IMT2020523_Assignment1_Classification.ipynb

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              "   variance  skewness  curtosis  entropy  class\n",
              "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
              "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
              "2   3.86600   -2.6383    1.9242  0.10645      0\n",
              "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
              "4   0.32924   -4.4552    4.5718 -0.98880      0"
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              "      <th></th>\n",
              "      <th>variance</th>\n",
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              "      <th>curtosis</th>\n",
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              "      <th>0</th>\n",
              "      <td>3.62160</td>\n",
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              "      <th>1</th>\n",
              "      <td>4.54590</td>\n",
              "      <td>8.1674</td>\n",
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              "      <th>2</th>\n",
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              "      <td>1.9242</td>\n",
              "      <td>0.10645</td>\n",
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              "      <th>3</th>\n",
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              "      <td>-3.59440</td>\n",
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      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "data=pd.read_csv(\"/content/data_banknote_authentication.txt\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for null and '?' values"
      ],
      "metadata": {
        "id": "BQNaZHfIrjcr"
      }
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      "cell_type": "code",
      "source": [
        "data.isna().sum()"
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        "id": "6Z3jktMJrXv7",
        "outputId": "bd03c938-b1aa-4ef4-86ec-b2d27044aeb5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "variance    0\n",
              "skewness    0\n",
              "curtosis    0\n",
              "entropy     0\n",
              "class       0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 3
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        "(data=='?').sum()"
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      "outputs": [
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            "text/plain": [
              "variance    0\n",
              "skewness    0\n",
              "curtosis    0\n",
              "entropy     0\n",
              "class       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for duplicate rows"
      ],
      "metadata": {
        "id": "q0VJN1sMroMJ"
      }
