Spring_AI_Bootcamp.ipynb

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    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a0febc5c",
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   "source": [
    "data = pd.read_table('Downloads/fruit_data_with_colors.txt')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e68260c0",
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       "      <td>apple</td>\n",
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      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
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    "data.head()"
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       "    fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "54            4      lemon       unknown   116    6.1     8.5         0.71\n",
       "55            4      lemon       unknown   116    6.3     7.7         0.72\n",
       "56            4      lemon       unknown   116    5.9     8.1         0.73\n",
       "57            4      lemon       unknown   152    6.5     8.5         0.72\n",
       "58            4      lemon       unknown   118    6.1     8.1         0.70"
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    "data.tail()"
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   "execution_count": 57,
   "id": "4b562da7",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59 entries, 0 to 58\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   fruit_label    59 non-null     int64  \n",
      " 1   fruit_name     59 non-null     object \n",
      " 2   fruit_subtype  59 non-null     object \n",
      " 3   mass           59 non-null     int64  \n",
      " 4   width          59 non-null     float64\n",
      " 5   height         59 non-null     float64\n",
      " 6   color_score    59 non-null     float64\n",
      "dtypes: float64(3), int64(2), object(2)\n",
      "memory usage: 3.4+ KB\n"
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    "data.info()"
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    "data.describe()"
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   "execution_count": 58,
   "id": "41e53d6d",
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       "      <td>59.000000</td>\n",
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       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2.542373</td>\n",
       "      <td>163.118644</td>\n",
       "      <td>7.105085</td>\n",
       "      <td>7.693220</td>\n",
       "      <td>0.762881</td>\n",
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       "      <th>std</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>0.550000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>7.200000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>7.600000</td>\n",
       "      <td>0.750000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.810000</td>\n",
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       "      <td>0.930000</td>\n",
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      "text/plain": [
       "       fruit_label        mass      width     height  color_score\n",
       "count    59.000000   59.000000  59.000000  59.000000    59.000000\n",
       "mean      2.542373  163.118644   7.105085   7.693220     0.762881\n",
       "std       1.208048   55.018832   0.816938   1.361017     0.076857\n",
       "min       1.000000   76.000000   5.800000   4.000000     0.550000\n",
       "25%       1.000000  140.000000   6.600000   7.200000     0.720000\n",
       "50%       3.000000  158.000000   7.200000   7.600000     0.750000\n",
       "75%       4.000000  177.000000   7.500000   8.200000     0.810000\n",
       "max       4.000000  362.000000   9.600000  10.500000     0.930000"
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     "execution_count": 58,
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    "data.describe()"
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   "id": "adf7873b",
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      "C:\\Users\\anvay\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
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       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.pairplot(data, x_vars=['mass'], y_vars=['color_score'], size=7, kind='scatter')\n",
    "plt.xlabel('Mass')\n",
    "plt.ylabel('Color Score')\n",
    "plt.title('Color Score Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5728e558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fruit_label', 'fruit_name', 'fruit_subtype', 'mass', 'width', 'height',\n",
       "       'color_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "04cb2254",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['mass']\n",
    "y = data['color_score']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a3539a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4ced2a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ffeb7fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8e94ac33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X_addC = sm.add_constant(X_train)\n",
    "model = sm.OLS(y_train, X_addC).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8af018a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    const  height\n",
      "36    1.0     7.6\n",
      "31    1.0     8.0\n",
      "8     1.0     7.8\n",
      "17    1.0     7.6\n",
      "6     1.0     4.3\n",
      "4     1.0     4.6\n",
      "54    1.0     8.5\n",
      "19    1.0     7.2\n",
      "30    1.0     7.5\n",
      "50    1.0     8.2\n",
      "44    1.0    10.5\n",
      "55    1.0     7.7\n",
      "15    1.0     7.1\n",
      "9     1.0     7.0\n",
      "27    1.0     9.2\n",
      "26    1.0     9.2\n",
      "16    1.0     7.5\n",
      "24    1.0     9.4\n",
      "33    1.0     8.1\n",
      "56    1.0     8.1\n",
      "41    1.0     8.2\n",
      "11    1.0     7.6\n",
      "32    1.0     7.0\n",
      "52    1.0     8.0\n",
      "40    1.0     7.5\n",
      "37    1.0     7.3\n",
      "29    1.0     7.4\n",
