  ...     waist       hip  time.ppn\n","353 -0.041417 -0.472007  1.080314  ... -0.332110 -0.538447 -0.472201\n","233 -0.266969  0.290480  0.797224  ... -0.507293 -1.070731 -0.910336\n","3   -2.928491 -0.220006 -4.235595  ... -0.857660 -0.893303  0.452752\n","18  -0.312080 -0.524198 -0.868755  ... -1.383211 -1.603015 -0.715609\n","131 -0.447411 -0.630551 -0.184903  ...  1.594909  1.768115 -0.650700\n","..        ...       ...       ...  ...       ...       ...       ...\n","71  -0.988738 -0.394885 -0.545865  ...  1.069359  0.703548  0.258025\n","106  1.920891 -0.420440  2.072602  ...  0.543808 -0.183592 -0.326155\n","270 -1.372177 -0.394885 -1.043925  ...  0.193441 -0.361020  0.452752\n","348 -1.936059 -0.498023 -0.324229  ...  0.543808  0.348692 -0.812973\n","102 -0.199303  0.610882  0.012883  ...  0.718992  1.413259 -1.072608\n","\n","[322 rows x 15 columns]"]},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"vsuPAaxzW-gP"},"source":["### Unique Values Preprocessing"]},{"cell_type":"code","metadata":{"id":"T9CM15-QW-gP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705326,"user_tz":-330,"elapsed":21,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"4420f575-1078-4854-c179-4827b9398dd1"},"source":["x_train[\"location\"].unique()"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Louisa', 'Buckingham'], dtype=object)"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"id":"S4PfjMyGW-gQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705326,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"261af572-5de8-44c1-85b7-147e6b3ec50f"},"source":["x_train[\"gender\"].unique()"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['male', 'female'], dtype=object)"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"HiY_a9bNW-gQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705326,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"ed7737a8-3004-4950-fafb-5e82fe3fed7b"},"source":["x_train[\"frame\"].unique()"],"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['small', 'medium', 'large'], dtype=object)"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"JjSiHwA_W-gQ"},"source":["### Label Encoding"]},{"cell_type":"code","metadata":{"id":"6ML6SzxPW-gQ","executionInfo":{"status":"ok","timestamp":1630253705327,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":37,"outputs":[]},{"cell_type":"code","metadata":{"id":"BaeIJZ76W-gQ","executionInfo":{"status":"ok","timestamp":1630253705327,"user_tz":-330,"elapsed":19,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["le = LabelEncoder()"],"execution_count":38,"outputs":[]},{"cell_type":"code","metadata":{"id":"EXPZtCzYW-gR","executionInfo":{"status":"ok","timestamp":1630253705327,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["combine = x_train.append(x_test)"],"execution_count":39,"outputs":[]},{"cell_type":"code","metadata":{"id":"s3hiOMzBW-gR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253705328,"user_tz":-330,"elapsed":19,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"92c19380-6e59-4490-b534-28f207140d15"},"source":["combine.shape"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(403, 15)"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"DXrKHLEZW-gR","executionInfo":{"status":"ok","timestamp":1630253705328,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["combine[\"location\"] = le.fit_transform(combine[\"location\"])"],"execution_count":41,"outputs":[]},{"cell_type":"code","metadata":{"id":"EWgzklitW-gR","executionInfo":{"status":"ok","timestamp":1630253705328,"user_tz":-330,"elapsed":17,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["combine[\"gender\"] = le.fit_transform(combine[\"gender\"])"],"execution_count":42,"outputs":[]},{"cell_type":"code","metadata":{"id":"c8p-uqB4W-gS","executionInfo":{"status":"ok","timestamp":1630253705329,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["combine[\"frame\"] = le.fit_transform(combine[\"frame\"])"],"execution_count":43,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xrgKo5UFfBjy"},"source":["#Feature Importance"]},{"cell_type":"code","metadata":{"id":"-UUq2VWsiI02","executionInfo":{"status":"ok","timestamp":1630253705329,"user_tz":-330,"elapsed":17,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["import matplotlib.pyplot as plt\n","import matplotlib\n","%matplotlib inline"],"execution_count":44,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UOPYjbmnTEOm"},"source":["### Tree"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ss9lfuskf4JH","executionInfo":{"status":"ok","timestamp":1630253706299,"user_tz":-330,"elapsed":987,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a53127d1-e10a-41f2-e9b5-d232b0d7b92b"},"source":["from sklearn.ensemble import RandomForestRegressor\n","# define the model\n","model = RandomForestRegressor()\n","# fit the model\n","model.fit(combine, y)\n","# get importance\n","importance = model.feature_importances_\n","print(importance)"],"execution_count":45,"outputs":[{"output_type":"stream","text":["[0.09268437 0.13731439 0.05814658 0.08284387 0.01357137 0.09611538\n"," 0.00698205 0.05881508 0.07356992 0.0216631  0.0746288  0.07111601\n"," 0.07052907 0.07345457 0.06856545]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"by-Qw3cUlFCA","executionInfo":{"status":"ok","timestamp":1630253706300,"user_tz":-330,"elapsed":24,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["feature_importances = pd.DataFrame(importance, index =combine.columns,  columns=['importance']).sort_values('importance', ascending=False)"],"execution_count":46,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sichfZ-Y2VH_"},"source":[""]},{"cell_type":"code","metadata":{"id":"kFdAYM68lZmb","colab":{"base_uri":"https://localhost:8080/","height":514},"executionInfo":{"status":"ok","timestamp":1630253706300,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"cf32b093-4524-4387-b4bf-700a7da5dae8"},"source":["feature_importances"],"execution_count":47,"outputs":[{"output_type":"execute_result","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>importance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>stab.glu</th>\n","      <td>0.137314</td>\n","    </tr>\n","    <tr>\n","      <th>age</th>\n","      <td>0.096115</td>\n","    </tr>\n","    <tr>\n","      <th>chol</th>\n","      <td>0.092684</td>\n","    </tr>\n","    <tr>\n","      <th>ratio</th>\n","      <td>0.082844</td>\n","    </tr>\n","    <tr>\n","      <th>bp.1s</th>\n","      <td>0.074629</td>\n","    </tr>\n","    <tr>\n","      <th>weight</th>\n","      <td>0.073570</td>\n","    </tr>\n","    <tr>\n","      <th>hip</th>\n","      <td>0.073455</td>\n","    </tr>\n","    <tr>\n","      <th>bp.1d</th>\n","      <td>0.071116</td>\n","    </tr>\n","    <tr>\n","      <th>waist</th>\n","      <td>0.070529</td>\n","    </tr>\n","    <tr>\n","      <th>time.ppn</th>\n","      <td>0.068565</td>\n","    </tr>\n","    <tr>\n","      <th>height</th>\n","      <td>0.058815</td>\n","    </tr>\n","    <tr>\n","      <th>hdl</th>\n","      <td>0.058147</td>\n","    </tr>\n","    <tr>\n","      <th>frame</th>\n","      <td>0.021663</td>\n","    </tr>\n","    <tr>\n","      <th>location</th>\n","      <td>0.013571</td>\n","    </tr>\n","    <tr>\n","      <th>gender</th>\n","      <td>0.006982</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          importance\n","stab.glu    0.137314\n","age         0.096115\n","chol        0.092684\n","ratio       0.082844\n","bp.1s       0.074629\n","weight      0.073570\n","hip         0.073455\n","bp.1d       0.071116\n","waist       0.070529\n","time.ppn    0.068565\n","height      0.058815\n","hdl         0.058147\n","frame       0.021663\n","location    0.013571\n","gender      0.006982"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"6MtYDlvAhs6v","executionInfo":{"status":"ok","timestamp":1630253706301,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["coef = pd.Series(model.feature_importances_, index = combine.columns)"],"execution_count":48,"outputs":[]},{"cell_type":"code","metadata":{"id":"JkU7jVnchvYL","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1630253706301,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"92936186-2d9b-4b66-9ed3-40f2d0c1024c"},"source":["matplotlib.rcParams['figure.figsize'] = (8, 8)\n","coef.nlargest(9).plot(kind = \"bar\",color='green')\n","plt.title(\"Coefficients in the Random Forest Model\")"],"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Coefficients in the Random Forest Model')"]},"metadata":{},"execution_count":49},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"De_QoBtOW-gS"},"source":["### One Hot Encoding"]},{"cell_type":"code","metadata":{"id":"zhkvV4mtW-gS","executionInfo":{"status":"ok","timestamp":1630253706301,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["#from sklearn.preprocessing import OneHotEncoder"],"execution_count":50,"outputs":[]},{"cell_type":"code","metadata":{"id":"nFKW__tZW-gS","executionInfo":{"status":"ok","timestamp":1630253706302,"user_tz":-330,"elapsed":19,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["#combine = pd.concat([combine,pd.get_dummies(combine[\"frame\"])],axis=1)\n","#combine.drop(columns=\"frame\",inplace=True)"],"execution_count":51,"outputs":[]},{"cell_type":"code","metadata":{"id":"S1Z4qpJ5W-gT","executionInfo":{"status":"ok","timestamp":1630253706302,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["combine.drop(columns=[\"frame\",\"waist\",\"hdl\",\"height\",\"location\",\"gender\"],inplace=True)"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"xGGIdof2Yxko","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253706302,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"d09b0512-8bd8-4659-c238-7fc3f33b5361"},"source":["combine"],"execution_count":53,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>ratio</th>\n","      <th>age</th>\n","      <th>weight</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>353</th>\n","      <td>-0.041417</td>\n","      <td>-0.472007</td>\n","      <td>-1.001985</td>\n","      <td>0.458841</td>\n","      <td>-0.610801</td>\n","      <td>0.223454</td>\n","      <td>1.073274</td>\n","      <td>-0.538447</td>\n","      <td>-0.472201</td>\n","    </tr>\n","    <tr>\n","      <th>233</th>\n","      <td>-0.266969</td>\n","      <td>0.290480</td>\n","      <td>-0.816145</td>\n","      <td>0.352171</td>\n","      <td>-0.933853</td>\n","      <td>1.679430</td>\n","      <td>1.778906</td>\n","      <td>-1.070731</td>\n","      <td>-0.910336</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-2.928491</td>\n","      <td>-0.220006</td>\n","      <td>1.224437</td>\n","      <td>1.148155</td>\n","      <td>-1.455706</td>\n","      <td>-1.310909</td>\n","      <td>-3.037533</td>\n","      <td>-0.893303</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>-0.312080</td>\n","      <td>-0.524198</td>\n","      <td>0.690563</td>\n","      <td>-0.541273</td>\n","      <td>-1.281755</td>\n","      <td>-1.310909</td>\n","      <td>-0.479763</td>\n","      <td>-1.603015</td>\n","      <td>-0.715609</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>-0.447411</td>\n","      <td>-0.630551</td>\n","      <td>-0.033104</td>\n","      <td>-0.947960</td>\n","      <td>1.228108</td>\n","      <td>-0.652145</td>\n","      <td>-0.982130</td>\n","      <td>1.768115</td>\n","      <td>-0.650700</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>0.793128</td>\n","      <td>-1.369639</td>\n","      <td>-0.102494</td>\n","      <td>-0.466755</td>\n","      <td>1.377209</td>\n","      <td>-1.310909</td>\n","      <td>-0.015589</td>\n","      <td>2.477827</td>\n","      <td>-0.812973</td>\n","    </tr>\n","    <tr>\n","      <th>90</th>\n","      <td>-1.101514</td>\n","      <td>-0.344215</td>\n","      <td>-0.398089</td>\n","      <td>-0.394224</td>\n","      <td>-0.213199</td>\n","      <td>0.131908</td>\n","      <td>-0.243268</td>\n","      <td>-0.538447</td>\n","      <td>1.134296</td>\n","    </tr>\n","    <tr>\n","      <th>248</th>\n","      <td>0.657797</td>\n","      <td>0.356161</td>\n","      <td>0.636739</td>\n","      <td>1.381418</td>\n","      <td>-0.088949</td>\n","      <td>1.152068</td>\n","      <td>-0.560674</td>\n","      <td>0.171264</td>\n","      <td>-0.131429</td>\n","    </tr>\n","    <tr>\n","      <th>337</th>\n","      <td>1.199123</td>\n","      <td>-0.028022</td>\n","      <td>-0.907566</td>\n","      <td>0.458841</td>\n","      <td>0.507454</td>\n","      <td>0.746684</td>\n","      <td>0.687334</td>\n","      <td>1.058404</td>\n","      <td>-1.040154</td>\n","    </tr>\n","    <tr>\n","      <th>390</th>\n","      <td>-0.266969</td>\n","      <td>0.399485</td>\n","      <td>-1.099606</td>\n","      <td>0.458841</td>\n","      <td>-0.759902</td>\n","      <td>0.402688</td>\n","      <td>0.818708</td>\n","      <td>-0.183592</td>\n","      <td>0.452752</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>403 rows × 9 columns</p>\n","</div>"],"text/plain":["         chol  stab.glu     ratio  ...     bp.1d       hip  time.ppn\n","353 -0.041417 -0.472007 -1.001985  ...  1.073274 -0.538447 -0.472201\n","233 -0.266969  0.290480 -0.816145  ...  1.778906 -1.070731 -0.910336\n","3   -2.928491 -0.220006  1.224437  ... -3.037533 -0.893303  0.452752\n","18  -0.312080 -0.524198  0.690563  ... -0.479763 -1.603015 -0.715609\n","131 -0.447411 -0.630551 -0.033104  ... -0.982130  1.768115 -0.650700\n","..        ...       ...       ...  ...       ...       ...       ...\n","45   0.793128 -1.369639 -0.102494  ... -0.015589  2.477827 -0.812973\n","90  -1.101514 -0.344215 -0.398089  ... -0.243268 -0.538447  1.134296\n","248  0.657797  0.356161  0.636739  ... -0.560674  0.171264 -0.131429\n","337  1.199123 -0.028022 -0.907566  ...  0.687334  1.058404 -1.040154\n","390 -0.266969  0.399485 -1.099606  ...  0.818708 -0.183592  0.452752\n","\n","[403 rows x 9 columns]"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","metadata":{"id":"hR2TQfRXW-gT"},"source":["### Separation OF Train And Test after using embedding"]},{"cell_type":"code","metadata":{"id":"BMI18TDEW-gT","executionInfo":{"status":"ok","timestamp":1630253706303,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["x_train = combine.iloc[:322]\n","x_test = combine.iloc[322:]"],"execution_count":54,"outputs":[]},{"cell_type":"code","metadata":{"id":"djYSY1GpW-gT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253706303,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"32de371f-bc72-4ee0-be6e-7a43efb1b78c"},"source":["x_train.shape,y_train.shape,x_test.shape,y_test.shape"],"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((322, 9), (322,), (81, 9), (81,))"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","metadata":{"id":"t4XXMB1xL-Ne","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1630253706303,"user_tz":-330,"elapsed":15,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"976ca4fd-bc95-4a19-8651-ca5ff69cfdee"},"source":["# data now\n","x_train.head()"],"execution_count":56,"outputs":[{"output_type":"execute_result","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>chol</th>\n","      <th>stab.glu</th>\n","      <th>ratio</th>\n","      <th>age</th>\n","      <th>weight</th>\n","      <th>bp.1s</th>\n","      <th>bp.1d</th>\n","      <th>hip</th>\n","      <th>time.ppn</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>353</th>\n","      <td>-0.041417</td>\n","      <td>-0.472007</td>\n","      <td>-1.001985</td>\n","      <td>0.458841</td>\n","      <td>-0.610801</td>\n","      <td>0.223454</td>\n","      <td>1.073274</td>\n","      <td>-0.538447</td>\n","      <td>-0.472201</td>\n","    </tr>\n","    <tr>\n","      <th>233</th>\n","      <td>-0.266969</td>\n","      <td>0.290480</td>\n","      <td>-0.816145</td>\n","      <td>0.352171</td>\n","      <td>-0.933853</td>\n","      <td>1.679430</td>\n","      <td>1.778906</td>\n","      <td>-1.070731</td>\n","      <td>-0.910336</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-2.928491</td>\n","      <td>-0.220006</td>\n","      <td>1.224437</td>\n","      <td>1.148155</td>\n","      <td>-1.455706</td>\n","      <td>-1.310909</td>\n","      <td>-3.037533</td>\n","      <td>-0.893303</td>\n","      <td>0.452752</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>-0.312080</td>\n","      <td>-0.524198</td>\n","      <td>0.690563</td>\n","      <td>-0.541273</td>\n","      <td>-1.281755</td>\n","      <td>-1.310909</td>\n","      <td>-0.479763</td>\n","      <td>-1.603015</td>\n","      <td>-0.715609</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>-0.447411</td>\n","      <td>-0.630551</td>\n","      <td>-0.033104</td>\n","      <td>-0.947960</td>\n","      <td>1.228108</td>\n","      <td>-0.652145</td>\n","      <td>-0.982130</td>\n","      <td>1.768115</td>\n","      <td>-0.650700</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         chol  stab.glu     ratio  ...     bp.1d       hip  time.ppn\n","353 -0.041417 -0.472007 -1.001985  ...  1.073274 -0.538447 -0.472201\n","233 -0.266969  0.290480 -0.816145  ...  