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    {
      "cell_type": "code",
      "source": [
        "data.index[data.duplicated()]"
      ],
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        "id": "8pJlvClstJfU",
        "outputId": "9ddb2cee-7c13-4cd7-c1b6-aa3c3424e6ca"
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      "execution_count": 5,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([ 47, 139, 190, 195, 226, 268, 284, 300, 315, 320, 345, 351, 352,\n",
              "            404, 427, 436, 476, 498, 604, 615, 657, 691, 716, 727],\n",
              "           dtype='int64')"
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "data=data.drop_duplicates()"
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      "execution_count": 6,
      "outputs": []
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        "data.index[data.duplicated()]"
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        "id": "kTxMIoy66yDM",
        "outputId": "135398c4-2bab-45e3-a835-8ac75196106e"
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      "execution_count": 7,
      "outputs": [
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      "cell_type": "markdown",
      "source": [
        "Checking the number of classes and their count"
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        "id": "uG9sf9nYrsoi"
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      "cell_type": "code",
      "source": [
        "sns.displot(data,x=\"class\")"
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      "execution_count": 8,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f840faba610>"
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          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking outliers"
      ],
      "metadata": {
        "id": "Czls9G_lr2TK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for c in list(data.columns)[:4]:\n",
        "  sns.catplot(kind=\"box\",x=\"class\",y=c,data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-OS_QS8q8S3p",
        "outputId": "20588e6c-bdc6-478d-b2c2-9a7bdbf31783"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASr0lEQVR4nO3df5DcdX3H8dcrdwLhR7TEgJ2DGOhGHEA7MIv4Y1orYHtSCu1MnYFRidqS0dZjdXT8Aamj08o4tUN7XKmdjKWGFmutv7VnILH80A5EjhAKMaluU4K5EbkcUoiJSS5594/dODFekj1z3+/79rvPx0wG9sfs530T5slnPvfdXUeEAADlm5c9AAD0KgIMAEkIMAAkIcAAkIQAA0CS/uwBZmJwcDBWr16dPQYAzJSnu7OrdsDbt2/PHgEAZk1XBRgAqoQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEm66sN4us3IyIiazWbK2uPj45KkgYGB0teu1WoaGhoqfV2g2xDgitq1a1f2CACOwt30pZz1ej3Gxsayx+gKjUZDkjQ8PJw8CQBV4eMoAaBKCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQJLUANt+ge3P295se5PtV2XOAwBlyv44ymFJqyPiD20fJ+nE5HkAoDRpAbb9fEm/KemtkhQReyTtyZoHAMqWeQRxlqQJSf9o+2Hbn7J90qFPsr3c9pjtsYmJifKnBICCZAa4X9KFkj4ZERdI+omkDx76pIhYGRH1iKgvWrSo7BkBoDCZAd4maVtErGvf/rxaQQaAnpAW4Ih