ML use_case_one.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77e8d317",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import  r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2548c1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "n = 15\n",
    "x = np.linspace(0,10,n) + np.random.randn(n)/5\n",
    "y= np.sin(x)+x/6 + np.random.randn(n)/10\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f05958d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(11,1)\n",
    "Y_train = Y_train.reshape(11,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba1920cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.26920155],\n",
       "        [0.30152425],\n",
       "        [0.33384695],\n",
       "        [0.36616966],\n",
       "        [0.39849236],\n",
       "        [0.43081507],\n",
       "        [0.46313777],\n",
       "        [0.49546047],\n",
       "        [0.52778318],\n",
       "        [0.56010588],\n",
       "        [0.59242858],\n",
       "        [0.62475129],\n",
       "        [0.65707399],\n",
       "        [0.6893967 ],\n",
       "        [0.7217194 ],\n",
       "        [0.7540421 ],\n",
       "        [0.78636481],\n",
       "        [0.81868751],\n",
       "        [0.85101021],\n",
       "        [0.88333292],\n",
       "        [0.91565562],\n",
       "        [0.94797832],\n",
       "        [0.98030103],\n",
       "        [1.01262373],\n",
       "        [1.04494644],\n",
       "        [1.07726914],\n",
       "        [1.10959184],\n",
       "        [1.14191455],\n",
       "        [1.17423725],\n",
       "        [1.20655995],\n",
       "        [1.23888266],\n",
       "        [1.27120536],\n",
       "        [1.30352807],\n",
       "        [1.33585077],\n",
       "        [1.36817347],\n",
       "        [1.40049618],\n",
       "        [1.43281888],\n",
       "        [1.46514158],\n",
       "        [1.49746429],\n",
       "        [1.52978699],\n",
       "        [1.56210969],\n",
       "        [1.5944324 ],\n",
       "        [1.6267551 ],\n",
       "        [1.65907781],\n",
       "        [1.69140051],\n",
       "        [1.72372321],\n",
       "        [1.75604592],\n",
       "        [1.78836862],\n",
       "        [1.82069132],\n",
       "        [1.85301403]]),\n",
       " array([[ 1.18821982],\n",
       "        [ 0.94624845],\n",
       "        [ 0.70427709],\n",
       "        [ 0.46230573],\n",
