nebraska_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a78373d9-04d5-4be5-8e3b-6ed3afe49b3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.1.1'.replace('rc', '-rc.');\n",
       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks;\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      run_callbacks();\n",
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       "    if (!reloading) {\n",
       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    }\n",
       "\n",
       "    function on_load() {\n",
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       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "    window._bokeh_on_load = on_load\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"jspanel\"], function(jsPanel) {\n",
       "\twindow.jsPanel = jsPanel\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-modal\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-tooltip\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-hint\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-layout\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-contextmenu\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-dock\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 9;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    var existing_stylesheets = []\n",
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       "      var link = links[i]\n",
       "      if (link.href != null) {\n",
       "\texisting_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
       "\ton_load()\n",
       "\tcontinue;\n",
       "      }\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
       "    for (var i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "\texisting_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
       "      `\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var js_exports = {};\n",
       "  var css_urls = [];\n",
       "  var inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      Bokeh = root.Bokeh;\n",
       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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     "output_type": "display_data"
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     "data": {
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       "  var output = handle.output;\n",
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       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
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      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
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       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
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       "    > .lm-Widget\n",
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       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "# Import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "import hvplot.pandas\n",
    "import holoviews as hv\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3b1ba3-e9a4-433e-9473-c61dcc41532d",
   "metadata": {},
   "source": [
    "# Regression using Offense Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd3f3ac1-3628-4165-86f9-8d68787b9cdd",
   "metadata": {
    "tags": []
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-09-02</td>\n",
       "      <td>home</td>\n",
       "      <td>San Jose State</td>\n",
       "      <td>W (49-13)</td>\n",
       "      <td>5</td>\n",
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       "      <td>...</td>\n",
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       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-09-09</td>\n",
       "      <td>away</td>\n",
       "      <td>Notre Dame</td>\n",
       "      <td>W (27-24)</td>\n",
       "      <td>7</td>\n",
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       "      <td>46.7</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>2000-09-23</td>\n",
       "      <td>home</td>\n",
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       "      <td>W (42-13)</td>\n",
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       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-09-30</td>\n",
       "      <td>home</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>W (42-24)</td>\n",
       "      <td>11</td>\n",
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       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-10-07</td>\n",
       "      <td>away</td>\n",
       "      <td>Iowa State</td>\n",
       "      <td>W (49-27)</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>41.2</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>49</td>\n",
       "      <td>27</td>\n",
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       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_away        opponent      score  passing_cmp  passing_att  \\\n",
       "0  2000-09-02      home  San Jose State  W (49-13)            5           13   \n",
       "1  2000-09-09      away      Notre Dame  W (27-24)            7           15   \n",
       "2  2000-09-23      home            Iowa  W (42-13)           10           13   \n",
       "3  2000-09-30      home        Missouri  W (42-24)           11           23   \n",
       "4  2000-10-07      away      Iowa State  W (49-27)            7           17   \n",
       "\n",
       "   passing_pct  passing_yds  passing_td  rushing_att  ...  first_down_pen  \\\n",
       "0         38.5           91           1           60  ...               0   \n",
       "1         46.7          103           0           59  ...               1   \n",
       "2         76.9          159           5           51  ...               0   \n",
