ML-SVR_ad.py-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38a66dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Rate_Overall\n",
      "DATE                   \n",
      "1/1/2000            4.0\n",
      "2/1/2000            4.1\n",
      "3/1/2000            4.0\n",
      "4/1/2000            3.8\n",
      "5/1/2000            4.0\n",
      "6/1/2000            4.0\n",
      "7/1/2000            4.0\n",
      "8/1/2000            4.1\n",
      "9/1/2000            3.9\n",
      "10/1/2000           3.9\n",
      "11/1/2000           3.9\n",
      "12/1/2000           3.9\n",
      "1/1/2001            4.2\n",
      "2/1/2001            4.2\n",
      "3/1/2001            4.3\n",
      "4/1/2001            4.4\n",
      "5/1/2001            4.3\n",
      "6/1/2001            4.5\n",
      "7/1/2001            4.6\n",
      "8/1/2001            4.9\n"
     ]
    }
   ],
   "source": [
    "from pandas import read_csv\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "# load dataset\n",
    "series = read_csv('Resources/overall_monthly.csv', header=0, index_col=0)\n",
    "# display first few rows\n",
    "print(series.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82139ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot of dataset\n",
    "series.plot()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc8359e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 257, Validation 7\n"
     ]
    }
   ],
   "source": [
    "# split the dataset\n",
    "split_point = len(series) - 7\n",
    "dataset, validation = series[0:split_point], series[split_point:]\n",
    "print('Dataset %d, Validation %d' % (len(dataset), len(validation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e38dae03",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('dataset.csv', index=False)\n",
    "validation.to_csv('validation.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97d24e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Pipeline in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (0.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fcab5120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (4.17.0)\n",
      "Requirement already satisfied: filelock in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (3.0.12)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,>=0.11.1 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (0.11.6)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (2021.4.4)\n",
      "Requirement already satisfied: importlib-metadata in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (4.11.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (5.4.1)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from transformers) (21.3)\n",
      "Requirement already satisfied: sacremoses in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (0.0.49)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from transformers) (1.21.5)\n",
      "Requirement already satisfied: requests in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (2.25.1)\n",
      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (4.59.0)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from transformers) (0.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from packaging>=20.0->transformers) (3.0.7)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from importlib-metadata->transformers) (3.4.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from requests->transformers) (1.26.4)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from requests->transformers) (4.0.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from requests->transformers) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: six in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from sacremoses->transformers) (1.15.0)\n",
      "Requirement already satisfied: click in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from sacremoses->transformers) (7.1.2)\n",
      "Requirement already satisfied: joblib in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from sacremoses->transformers) (1.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a96981a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svr', SVR(epsilon=0.2))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "n_samples, n_features = 10, 5\n",
    "rng = np.random.RandomState(0)\n",
    "y = rng.randn(n_samples)\n",
    "X = rng.randn(n_samples, n_features)\n",
    "regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))\n",
    "regr.fit(X, y)\n",
    "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
    "                ('svr', SVR(epsilon=0.2))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "666b9aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the data sets\n",
    "\n",
    "datasets = pd.read_csv('Resources/overall_monthly.csv')\n",
    "X = datasets.iloc[:, [0,1]].values\n",
    "Y = datasets.iloc[:, 1].values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ef07399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use pandas library to randomly sample data from the dataset\n",
    "## sklean has \"train test split\", this function randomly samples the data and splits into 2 groups \n",
    "## a treatment group and a control group or a training group for the model and a test group for the model\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e5a3fac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.86755799,\n",
       "        -0.97727788,  0.95008842]),\n",
       " array([-0.15135721, -0.10321885,  0.4105985 ])]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test_split(y, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8b00f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['20000101']\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "mylist = [\"1/1/2000\"]\n",
    "list2=[]\n",
    "\n",
    "for x in mylist:\n",
    "    final_date = datetime.strptime(\"1/1/2000\", \"%d/%m/%Y\").strftime('%Y%m%d')\n",
    "    list2.append(final_date)\n",
    "print(list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b3a1f8d2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '1/1/2000'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2404\\3994340809.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mregressor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSVR\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rbf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mregressor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mregressor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\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[0mscore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mregressor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    194\u001b[0m                 \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"C\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m                 \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 196\u001b[1;33m                 \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    197\u001b[0m             )\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    574\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 576\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    966\u001b[0m         \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    967\u001b[0m         \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 968\u001b[1;33m         \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    969\u001b[0m     )\n\u001b[0;32m    970\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    736\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    737\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 738\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    739\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    740\u001b[0m                 raise ValueError(\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '1/1/2000'"
     ]
    }
   ],
   "source": [
    "regressor = SVR(kernel='rbf')\n",
    "regressor.fit(X, y)\n",
    "y_pred = regressor.predict(X)\n",
    "\n",
    "score=regressor.score(X, y)\n",
    "print(\"model score\")\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "956ad781",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizations\n",
    "import plotly.graph_objects as go # for data visualization\n",
    "import plotly.express as px # for data visualization\n",
    "from plotly.subplots import make_subplots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f67626c7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2404\\3328825731.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# # Create a scatter plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m fig = px.scatter(df, x=df['DATE'], y=df['Rate_Overall'], \n\u001b[0m\u001b[0;32m      4\u001b[0m opacity=0.8, color_discrete_sequence=['black'])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# # Create a scatter plot\n",
    "\n",
    "fig = px.scatter(df, x=df['DATE'], y=df['Rate_Overall'], \n",
    "opacity=0.8, color_discrete_sequence=['black'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fdedcb90",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fig' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2404\\2137536598.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Change chart background color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_bgcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'white'\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[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Update axes lines\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey', \n",
      "\u001b[1;31mNameError\u001b[0m: name 'fig' is not defined"
     ]
    }
   ],
   "source": [
    "# Change chart background color\n",
    "fig.update_layout(dict(plot_bgcolor = 'white'))\n",
    "\n",
    "# Update axes lines\n",
    "fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey', \n",
    "zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey', \n",
    "showline=True, linewidth=1, linecolor='black')\n",
    "\n",
    "fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgrey', \n",
    "zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey', \n",
    "showline=True, linewidth=1, linecolor='black')\n",
    "\n",
    "# Set figure title\n",
    "fig.update_layout(title=dict(text=\"Testing the SVR ML model with overall_monthly.csv\", \n",
    "font=dict(color='black')))\n",
    "\n",
    "# Update marker size\n",
    "fig.update_traces(marker=dict(size=3))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70da7de4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f4b0286",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3063fd08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}