       "        [ 0.22033436],\n",
       "        [-0.021637  ],\n",
       "        [-0.26360836],\n",
       "        [-0.50557973],\n",
       "        [-0.74755109],\n",
       "        [-0.98952245],\n",
       "        [-1.23149382],\n",
       "        [-1.47346518],\n",
       "        [-1.71543654],\n",
       "        [-1.95740791],\n",
       "        [-2.19937927],\n",
       "        [-2.44135063],\n",
       "        [-2.683322  ],\n",
       "        [-2.92529336],\n",
       "        [-3.16726472],\n",
       "        [-3.40923608],\n",
       "        [-3.65120745],\n",
       "        [-3.89317881],\n",
       "        [-4.13515017],\n",
       "        [-4.37712154],\n",
       "        [-4.6190929 ]]),\n",
       " array([[ 7.05917170e+00],\n",
       "        [ 3.31394770e-02],\n",
       "        [-6.99289275e+00],\n",
       "        [-1.40189250e+01],\n",
       "        [-2.10449572e+01],\n",
       "        [-2.80709894e+01],\n",
       "        [-3.50970216e+01],\n",
       "        [-4.21230539e+01],\n",
       "        [-4.91490861e+01],\n",
       "        [-5.61751183e+01]]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def question_one(degree):\n",
    "    polynomial_features= PolynomialFeatures(degree)\n",
    "    x_poly = polynomial_features.fit_transform(X_train)\n",
    "    predict= np.linspace(0,10,100)\n",
    "    predict = np.reshape(predict, (-1, degree+1))\n",
    "    model = LinearRegression()\n",
    "    model.fit(x_poly,Y_train)\n",
    "    y_poly_pred = model.predict(predict)\n",
    "    return y_poly_pred\n",
    "\n",
    "def q_1():\n",
    "    x = question_one(1)\n",
    "    y = question_one(3)\n",
    "#    z = question_one(6)\n",
    "    a = question_one(9)\n",
    "#     array_of_tuples = (tuple, x,y,a)\n",
    "\n",
    "    return x,y,a\n",
    "q_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d1c33bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<class 'tuple'>, 1.0, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.24828904522027617, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.5174107798758666, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.5937394085468037, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.6102831396995204, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.6044666513025782, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.589473847801035, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.5707290854099303, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.550726408575471, -2.928577561678502)\n",