       "3         47.8          173           2           53  ...               4   \n",
       "4         41.2          164           0           60  ...               0   \n",
       "\n",
       "   first_down_total  penalties  penalty_yds  fumbles  interceptions  \\\n",
       "0                28          4           31        0              3   \n",
       "1                20          6           44        0              1   \n",
       "2                23          5           55        1              0   \n",
       "3                27          7           53        0              0   \n",
       "4                20          5           38        0              1   \n",
       "\n",
       "   turnovers  outcome  points_for  points_against  \n",
       "0          3        W          49              13  \n",
       "1          1        W          27              24  \n",
       "2          1        W          42              13  \n",
       "3          0        W          42              24  \n",
       "4          1        W          49              27  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Read the CSV file from the output folder into a Pandas DataFrame\n",
    "df_data = pd.read_csv(\"output/nebraska_offense.csv\")\n",
    "\n",
    "# Display the data\n",
    "df_data.head()"
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  {
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   "execution_count": 3,
   "id": "2089cf54-5303-4e35-bd8f-b12d6681d32d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 287 entries, 0 to 286\n",
      "Data columns (total 28 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   date              287 non-null    object \n",
      " 1   home_away         287 non-null    object \n",
      " 2   opponent          287 non-null    object \n",
      " 3   score             287 non-null    object \n",
      " 4   passing_cmp       287 non-null    int64  \n",
      " 5   passing_att       287 non-null    int64  \n",
      " 6   passing_pct       287 non-null    float64\n",
      " 7   passing_yds       287 non-null    int64  \n",
      " 8   passing_td        287 non-null    int64  \n",
      " 9   rushing_att       287 non-null    int64  \n",
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      " 17  first_down_rush   287 non-null    int64  \n",
      " 18  first_down_pen    287 non-null    int64  \n",
      " 19  first_down_total  287 non-null    int64  \n",
      " 20  penalties         287 non-null    int64  \n",
      " 21  penalty_yds       287 non-null    int64  \n",
      " 22  fumbles           287 non-null    int64  \n",
      " 23  interceptions     287 non-null    int64  \n",
      " 24  turnovers         287 non-null    int64  \n",
      " 25  outcome           287 non-null    object \n",
      " 26  points_for        287 non-null    int64  \n",
      " 27  points_against    287 non-null    int64  \n",
      "dtypes: float64(3), int64(20), object(5)\n",
      "memory usage: 62.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the dataset to determine initial processing steps\n",
    "df_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80bfbc35-aad9-4068-bcf8-769e477d58ea",
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       "      <td>505</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-09</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>46.7</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>274</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-23</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>76.9</td>\n",
       "      <td>159</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>331</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-30</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>47.8</td>\n",
       "      <td>173</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>311</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>2000-10-07</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>41.2</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>336</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>27</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            home_away  passing_cmp  passing_att  passing_pct  passing_yds  \\\n",
       "date                                                                        \n",
       "2000-09-02          0            5           13         38.5           91   \n",
       "2000-09-09          1            7           15         46.7          103   \n",
       "2000-09-23          0           10           13         76.9          159   \n",
       "2000-09-30          0           11           23         47.8          173   \n",
       "2000-10-07          1            7           17         41.2          164   \n",
       "\n",
       "            passing_td  rushing_att  rushing_yds  rushing_avg  rushing_td  \\\n",
       "date                                                                        \n",
       "2000-09-02           1           60          505          8.4           6   \n",
       "2000-09-09           0           59          274          4.6           4   \n",
       "2000-09-23           5           51          331          6.5           0   \n",
       "2000-09-30           2           53          311          5.9           2   \n",
       "2000-10-07           0           60          336          5.6           6   \n",
       "\n",
       "            ...  first_down_pen  first_down_total  penalties  penalty_yds  \\\n",
       "date        ...                                                             \n",
       "2000-09-02  ...               0                28          4           31   \n",