      "43    1.0    10.3\n",
      "1     1.0     6.8\n",
      "21    1.0     7.4\n",
      "2     1.0     7.2\n",
      "46    1.0    10.2\n",
      "39    1.0     7.4\n",
      "35    1.0     7.9\n",
      "23    1.0     7.9\n",
      "48    1.0    10.1\n",
      "10    1.0     7.3\n",
      "22    1.0     7.1\n",
      "18    1.0     7.1\n",
      "58    1.0     8.1\n",
      "20    1.0     7.5\n",
      "7     1.0     4.0\n",
      "42    1.0     7.2\n",
      "14    1.0     7.3\n",
      "28    1.0     7.1\n",
      "51    1.0     7.5\n",
      "38    1.0     7.8\n"
     ]
    }
   ],
   "source": [
    "print(X_addC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fdd8d9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>color_score</td>   <th>  R-squared:         </th> <td>   0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 17 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>0.0922</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:54:31</td>     <th>  Log-Likelihood:    </th> <td>  56.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    47</td>      <th>  AIC:               </th> <td>  -108.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>  -104.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.8835</td> <td>    0.067</td> <td>   13.187</td> <td> 0.000</td> <td>    0.749</td> <td>    1.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>height</th> <td>   -0.0148</td> <td>    0.009</td> <td>   -1.720</td> <td> 0.092</td> <td>   -0.032</td> <td>    0.003</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.323</td> <th>  Durbin-Watson:     </th> <td>   2.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.516</td> <th>  Jarque-Bera (JB):  </th> <td>   0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.028</td> <th>  Prob(JB):          </th> <td>   0.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.525</td> <th>  Cond. No.          </th> <td>    48.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            color_score   R-squared:                       0.062\n",
       "Model:                            OLS   Adj. R-squared:                  0.041\n",
       "Method:                 Least Squares   F-statistic:                     2.960\n",
       "Date:                Fri, 17 Mar 2023   Prob (F-statistic):             0.0922\n",
       "Time:                        21:54:31   Log-Likelihood:                 56.186\n",
       "No. Observations:                  47   AIC:                            -108.4\n",
       "Df Residuals:                      45   BIC:                            -104.7\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.8835      0.067     13.187      0.000       0.749       1.018\n",
       "height        -0.0148      0.009     -1.720      0.092      -0.032       0.003\n",
       "==============================================================================\n",
       "Omnibus:                        1.323   Durbin-Watson:                   2.026\n",
       "Prob(Omnibus):                  0.516   Jarque-Bera (JB):                0.547\n",
       "Skew:                          -0.028   Prob(JB):                        0.761\n",
       "Kurtosis:                       3.525   Cond. No.                         48.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "37727f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.061720228196369575 0.040869566600733376\n"
     ]
    }
   ],
   "source": [
    "print(model.rsquared, model.rsquared_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "eb484dec",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'linearRegression' from 'sklearn.linear_model' (C:\\Users\\anvay\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19856\\373623727.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlinearRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'linearRegression' from 'sklearn.linear_model' (C:\\Users\\anvay\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import linearRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.array(X_train).reshape((-1, 1))\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "plt.scatter(X_train, y_train, color='g')\n",
    "plt.scatter(X_train, y_train, color='b')\n",
    "plt.plot(X_train, model.predict(X_train), color='r')\n",
    "ax.text(1, 4, r'LR equation: $Y = a + bX$', fontsize=10)\n",
    "\n",
    "print(model.coef_[0], model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "73bf53d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_c = sm.add_constant(X_test)\n",
    "y_pred = model.predict(X_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d060ee0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['Prediction'] = y_pred\n",
    "X_test['y_test'] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3e0a7aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                           7.3\n",
       "5                                                           4.3\n",
       "34                                                          7.8\n",
       "13                                                          7.7\n",
       "45                                                          9.2\n",
       "53                                                          8.4\n",
       "57                                                          8.5\n",
       "25                                                          9.2\n",
       "47                                                          9.7\n",
       "12                                                          7.1\n",
       "49                                                          8.7\n",
       "3                                                           4.7\n",
       "Prediction    0     0.775516\n",
       "5     0.819900\n",
       "34    0.768119\n",
       "1...\n",
       "y_test        0     0.55\n",
       "5     0.77\n",
       "34    0.75\n",
       "13    0.70\n",
       "45...\n",
       "Name: height, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  }
 ],
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