      "(<class 'tuple'>, -0.530692893880487, -2.928577561678502)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def question_two(i):\n",
    "    polynomial_features = PolynomialFeatures(degree=i)\n",
    "    x_poly = polynomial_features.fit_transform(X_train)\n",
    "    x_poly_test = polynomial_features.fit_transform(Y_train)\n",
    "    model = LinearRegression()\n",
    "    model.fit(x_poly,Y_train)\n",
    "    y_pred = model.predict(x_poly)\n",
    "    r2_train = r2_score(x_poly, x_poly_test)\n",
    "    r2_test = r2_score(X_test, Y_test)\n",
    "    array_of_tuples = (tuple, r2_train,r2_test)\n",
    "    return array_of_tuples\n",
    "\n",
    "for i in range(0,10):\n",
    "    answer  = question_two(i)\n",
    "    print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aaf93f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96992369",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model is underfitting when model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called X) and the target values (often called Y).\n",
      " \n",
      "\n",
      "Model is overfitting when you see that the model performs well on the training data but does not perform well on the evaluation data. This is because the model is memorizing the data it has seen and is unable to generalize to unseen examples.\n"
     ]
    }
   ],
   "source": [
    "print(\"Model is underfitting when model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called X) and the target values (often called Y).\\n \\n\" )\n",
    "print(\"Model is overfitting when you see that the model performs well on the training data but does not perform well on the evaluation data. This is because the model is memorizing the data it has seen and is unable to generalize to unseen examples.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05596ec",
   "metadata": {},
   "source": [
    "#### Model is underfitting when model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called X) and the target values (often called Y).\n",
    " \n",
    "\n",
    "##### Model is overfitting when you see that the model performs well on the training data but does not perform well on the evaluation data. This is because the model is memorizing the data it has seen and is unable to generalize to unseen examples.\n",