       "2000-09-09  ...               1                20          6           44   \n",
       "2000-09-23  ...               0                23          5           55   \n",
       "2000-09-30  ...               4                27          7           53   \n",
       "2000-10-07  ...               0                20          5           38   \n",
       "\n",
       "            fumbles  interceptions  turnovers  outcome  points_for  \\\n",
       "date                                                                 \n",
       "2000-09-02        0              3          3        0          49   \n",
       "2000-09-09        0              1          1        0          27   \n",
       "2000-09-23        1              0          1        0          42   \n",
       "2000-09-30        0              0          0        0          42   \n",
       "2000-10-07        0              1          1        0          49   \n",
       "\n",
       "            points_against  \n",
       "date                        \n",
       "2000-09-02              13  \n",
       "2000-09-09              24  \n",
       "2000-09-23              13  \n",
       "2000-09-30              24  \n",
       "2000-10-07              27  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the index to the date column\n",
    "df_data = df_data.set_index('date')\n",
    "\n",
    "# Encode the home_away column (home = 0, away = 1, N = 1)\n",
    "df_data['home_away'] = df_data['home_away'].replace('home', 0)\n",
    "df_data['home_away'] = df_data['home_away'].replace('away', 1)\n",
    "df_data['home_away'] = df_data['home_away'].replace('N', 1)\n",
    "\n",
    "# Encode the outcome column (win = 0, loss = 1)\n",
    "df_data['outcome'] = df_data['outcome'].replace('W', 0)\n",
    "df_data['outcome'] = df_data['outcome'].replace('L', 1)\n",
    "\n",
    "# Drop the unnecessary columns and set the team column to the index\n",
    "columns_to_drop = ['opponent', 'score']\n",
    "df_data = df_data.drop(columns_to_drop, axis=1)\n",
    "\n",
    "#Preview the dataframe\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "3b772a62-37fb-4b41-b2e9-4a8263c546d9",
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    {
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       "      <th></th>\n",
       "      <th>home_away</th>\n",
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       "      <td>2.240868</td>\n",
       "      <td>2.365558</td>\n",
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       "      <td>1.909233</td>\n",
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       "      <td>1.383476</td>\n",
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       "      <th>2000-09-09</th>\n",
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       "      <td>0.699274</td>\n",
       "      <td>0.032200</td>\n",
       "      <td>1.154291</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.346805</td>\n",
       "      <td>-0.114387</td>\n",
       "      <td>-0.150704</td>\n",
       "      <td>-0.471387</td>\n",
       "      <td>-0.894651</td>\n",
       "      <td>0.026244</td>\n",
       "      <td>-0.640940</td>\n",
       "      <td>-0.841625</td>\n",
       "      <td>-0.213757</td>\n",
       "      <td>-0.016184</td>\n",
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       "      <th>2000-09-23</th>\n",
       "      <td>-0.897235</td>\n",
       "      <td>-0.834361</td>\n",
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       "      <td>1.571151</td>\n",
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       "      <th>2000-09-30</th>\n",
       "      <td>-0.897235</td>\n",
       "      <td>-0.691909</td>\n",
       "      <td>-0.431572</td>\n",
       "      <td>-0.743627</td>\n",
       "      <td>-0.341494</td>\n",
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       "      <td>1.054616</td>\n",
       "      <td>0.787797</td>\n",
       "      <td>-0.056976</td>\n",
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       "      <td>2.080831</td>\n",
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       "      <th>2000-10-07</th>\n",
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       "      <td>1.532764</td>\n",
       "      <td>1.294712</td>\n",
       "      <td>0.613429</td>\n",
       "      <td>2.365558</td>\n",
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       "      <td>-0.114387</td>\n",
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       "      <td>-0.715370</td>\n",
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       "      <td>-0.640940</td>\n",
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       "      <td>1.383476</td>\n",
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      "text/plain": [
       "            home_away  passing_cmp  passing_att  passing_pct  passing_yds  \\\n",
       "date                                                                        \n",
       "2000-09-02  -0.897235    -1.546620    -1.386554    -1.483401    -1.221747   \n",
       "2000-09-09   1.114535    -1.261716    -1.195557    -0.831127    -1.092929   \n",
       "2000-09-23  -0.897235    -0.834361    -1.386554     1.571151    -0.491781   \n",
       "2000-09-30  -0.897235    -0.691909    -0.431572    -0.743627    -0.341494   \n",
       "2000-10-07   1.114535    -1.261716    -1.004561    -1.268628    -0.438107   \n",
       "\n",
       "            passing_td  rushing_att  rushing_yds  rushing_avg  rushing_td  \\\n",
       "date                                                                        \n",
       "2000-09-02   -0.369818     1.532764     2.917762     2.240868    2.365558   \n",