4UtIPbJ/TvutSSd/NmgcAypZ9FcSQpDvaV0BskfS25HkAoDSpAY6IDZLqmTMAQBbeCQcASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJAkPcC2+2w/bPvr2bMAQJnSAyypIWlT9hAAULbUANs+Q9LvSvpU5hwAkCF7B/w3kt4vaf/hnmB7ue0x22MTExPlTQYABevPWtj2FZKeioiHbP/W4Z4XESslrZSker0ev8xaIyMjajabv9Sc3erAz9toNJInKU+tVtPQ0FD2GEDH0gIs6TWSrrR9uaQTJC2w/c8R8ebZXqjZbGrDY5u078RTZ/ul56x5e1r/r3poy4+SJylH386ns0cAZiwtwBHxIUkfkqT2Dvh9RcT3gH0nnqpdL728qJdHsvmbR7NHAGYs+wwYAHpW5hHEz0TEPZLuSR4DAErFDhgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJLMiW9FLtr4+Lj6dv6f5m8ezR4FBenbOanx8ansMYAZYQcMAEl6Ygc8MDCgJ3f3a9dLL88eBQWZv3lUAwOnZ48BzAg7YABIQoABIElPHEEAVTQyMqJms1n6uuPj45JaR3tlq9VqGhoaKn3dohBgADOya9eu7BEqgwADXSprJ9hoNCRJw8PDKetXCWfAAJCEAANAkrQA2z7T9t22v2t7o+1G1iwAkCHzDHhK0nsjYr3tUyQ9ZHtNRHw3cSYAKE3aDjgifhgR69v//pykTZLKv64FAJLMiTNg20skXSBpXe4kAFCe9ADbPlnSFyS9OyKenebx5bbHbI9NTEyUPyAAFCQ1wLafp1Z874iIL073nIhYGRH1iKgvWrSo3AEBoECZV0FY0j9I2hQRN2fNAQBZMnfAr5H0FkmX2N7Q/sPnRQLoGWmXoUXEtyU5a30AyJb+SzgA6FUEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSZH4rcqn6dj6t+ZtHs8cozbyftr7daf8JC5InKUffzqclnZ49BjAjPRHgWq2WPULpms3nJEm1s3slSqf35N8zultPBHhoaCh7hNI1Gg1J0vDwcPIkAA6HM2AASHLUALvlzbY/3L692PYrih8NAKqtkx3w30l6laRr2refk3RrYRMBQI/o5Az44oi40PbDkhQRP7Z9XMFzAUDldbID3mu7T1JIku1FkvYXOhUA9IBOAnyLpC9JOs32xyR9W9JNhU4FAD3gqEcQEXGH7YckXSrJkn4/IjYVPhkAVNxRA2z7lZI2RsSt7dsLbF8cEesKnw4AKqyTI4hPStpx0O0d7fsAAMegkwA7IuLAjYjYrx55Bx0AFKmTAG+xfb3t57X/NCRtKXowAKi6TgL8DkmvljQuaZukiyUtL3IoAOgFnVwF8ZSkq0uYBQB6SidXQSySdJ2kJQc/PyLeXtxYAFB9nfwy7SuSviVpraR9xY4DAL2jkwCfGBEfKGJx24OShiX1SfpURHy8iHUAYC7q5JdwX7d9+Wwv3P58iVslvUHSuZKusX3ubK8DAHNVJwFuqBXhXbaftf2c7WdnYe1XSGpGxJaI2CPps5KumoXXBYCu0MlVEKcUtPaApB8cdPvAJW4/x/ZytS97W7x4cUGjAED5OnpHm+1fkbRU0gkH7ouI+4oa6mARsVLSSkmq1+txlKcDQNfo5DK0P1brGOIMSRskvVLS/ZIuOca1xyWdedDtM9r3AUBP6PQM+CJJWyPidZIukPTMLKz9oKSlts9qf8PG1ZK+OguvCwBdoZMjiJ9GxE9ty/bxEbHZ9jnHunBETNl+l6Q71boM7baI2HisrwsA3aKTAG+z/QJJX5a0xvaPJW2djcUjYlTS6Gy8FgB0m06ugviD9r9+xPbdkp4vaXWhUwFADzhsgG0viIhnbZ960N2Ptv95sqSnC50MACruSDvgz0i6QtJDan0jsg/559mFTwcAFXbYAEfEFbYt6bUR8USJMwFATzjiGXBEhO1/l/SykuYBusrIyIiazWb2GKU68PM2Go3kScpTq9U0NDQ066/byVUQ621fFBEPzvrqQJdrNpv6/saHtfjk3vmk1uP2tt4+sHvrWPIk5XhiR19hr91JgC+W9CbbWyX9RO0z4Ih4eWFTAV1k8cn7dMOFs/H5VJiLblq/oLDX7iTAv1PY6gDQwzq5DnirJNk+TQd9GA8A4Ngc9bMgbF9p+/uS/lfSvZIel/SNgucCgMrr5MN4/lytT0D7XkScJelSSQ8UOhUA9IBOArw3IiYlzbM9LyLullQveC4AqLxOfgn3jO2T1fpm5DtsP6XW1RAAgGPQyQ74wAfwNNT6EJ7/kfR7RQ4FAL2gkwD3S7pL0j2STpH0r+0jCQDAMThqgCPioxFxnqQ/lfSrku61vbbwyQCg4jrZAR/wlKQnJU1KOq2YcQCgd3RyHfCf2L5H0jclLZR0HW9DBoBj18lVEGdKendEbCh6GADoJZ28FflDZQwCAL1mJmfAAIBZRIABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASNLJW5EBHMb4+Lh+8lxfoV9djlxbn+vTSePjhbw2O2AASJKyA7b9CbW+VWOPWt+w8baIeCZjFuBYDAwMaPfUD3XDhc9mj4KC3LR+gY4fGCjktbN2wGsknd/+WMvvSeIDfwD0nJQAR8RdETHVvvmApDMy5gCATHPhDPjtkr5xuAdtL7c9ZntsYmKixLEAoFiFnQG3vzfuRdM8dGNEfKX9nBslTUm643CvExErJa2UpHq9HgWMCgApCgtwRFx