1.778906 -1.070731 -0.910336\n","3   -2.928491 -0.220006  1.224437  ... -3.037533 -0.893303  0.452752\n","18  -0.312080 -0.524198  0.690563  ... -0.479763 -1.603015 -0.715609\n","131 -0.447411 -0.630551 -0.033104  ... -0.982130  1.768115 -0.650700\n","\n","[5 rows x 9 columns]"]},"metadata":{},"execution_count":56}]},{"cell_type":"code","metadata":{"id":"jGj7D50tW-gv","executionInfo":{"status":"ok","timestamp":1630253706304,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["## Metric Table Function\n","from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error\n","from prettytable import PrettyTable\n","\n","def metrics(y_test,y_pred):\n","    r2score = r2_score(y_test,y_pred)\n","    MAD = np.mean(np.abs(y_pred-np.mean(y_test)))\n","    MAE = mean_absolute_error(y_test,y_pred)\n","    RMSE = np.sqrt(mean_squared_error(y_test,y_pred))\n","    MSE=mean_squared_error(y_test,y_pred)\n","    t = PrettyTable(['Error Metric', 'Value'])\n","    t.add_row([\"r2_score\",r2score])\n","    t.add_row([\"MSE\",MSE])\n","    t.add_row(['MAE', MAE])\n","    t.add_row([\"RMSE\",RMSE,])\n","    t.add_row([\"MAD\",MAD])\n","    return t,r2score,MAD,MAE,RMSE,MSE"],"execution_count":57,"outputs":[]},{"cell_type":"code","metadata":{"id":"guGA0Pyxi1za","executionInfo":{"status":"ok","timestamp":1630253706304,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["# Lets create a table for storing data \n","\n","df = pd.DataFrame(columns=[\"Model\",\"R2score\",\"MSE\",\"MAE\",\"MAD\",\"RMSE\"])\n","# Data for storing the weights\n","df_weights = pd.DataFrame(columns = [\"Ridge\",\"Linear Reg\",\"Lasso\",\"ANN\"])"],"execution_count":58,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wQM2onytTKjr"},"source":["# Machine Learning"]},{"cell_type":"markdown","metadata":{"id":"Dpuy0XNUW-hB"},"source":["## Models used for this Ensemble\n","1. Ridge()\n","2. ANN\n","3. Lasso\n","4. Linear Reg\n"]},{"cell_type":"markdown","metadata":{"id":"BNf77RvHEErF"},"source":["##1. Ridge Regression :"]},{"cell_type":"code","metadata":{"id":"VOSFO64dW-hB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253709820,"user_tz":-330,"elapsed":3530,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"6d53cd50-2e43-4ce3-f23a-b93fdc310b6f"},"source":["### Ridge\n","from sklearn.linear_model import Ridge\n","ridge_reg = Ridge()\n","from sklearn.model_selection import GridSearchCV\n","params_Ridge = {'alpha': [1,0.1,0.01,0.001,0.0001,0] , \"fit_intercept\": [True, False], \"solver\": ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']}\n","Ridge_GS = GridSearchCV(ridge_reg, param_grid=params_Ridge, n_jobs=-1, cv=10)\n","Ridge_GS.fit(x_train,y_train)\n","print(Ridge_GS.best_params_)\n","Ridge_best_model = Ridge_GS.best_estimator_\n","Ridge_best_model.fit(x_train,y_train)"],"execution_count":59,"outputs":[{"output_type":"stream","text":["{'alpha': 1, 'fit_intercept': True, 'solver': 'saga'}\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n","      random_state=None, solver='saga', tol=0.001)"]},"metadata":{},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"EVwKe2M-W-hC","executionInfo":{"status":"ok","timestamp":1630253709821,"user_tz":-330,"elapsed":24,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["y_Ridge_GS_pred= Ridge_best_model.predict(x_test)"],"execution_count":60,"outputs":[]},{"cell_type":"code","metadata":{"id":"_hQ48ShbjNQX","executionInfo":{"status":"ok","timestamp":1630253709821,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,y_Ridge_GS_pred)\n","df.loc[len(df.index)] = [\"Ridge\",r2score,MSE,MAE,MAD,RMSE]"],"execution_count":61,"outputs":[]},{"cell_type":"code","metadata":{"id":"J0uIL76CW-hC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253709821,"user_tz":-330,"elapsed":23,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"fbf720b2-8c49-46f3-888f-bf48b3048d0b"},"source":["# Metric table\n","print(t)"],"execution_count":62,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8073796043905681 |\n","|     MSE      | 1.0392481098540232 |\n","|     MAE      | 0.7622254015790835 |\n","|     RMSE     | 1.0194351915909237 |\n","|     MAD      |  1.29813981786459  |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZYyyKnHXZ97g"},"source":["## 2. Linear Regression"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2BAbO12VaEw0","executionInfo":{"status":"ok","timestamp":1630253709822,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"09be0cae-02c0-4477-c707-77248972d410"},"source":["from sklearn.linear_model import LinearRegression\n","model_lr = LinearRegression()\n","params_lr = {\"fit_intercept\": [True, False]}\n","lr_GS = GridSearchCV(model_lr, param_grid=params_lr, n_jobs=-1,cv=10)\n","lr_GS.fit(x_train,y_train)\n","print(lr_GS.best_params_)\n","Lr_best_model = lr_GS.best_estimator_\n","Lr_best_model.fit(x_train,y_train)"],"execution_count":63,"outputs":[{"output_type":"stream","text":["{'fit_intercept': True}\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"16ZzZr1FaE27","executionInfo":{"status":"ok","timestamp":1630253709822,"user_tz":-330,"elapsed":16,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"51146e06-d18f-4513-d572-0a371e9d6712"},"source":["y_lr_GS_pred= Lr_best_model.predict(x_test)\n","t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,y_lr_GS_pred)\n","df.loc[len(df.index)] = [\"Linear Reg\",r2score,MSE,MAE,MAD,RMSE]\n","print(t)"],"execution_count":64,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8078997940731456 |\n","|     MSE      | 1.0364415215762148 |\n","|     MAE      | 0.762101101310602  |\n","|     RMSE     | 1.0180577201594292 |\n","|     MAD      | 1.3020520821124373 |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CBZW1CZoEUKR"},"source":["## 3. Lasso"]},{"cell_type":"code","metadata":{"id":"IvWV7Np0W-hC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630253710469,"user_tz":-330,"elapsed":660,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"758949e9-d60f-488d-8fb9-41c914a996af"},"source":["# lasso\n","from sklearn.linear_model import Lasso\n","lasso_reg = Lasso()\n","params_lasso = {'alpha':[0.02, 0.024, 0.025, 0.026, 0.03] , \"fit_intercept\": [True, False]}\n","lasso_GS = GridSearchCV(lasso_reg, param_grid=params_lasso, n_jobs=-1,cv=10)\n","lasso_GS.fit(x_train,y_train)\n","print(lasso_GS.best_params_)\n","Lasso_best_model = lasso_GS.best_estimator_\n","Lasso_best_model.fit(x_train,y_train)"],"execution_count":65,"outputs":[{"output_type":"stream","text":["{'alpha': 0.03, 'fit_intercept': True}\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Lasso(alpha=0.03, copy_X=True, fit_intercept=True, max_iter=1000,\n","      normalize=False, positive=False, precompute=False, random_state=None,\n","      selection='cyclic', tol=0.0001, warm_start=False)"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","metadata":{"id":"KFbo9JTOW-hC","executionInfo":{"status":"ok","timestamp":1630253710469,"user_tz":-330,"elapsed":10,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["y_lasso_GS_pred= Lasso_best_model.predict(x_test)"],"execution_count":66,"outputs":[]},{"cell_type":"code","metadata":{"id":"bVItQIHMW-hD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010569844,"user_tz":-330,"elapsed":10,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"b04d17c6-0576-48f4-c0a4-7698d4dda716"},"source":["t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,y_lasso_GS_pred)\n","df.loc[len(df.index)] = [\"Lasso\",r2score,MSE,MAE,MAD,RMSE]\n","print(t)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8059940600121166 |\n","|     MSE      | 1.0467235611003405 |\n","|     MAE      | 0.7695319336334419 |\n","|     RMSE     | 1.023095088982613  |\n","|     MAD      | 1.2643779177776455 |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"cyTty_I_saJi"},"source":["## 5. SVR"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBddR9lfse4i","executionInfo":{"status":"ok","timestamp":1630254399255,"user_tz":-330,"elapsed":467830,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"872b4447-940d-4f83-c007-6859952ec476"},"source":["from sklearn.svm import SVR\n","svr_gs = SVR()\n","param_svr = {'kernel' : ('linear', 'poly', 'rbf', 'sigmoid'),'C' : [1,5,10],'degree' : [3,8],\n","             'coef0' : [0.01,10,0.5],'gamma' : ('auto','scale')}\n","Svr_grid= GridSearchCV(estimator = svr_gs, param_grid = param_svr, cv = 10, n_jobs = -1, verbose = 2)\n","Svr_grid.fit(x_train,y_train)\n","print(Svr_grid.best_params_)\n","SVR_best_model = Svr_grid.best_estimator_\n","SVR_best_model.fit(x_train,y_train)"],"execution_count":67,"outputs":[{"output_type":"stream","text":["Fitting 10 folds for each of 144 candidates, totalling 1440 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n","[Parallel(n_jobs=-1)]: Done 228 tasks      | elapsed:    9.2s\n","[Parallel(n_jobs=-1)]: Done 521 tasks      | elapsed:  1.5min\n","[Parallel(n_jobs=-1)]: Done 998 tasks      | elapsed:  4.0min\n"],"name":"stderr"},{"output_type":"stream","text":["{'C': 1, 'coef0': 0.01, 'degree': 3, 'gamma': 'auto', 'kernel': 'linear'}\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=-1)]: Done 1440 out of 1440 | elapsed:  7.8min finished\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["SVR(C=1, cache_size=200, coef0=0.01, degree=3, epsilon=0.1, gamma='auto',\n","    kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"-iSQLai-wFLH","executionInfo":{"status":"ok","timestamp":1630254714991,"user_tz":-330,"elapsed":640,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}}},"source":["y_svr=SVR_best_model.predict(x_test)"],"execution_count":68,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZSV2iDvvv95L","executionInfo":{"status":"ok","timestamp":1630254725163,"user_tz":-330,"elapsed":634,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"caedb537-38d3-4acb-b82d-f826f990c557"},"source":["t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,y_svr)\n","print(t)"],"execution_count":69,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.7656081489291227 |\n","|     MSE      | 1.2646183568458378 |\n","|     MAE      | 0.7814313361649005 |\n","|     RMSE     | 1.1245525140454036 |\n","|     MAD      | 1.2290411568976816 |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"U0UVhlNnbdvH"},"source":["## 4. ANN"]},{"cell_type":"code","metadata":{"id":"dRm4rFuSbhxK"},"source":["from keras.models import Sequential\n","from keras.layers import Dense\n","from sklearn.model_selection import cross_val_score\n","from sklearn.model_selection import KFold\n","from keras.callbacks import ModelCheckpoint\n","from keras.models import load_model\n","from keras.wrappers.scikit_learn import KerasRegressor"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WG25q73fafPy"},"source":["# # Function to create model, required for KerasClassifier\n","# def create_model():\n","#   model = Sequential()\n","#   model.add(Dense(6, input_dim=9, kernel_initializer='normal', activation='relu'))\n","#   model.add(Dense(3, activation='relu'))\n","#   model.add(Dense(1, activation='linear'))\n","#   model.compile(loss='mse', optimizer='adam', metrics=['mse'])\n","#   mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n","#   return model\n","\n","# # create model\n","# model = KerasRegressor(build_fn=create_model, verbose=1)\n","# # define the grid search parameters\n","\n","# batch_size = [5, 10, 20, 40, 60, 80, 100]\n","# epochs = [10, 50, 100]\n","\n","\n","# param_grid = dict(batch_size=batch_size, epochs=epochs)\n","# grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n","# grid_result = grid.fit(x_train, y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qlT3N7m2boM2"},"source":["# # summarize results\n","# print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n","\n","# means = grid_result.cv_results_['mean_test_score']\n","# stds = grid_result.cv_results_['std_test_score']\n","# params = grid_result.cv_results_['params']\n","# for mean, stdev, param in zip(means, stds, params):\n","#     print(\"%f (%f) with: %r\" % (mean, stdev, param))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bwiQaijXbdi8"},"source":["# model = Sequential()\n","# model.add(Dense(6, input_dim=9, kernel_initializer='normal', activation='relu'))\n","# model.add(Dense(3, activation='relu'))\n","# model.add(Dense(1, activation='linear'))\n","# model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0TYfwgcbdbeo"},"source":["# # Compile model\n","# model.compile(loss='mse', optimizer='adam', metrics=['mse'])\n","# mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xTzuaIQqeM6J"},"source":["#history=model.fit(x_train, y_train, epochs=50, batch_size=5, verbose=1, validation_split=0.2,callbacks=[mc])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"B2wIk5sSpu2M","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010586458,"user_tz":-330,"elapsed":14688,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a87f9289-19e1-42c1-ae9e-686ae5b7ca50"},"source":["saved_model = load_model('/gdrive/MyDrive/best_model.h5')\n","\n","y_ann_pred = saved_model.predict(x_test)\n","score = saved_model.evaluate(x_test,y_test, verbose=1)\n","\n","t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,y_ann_pred)\n","df.loc[len(df.index)] = [\"ANN\",r2score,MSE,MAE,MAD,RMSE]\n","print(t)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["3/3 [==============================] - 6s 3ms/step - loss: 1.0557 - mse: 1.0557\n","+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8043222777925207 |\n","|     MSE      | 1.0557433562591323 |\n","|     MAE      | 0.7344105772020009 |\n","|     RMSE     | 1.0274937256543868 |\n","|     MAD      |     1.2282964      |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YQ1BEx2XTlj8"},"source":["history=saved_model.history"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9wvmlPAWUx9a","executionInfo":{"status":"ok","timestamp":1628010586459,"user_tz":-330,"elapsed":29,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"bbc7251d-3939-4740-9949-0cc1c3444042"},"source":["history.history"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{}"]},"metadata":{"tags":[]},"execution_count":74}]},{"cell_type":"code","metadata":{"id":"zxCz0v_ugdle"},"source":["# print(history.history.keys())\n","# # \"Loss\"\n","# plt.plot(history.history['loss'])\n","# plt.plot(history.history['val_loss'])\n","# plt.title('model loss')\n","# plt.ylabel('loss')\n","# plt.xlabel('epoch')\n","# plt.legend(['train', 'validation'], loc='upper left')\n","# plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YoqyM18p9rke"},"source":["y_ann_pred=y_ann_pred.reshape((81,))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nKgF3Jjz-ZuM","executionInfo":{"status":"ok","timestamp":1628010586460,"user_tz":-330,"elapsed":25,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"48751e6f-b4a5-49fa-903f-2f01a81cc6f5"},"source":["y_ann_pred.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(81,)"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173},"id":"ifTrmaP7X4ut","executionInfo":{"status":"ok","timestamp":1628010586461,"user_tz":-330,"elapsed":21,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"6b04b707-bd0c-42b7-8d9a-720a21a6ea31"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Model</th>\n","      <th>R2score</th>\n","      <th>MSE</th>\n","      <th>MAE</th>\n","      <th>MAD</th>\n","      <th>RMSE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Ridge</td>\n","      <td>0.807426</td>\n","      <td>1.038995</td>\n","      <td>0.762401</td>\n","      <td>1.298893</td>\n","      <td>1.019311</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Linear Reg</td>\n","      <td>0.807900</td>\n","      <td>1.036442</td>\n","      <td>0.762101</td>\n","      <td>1.302052</td>\n","      <td>1.018058</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Lasso</td>\n","      <td>0.805994</td>\n","      <td>1.046724</td>\n","      <td>0.769532</td>\n","      <td>1.264378</td>\n","      <td>1.023095</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ANN</td>\n","      <td>0.804322</td>\n","      <td>1.055743</td>\n","      <td>0.734411</td>\n","      <td>1.228296</td>\n","      <td>1.027494</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Model   R2score       MSE       MAE       MAD      RMSE\n","0       Ridge  0.807426  1.038995  0.762401  1.298893  1.019311\n","1  Linear Reg  0.807900  1.036442  0.762101  1.302052  1.018058\n","2       Lasso  0.805994  1.046724  0.769532  1.264378  1.023095\n","3         ANN  0.804322  1.055743  0.734411  1.228296  1.027494"]},"metadata":{"tags":[]},"execution_count":78}]},{"cell_type":"markdown","metadata":{"id":"qKBllY7VW-hD"},"source":["## Ensemble Learning with Average weights"]},{"cell_type":"code","metadata":{"id":"dRcJZBKGW-hE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010586461,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"0f2c7a92-b22c-4503-c902-aad29d8fb488"},"source":["Ensemble_average_prediction_1 = (y_ann_pred + y_lr_GS_pred+ y_lasso_GS_pred+  + y_Ridge_GS_pred ) / 4\n","t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,Ensemble_average_prediction_1)\n","df.loc[len(df.index)] = [\"Ensemble with AVG\",r2score,MSE,MAE,MAD,RMSE]\n","print(t)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8121820924241245 |\n","|     MSE      | 1.0133371641533924 |\n","|     MAE      | 0.7394892834966859 |\n","|     RMSE     | 1.0066464941345559 |\n","|     MAD      | 1.269054853573645  |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GRXTT1m1W-hI"},"source":["## Ensemble Learning with Optimzed Weights for\n","1. Ridge()\n","2. ANN\n","3. Lasso\n","4. Linear Reg\n"]},{"cell_type":"code","metadata":{"id":"-5NLYNY1tuXU"},"source":["from scipy.optimize import minimize"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"s5GfUhazppUm"},"source":["rmse_optimize =[]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-ZYXUGXjs9My"},"source":["def rmse_loss_func(weights):\n","    ''' scipy minimize will pass the weights as a numpy array '''\n","    final_prediction = 0\n","    for weight, prediction in zip(weights, predictions):\n","            final_prediction += weight*prediction\n","\n","    print(np.sqrt(mean_squared_error(y_test,final_prediction)),weights)\n","    rmse_optimize.append(np.sqrt(mean_squared_error(y_test,final_prediction)))\n","    return np.sqrt(mean_squared_error(y_test,final_prediction))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eNw6fPB5W-hI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010587241,"user_tz":-330,"elapsed":794,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"54bc0a67-34e1-4072-9677-28fd612240cf"},"source":["# add every model's prediction into list\n","predictions = []\n","predictions.append(y_Ridge_GS_pred)\n","predictions.append(y_ann_pred)\n","predictions.append(y_lasso_GS_pred)\n","predictions.append(y_lr_GS_pred)\n","#starting Value\n","starting_values = [0.5]*len(predictions)\n","#cons = ({'type':'eq','fun':lambda w: 1-sum(w)})\n","#our weights are bound between 0 and 1\n","bounds = [(0,1)]*len(predictions)\n","res = minimize(rmse_loss_func, starting_values, method='Powell')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["5.945198192249125 [0.5 0.5 0.5 0.5]\n","5.945198192249125 [0.5 0.5 0.5 0.5]\n","11.913649487434935 [1.5 0.5 0.5 0.5]\n","3.989785876486906 [-1.118034  0.5       0.5       0.5     ]\n","1.409341475486176 [-0.6413661  0.5        0.5        0.5      ]\n","1.409341475486176 [-0.6413661  0.5        0.5        0.5      ]\n","1.9021068272745543 [-0.20540306  0.5         0.5         0.5       ]\n","2.3173385185476656 [-0.82343703  0.5         0.5         0.5       ]\n","0.9947675049548671 [-0.48048115  0.5         0.5         0.5       ]\n","0.9960250654343182 [-0.46531981  0.5         0.5         0.5       ]\n","0.9983921841909147 [-0.49028596  0.5         0.5         0.5       ]\n","0.9947675049548671 [-0.48048115  0.5         0.5         0.5       ]\n","5.93096380659327 [-0.48048115  1.5         0.5         0.5       ]\n","9.59961808867216 [-0.48048115 -1.118034    0.5         0.5       ]\n","0.9947675049548671 [-0.48048115  0.5         0.5         0.5       ]\n","3.7999802190284386 [-0.48048115 -0.11803397  0.5         0.5       ]\n","2.4258567932014166 [-0.48048115  0.881966    0.5         0.5       ]\n","1.022721188635252 [-0.48048115  0.4648887   0.5         0.5       ]\n","0.9972902168911694 [-0.48048115  0.51899174  0.5         0.5       ]\n","0.9942086593940832 [-0.48048115  0.50562784  0.5         0.5       ]\n","0.9942086332005377 [-0.48048115  0.50568412  0.5         0.5       ]\n","0.9942087368722635 [-0.48048115  0.50574096  0.5         0.5       ]\n","0.9942086332005377 [-0.48048115  0.50568412  0.5         0.5       ]\n","6.088247095564879 [-0.48048115  0.50568412  1.5         0.5       ]\n","9.762503799380333 [-0.48048115  0.50568412 -1.118034    0.5       ]\n","0.9942086332005377 [-0.48048115  0.50568412  0.5         0.5       ]\n","3.838878041084994 [-0.48048115  0.50568412 -0.11803397  0.5       ]\n","2.501957941143955 [-0.48048115  0.50568412  0.881966    0.5       ]\n","1.0219681590053178 [-0.48048115  0.50568412  0.46014746  0.5       ]\n","0.9968796654465444 [-0.48048115  0.50568412  0.51170884  0.5       ]\n","0.9942050926311535 [-0.48048115  0.50568412  0.49949423  0.5       ]\n","0.9942050278008224 [-0.48048115  0.50568412  0.49955377  0.5       ]\n","0.9942050281184502 [-0.48048115  0.50568412  0.49955823  0.5       ]\n","0.9942050282051464 [-0.48048115  0.50568412  0.4995493   0.5       ]\n","0.9942050278008224 [-0.48048115  0.50568412  0.49955377  0.5       ]\n","6.09314395759435 [-0.48048115  0.50568412  0.49955377  1.5       ]\n","9.781146550605564 [-0.48048115  0.50568412  0.49955377 -1.118034  ]\n","0.9942050278008224 [-0.48048115  0.50568412  0.49955377  0.5       ]\n","3.8482342189338112 [-0.48048115  0.50568412  0.49955377 -0.11803397]\n","2.5013775175098254 [-0.48048115  0.50568412  0.49955377  0.881966  ]\n","1.0223647034589145 [-0.48048115  0.50568412  0.49955377  0.46060314]\n","0.9969858416840245 [-0.48048115  0.50568412  0.49955377  0.51261259]\n","0.9942040874300457 [-0.48048115  0.50568412  0.49955377  0.50017584]\n","0.9942040238356047 [-0.48048115  0.50568412  0.49955377  0.50023518]\n","0.9942040239189952 [-0.48048115  0.50568412  0.49955377  0.50023283]\n","0.9942040239533527 [-0.48048115  0.50568412  0.49955377  0.50023753]\n","5.9446660132302815 [-1.4609623   0.51136824  0.49910753  0.50047036]\n","0.9942040238356047 [-0.48048115  0.50568412  0.49955377  0.50023518]\n","6.092546287208881 [0.51951885 0.50568412 0.49955377 0.50023518]\n","9.775811120425997 [-2.09851515  0.50568412  0.49955377  0.50023518]\n","0.9942040238356047 [-0.48048115  0.50568412  0.49955377  0.50023518]\n","3.8452557049031237 [-1.09851513  0.50568412  0.49955377  0.50023518]\n","2.502114653053248 [-0.09851515  0.50568412  0.49955377  0.50023518]\n","1.0222205867959901 [-0.52006852  0.50568412  0.49955377  0.50023518]\n","0.9969452425507914 [-0.46823305  0.50568412  0.49955377  0.50023518]\n","0.9942040537849448 [-0.4805841   0.50568412  0.49955377  0.50023518]\n","0.9942039895058036 [-0.48052461  0.50568412  0.49955377  0.50023518]\n","0.9942039895090609 [-0.48052504  0.50568412  0.49955377  0.50023518]\n","0.9942039895094089 [-0.48052417  0.50568412  0.49955377  0.50023518]\n","0.9942039895058036 [-0.48052461  0.50568412  0.49955377  0.50023518]\n","5.9624153398619075 [-0.48052461  1.50568412  0.49955377  0.50023518]\n","9.567888129116973 [-0.48052461 -1.11234988  0.49955377  0.50023518]\n","0.9942039895058036 [-0.48052461  0.50568412  0.49955377  0.50023518]\n","3.769197786443375 [-0.48052461 -0.11234985  0.49955377  0.50023518]\n","2.4549885094484356 [-0.48052461  0.88765012  0.49955377  0.50023518]\n","1.0206377757028227 [-0.48052461  0.46668208  0.49955377  0.50023518]\n","0.9966793939584706 [-0.48052461  0.51786947  0.49955377  0.50023518]\n","0.9942030124078792 [-0.48052461  0.50587458  0.49955377  0.50023518]\n","0.9942029684260932 [-0.48052461  0.50592708  0.49955377  0.50023518]\n","0.9942029691256825 [-0.48052461  0.50592454  0.49955377  0.50023518]\n","0.9942029574251118 [-0.48052461  0.50593806  0.49955377  0.50023518]\n","0.9945655657332121 [-0.48052461  0.51049545  0.49955377  0.50023518]\n","0.994256237845929 [-0.48052461  0.50767883  0.49955377  0.50023518]\n","0.9942108873401272 [-0.48052461  0.50660298  0.49955377  0.50023518]\n","0.9942041641366055 [-0.48052461  0.50619204  0.49955377  0.50023518]\n","0.9942031535609128 [-0.48052461  0.50603507  0.49955377  0.50023518]\n","0.9942029696456265 [-0.48052461  0.50597512  0.49955377  0.50023518]\n","0.994202967054994 [-0.48052461  0.50595222  0.49955377  0.50023518]\n","0.9942029655803348 [-0.48052461  0.5059406   0.49955377  0.50023518]\n","0.9942029713153563 [-0.48052461  0.50593387  0.49955377  0.50023518]\n","0.9942029574251118 [-0.48052461  0.50593806  0.49955377  0.50023518]\n","6.088210562730262 [-0.48052461  0.50593806  1.49955377  0.50023518]\n","9.762539149905717 [-0.48052461  0.50593806 -1.11848023  0.50023518]\n","0.9942029574251118 [-0.48052461  0.50593806  0.49955377  0.50023518]\n","3.8389114707301912 [-0.48052461  0.50593806 -0.11848021  0.50023518]\n","2.5019225907903886 [-0.48052461  0.50593806  0.88151977  0.50023518]\n","1.0219653165410705 [-0.48052461  0.50593806  0.45970538  0.50023518]\n","0.9968749049727497 [-0.48052461  0.50593806  0.51127045  0.50023518]\n","0.9941995134035924 [-0.48052461  0.50593806  0.49905402  0.50023518]\n","0.9941994485882822 [-0.48052461  0.50593806  0.49911355  0.50023518]\n","0.9941994488974976 [-0.48052461  0.50593806  0.49911795  0.50023518]\n","0.9941994489816803 [-0.48052461  0.50593806  0.49910915  0.50023518]\n","0.9941994485882822 [-0.48052461  0.50593806  0.49911355  0.50023518]\n","6.093143083638896 [-0.48052461  0.50593806  0.49911355  1.50023518]\n","9.781145946828518 [-0.48052461  0.50593806  0.49911355 -1.11779882]\n","0.9941994485882822 [-0.48052461  0.50593806  0.49911355  0.50023518]\n","3.8482327419179176 [-0.48052461  0.50593806  0.49911355 -0.1177988 ]\n","2.5013753338431144 [-0.48052461  0.50593806  0.49911355  0.88220118]\n","1.0223594113807029 [-0.48052461  0.50593806  0.49911355  0.46083822]\n","0.9969803275480779 [-0.48052461  0.50593806  0.49911355  0.51284787]\n","0.9941985082674478 [-0.48052461  0.50593806  0.49911355  0.50041101]\n","0.9941984446698354 [-0.48052461  0.50593806  0.49911355  0.50047035]\n","0.9941984447532215 [-0.48052461  0.50593806  0.49911355  0.500468  ]\n","0.9941984447875786 [-0.48052461  0.50593806  0.49911355  0.5004727 ]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"Lb7Cr675uUCz","executionInfo":{"status":"error","timestamp":1628010587937,"user_tz":-330,"elapsed":719,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"1c3b3b21-4992-4ebd-a702-03d89ef2edcf"},"source":["\n","g = sns.relplot(x=range(len(rmse_optimize)), y=rmse_optimize, kind=\"line\")\n","g.fig.set_size_inches(10,8)\n","plt.title(\"RMSE Weights Optimization using Powell algorithm)\n","plt.ylabel('RMSE')\n","plt.xlabel('Number of Iterations')\n","g.fig.autofmt_xdate()\n"],"execution_count":null,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-84-e315c21a6fc6>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    plt.title(\"RMSE Weights Optimization using Powell algorithm)\u001b[0m\n\u001b[0m                                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"]}]},{"cell_type":"code","metadata":{"id":"K-_CXQz7W-hJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010603570,"user_tz":-330,"elapsed":5,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"c7c10789-ffc5-46b5-f146-1f5a1b61cd85"},"source":["print('Ensemble Score: {best_score}'.format(best_score=res['fun']))\n","print('Best Weights: {weights}'.format(weights=res['x']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Ensemble Score: 0.9941984446698354\n","Best Weights: [-0.48052461  0.50593806  0.49911355  0.50047035]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OQ4aNe4oW-hJ"},"source":["1. Ridge()\n","2. ANN\n","3. Lasso\n","4. Linear Reg\n"]},{"cell_type":"code","metadata":{"id":"SL2YXDYovdxe"},"source":["best_weights=res[\"x\"]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7ayNt_ZSvXe2","executionInfo":{"status":"ok","timestamp":1628010604296,"user_tz":-330,"elapsed":5,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"c421756a-a71f-49b5-fa70-b0856310d36b"},"source":["w1,w2,w3,w4 = best_weights[0],best_weights[1],best_weights[2],best_weights[3]\n","w1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.48052460728723473"]},"metadata":{"tags":[]},"execution_count":88}]},{"cell_type":"code","metadata":{"id":"2sDLOYiWYwDR"},"source":["df_weights.loc[len(df.index)] = [w1,w4,w3,w2]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mSZGm9_FaOmK"},"source":["#df_weights.to_csv(\"Weights.csv\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qop3TTIpW-hJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010604979,"user_tz":-330,"elapsed":5,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"59afbb1d-aa4e-4afc-9fd9-b8f495b8b092"},"source":["Ensemble_average_prediction_2 = ( y_lr_GS_pred*w4+ y_lasso_GS_pred*w3 + y_ann_pred*w2 +\n","                                 y_Ridge_GS_pred*w1)\n","t,r2score,MAD,MAE,RMSE,MSE = metrics(y_test,Ensemble_average_prediction_2)\n","df.loc[len(df.index)] = [\"Ensemble with Opt Weights\",r2score,MSE,MAE,MAD,RMSE]\n","print(t)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["+--------------+--------------------+\n","| Error Metric |       Value        |\n","+--------------+--------------------+\n","|   r2_score   | 0.8167984321893247 |\n","|     MSE      | 0.9884305473839203 |\n","|     MAE      |  0.74112686709868  |\n","|     RMSE     | 0.9941984446698356 |\n","|     MAD      | 1.2111301373774852 |\n","+--------------+--------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"Envm1D5BYTbM","executionInfo":{"status":"ok","timestamp":1628010605657,"user_tz":-330,"elapsed":6,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"858bcce2-85e0-406a-db83-d84b68606043"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Model</th>\n","      <th>R2score</th>\n","      <th>MSE</th>\n","      <th>MAE</th>\n","      <th>MAD</th>\n","      <th>RMSE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Ridge</td>\n","      <td>0.807426</td>\n","      <td>1.038995</td>\n","      <td>0.762401</td>\n","      <td>1.298893</td>\n","      <td>1.019311</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Linear Reg</td>\n","      <td>0.807900</td>\n","      <td>1.036442</td>\n","      <td>0.762101</td>\n","      <td>1.302052</td>\n","      <td>1.018058</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Lasso</td>\n","      <td>0.805994</td>\n","      <td>1.046724</td>\n","      <td>0.769532</td>\n","      <td>1.264378</td>\n","      <td>1.023095</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ANN</td>\n","      <td>0.804322</td>\n","      <td>1.055743</td>\n","      <td>0.734411</td>\n","      <td>1.228296</td>\n","      <td>1.027494</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Ensemble with AVG</td>\n","      <td>0.812182</td>\n","      <td>1.013337</td>\n","      <td>0.739489</td>\n","      <td>1.269055</td>\n","      <td>1.006646</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Ensemble with Opt Weights</td>\n","      <td>0.816798</td>\n","      <td>0.988431</td>\n","      <td>0.741127</td>\n","      <td>1.211130</td>\n","      <td>0.994198</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                       Model   R2score       MSE       MAE       MAD      RMSE\n","0                      Ridge  0.807426  1.038995  0.762401  1.298893  1.019311\n","1                 Linear Reg  0.807900  1.036442  0.762101  1.302052  1.018058\n","2                      Lasso  0.805994  1.046724  0.769532  1.264378  1.023095\n","3                        ANN  0.804322  1.055743  0.734411  1.228296  1.027494\n","4          Ensemble with AVG  0.812182  1.013337  0.739489  1.269055  1.006646\n","5  Ensemble with Opt Weights  0.816798  0.988431  0.741127  1.211130  0.994198"]},"metadata":{"tags":[]},"execution_count":92}]},{"cell_type":"code","metadata":{"id":"DFCM7btqYXaX"},"source":["#df.to_csv(\"Model Data.csv\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EscvINY1kY04"},"source":["## Model Comparison Charts\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"IpOw0Nw2z08o","executionInfo":{"status":"ok","timestamp":1628010607639,"user_tz":-330,"elapsed":5,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"a6a7587c-226e-4cde-85a1-71c898e5c9bc"},"source":["fig = plt.figure(figsize=(10,5))\n","ax = fig.add_axes([0,0,1,1])\n","models = ['Ridge Regression', 'LASSO Regression', 'Linear Regression', 'ANN', 'AVG Ensemble','Optimized Ensemble']\n","\n","r2_score_list = [round(r2_score(y_test,y_Ridge_GS_pred),3),\n","                 round(r2_score(y_test,y_lasso_GS_pred),3),\n","                 round(r2_score(y_test,y_lr_GS_pred),3),\n","                 round(r2_score(y_test,y_ann_pred),3),\n","                 round(r2_score(y_test,Ensemble_average_prediction_1),3),\n","                 round(r2_score(y_test,Ensemble_average_prediction_2),3)]\n","\n","ax.bar(models,r2_score_list)\n","ax.set_ylabel('R2 Score',fontsize= 12)\n","ax.set_xlabel('Models',fontsize= 12)\n","ax.set_title('Model Comparison')\n","for i, v in enumerate(r2_score_list):\n","  ax.text(i - 0.1,v + 0.01, str(v), color='black', fontweight='bold')\n","#plt.savefig('barplot_1.png',dpi=200, format='png', bbox_inches='tight')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"54Dnxwe0kYNs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1628010612489,"user_tz":-330,"elapsed":4187,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"fee92d55-284b-412c-f093-d1a8796d81fb"},"source":["!pip install regressormetricgraphplot"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting regressormetricgraphplot\n","  Downloading regressormetricgraphplot-0.0.3-py3-none-any.whl (4.0 kB)\n","Installing collected packages: regressormetricgraphplot\n","Successfully installed regressormetricgraphplot-0.0.3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XZCQY666l4w_"},"source":["from regressormetricgraphplot import CompareModels"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XVXVvuGDmx9R"},"source":["plot = CompareModels()\n","plot.add(model_name='Ridge Regression', y_test=y_test, y_pred=y_Ridge_GS_pred)\n","#plot.show(figsize=(10, 5))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vdWQLAQvnKJL"},"source":["plot.add(model_name='LASSO Regression', y_test=y_test, y_pred=y_lasso_GS_pred)\n","plot.add(model_name='Linear Regression', y_test=y_test, y_pred=y_lr_GS_pred)\n","plot.add(model_name='ANN', y_test=y_test, y_pred=y_ann_pred)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":320},"id":"e1JrjaLCnngK","executionInfo":{"status":"ok","timestamp":1628010612491,"user_tz":-330,"elapsed":9,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"ae0d4c76-4de8-4bf4-a3ef-175bf5f3878d"},"source":["plot.show(figsize=(5,5))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"zDVjbMFzoFcC"},"source":["plot.add(model_name='Ensemble Model', y_test=y_test, y_pred=Ensemble_average_prediction_2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"q1pqdgv_oSVU","executionInfo":{"status":"ok","timestamp":1628010614068,"user_tz":-330,"elapsed":1585,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"78ebde13-f0b3-4ed7-ef14-e86b3ba73c83"},"source":["plot.show(figsize=(10, 10))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K7RC33pJK9pT","executionInfo":{"status":"ok","timestamp":1628010614068,"user_tz":-330,"elapsed":16,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"e22ac0e8-5cdf-4e07-ba33-6d5001c7e155"},"source":["y_test_array=y_test.to_numpy()\n","y_test_array"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 4.59000015,  4.86999989,  4.42999983,  5.65999985,  4.38000011,\n","        4.28999996,  6.48999977,  4.96000004,  9.27999973,  7.21999979,\n","       10.06999969,  3.93000007,  4.67000008,  4.6500001 , 12.97000027,\n","        5.05000019,  8.81000042,  5.36000013,  5.78000021,  4.30999994,\n","        6.3499999 , 16.11000061,  4.13999987,  5.38000011,  4.80999994,\n","        6.78000021,  5.28000021, 10.09000015,  4.96000004,  3.41000009,\n","        5.09000015,  4.84000015,  4.30999994,  4.82999992,  4.23999977,\n","        4.67999983,  5.19999981,  4.19999981,  5.51999998,  4.94999981,\n","        5.03000021,  4.82999992,  4.9000001 ,  4.30999994,  4.05000019,\n","        3.02999997,  4.17999983,  6.48000002, 11.59000015,  3.83999991,\n","        4.03999996, 12.73999977,  4.84000015,  6.13999987,  5.53000021,\n","        3.61999989,  5.19999981,  4.38999987,  7.90999985,  4.40999985,\n","        4.98000002,  4.4000001 ,  5.11000013,  4.86999989,  3.79999995,\n","        4.84000015,  4.38999987,  9.77000046,  4.46999979,  4.30999994,\n","        5.73999977,  4.42999983,  5.46999979,  7.4000001 ,  4.80999994,\n","        4.80999994,  4.40999985,  5.01999998,  7.51000023,  5.11999989,\n","        9.36999989])"]},"metadata":{"tags":[]},"execution_count":102}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ASalXceULHKr","executionInfo":{"status":"ok","timestamp":1628010614069,"user_tz":-330,"elapsed":13,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"e522e6aa-a564-4f87-c683-a62864126b36"},"source":["Ensemble_average_prediction_2"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 