       "2000-09-09   -1.180028     1.444055     0.699274     0.032200    1.154291   \n",
       "2000-09-23    2.871026     0.734391     1.246693     1.136534   -1.268243   \n",
       "2000-09-30    0.440393     0.911807     1.054616     0.787797   -0.056976   \n",
       "2000-10-07   -1.180028     1.532764     1.294712     0.613429    2.365558   \n",
       "\n",
       "            ...  first_down_pen  first_down_total  penalties  penalty_yds  \\\n",
       "date        ...                                                             \n",
       "2000-09-02  ...       -1.156017          1.246407  -0.902913    -1.000016   \n",
       "2000-09-09  ...       -0.346805         -0.114387  -0.150704    -0.471387   \n",
       "2000-09-23  ...       -1.156017          0.395910  -0.526808    -0.024087   \n",
       "2000-09-30  ...        2.080831          1.076307   0.225401    -0.105414   \n",
       "2000-10-07  ...       -1.156017         -0.114387  -0.526808    -0.715370   \n",
       "\n",
       "             fumbles  interceptions  turnovers   outcome  points_for  \\\n",
       "date                                                                   \n",
       "2000-09-02 -0.894651       1.909233   0.807484 -0.841625    1.383476   \n",
       "2000-09-09 -0.894651       0.026244  -0.640940 -0.841625   -0.213757   \n",
       "2000-09-23  0.085367      -0.915251  -0.640940 -0.841625    0.875265   \n",
       "2000-09-30 -0.894651      -0.915251  -1.365152 -0.841625    0.875265   \n",
       "2000-10-07 -0.894651       0.026244  -0.640940 -0.841625    1.383476   \n",
       "\n",
       "            points_against  \n",
       "date                        \n",
       "2000-09-02       -0.746078  \n",
       "2000-09-09       -0.016184  \n",
       "2000-09-23       -0.746078  \n",
       "2000-09-30       -0.016184  \n",
       "2000-10-07        0.182878  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `StandardScaler()` module from scikit-learn to normalize the data\n",
    "data_scaled = StandardScaler().fit_transform(df_data)\n",
    "\n",
    "# Creating a DataFrame with with the scaled data\n",
    "df_scaled = pd.DataFrame(data_scaled, columns=list(df_data.columns))\n",
    "\n",
    "# Set the index to the team value\n",
    "df_scaled['date'] = df_data.index\n",
    "df_scaled = df_scaled.set_index('date')\n",
    "\n",
    "# Preview the DataFrame\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d410de81-167a-4e29-ad99-e3b5fec8943c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Actual</th>\n",
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       "      <th>2019-09-21</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prediction  Actual\n",
       "date                          \n",
       "2021-09-04           0       0\n",
       "2019-09-21           0       0\n",
       "2014-10-04           1       1\n",
       "2007-09-22           0       0\n",
       "2022-11-25           0       0\n",
       "...                ...     ...\n",
       "2009-10-17           1       1\n",
       "2014-11-15           1       1\n",
       "2007-09-29           0       0\n",
       "2002-11-16           1       1\n",
       "2013-08-31           0       0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate the y variable, the labels\n",
    "y = df_data['outcome']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "X = df_data.drop('outcome', axis=1)\n",
    "\n",
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter=200,\n",
    "                                random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28e3154b-925a-493b-9e06-9616327409b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the accuracy score for the test dataset.\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a68b5e3c-4d37-4ae3-b972-fe159077851d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[40,  0],\n",
       "       [ 0, 32]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusiong matrix for the test dataset.\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f88ec6b-6c91-46a5-a25f-749bade05cf1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         win       1.00      1.00      1.00        40\n",
      "        loss       1.00      1.00      1.00        32\n",
      "\n",
      "    accuracy                           1.00        72\n",
      "   macro avg       1.00      1.00      1.00        72\n",
      "weighted avg       1.00      1.00      1.00        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Final Report\n",
    "target_names = [\"win\", \"loss\"]\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195afcd9-3dbc-4e84-b32b-52e447d7a9be",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Oversample the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "172460f3-f41e-4067-a326-03d179da192c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prediction  Actual\n",
       "date                          \n",
       "2021-09-04           0       0\n",
       "2019-09-21           0       0\n",
       "2014-10-04           1       1\n",
       "2007-09-22           0       0\n",
       "2022-11-25           0       0\n",
       "...                ...     ...\n",
       "2009-10-17           1       1\n",
       "2014-11-15           1       1\n",
       "2007-09-29           0       0\n",
       "2002-11-16           1       1\n",
       "2013-08-31           0       0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the random oversampler model\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "\n",
    "# Fit the original training data to the RandomOverSampler model\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter=200,\n",