2pMdtv1XSFZIujQjCCqDnZF0FMSjp/ZJeGxE7M2YAgGxZZ8B/K+kUSWtsb7D990lzAECalB1wRNQy1gWAuWQuXAUBAD2JAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhO+EK9DIyIiazWbK2gfWbTQapa9dq9U0NDRU+rpAtyHAFTV//vzsEQAcBQEuELtAAEfCGTAAJCHAAJCEAANAEs6AgWP0xI4+3bR+QfYYpfnRzta+7fQT9ydPUo4ndvRpaUGvTYCBY1Cr9d53C+xpX+J4/It742dfquL+nt1N34dZr9djbGwsewygpx24tnx4eDh5kq7i6e7kDBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkqQG2PZ7bYftF2bOAQAZ0gJs+0xJvy3piawZACBT5g74ryW9X1L3fCAxAMyilADbvkrSeEQ8krE+AMwFhX0lke21kl40zUM3SrpBreOHTl5nuaTlkrR48eJZmw8AshW2A46IyyLi/EP/SNoi6SxJj9h+XNIZktbbni7WioiVEVGPiPqiRYuKGrdyJicndf3112tycjJ7FACHUfoRREQ8GhGnRcSSiFgiaZukCyPiybJnqbJVq1bp0Ucf1e233549CoDD4DrgCpqcnNTq1asVEVq9ejW7YGCOSg9weye8PXuOKlm1apX2798vSdq3bx+7YGCOSg8wZt/atWs1NTUlSZqamtKaNWuSJwIwHQJcQZdddpn6+1sXuPT39+v1r3998kQApkOAK2jZsmWaN6/1V9vX16drr702eSIA0yHAFbRw4UINDg7KtgYHB7Vw4cLskQBMo7A3YiDXsmXL9Pjjj7P7BeYwAlxRCxcu1C233JI9BoAj4AgCAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIkhZg20O2N9veaPsvs+YAgCz9GYvafp2kqyT9ekTstn1axhwAkClrB/xOSR+PiN2SFBFPJc0BAGmyAvwSSb9he53te21fdLgn2l5ue8z22MTERIkjAkCxCjuCsL1W0oumeejG9rqnSnqlpIskfc722RERhz45IlZKWilJ9Xr9Fx4HgG5VWIAj4rLDPWb7nZK+2A7ud2zvl/RCSWxxAfSMrCOIL0t6nSTZfomk4yRtT5oFAFKkXAUh6TZJt9l+TNIeScumO34AgCpLCXBE7JH05oy1AWCu4J1wAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACTJeiccgGM0MjKiZrNZ+roH1mw0GqWvXavVNDQ0VPq6RWEHDGBGjj/+eO3evVt79+7NHqXrsQMGulTWTvDmm2/W1772NS1dulTvec97UmaoCnbAADo2OTmp0dFRRYRGR0c1OTmZPVJXI8AAOrZq1SpNTU1Jkvbu3avbb789eaLuRoABdOyuu+76udt33nln0iTVQIABdKy/v/+ItzEzBBhAx3bs2HHE25gZAgygY0uWLDnibcwMAQbQsRUrVhzxNmaGAAPoWK1W+9mud8mSJarVarkDdTkCDGBGVqxYoZNOOond7yxwN30Zcb1ej7GxsewxAGCmPN2d7IABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASNJVb8SwPSFpa/YcXeSFkrZnD4FK4r+tmdkeEYOH3tlVAcbM2B6LiHr2HKge/tuaHRxBAEASAgwASQhwta3MHgCVxX9bs4AzYABIwg4YAJIQYABIQoAryPag7f+23bT9wex5UB22b7P9lO3HsmepAgJcMbb7JN0q6Q2SzpV0je1zc6dChXxa0i+8oQC/HAJcPa+Q1IyILRGxR9JnJV2VPBMqIiLuk/R09hxVQYCrZ0DSDw66va19H4A5hgADQBICXD3jks486PYZ7fsAzDEEuHoelLTU9lm2j5N0taSvJs8EYBoEuGIiYkrSuyTdKWmTpM9FxMbcqVAVtv9F0v2SzrG9zfYfZc/UzXgrMgAkYQcMAEkIMAAkIcAAkIQAA0ASAgwASQgweobtj9h+X/YcwAEEGACSEGBUlu1rbf+X7Uds/9Mhj11n+8H2Y1+wfWL7/jfafqx9/33t+86z/R3bG9qvtzTj50H18EYMVJLt8yR9SdKrI2K77VMlXS9pR0T8le2FETHZfu5fSPpRRIzYflTSYESM235BRDxje0TSAxFxR/vt3X0RsSvrZ0N1sANGVV0i6d8iYrskRcShn2F7vu1vtYP7Jknnte//T0mftn2dpL72ffdLusH2ByS9mPhithBg9KpPS3pXRLxM0kclnSBJEfEOSSvU+kS5h9o75c9IulLSLkmjti/JGRlVQ4BRVf8h6Y22F0pS+wjiYKdI+qHt56m1A1b7eb8WEesi4sOSJiSdaftsSVsi4hZJX5H08lJ+AlRef/YAQBEiYqPtj0m61/Y+SQ9Levygp/yZpHVqRXadWkGWpE+0f8lmSd+U9IikD0h6i+29kp6UdFMpPwQqj1/CAUASjiAAIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACS/D+/0VExPOgQKQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing outliers"
      ],
      "metadata": {
        "id": "xsziNxEWr5N6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def manageOutliers(dt,c):\n",
