07 - Polynomial Regression/Ploynomial Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0a2f622c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f1d7f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data_poly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf92d768",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     x    y\n",
       "0    0    3\n",
       "1    1    4\n",
       "2    2    5\n",
       "3    3    7\n",
       "4    4   10\n",
       "5    5    8\n",
       "6    6    9\n",
       "7    7   10\n",
       "8    8   10\n",
       "9    9   23\n",
       "10  10   27\n",
       "11  11   44\n",
       "12  12   50\n",
       "13  13   63\n",
       "14  14   67\n",
       "15  15   60\n",
       "16  16   62\n",
       "17  17   70\n",
       "18  18   75\n",
       "19  19   88\n",
       "20  20   81\n",
       "21  21   87\n",
       "22  22   95\n",
       "23  23  100\n",
       "24  24  108\n",
       "25  25  135\n",
       "26  26  151\n",
       "27  27  160\n",
       "28  28  169\n",
       "29  29  179"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "320d029e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'x', y = 'y', data=data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbfd3ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(data[['x']], data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63941aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pred = model.predict(data[['x']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ce10e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0f90ed250>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'x', y = 'y', data=data )\n",
    "plt.plot(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5ee7336",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, z\n",
    "\n",
    "1,x,z,x^2,z^2,xz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7dd92e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x\n",
    "\n",
    "1, x^2, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "071f5fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly_3 = PolynomialFeatures(degree=3, include_bias=False)\n",
    "poly_5 = PolynomialFeatures(degree=5, include_bias=False)\n",
    "poly_15 = PolynomialFeatures(degree=15, include_bias=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e2ce93f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_x = poly.fit_transform(data[['x']])\n",
    "poly_x3 = poly_3.fit_transform(data[['x']])\n",
    "poly_x5 = poly_5.fit_transform(data[['x']])\n",
    "poly_x15 = poly_15.fit_transform(data[['x']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dcce94aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model_3 = LinearRegression()\n",
    "model_5 = LinearRegression()\n",
    "model_15 = LinearRegression()\n",
    "\n",
    "\n",
    "model.fit(poly_x, data['y'])\n",
    "model_3.fit(poly_x3, data['y'])\n",
    "model_5.fit(poly_x5, data['y'])\n",
    "model_15.fit(poly_x15, data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1a06f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pred_poly = model.predict(poly_x)\n",
    "\n",
    "data_pred_poly_3 = model_3.predict(poly_x3)\n",
    "data_pred_poly_5 = model_5.predict(poly_x5)\n",
    "data_pred_poly_15 = model_15.predict(poly_x15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e9d0190f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0f11fa640>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'x', y = 'y', data=data )\n",
    "plt.plot(data_pred,color = 'red')\n",
    "plt.plot(data_pred_poly, color = 'green')\n",
    "plt.plot(data_pred_poly_3, color = 'black')\n",
    "plt.plot(data_pred_poly_5, color = 'pink')\n",
    "plt.plot(data_pred_poly_15, color = 'yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e77e033e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE of Straight Line =  0.7996999195631865\n",
      "MAPE of Polynomial degree 2 Line =  0.2031995437736923\n",
      "MAPE of Polynomial degree 3 Line =  0.32043050951182656\n",
      "MAPE of Polynomial degree 5 Line =  0.2942388626809141\n",
      "MAPE of Polynomial degree 15 Line =  0.17432462595126647\n"
     ]
    }
   ],
   "source": [
    "print(\"MAPE of Straight Line = \",mean_absolute_percentage_error(data['y'],data_pred))\n",
    "print(\"MAPE of Polynomial degree 2 Line = \",mean_absolute_percentage_error(data['y'],data_pred_poly))\n",
    "print(\"MAPE of Polynomial degree 3 Line = \",mean_absolute_percentage_error(data['y'],data_pred_poly_3))\n",
    "print(\"MAPE of Polynomial degree 5 Line = \",mean_absolute_percentage_error(data['y'],data_pred_poly_5))\n",
    "print(\"MAPE of Polynomial degree 15 Line = \",mean_absolute_percentage_error(data['y'],data_pred_poly_15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "728bed2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy of simple model = 85% \n",
    "acuuracy of little complex = 92% #1~ hour # 7% improvement\n",
    "accuracy of very much complex = 93% # 5~ hours # 1% improvement\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "44c9143f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07608695652173914"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(92 -85)/92"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "25beed40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010752688172043012"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(93-92)/93"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a17d385",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy of simple model = 85% \n",
    "acuuracy of little complex = 87% #1~ hour # 2% improvement\n",
    "accuracy of very much complex = 90% # 5~ hours # 3% improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "72b4a249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022988505747126436"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(87 -85)/87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "741b9d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03333333333333333"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(90 -87)/90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ffe38a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e1c050f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ca8b0217",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y  = train_test_split(data[['x']],data['y'], test_size=0.35, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ac04b7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19, 1), (11, 1), (19,), (11,))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape, test_X.shape, train_y.shape, test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ea3e9d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_run(model, train_X, test_X, train_y, test_y):\n",
    "    model.fit(train_X,train_y)\n",
    "    train_pred = model.predict(train_X)\n",