    "                                random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "classifier.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35ec31a2-cf2a-4ab4-adfb-54e584d658b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  1],\n",
       "       [ 0, 32]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the accuracy score for the test dataset.\n",
    "accuracy_score(y_test, predictions)\n",
    "\n",
    "# Display the confusiong matrix for the test dataset.\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "236c38e4-fd69-4bd8-97c8-b951f83ec86c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        wins       1.00      0.97      0.99        40\n",
      "      losses       0.97      1.00      0.98        32\n",
      "\n",
      "    accuracy                           0.99        72\n",
      "   macro avg       0.98      0.99      0.99        72\n",
      "weighted avg       0.99      0.99      0.99        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "target_names = [\"wins\", \"losses\"]\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15560e89-36a7-410a-a26d-d2bccac61cbe",
   "metadata": {},
   "source": [
    "### Drop points against to see if that impacts the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fcae5ed2-85e8-486a-b3a3-da701c99fda3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prediction  Actual\n",
       "date                          \n",
       "2021-09-04           0       0\n",
       "2019-09-21           0       0\n",
       "2014-10-04           1       1\n",
       "2007-09-22           0       0\n",
       "2022-11-25           0       0\n",
       "...                ...     ...\n",
       "2009-10-17           1       1\n",
       "2014-11-15           1       1\n",
       "2007-09-29           0       0\n",
       "2002-11-16           1       1\n",
       "2013-08-31           0       0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate the y variable, the labels\n",
    "y = df_data['outcome']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "columns_to_drop = ['outcome', 'points_against']\n",
    "X = df_data.drop(columns_to_drop, axis=1)\n",
    "\n",
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter=200,\n",
    "                                random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8a5201b-978a-4eb0-b53d-32ace2c51033",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7638888888888888"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the accuracy score for the test dataset.\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6531ea00-2d43-425e-9948-6c5b26379848",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32,  8],\n",
       "       [ 9, 23]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusiong matrix for the test dataset.\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b74f7f73-4bcb-47c0-bb90-0ef58fdce1ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         win       0.78      0.80      0.79        40\n",
      "        loss       0.74      0.72      0.73        32\n",
      "\n",
      "    accuracy                           0.76        72\n",
      "   macro avg       0.76      0.76      0.76        72\n",
      "weighted avg       0.76      0.76      0.76        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Final Report\n",
    "target_names = [\"win\", \"loss\"]\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0930168-102b-4006-93cd-cb4d4bcc1dbd",
   "metadata": {},
   "source": [
    "# Regression using Defense Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf75366d-9d2a-48f2-9ab1-9b92d180134e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>home_away</th>\n",
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       "      <th>passing_yds</th>\n",
       "      <th>passing_td</th>\n",
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       "      <th>...</th>\n",
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       "      <th>first_down_total</th>\n",
       "      <th>penalties</th>\n",
       "      <th>penalty_yds</th>\n",
       "      <th>fumbles</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>turnovers</th>\n",
       "      <th>outcome</th>\n",
       "      <th>points_for</th>\n",
       "      <th>points_against</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-09-02</td>\n",
       "      <td>home</td>\n",
       "      <td>San Jose State</td>\n",
       "      <td>W (49-13)</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>37.8</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-09-09</td>\n",
       "      <td>away</td>\n",
       "      <td>Notre Dame</td>\n",
       "      <td>W (27-24)</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-09-23</td>\n",
       "      <td>home</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>W (42-13)</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>47.5</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-09-30</td>\n",
       "      <td>home</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>W (42-24)</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>48.7</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-10-07</td>\n",
       "      <td>away</td>\n",
       "      <td>Iowa State</td>\n",
       "      <td>W (49-27)</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "      <td>51.2</td>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>49</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_away        opponent      score  passing_cmp  passing_att  \\\n",
       "0  2000-09-02      home  San Jose State  W (49-13)           14           37   \n",
       "1  2000-09-09      away      Notre Dame  W (27-24)            3           15   \n",