    "\n",
    "#### Training models on high degree polynomial features can result in overly complex models that overfit,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ae2963d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 -2.928577561678502\n",
      "-0.24828904522027617 -2.928577561678502\n",
      "-0.5174107798758666 -2.928577561678502\n",
      "-0.5937394085468037 -2.928577561678502\n",
      "-0.6102831396995204 -2.928577561678502\n",
      "-0.6044666513025782 -2.928577561678502\n",
      "-0.589473847801035 -2.928577561678502\n",
      "-0.5707290854099303 -2.928577561678502\n",
      "-0.550726408575471 -2.928577561678502\n",
      "-0.530692893880487 -2.928577561678502\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cf4c02a7f0>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02a820>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02a8e0>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02a9a0>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02aa60>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02ab20>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02abe0>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02aca0>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02ad60>,\n",
       " <matplotlib.lines.Line2D at 0x1cf4c02ae20>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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M0JY1BWP6XzRDnbJEJL8VVdLftPn7AHR37zui/TrDOv0Tx2sMfRHJb0WV9Ds6tgFQWTnopF0D7Ors7l1WpywRyXdFlcXcg161paWp37H/JeyU9ZZJ48YkJhGRTCqqpJ+OeKesqzWypogUACX9YTwTzol79uTxWY5ERGTklPSH0BWL9S5PV6csESkAaSd9MzvWzFYm/Bw0s8+b2TfNbFtC+QUJ+9xoZuvN7DUzO290LmHsrGkJOmVVmGU5EhGR0THsdImDcffXgMUAZlYKbAN+DlwNfNfdv524vZmdACwBTgRmA781s2MS5tDNOQ817wfgurnqlCUihWG0qnfOBTa4+5YhtrkQuM/dO919E7AeOHOUzj8mlm0NOmVdUDs5u4GIiIyS0Ur6S4B7E95/1sxWm9kKM4t3Y60DtiZs0xSWDWBmS82s0cwam5ubRyXAWCw6/EYJ3PsGWTuxiGfK6nFnZ0JfBRHJbyNO+mZWAXwI+M+w6HZgIUHVzw7gO/FNk+zuScpw9+Xu3uDuDbW1o1O10tn5JgCRyMyUtt+WkOjKS4qzTr+jJ8a1azfzwRfW0RLN2Vo4ETkCo3Gn/37gBXffCeDuO929x91jwA/oq8JpAuYm7DcH2D4K509JR0cTAJWVc1LaPt4p6x01xdlU82C0h8tWb+SR5gNcUzeNCWWl2Q5JREbBaCT9S0mo2jGzWQnrPgysCZcfBpaYWcTM6oFFwLOjcP6UtLcHSb+qKrWkv6Ip6JR1xezi65T1Zmc3F72wjucOtHLbCUdx3bzp2Q5JREZJ2q13AMysGngvcG1C8f8xs8UEVTeb4+vcfa2Z3Q+8DESBGzLZcudI7/RXtrQBcFaRDb+wvq2DJas2sK+7h5+csoB3TtFMYSKFZERJ393bgKn9yj4xxPY3ATeN5Jzpau8IniFXVc4dZkto6ynOTlkvHGjlipc2YhgPnnY0p06oznZIIjLKiqZHbrx6p7Jq+BE2V4V3+eNLi+bj4Yk9B7l45QYmlJbyyOmLlPBFClTRZLV49U4qd/q9M2UVSaes+9/cy1UvbeTo6gi/PH0R9dWRbIckImNkRNU7+aSvyeaMYbeNj6z53qmTxjSmbHN3vv/GLv5p4w7eXjOeFSfVq5WOSIErmqQfV1IydB19sXTKirnzzfXbWd7UzEXTJ/O94+cR0SQxIgWv6JL+cDa1d/UuF2qnrM5YjM+98ga/2LWfpXNq+ebRsynRoHIiRUFJv58/728B4NwpE7Mcydg4FO3hU2s28eS+Q3xtwSxumDcdU8IXKRpK+v38OOyUddnsKVmOZPQ1d3Vz+aqNrG1t53vHzePjswrvGkVkaEr6/bzc2gHAmQXWKWtTWydLVm1gV1eUu09ewLlTC/MvGREZmpJ+gsRBxWorCqdT1qqWNi5ftZEYzgOLF3J6gX2hiUjq1FwjwQsHg05ZU8sL57vwj3tb+MiL66ksNR4+fZESvkiRK4qk39MTTHsYTPA1uId27QNg6ZzC6JT18537uGL1Ro6qrOCR04/h6OrKbIckIllWFEm/oyMYwXm4wdbu2bEXgHOm5v8gY8u27uL6l7fQMKman592NDOLaAwhERlc4dRjDKFvsLXBk34soVPWCePyt1NWzJ1/2rCD27bu4gO1k/j+8UdRWURjCInI0Ioi6Xe0bwOgcoix9F9v6+hdztdOWd0x5+9ffYOf7dzHJ+umcdOiOkrVBl9EEhRF0k9lWOU/7QtmyvpAbX6Ot9Ma7eHTazfz+70tfLl+Jp87aoY6XYnIAEWR9Ds6wjv9ysGHVY53yvrojJpBt8lVu7uiXLF6I6tb2vjOsXO5fPbU4XcSkaI0ospeM9tsZi+Z2UozawzLppjZ42a2LnytSdj+RjNbb2avmdl5Iw0+Ve3t4Z1+1eB3+hvaOwFoyLMmjVvaO/nQC+t4tbWdH59cr4QvIkMajSd873L3xe7eEL7/MvCEuy8CngjfY2YnAEuAE4HzgdtsuDaUo6TvTj95nf6ermjvcj51ylp7qJ2/fmEde7uj3H/qQs6blp9VUyKSOWPRrONC4K5w+S7gooTy+9y90903AeuBM8fg/AN0dwdNMSsqko818/zBVgDq8qhZ45/2tXDRC+soM+Oh0xdx5uTx2Q5JRPLASJO+A78xs+fNbGlYNsPddwCEr9PD8jpga8K+TWHZAGa21Mwazayxubl5hCEmHjf5HxbxmbI+lSedsh7etZ/LVm1kdmUFj5y+iGPHqdOViKRmpA9y3+ru281sOvC4mb06xLbJmpJ4kjLcfTmwHKChoSHpNqPpZzuDnrhvq8n9u+UVTc18dd02/mrSOO4+uZ7JBTRkhIiMvRHd6bv79vB1F/BzguqanWY2CyB83RVu3gQkPkmdA2wfyflHQzSWH52y3J2bN+7gK+u28b5pE/mPUxcq4YvIEUs76ZvZODObEF8G3gesAR4Grgo3uwp4KFx+GFhiZhEzqwcWAc+me/7R8nJre+9yrnbKisacL7y2lX/bspMrZk3lRyfWU6VetiKShpHcKs4Afh52ACoD7nH3X5nZc8D9ZnYN8AZwCYC7rzWz+4GXgShwg7v3JD905sQ7ZX14+uTsBjKItp4Y167dzON7DvKF+TP4n/NnqtOViKQt7aTv7huBU5OU7wHOHWSfm4Cb0j3nWFixLXhQfOH03OuUtbc7ypWrN/L8wTb+5Zg5XFU3LdshiUieK/hKYffYkOubOroBOGNSdSbCSVlTRxeXrtrAGx1d/PCk+XygdnK2QxKRAlDwST8aPQhAaenAnrY7O7t7l3OpU9Yrh9q5bPVGWnt6uPeUhZydB62KRCQ/FHzSj4+ln2xY5ecOBJ2y6qsqMhrTUJ7ef4irXtpEVUkJD522iOPH526LIhHJPwXfBCQ+wmayYZV/Ec6U9ckcqSt/rHk/H1+1gdqKMn55hhK+iIy+gk/6vWPpJxlh85HmAwD8jxwYwuDubbu5Zs1mThpfxcOnL2JuZe789SEihaPgq3cGG0u/M9b3gDebnbLcne9s3sm3N7/Je6ZOZNmJRzGuNCPj0IlIESr4pN87wmbV4Xf6L7X0dcoqy1KnrB53bny9ibu37+HjM6fw7WPn5mwHMREpDAWb9N2dZ555Bif5nf6f9rUAsGRm8pE3x1p7T4zPvLyFx3Yf4O/mTefGBbPU6UpExlzB1um//vrr/OpXv6K1dQsAlZWzD1v/o23BTFmnx/5Ed/e+jMa2vzvKpas28KvdB/inRXV8ZeFsJXwRyYiCTfrt7UH1jVkwI1ZZ2cTede5Oczhxyvid3yaTH8OOzi4uenE9Lxxs444Tj+LTeTKcs4gUhoKt3unu7j7svVlfYt/a0dW7PHfCUZSXZ2bGqddbO7h01QYORHu459QFvK1mQkbOKyISVzRJP1HjwTYA5vIGNVPOzkg8jQda+cTqjZSVGD8/7WhOnpBbwz6ISHEo2OqdaDQ66LpfhJOmvMt/w5SasU/6v9l9gEtWrmdyeSmPnL5ICV9EsqZgk/5Qd/q/2ROMx3OcbWDSpIZBtxsN9+zYw9VrNnHMuEoePn0RR1VFxvR8IiJDKbrqndaeviH8T55US2np2CRhd+fWLbv45007eNeUCfzwxPmMK1OnKxHJroJN+oNV76wM6/MBaseoPr/Hnf+1bhsrtu3mozNquOW4uVSUFOwfVSKSR0YyXeJcM/u9mb1iZmvN7HNh+TfNbJuZrQx/LkjY50YzW29mr5nZeaNxAYPp7u7GbODEXPGZst7tv2HKGCT9zliM69ZuYcW23Vw3t5Zbj5+nhC8iOWMkd/pR4Ivu/kI4V+7zZvZ4uO677v7txI3N7ARgCXAiMBv4rZkdM1ZTJnZ3d1NWHrTRLy/v63W7IuyUdUbpK0yY8MVRPefBaA+ffGkTf9l/iG8snM3186aP6vFFREZqJNMl7gB2hMstZvYKMHAoyz4XAve5eyewyczWA2cCT6Ubw1Ci0SiVkWC8/PhY+u7OgWjwHdMwuQaz0atj39nZzWWrN/BaawffP34eF2dpeAcRkaGMSr2Dmc0HTgOeCYs+a2arzWyFmcUnn62DcCCcQBODfEmY2VIzazSzxubm5rRi6u7uprIyqMqJj6W/sb2zd/3CqaenddxkNrR18MEX1rGpvYufnLJACV9EctaIk76ZjQceAD7v7geB24GFwGKCvwS+E980ye6e7JjuvtzdG9y9obY2vWEKotEokTDpx+/0nw1nyprvG5gy5a1pHbe/Fw+28dcvrKOtJ8aDi4/mnCkTh99JRCRLRtR6x8zKCRL+T939QQB335mw/gfAI+HbJiBxqMs5wPaRnH8o3d3djJ8Qv9MPTvvQzv0AnFv2PFVVH0m634+37eaBN/diGCXW901VYoZBb5lh/DEcqRPg6OoI39n8Zu/6Eoz4GGrx5aAczKz329YsXN/v2PERluPnTVxfQrCQeJ6ScJsRHzsM2hLWl4THMsJjp3DuwY494PPsd2xLiDPZsePL1nt9Qxy73zVbv/2Tnzv5scvMqCrVA3nJf2knfQuGhfwR8Iq735JQPius7wf4MLAmXH4YuMfMbiF4kLsIeDbd8w8nGo32Vu9UhbNm/SFM0mdOmpB0VMuXD7Xz1debWDSukhkVZcQ8+FPEgZgHf5R0O7jDcwcP9e43O1JOdWkJOzu7iQGOJ923r6xvfQwnXB3s62FZwvKg+zphCQOOFz+3jI4yg1+ctoiGSeOyHYrIiIzkTv+twCeAl8xsZVj2FeBSM1tMkHM2A9cCuPtaM7sfeJmg5c8NY9VyB+J1+kF1TmXlXA5097XbP2P6CQO2d3e+8noTk8tL+cVpR1NTnvyjcXdu29rMcwdbedvk8fz45Hom5HCnK/fBvxRiBAvBF1WwbXw52P7wL5xYeKz+y97v2PEvvsQvn8P3HXx977okxw2W+64p8Yuxb3noY3e7s7m9k41tnaxv62Rzeydd8W/dfqaWl7GgKkJ9dQXHVFdy/PjK0fynEcmKkbTe+RPJ6+kfHWKfm4Cb0j3nkUh8kBuJ1PLsgaE7ZT20az9PH2jlX4+dM2jCj7nzv9dvZ1lTMx+aPpn/e/w8IjneBt8SqjrCkixGkxnRmLOts4sNbZ1sbO9kU/y1vZOtHV30JOT4SWWlnFgVYUF1hPrE16oKJg3yeyCSzwr2tzronBX8311aOo4/7X8TgAvK/kIk8pnDtm2N9vDN9ds5ZUIVl82amvR4XbEYn391Kw/u3Men50zjW0fX9dZ/S+bF3Nne2d2b0DcmJPYt7V10J9y9jystYUFVhFMnVPPh6TXUV0eCO/iqCFPKSzWBjRSVgk36icMwmJXwo6ag6efbJw685O9u2cmbXd386KT5lCZJAIeiPVyzZjN/3NfCVxfM4rPzpitRZIC7s7Mrysa2IJknJvct7Z10xPoSe1WJMb8qwrHjKjl/2iQWhIl9QVWE2ooy/XuJhAo26XvCnV6Pe2+CeOuMRYdtt6Gtg2Vbm/n4zCmckeQhXXNXN5ev3sjaQ+3823FzWTLIXwKSHndnd3c0oQqmK0zsHWxq76KtJ9a7bYUZR1VVsKA6wrumTAiSepjcZ0bK9ZeXSAoKNukner21o3d5wbS+oZTdna+t20ZlifG1hbMG7Le5vZMlqzawszPKXScv4D1T1QY/Xfu7o4dXwyRUxxyM9iX2UoN5lRUsqKrk7MnjD6tnn1NZkfQvMRFJXVEk/XinrONLt1JWtri3/Ne7D/L7vS186+jZ1FaUH7bP6pY2Llu1kR53frZ4YdK/AuRwh6I9vYl9U3snG8LXTe2d7O3ua6hlwJzKChZURfjIjHEsrIr01rPPraygvESJXWSsFEXSf/DNYJC1D0xq7y1r74nx9fXbOHZcJVfXHd7r98m9LVy9ZhOTy0q579SjWTROTfXi2npivU0e+9ezxyebj5sdKae+KsIHaieHLWKCu/ajqipyvtWTSKEq4KTfV6f/zMGgeue40m00Nz+OlZRzx85q3uio4K5joKP1VbpKyikpqeCXe3r44voDLKyq4L7Fi5gZKR/sBAWrMxZjS2/depjcwyS/vfPwyWlqK8pYWBXhPVMn9raIWVAdYX5VhGr1YBXJOQWb9EtKgrvOg/TVw1fsuo3Vu2I0U8syvsdb+DNlr93S2y34MT7AT+xTHOdr+ULrzcT2fB1mX5KF6Mded8zZ2tEV3ql3sLG9q7eefVtHF7GEbaeUl1JfFeGtNeMPS+z1VZGc7pgmIgMVbNIvrwju7tdxDABlRDn7zP/CvZvPrOuirAVuOnYxM8t+SE9PF9/dWcVP9kzkLbaS6/xmpow/etQGZcuWHneaOroOu1OP17O/0a+T0sSyEuqrIjRMrOaSmTWH1bNPViclkYJRsP83x8fSf5mTAfjYpN2MH9/AH/Ye5PEDG/nKglmcNHMG3THnC6+9wX/u2cf7y57lsu5/Zd6cKzh64ZfHbP7c0RRzZ0dnd29iT6yO2dLeddgQA9VhJ6WTxlfzoek11FdVhPXslUxVJyWRolC4ST8cguHXvB+Ad9bOoSsW42vrtlFfVcG1c2tp7enhb9Zs5nd7W7jEHuDi2KOcePLt1Na+J5uhD+DuNHdFB/Q83RiOHdOe0EkpEnZSWlRdyfumTTqsOmaGOimJFL2CTvpRSvFwdqy3Tj+GHzTtZn1bJz85ZQGHojEuX72O1S3tXOPL+Mikg5x4wiNUVg5sr58J7s7e7p4BLWLi9eytCZ2UysNOSvVVEd5RM6G3g1J9dYTZ6qQkIkMo2KQfqWzlDeb3vo9i3LL5Td43dSLHVEf4YOMatnV28zm/hUvqz6Z+/mdGdfrEwRzojgYPTZPUs8encoRgLPd5YWI/c9K43vr1hdUR6iIVlKktu4ikoWCTfmXlIV4k6H17atl+/nHDdqLufHzmFN7/3Go6ejr5etkyLj7p76mpOXNUz90a76SU2PO0LWgpsydhiGcjaMu+sDrCRdMnHzbS47zKCirUll1ERllBJ/2/8HYA5k6YygM79/FXEyv425dfozLWwi2Tf8sFJ99KeXnNMEdKrj3spJR4px6/c9/Zr5PSzIpy6qsreP+0SeEdewX11RHmV0aoVFt2Ecmggk36kUgbGyxorvnEgaA+/LmDXdTRzB31h2iY/8/DPtTsCjspJbaMSeyklDj1xrTyMhZURzhnysS+gcCqI8yvqmBcqdqyi0huKNikv5cpvcvx1i3HlWzm/51yDHNr+mbOiiZ0Ukq8W9/YFky4kdhJaXJZKQuqI/yPfgOBLaiOMFGdlEQkD2Q86ZvZ+cD3gFLgh+5+81icJ94pK25h2T6+dOzZ/LathE27t/XWt2/p6CSacMs+PmzLftrEaj4yo+awljFT1ElJRPKc+SDzg47JyYLmMa8D7wWagOeAS9395cH2aWho8MbGxiM+11G/e4pOq0q6rqqkhPqwXn1BODXe/Koy6itLmVoWzsrqUdx7wp9gOdZblvAa6/feeygtrcr73ryDif++xH9rel+933t8mPWHb9fvZZBzDHLuYWJg0PUDtxt8m+QxDIg1xc9hqDj6/y850s8h8dj9P+f0j538c0h2/sGub+SfsQ9cd4QxDL5/8s9hqDgGbDvCzwHgE7Ompt1Sz8yed/eG/uWZvnU9E1jv7hvDoO4DLiSYLH1UDZbwI3QyMbafva3OnlZodMfDeWO93/yx6ZWXYNZDecWasCxcN8KENNj6ZOdgkF/CdBOziGTHpTOnUDbK81pnOunXAVsT3jcBb+m/kZktBZYCzJs3L60TzfJt7LA6FrORcSU9lJWWUVpWhsX/M4Pe1xJKAKykd51hYBau6du+xPreB9uWhOWWsH8Z5WUTiT8n7v9PFn8ff5Bs/ct71/cvP3z7xAMPPIYNcozUYug9bpJr6B/H4OdIHsOAmIeIYdjPot9BU72+gcftO9CAOI7w+o70M04Wx/C/C0PH0P9ahopjsM9isPX9j53s3+JIP4uBn3Wqv2MJ50z5dznVzzh5DEmPPUwM6X7GkTHoj5PppJ/sCgbcULr7cmA5BNU76ZzoxXd/IFxanM7uIiIFKdONxJuAuQnv5wDbMxyDiEjRynTSfw5YZGb1ZlYBLAEeznAMIiJFK6PVO+4eNbPPAr8maLK5wt3XZjIGEZFilvGG5+7+KPBops8rIiKZr94REZEsUtIXESkiSvoiIkVESV9EpIhkdOyddJhZM7Alzd2nAbtHMZxco+vLf4V+jbq+7DnK3Wv7F+Z80h8JM2tMNuBQodD15b9Cv0ZdX+5R9Y6ISBFR0hcRKSKFnvSXZzuAMabry3+Ffo26vhxT0HX6IiJyuEK/0xcRkQRK+iIiRaQgkr6ZnW9mr5nZejP7cpL1Zma3hutXm9np2YgzXSlc3+Xhda02s7+Y2anZiDNdw11fwnZ/ZWY9ZvbRTMY3Uqlcn5mdY2YrzWytmf0x0zGORAq/n5PM7Jdmtiq8vquzEWe6zGyFme0yszWDrM+v/OLuef1DMETzBmABUAGsAk7ot80FwGMEM3edBTyT7bhH+frOBmrC5fcX2vUlbPc7ghFaP5rtuEf5328ywTzR88L307Md9yhf31eAfwmXa4G9QEW2Yz+Ca3wHcDqwZpD1eZVfCuFOv3eydXfvAuKTrSe6ELjbA08Dk81sVqYDTdOw1+fuf3H3feHbpwlmJMsXqfz7Afwt8ACwK5PBjYJUru8y4EF3fwPA3fPpGlO5PgcmWDCB7HiCpB/NbJjpc/cnCWIeTF7ll0JI+skmW69LY5tcdaSxX0Nw15Evhr0+M6sDPgzckcG4Rksq/37HADVm9gcze97MrsxYdCOXyvX9O3A8wdSoLwGfc/dYZsLLiLzKLxmfRGUMpDLZekoTsueolGM3s3cRJP23jWlEoyuV6/s34Evu3hPcLOaVVK6vDDgDOBeoAp4ys6fd/fWxDm4UpHJ95wErgXcDC4HHzey/3f3gGMeWKXmVXwoh6acy2Xo+T8ieUuxmdgrwQ+D97r4nQ7GNhlSurwG4L0z404ALzCzq7r/ISIQjk+rv5253bwVazexJ4FQgH5J+Ktd3NXCzBxXg681sE3Ac8GxmQhxzeZVfCqF6J5XJ1h8Grgyfsp8FHHD3HZkONE3DXp+ZzQMeBD6RJ3eHiYa9Pnevd/f57j4f+BnwmTxJ+JDa7+dDwNvNrMzMqoG3AK9kOM50pXJ9bxD8FYOZzQCOBTZmNMqxlVf5Je/v9H2QydbN7Lpw/R0ELT4uANYDbQR3Hnkhxev7OjAVuC28G456noz8l+L15a1Urs/dXzGzXwGrgRjwQ3dP2jww16T47/ePwJ1m9hJBVciX3D1XhyMewMzuBc4BpplZE/ANoBzyM79oGAYRkSJSCNU7IiKSIiV9EZEioqQvIlJElPRFRIqIkr6ISA4ZboC3ftseZWZPhAO9/cHMhh2CRUlfRCS33Amcn+K23yYY9+cU4FvAPw+3g5K+iEgOSTbAm5ktNLNfhWMz/beZHReuOgF4Ilz+PckHKzyMkr6ISO5bDvytu58B/ANwW1i+Crg4XP4wwWimU4c6UN73yBURKWRmNp5gzoz/TBhwMBK+/gPw72b2SeBJYBvDDFutpC8ikttKgP3uvrj/CnffDnwEer8cLnb3A8MdTEREclQ4BPUmM7sEeqdnPDVcnmZm8Tx+I7BiuOMp6YuI5JBwgLengGPNrMnMrgEuB64xs1XAWvoe2J4DvGZmrwMzgJuGPb4GXBMRKR660xcRKSJK+iIiRURJX0SkiCjpi4gUESV9EZEioqQvIlJElPRFRIrI/wfenm4iFitbbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    polynomial_features = PolynomialFeatures(degree=i)\n",
    "    x_poly = polynomial_features.fit_transform(X_train)\n",
    "    x_poly_test = polynomial_features.fit_transform(Y_train)\n",
    "    model = LinearRegression()\n",
    "    model.fit(x_poly,Y_train)\n",
    "    y_pred = model.predict(x_poly)\n",
    "    r2_train = r2_score(x_poly, x_poly_test)\n",
    "    r2_test = r2_score(X_test, Y_test)\n",
    "    array_of_tuples = (tuple, r2_train,r2_test)\n",
    "    print(r2_train, r2_test)\n",
    "    plt.plot(x_poly, x_poly_test, label='training data')\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "# plt.figure()\n",
    "# plt.scatter(r2_train, r2_test, label='training data')\n",
    "\n",
    "# plt.plot(X_test, Y_test, label='training data')\n",
    "# plt.plot(x_poly, x_poly_test, label='training data')\n",
    "# plt.scatter(X_test, Y_test, label='test data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9b5841",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}