4.94314327,  6.28937497,  5.66128372,  5.37817348,  4.61836205,\n","        5.32696335,  6.7543181 ,  5.04842252,  8.44763742,  6.61289779,\n","       10.40822099,  4.29257427,  4.24445683,  4.89662924, 12.11229809,\n","        6.07606121,  7.18171125,  5.10000075,  5.47838699,  4.84801987,\n","        5.480271  , 12.29827993,  5.0489712 ,  5.58195699,  5.84168875,\n","        6.26883918,  5.03178633,  9.41059133,  5.34387472,  4.56936406,\n","        5.18833334,  3.79979804,  4.75746826,  4.34306298,  5.0427224 ,\n","        4.6264495 ,  6.83935377,  4.75523741,  5.0634817 ,  4.35576728,\n","        5.63339801,  5.25212313,  5.11572297,  4.2396029 ,  4.85459975,\n","        4.54168007,  4.90078092,  5.68049224, 11.74885581,  4.55005389,\n","        5.29744051, 10.39986243,  5.69415007,  8.36672742,  6.41222501,\n","        4.89056627,  4.75527516,  4.00239645,  9.07598661,  4.17256458,\n","        5.65623591,  4.40054404,  5.61889357,  6.50837292,  4.71588644,\n","        5.34949067,  5.2595088 ,  8.28983768,  5.192878  ,  4.40761161,\n","        5.34372005,  4.50674845,  5.71465785,  6.07772537,  4.80382987,\n","        4.97795285,  3.64033168,  4.97727562,  6.77518996,  5.38052495,\n","        6.18269283])"]},"metadata":{"tags":[]},"execution_count":103}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":812},"id":"jSwcOhBJKYmM","executionInfo":{"status":"error","timestamp":1628010614069,"user_tz":-330,"elapsed":9,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"3b3f404f-92ef-4e44-a3b1-86b6aa66aafa"},"source":["#Ensemble_average_prediction_2\n","labels =[\"Actual Values\", \"Predicted Values\"]\n","plt.plot(y_test_array)\n","plt.plot(Ensemble_average_prediction_2)\n","plt.xlabel(\"Frequency\")\n","plt.ylabel(\"Glyhb\")\n","plt.title(\"Ensemble Predicted Values VS Actual Values\")\n","plt.legend(labels)\n","plt.show(figsize=(10, 10))"],"execution_count":null,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-104-b4920a37681b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ensemble Predicted Values VS Actual Values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    270\u001b[0m     \"\"\"\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: show() got an unexpected keyword argument 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\n","text/plain":["<Figure size 576x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"IOF0XZ93w72s"},"source":["\n","\n","---\n","\n"]},{"cell_type":"markdown","metadata":{"id":"mvOzWDYVw-gy"},"source":["# Feature **Importance**"]},{"cell_type":"code","metadata":{"id":"1al_5fTv2Q4a"},"source":["import matplotlib.pyplot as plt\n","import matplotlib\n","%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jr7d-pzC2w-1"},"source":["### Lasso Important features"]},{"cell_type":"code","metadata":{"id":"zwA-8OVF1d_S","colab":{"base_uri":"https://localhost:8080/","height":180},"executionInfo":{"status":"error","timestamp":1628010466607,"user_tz":-330,"elapsed":674,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"41121eae-b33b-4278-df51-57f29ea96550"},"source":["# lasso \n","coef = pd.Series(Lasso_best_model.coef_, index = x_train.columns)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-ef86d0e3eff4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# lasso\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcoef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLasso_best_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":180},"id":"D64NndnV150w","executionInfo":{"status":"error","timestamp":1628010467067,"user_tz":-330,"elapsed":7,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"93a0d47d-596c-4efb-fc1c-3f17a7e3048a"},"source":["print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-9fccceedee5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Lasso picked \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" variables and eliminated the other \"\u001b[0m \u001b[0;34m+\u001b[0m  \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" variables\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'coef' is not defined"]}]},{"cell_type":"code","metadata":{"id":"M3d6aENm2dI4"},"source":["imp_coef = pd.concat([coef.sort_values().head(11),\n","                     coef.sort_values().tail(11)])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"z3YQQeyS2Bxy"},"source":["matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n","imp_coef.plot(kind = \"barh\")\n","plt.title(\"Coefficients in the Lasso Model\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nJV5zefS3QS2"},"source":["### XGBRegressor"]},{"cell_type":"code","metadata":{"id":"Mm8JOHVN3ORd"},"source":["# XGBRegressor\n","coef = pd.Series(xgb_best_model.feature_importances_, index = x_train.columns)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CgZeQqPj3ORf"},"source":["imp_coef = pd.concat([coef.sort_values().head(11),\n","                     coef.sort_values().tail(11)])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Y8zuZaK3ORf","executionInfo":{"status":"ok","timestamp":1625477772207,"user_tz":-330,"elapsed":19,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"e7779a90-7a14-4fd5-9cb9-d395ef4961c9"},"source":["matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n","imp_coef.plot(kind = \"barh\")\n","plt.title(\"Coefficients in the Lasso Model\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Coefficients in the Lasso Model')"]},"metadata":{"tags":[]},"execution_count":89},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"WJoAqGXf35nh"},"source":["##  Random forest"]},{"cell_type":"code","metadata":{"id":"qDWFlGjB4FHp"},"source":["# Random forest\n","coef = pd.Series(rf.feature_importances_, index = x_train.columns)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"g_AyX0oW4FHq"},"source":["imp_coef = pd.concat([coef.sort_values().head(11),\n","                     coef.sort_values().tail(11)])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mb4-azlA4FHq","executionInfo":{"status":"ok","timestamp":1625477772208,"user_tz":-330,"elapsed":17,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"e88088d9-9b67-428f-f54b-d70588aca5f5"},"source":["matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n","imp_coef.plot(kind = \"barh\")\n","plt.title(\"Coefficients in the Lasso Model\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Coefficients in the Lasso Model')"]},"metadata":{"tags":[]},"execution_count":92},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"LE5MiSoZ4exO"},"source":["### Ridge Important features"]},{"cell_type":"code","metadata":{"id":"7QnapuJd4exS"},"source":["# Ridge\n","coef = pd.Series(Ridge_best_model.coef_, index = x_train.columns)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jnN6w67K4exT"},"source":["imp_coef = pd.concat([coef.sort_values().head(11),\n","                     coef.sort_values().tail(11)])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J5qu8lA14exT","executionInfo":{"status":"ok","timestamp":1625477772209,"user_tz":-330,"elapsed":16,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"26fb624e-990d-4c3e-8ee9-236c4f3addad"},"source":["matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n","imp_coef.plot(kind = \"barh\")\n","plt.title(\"Coefficients in the Ridge Model\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Coefficients in the Ridge Model')"]},"metadata":{"tags":[]},"execution_count":95},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p0dXGGJIJimi","executionInfo":{"status":"ok","timestamp":1625477772209,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"cc8752b7-c998-4b0f-eaee-eb94aa555796"},"source":["x_train.columns"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index([    'chol', 'stab.glu',      'hdl',    'ratio', 'location',      'age',\n","         'gender',   'height',   'weight',    'bp.1s',    'bp.1d',    'waist',\n","            'hip', 'time.ppn',          0,          1,          2],\n","      dtype='object')"]},"metadata":{"tags":[]},"execution_count":96}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g_-lL2WjJn5U","executionInfo":{"status":"ok","timestamp":1625477772209,"user_tz":-330,"elapsed":14,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"6376aaa6-76eb-41f4-b0e2-c98bbd3380ec"},"source":["y_train"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["353    4.88\n","233    4.34\n","3      4.63\n","18     5.28\n","131    4.79\n","       ... \n","71     5.23\n","106    5.17\n","270    4.62\n","348    3.98\n","102    5.44\n","Name: glyhb, Length: 322, dtype: float64"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3m3Yv-T2_Tls","executionInfo":{"status":"ok","timestamp":1625477773188,"user_tz":-330,"elapsed":992,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"ff8f3754-8b8d-430e-e59a-891d2c465665"},"source":["plt.figure(figsize=(20,10))\n","sns.jointplot(x=x_train[2],y=y_train,kind=\"reg\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x720 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"v-vRR12tL6sy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625477774799,"user_tz":-330,"elapsed":1616,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"09e00608-e686-45e2-837d-df035b52a0a0"},"source":["# Plot for mse \n","from matplotlib import pyplot\n","from sklearn.metrics import mean_squared_error\n","# real value\n","expected = y_test.to_numpy()\n","# predicted value\n","predicted = Ensemble_average_prediction_2\n","# calculate errors\n","errors = list()\n","for i in range(len(expected)):\n","\t# calculate error\n","\terr = (expected[i] - predicted[i])**2\n","\t# store error\n","\terrors.append(err)\n","\t# report error\n","\tprint('>%.1f, %.1f = %.3f' % (expected[i], predicted[i], err))\n","# plot errors\n","pyplot.plot(errors)\n","pyplot.xticks(ticks=[i for i in range(len(errors))], labels=predicted)\n","pyplot.xlabel('Predicted Value')\n","pyplot.ylabel('Mean Squared Error')\n","plt.xticks(rotation = 90)\n","pyplot.