    "    test_pred = model.predict(test_X)\n",
    "    r2_train = r2_score(train_y,train_pred)\n",
    "    r2_test = r2_score(test_y,test_pred)\n",
    "    return r2_train, r2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "30906d8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 1)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "d829ce72",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainerr = []\n",
    "testerr = []\n",
    "\n",
    "for i in range(0,10):\n",
    "    if i ==0:\n",
    "        model = LinearRegression()\n",
    "        mape_train, mape_test = model_run(model, train_X, test_X, train_y, test_y)\n",
    "        trainerr.append(1-mape_train)\n",
    "        testerr.append(1-mape_test)\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        poly = PolynomialFeatures(degree=i, include_bias=False)\n",
    "        poly_feature_train_x = poly.fit_transform(train_X)\n",
    "        poly_feature_test_x = poly.transform(test_X)\n",
    "        ploy_model = LinearRegression()\n",
    "        error_train, error_test = model_run(ploy_model, poly_feature_train_x, poly_feature_test_x, train_y, test_y)\n",
    "        trainerr.append(1-error_train)\n",
    "        testerr.append(1-error_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "1687efec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.22663642741156087,\n",
       " 0.22663642741156087,\n",
       " 0.1208845902586877,\n",
       " 0.15168780978673024,\n",
       " 0.09098213255413878,\n",
       " 0.08470345633138698,\n",
       " 0.022128846523611134,\n",
       " 0.031788993295670864,\n",
       " 0.039710433062164485,\n",
       " 0.09731107017417517,\n",
       " 0.22663642741156087,\n",
       " 0.22663642741156087,\n",
       " 0.22663642741156087]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapetest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3e15dbbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.051213134574498276,\n",
       " 0.051213134574498276,\n",
       " 0.011386977902735462,\n",
       " 0.01090985442225223,\n",
       " 0.008776381124455668,\n",
       " 0.008509637007715898,\n",
       " 0.004567371269360132,\n",
       " 0.004215816041595377,\n",
       " 0.002937429594913832,\n",
       " 0.0017142263104424904]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainerr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "4d0c5440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff0f11f31c0>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = range(0,10), y = trainerr)\n",
    "plt.scatter(x = range(0,10), y = testerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523fb539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "013a5ad7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "355fd44e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "06fd8ee9",
   "metadata": {},
   "source": [
    "a = 0\n",
    "a = 5\n",
    "b = 3\n",
    "\n",
    "a = a+b = 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f284e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "a + b = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5af1e3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 0 -> .fit()\n",
    "a+b    -> .transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05196602",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 0 , a+b .fit_transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce222c14",
   "metadata": {},
   "outputs": [],
   "source": [
    ".fit -> initializing\n",
    ".transform -> applying\n",
    ".fit_transform -> initializing and applying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25dd6959",
   "metadata": {},
   "outputs": [],
   "source": [
    "initializing -> Training \n",
    "transform/ applying -> train and test\n",
    "initializing can it happen on test -> NO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a8f26b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f42429",
   "metadata": {},
   "outputs": [],
   "source": [
    "Scaling ->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638e4b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = x-mean/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156489e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train -> Fit\n",
    "mean - column A = 23 \n",
    "Std - column A = 2\n",
    "\n",
    "transform ->\n",
    "z = x-mean/std\n",
    "\n",
    "Fit_transform\n",
    "Find mean and Std and find Z score\n",
    "\n",
    "\n",
    "\n",
    "Test -> Fit\n",
    "mean - column A = 12 \n",
    "Std - column A = 5\n",
    "\n",
    "\n",
    "Test -> Transform\n",
    "mean - column A = 12 \n",
    "Std - column A = 5\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc585ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "80 sample train mean = 30\n",
    "20 sample test  mean = 20\n",
    "\n",
    "80 sample train std = 2\n",
    "20 sample test  std = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280b119d",
   "metadata": {},
   "source": [
    "train \n",
    "X,y\n",
    "\n",
    "\n",
    "Test\n",
    "X\n",
    "\n",
    "\n",
    "------------------------\n",
    "\n",
    "train+ Test\n",
    "\n",
    "**X**, y   -> Fit\n",
    "**X**, na\n",
    "\n",
    "\n",
    "Realtime \n",
    "\n",
    "test data  -> never fit\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e48c9482",
   "metadata": {},
   "outputs": [],
   "source": [
    "Apple or Orange\n",
    "\n",
    "\n",
    "500 data 300 apple + 200 orange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a08792",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.<-> test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec7aa16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train - 200 apple+100 orange\n",
    "test - 100 apple+50 orange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa21ec7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train - > fit model\n",
    "extracting feautures -> color, shape , weight , height\n",
    "Class - - Object - function -> intialize -> feature ->\n",
    "Extracted the features\n",
    "\n",
    "Featute Object-> Live -> not deallocate \n",
    "\n",
    "Creating Model\n",
    "\n",
    "Model Object - > LinearRegression()\n",
    "Model Object-> Live -> not deallocate \n",
    "\n",
    "\n",
    "\n",
    "Test \n",
    "\n",
    "use same\n",
    "\n",
    "Featute Object - > extract the feature of test data\n",
    "\n",
    "Model Object -> Predict\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "0eafed62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5c379c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-21.630919501515642"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b39d784b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
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 },
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