       "2  2000-09-23      home            Iowa  W (42-13)           19           40   \n",
       "3  2000-09-30      home        Missouri  W (42-24)           19           39   \n",
       "4  2000-10-07      away      Iowa State  W (49-27)           22           43   \n",
       "\n",
       "   passing_pct  passing_yds  passing_td  rushing_att  ...  first_down_pen  \\\n",
       "0         37.8          153           2           29  ...               2   \n",
       "1         20.0           40           0           40  ...               2   \n",
       "2         47.5          252           1           31  ...               2   \n",
       "3         48.7          283           1           31  ...               1   \n",
       "4         51.2          346           2           25  ...               2   \n",
       "\n",
       "   first_down_total  penalties  penalty_yds  fumbles  interceptions  \\\n",
       "0                14         12           65        0              1   \n",
       "1                11          8           67        0              1   \n",
       "2                18          7           57        0              2   \n",
       "3                22         10           95        1              1   \n",
       "4                18          8           71        0              2   \n",
       "\n",
       "   turnovers  outcome  points_for  points_against  \n",
       "0          1        W          49              13  \n",
       "1          1        W          27              24  \n",
       "2          2        W          42              13  \n",
       "3          2        W          42              24  \n",
       "4          2        W          49              27  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file from the output folder into a Pandas DataFrame\n",
    "df_data = pd.read_csv(\"output/nebraska_defense.csv\")\n",
    "\n",
    "# Display the data\n",
    "df_data.head()"
   ]
  },
  {
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   "execution_count": 18,
   "id": "93224131-e441-4e5f-b60f-56b864931136",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>home_away</th>\n",
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       "      <td>27</td>\n",
       "      <td>24</td>\n",
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       "      <th>2000-09-23</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>47.5</td>\n",
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       "      <td>42</td>\n",
       "      <td>13</td>\n",
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       "      <th>2000-09-30</th>\n",
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       "      <th>2000-10-07</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "            home_away  passing_cmp  passing_att  passing_pct  passing_yds  \\\n",
       "date                                                                        \n",
       "2000-09-02          0           14           37         37.8          153   \n",
       "2000-09-09          1            3           15         20.0           40   \n",
       "2000-09-23          0           19           40         47.5          252   \n",
       "2000-09-30          0           19           39         48.7          283   \n",
       "2000-10-07          1           22           43         51.2          346   \n",
       "\n",
       "            passing_td  rushing_att  rushing_yds  rushing_avg  rushing_td  \\\n",
       "date                                                                        \n",
       "2000-09-02           2           29          193          6.7           0   \n",
       "2000-09-09           0           40          184          4.6           1   \n",
       "2000-09-23           1           31           47          1.5           0   \n",
       "2000-09-30           1           31          209          6.7           2   \n",
       "2000-10-07           2           25           37          1.5           1   \n",
       "\n",
       "            ...  first_down_pen  first_down_total  penalties  penalty_yds  \\\n",
       "date        ...                                                             \n",
       "2000-09-02  ...               2                14         12           65   \n",
       "2000-09-09  ...               2                11          8           67   \n",
       "2000-09-23  ...               2                18          7           57   \n",
       "2000-09-30  ...               1                22         10           95   \n",
       "2000-10-07  ...               2                18          8           71   \n",
       "\n",
       "            fumbles  interceptions  turnovers  outcome  points_for  \\\n",
       "date                                                                 \n",
       "2000-09-02        0              1          1        0          49   \n",
       "2000-09-09        0              1          1        0          27   \n",
       "2000-09-23        0              2          2        0          42   \n",
       "2000-09-30        1              1          2        0          42   \n",
       "2000-10-07        0              2          2        0          49   \n",
       "\n",
       "            points_against  \n",
       "date                        \n",
       "2000-09-02              13  \n",
       "2000-09-09              24  \n",
       "2000-09-23              13  \n",
       "2000-09-30              24  \n",
       "2000-10-07              27  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the index to the date column\n",
    "df_data = df_data.set_index('date')\n",
    "\n",