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":[">4.6, 5.1 = 0.240\n",">4.9, 6.5 = 2.536\n",">4.4, 5.8 = 1.780\n",">5.7, 5.4 = 0.053\n",">4.4, 4.7 = 0.111\n",">4.3, 5.3 = 1.120\n",">6.5, 6.7 = 0.051\n",">5.0, 5.1 = 0.009\n",">9.3, 8.8 = 0.209\n",">7.2, 6.5 = 0.474\n",">10.1, 10.5 = 0.209\n",">3.9, 4.5 = 0.317\n",">4.7, 4.3 = 0.111\n",">4.7, 5.2 = 0.251\n",">13.0, 11.8 = 1.430\n",">5.1, 5.9 = 0.652\n",">8.8, 7.0 = 3.277\n",">5.4, 5.4 = 0.000\n",">5.8, 5.5 = 0.094\n",">4.3, 4.7 = 0.157\n",">6.3, 4.9 = 2.180\n",">16.1, 12.5 = 13.077\n",">4.1, 4.8 = 0.446\n",">5.4, 5.3 = 0.004\n",">4.8, 5.3 = 0.198\n",">6.8, 6.2 = 0.393\n",">5.3, 5.1 = 0.021\n",">10.1, 9.6 = 0.264\n",">5.0, 5.1 = 0.024\n",">3.4, 4.8 = 1.955\n",">5.1, 5.0 = 0.003\n",">4.8, 3.6 = 1.605\n",">4.3, 5.1 = 0.601\n",">4.8, 4.3 = 0.328\n",">4.2, 5.0 = 0.518\n",">4.7, 5.0 = 0.084\n",">5.2, 6.7 = 2.145\n",">4.2, 4.5 = 0.106\n",">5.5, 5.0 = 0.241\n",">4.9, 3.9 = 1.050\n",">5.0, 5.5 = 0.209\n",">4.8, 5.0 = 0.028\n",">4.9, 5.4 = 0.229\n",">4.3, 4.0 = 0.128\n",">4.1, 4.8 = 0.496\n",">3.0, 4.2 = 1.378\n",">4.2, 4.5 = 0.119\n",">6.5, 5.6 = 0.694\n",">11.6, 12.0 = 0.173\n",">3.8, 4.6 = 0.640\n",">4.0, 5.4 = 1.816\n",">12.7, 10.4 = 5.483\n",">4.8, 5.6 = 0.632\n",">6.1, 8.7 = 6.706\n",">5.5, 6.6 = 1.209\n",">3.6, 4.8 = 1.291\n",">5.2, 4.7 = 0.232\n",">4.4, 3.6 = 0.660\n",">7.9, 9.1 = 1.469\n",">4.4, 4.2 = 0.035\n",">5.0, 5.4 = 0.150\n",">4.4, 4.2 = 0.052\n",">5.1, 5.7 = 0.313\n",">4.9, 6.7 = 3.422\n",">3.8, 4.6 = 0.688\n",">4.8, 5.4 = 0.328\n",">4.4, 5.5 = 1.193\n",">9.8, 8.3 = 2.223\n",">4.5, 5.2 = 0.468\n",">4.3, 4.0 = 0.129\n",">5.7, 5.6 = 0.032\n",">4.4, 4.5 = 0.012\n",">5.5, 6.0 = 0.233\n",">7.4, 6.1 = 1.618\n",">4.8, 4.7 = 0.006\n",">4.8, 5.1 = 0.102\n",">4.4, 3.7 = 0.523\n",">5.0, 4.9 = 0.007\n",">7.5, 6.6 = 0.797\n",">5.1, 5.6 = 0.276\n",">9.4, 6.3 = 9.416\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7mfhOlkm9Ntu","executionInfo":{"status":"ok","timestamp":1625477774799,"user_tz":-330,"elapsed":18,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"eec7a4e4-b801-434b-ca8e-d8f89512119d"},"source":["expected"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 4.59000015,  4.86999989,  4.42999983,  5.65999985,  4.38000011,\n","        4.28999996,  6.48999977,  4.96000004,  9.27999973,  7.21999979,\n","       10.06999969,  3.93000007,  4.67000008,  4.6500001 , 12.97000027,\n","        5.05000019,  8.81000042,  5.36000013,  5.78000021,  4.30999994,\n","        6.3499999 , 16.11000061,  4.13999987,  5.38000011,  4.80999994,\n","        6.78000021,  5.28000021, 10.09000015,  4.96000004,  3.41000009,\n","        5.09000015,  4.84000015,  4.30999994,  4.82999992,  4.23999977,\n","        4.67999983,  5.19999981,  4.19999981,  5.51999998,  4.94999981,\n","        5.03000021,  4.82999992,  4.9000001 ,  4.30999994,  4.05000019,\n","        3.02999997,  4.17999983,  6.48000002, 11.59000015,  3.83999991,\n","        4.03999996, 12.73999977,  4.84000015,  6.13999987,  5.53000021,\n","        3.61999989,  5.19999981,  4.38999987,  7.90999985,  4.40999985,\n","        4.98000002,  4.4000001 ,  5.11000013,  4.86999989,  3.79999995,\n","        4.84000015,  4.38999987,  9.77000046,  4.46999979,  4.30999994,\n","        5.73999977,  4.42999983,  5.46999979,  7.4000001 ,  4.80999994,\n","        4.80999994,  4.40999985,  5.01999998,  7.51000023,  5.11999989,\n","        9.36999989])"]},"metadata":{"tags":[]},"execution_count":100}]},{"cell_type":"markdown","metadata":{"id":"F45ifwjCOujT"},"source":["## Elastic Net"]},{"cell_type":"markdown","metadata":{"id":"eK7CRvfiwUok"},"source":["### DBSCAN outlier detector ( didnt work)\n"]},{"cell_type":"code","metadata":{"id":"veGv1QnYwTTm"},"source":["from sklearn.cluster import DBSCAN\n","from sklearn.preprocessing import StandardScaler"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NfRodBDBwheG","colab":{"base_uri":"https://localhost:8080/","height":180},"executionInfo":{"status":"error","timestamp":1629228427200,"user_tz":-330,"elapsed":20,"user":{"displayName":"Atharv Jairath","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GimYmfxjyVhkyags-cPXU89M7YrR7p8lIrOncB55kc=s64","userId":"02765993908853204784"}},"outputId":"268511a1-221e-46be-9a90-3ac5c1bdef01"},"source":["dbscan_dataset = dataset[[\"stab.glu\"]]\n","dbscan_dataset = StandardScaler().fit_transform(dbscan_dataset)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-3-5674f4c02bfb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdbscan_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stab.glu\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdbscan_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdbscan_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"]}]},{"cell_type":"code","metadata":{"id":"5n8Ma_9JxNtC"},"source":["db = DBSCAN(eps=32, min_samples=5).fit(dbscan_dataset)\n","core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n","core_samples_mask[db.core_sample_indices_] = True\n","labels = db.labels_\n","\n","n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n","n_noise_ = list(labels).count(-1)\n","\n","print('Estimated number of clusters: %d' % n_clusters_)\n","print('Estimated number of noise points: %d' % n_noise_)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BC2MnAoz_R58"},"source":["dataset[\"stab.glu\"].head()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JB38Mm4a2rnC"},"source":["dataset[\"labels\"]=db.labels_"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vRc_dN2_CT2W"},"source":["dataset = dataset[dataset.labels != -1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oLrdJGcvCY-g"},"source":["dataset.drop(columns=[\"labels\"],inplace=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KyORftmPOgCb"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HWy-qu28OtuV"},"source":["# Elastic Net\n","from numpy import arange\n","from sklearn.linear_model import ElasticNet\n","elasticNet_reg = ElasticNet()\n","params_elastic = {'alpha':[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0] , \"l1_ratio\": arange(0, 1, 0.01)}\n","elasticNet_GS = GridSearchCV(elasticNet_reg, param_grid=params_elastic, n_jobs=-1)\n","elasticNet_GS.fit(x_train,y_train)\n","print(elasticNet_GS.best_params_)\n","elastic_best_model = elasticNet_GS.best_estimator_\n","elastic_best_model.fit(x_train,y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"aa7CPEmBPnGS"},"source":["y_elastic_pred= elastic_best_model.predict(x_test)\n","print(metrics(y_test,y_elastic_pred))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KbD-okRpZghk"},"source":["Ensemble_Weighted_average_prediction_2 = y_Ridge_GS_pred*0.25 + y_lasso_GS_pred*0.5 + y_elastic_pred *0.25\n","print(metrics(y_test,Ensemble_Weighted_average_prediction_2))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TUZoQnscexPf"},"source":["# add every model's prediction into list\n","predictions = []\n","#predictions.append(y_Ridge_GS_pred)\n","predictions.append(y_rf)\n","predictions.append(y_lasso_GS_pred)\n","predictions.append(y_XGBReg_pred)\n","predictions.append(y_elastic_pred)\n","#starting Value\n","starting_values = [0.5]*len(predictions)\n","cons = ({'type':'eq','fun':lambda w: 1-sum(w)})\n","#our weights are bound between 0 and 1\n","bounds = [(0,1)]*len(predictions)\n","res = minimize(rmse_loss_func, starting_values, method='Powell', bounds=bounds, constraints=cons)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2MbOHYYAe6_l"},"source":["print('Ensamble Score: {best_score}'.format(best_score=res['fun']))\n","print('Best Weights: {weights}'.format(weights=res['x']))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IxwFArsKWlZX"},"source":["dataset_copy = dataset"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oOiNiOcKWmuR"},"source":["x_train,x_test,y_train,y_test = train_test_split(dataset_copy,y,test_size=0.2,random_state = 42, shuffle = True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"diVAAqaZW9SW"},"source":["x_train.head()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z-OUyIVibybL"},"source":[""],"execution_count":null,"outputs":[]}]}