    "# Encode the home_away column (home = 0, away = 1, N = 1)\n",
    "df_data['home_away'] = df_data['home_away'].replace('home', 0)\n",
    "df_data['home_away'] = df_data['home_away'].replace('away', 1)\n",
    "df_data['home_away'] = df_data['home_away'].replace('N', 1)\n",
    "\n",
    "# Encode the outcome column (win = 0, loss = 1)\n",
    "df_data['outcome'] = df_data['outcome'].replace('W', 0)\n",
    "df_data['outcome'] = df_data['outcome'].replace('L', 1)\n",
    "\n",
    "# Drop the unnecessary columns and set the team column to the index\n",
    "columns_to_drop = ['opponent', 'score']\n",
    "df_data = df_data.drop(columns_to_drop, axis=1)\n",
    "\n",
    "#Preview the dataframe\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa5d2921-c32b-4659-953b-397f8e4e8c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.245140</td>\n",
       "      <td>-1.364142</td>\n",
       "      <td>0.747045</td>\n",
       "      <td>0.663101</td>\n",
       "      <td>-0.709041</td>\n",
       "      <td>-0.065325</td>\n",
       "      <td>-0.462723</td>\n",
       "      <td>-0.841625</td>\n",
       "      <td>-0.213757</td>\n",
       "      <td>-0.016184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-23</th>\n",
       "      <td>-0.897235</td>\n",
       "      <td>0.176162</td>\n",
       "      <td>0.683770</td>\n",
       "      <td>-0.476344</td>\n",
       "      <td>0.444298</td>\n",
       "      <td>-0.166231</td>\n",
       "      <td>-0.599930</td>\n",
       "      <td>-1.091042</td>\n",
       "      <td>-1.270219</td>\n",
       "      <td>-0.998071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245140</td>\n",
       "      <td>-0.177752</td>\n",
       "      <td>0.389708</td>\n",
       "      <td>0.271678</td>\n",
       "      <td>-0.709041</td>\n",
       "      <td>0.872083</td>\n",
       "      <td>0.283353</td>\n",
       "      <td>-0.841625</td>\n",
       "      <td>0.875265</td>\n",
       "      <td>-0.746078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-30</th>\n",
       "      <td>-0.897235</td>\n",
       "      <td>0.176162</td>\n",
       "      <td>0.590455</td>\n",
       "      <td>-0.381843</td>\n",
       "      <td>0.782270</td>\n",
       "      <td>-0.166231</td>\n",
       "      <td>-0.599930</td>\n",
       "      <td>0.608978</td>\n",
       "      <td>1.497189</td>\n",
       "      <td>0.250061</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.527993</td>\n",
       "      <td>0.500185</td>\n",
       "      <td>1.461717</td>\n",
       "      <td>1.759086</td>\n",
       "      <td>0.578900</td>\n",
       "      <td>-0.065325</td>\n",
       "      <td>0.283353</td>\n",
       "      <td>-0.841625</td>\n",
       "      <td>0.875265</td>\n",
       "      <td>-0.016184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10-07</th>\n",
       "      <td>1.114535</td>\n",
       "      <td>0.579554</td>\n",
       "      <td>0.963715</td>\n",
       "      <td>-0.184967</td>\n",
       "      <td>1.469117</td>\n",
       "      <td>0.670756</td>\n",
       "      <td>-1.220399</td>\n",
       "      <td>-1.195981</td>\n",
       "      <td>-1.270219</td>\n",
       "      <td>-0.374005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245140</td>\n",
       "      <td>-0.177752</td>\n",
       "      <td>0.747045</td>\n",
       "      <td>0.819670</td>\n",
       "      <td>-0.709041</td>\n",
       "      <td>0.872083</td>\n",
       "      <td>0.283353</td>\n",
       "      <td>-0.841625</td>\n",
       "      <td>1.383476</td>\n",
       "      <td>0.182878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            home_away  passing_cmp  passing_att  passing_pct  passing_yds  \\\n",
       "date                                                                        \n",
       "2000-09-02  -0.897235    -0.496158     0.403824    -1.240222    -0.635032   \n",
       "2000-09-09   1.114535    -1.975262    -1.649111    -2.641978    -1.866995   \n",
       "2000-09-23  -0.897235     0.176162     0.683770    -0.476344     0.444298   \n",
       "2000-09-30  -0.897235     0.176162     0.590455    -0.381843     0.782270   \n",
       "2000-10-07   1.114535     0.579554     0.963715    -0.184967     1.469117   \n",
       "\n",
       "            passing_td  rushing_att  rushing_yds  rushing_avg  rushing_td  \\\n",
       "date                                                                        \n",
       "2000-09-02    0.670756    -0.806753     0.441075     1.497189   -0.998071   \n",
       "2000-09-09   -1.003218     0.330772     0.346629     0.379582   -0.374005   \n",
       "2000-09-23   -0.166231    -0.599930    -1.091042    -1.270219   -0.998071   \n",
       "2000-09-30   -0.166231    -0.599930     0.608978     1.497189    0.250061   \n",
       "2000-10-07    0.670756    -1.220399    -1.195981    -1.270219   -0.374005   \n",
       "\n",
       "            ...  first_down_pen  first_down_total  penalties  penalty_yds  \\\n",
       "date        ...                                                             \n",
       "2000-09-02  ...        0.245140         -0.855689   2.176390     0.584816   \n",
       "2000-09-09  ...        0.245140         -1.364142   0.747045     0.663101   \n",
       "2000-09-23  ...        0.245140         -0.177752   0.389708     0.271678   \n",
       "2000-09-30  ...       -0.527993          0.500185   1.461717     1.759086   \n",
       "2000-10-07  ...        0.245140         -0.177752   0.747045     0.819670   \n",
       "\n",
       "             fumbles  interceptions  turnovers   outcome  points_for  \\\n",
       "date                                                                   \n",
       "2000-09-02 -0.709041      -0.065325  -0.462723 -0.841625    1.383476   \n",
       "2000-09-09 -0.709041      -0.065325  -0.462723 -0.841625   -0.213757   \n",
       "2000-09-23 -0.709041       0.872083   0.283353 -0.841625    0.875265   \n",
       "2000-09-30  0.578900      -0.065325   0.283353 -0.841625    0.875265   \n",
       "2000-10-07 -0.709041       0.872083   0.283353 -0.841625    1.383476   \n",
       "\n",
       "            points_against  \n",
       "date                        \n",
       "2000-09-02       -0.746078  \n",
       "2000-09-09       -0.016184  \n",
       "2000-09-23       -0.746078  \n",
       "2000-09-30       -0.016184  \n",
       "2000-10-07        0.182878  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `StandardScaler()` module from scikit-learn to normalize the data\n",
    "data_scaled = StandardScaler().fit_transform(df_data)\n",
    "\n",
    "# Creating a DataFrame with with the scaled data\n",
    "df_scaled = pd.DataFrame(data_scaled, columns=list(df_data.columns))\n",
    "\n",
    "# Set the index to the team value\n",
    "df_scaled['date'] = df_data.index\n",
    "df_scaled = df_scaled.set_index('date')\n",
    "\n",
    "# Preview the DataFrame\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20780ae5-06ed-4c4c-b499-c2a444a7152b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prediction  Actual\n",
       "date                          \n",
       "2021-09-04           0       0\n",
       "2019-09-21           0       0\n",
       "2014-10-04           1       1\n",
       "2007-09-22           0       0\n",
       "2022-11-25           0       0\n",
       "...                ...     ...\n",
       "2009-10-17           1       1\n",
       "2014-11-15           1       1\n",
       "2007-09-29           0       0\n",
       "2002-11-16           1       1\n",
       "2013-08-31           0       0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate the y variable, the labels\n",
    "y = df_data['outcome']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "X = df_data.drop('outcome', axis=1)\n",
    "\n",
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter=200,\n",
    "                                random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "564743bf-4e34-4a2c-8428-27d7f1b5938e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the accuracy score for the test dataset.\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad97396a-e048-49ab-9626-44e2328affd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  1],\n",
       "       [ 1, 31]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusiong matrix for the test dataset.\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a356e80-65bc-4cae-aea3-5ca1958a666f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        wins       0.97      0.97      0.97        40\n",
      "      losses       0.97      0.97      0.97        32\n",
      "\n",
      "    accuracy                           0.97        72\n",
      "   macro avg       0.97      0.97      0.97        72\n",
      "weighted avg       0.97      0.97      0.97        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "target_names = [\"wins\", \"losses\"]\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac7ff81-b415-44d3-9d7a-089dd095e19e",
   "metadata": {},
   "source": [
    "### Drop points for to see if that impacts the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae87609a-580e-418b-97bc-2881121dce9b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prediction  Actual\n",
       "date                          \n",
       "2021-09-04           0       0\n",
       "2019-09-21           0       0\n",
       "2014-10-04           1       1\n",
       "2007-09-22           1       0\n",
       "2022-11-25           0       0\n",
       "...                ...     ...\n",
       "2009-10-17           1       1\n",
       "2014-11-15           1       1\n",
       "2007-09-29           0       0\n",
       "2002-11-16           1       1\n",
       "2013-08-31           1       0\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate the y variable, the labels\n",
    "y = df_data['outcome']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "columns_to_drop = ['outcome', 'points_against']\n",
    "X = df_data.drop(columns_to_drop, axis=1)\n",
    "\n",
    "# Split the data using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter=200,\n",
    "                                random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2f0af4bf-c0a6-442c-ba18-ecc5f2719d84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the accuracy score for the test dataset.\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3f8ff7c6-f0a7-45f9-954e-3540faae7ab2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33,  7],\n",
       "       [ 2, 30]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusiong matrix for the test dataset.\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf0ecd4b-4f44-481e-ad01-9967c4c4bc5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        wins       0.94      0.82      0.88        40\n",
      "      losses       0.81      0.94      0.87        32\n",
      "\n",
      "    accuracy                           0.88        72\n",
      "   macro avg       0.88      0.88      0.87        72\n",
      "weighted avg       0.88      0.88      0.88        72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "target_names = [\"wins\", \"losses\"]\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "999e2f47-11a0-4438-97c4-3e3d815c33d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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