        "  lq0=dt[c].loc[(dt['class']==0)].quantile(0.25)\n",
        "  lq1=dt[c].loc[(dt['class']==1)].quantile(0.25)\n",
        "  uq0=dt[c].loc[(dt['class']==0)].quantile(0.75)\n",
        "  uq1=dt[c].loc[(dt['class']==1)].quantile(0.75)\n",
        "  uw0=uq0+1.5*(uq0-lq0)\n",
        "  lw0=lq0-1.5*(uq0-lq0)\n",
        "  uw1=uq1+1.5*(uq1-lq1)\n",
        "  lw1=lq1-1.5*(uq1-lq1)\n",
        "  dt.drop(axis=\"rows\",labels=dt.index[(dt[\"class\"]==0) & ((dt[c]>uw0) | (dt[c]<lw0))],inplace=True)\n",
        "  dt.drop(axis=\"rows\",labels=dt.index[(dt[\"class\"]==1) & ((dt[c]>uw1) | (dt[c]<lw1))],inplace=True)\n",
        "  return dt"
      ],
      "metadata": {
        "id": "QRJa3SUSEUzh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for c in list(data.columns)[:4]:\n",
        "  sns.catplot(kind=\"box\",x=\"class\",y=c,data=data)\n",
        "  data=manageOutliers(data,c)\n",
        "  sns.catplot(kind=\"box\",x=\"class\",y=c,data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NiqlMCz4IfKq",
        "outputId": "72a6696b-7cce-42a8-b4b9-824084fe9949"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of data across columns"
      ],
      "metadata": {
        "id": "IJdQHDb6sDKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for c in list(data.columns)[:4]:\n",
        "  sns.displot(data,x=c,hue=data[\"class\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6EI659Jptjgx",
        "outputId": "11dc31a4-dd4b-4d17-ea37-427bde005957"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ],
            "image/png": 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WnnI6d0nS4csCkSQ1sUAkSU0sEElSEwtEktTEs7CkziWXjGzuOumIYIFInYvXn7Woz2/85AdGlERaGnrZhZXkmCTXJfl8kvuTPDfJsUluSfL33Z/f3Uc2SdJw+joG8i7gpqr6QeCZwP3AhcCtVXUycGv3WpI0pSZeIEmeAjwfuBygqr5ZVV8BzgGu6ha7CnjZpLNJkobXxxbIScAs8L4kn0ny3iRPBI6vqoe7ZR4Bju8hmyRpSH0UyHLgNOA9VfUjwD+z3+6qqiqg5vtwkg1JtiTZMjs7O/aw02zN6hnv4S2pN32chbUD2FFVm7vX1zEokC8mOaGqHk5yArBrvg9X1SZgE8DatWvnLZkjxbbtO6nb3t78+Zx50QjTSDrSTHwLpKoeAbYnOaUbOgu4D7gBWN+NrQc+OulskqTh9XUdyOuBq5McDTwI/ByDMru2u4nJNuDcnrJJkobQS4FU1WeBtfO8tbgruSRJE+NcWJKkJhaIJKmJBSJJamKBSJKaWCCSpCYWiCSpiQUiSWpigUiSmlggkqQmFogkqYkFIklqYoFIkppYIJKkJhaIJKmJBSJJamKBSJKaWCCSpCYWiCSpiQUiSWpigUiSmlggkqQmFogkqYkFIklqYoFIkppYIJKkJhaIJKmJBSJJatJbgSRZluQzSf68e31Sks1Jtib5UJKj+8omSTq0PrdA3gDcP+f1O4B3VtUPAF8GLugllSRpKL0USJIZ4MXAe7vXAc4ErusWuQp4WR/ZJEnD6WsL5PeBNwF7u9ffA3ylqnZ3r3cAK+f7YJINSbYk2TI7Ozv+pJKkeU28QJK8BNhVVXe0fL6qNlXV2qpau2LFihGnkyQNa3kP33kG8NIkPwk8AXgy8C7gmCTLu62QGWBnD9kkSUOa+BZIVb2lqmaqag1wHnBbVb0S+Bjw8m6x9cBHJ51NkjS8aboO5M3AxiRbGRwTubznPJKkg+hjF9a/qKq/Af6me/4gcHqfeSRJw5umLRBJ0hJigUiSmlggkqQmFogkqYkFIklq0utZWNLI5Chy5kV9p9Ao5SgG0+S1mVm1mu0PbRthIO3PAtHhofZy2ab2S4c2bnDy56lTe7ns5geaP77x7FNGGEbzsUCkEbrkkkv6jiBNjAUijdDF689a1Oc3fvIDI0oijZ8H0SVJTSwQSVITC0SS1MQCkSQ1sUAkSU0sEElSEwtEktTEApEkNbFAJElNLBBJUhMLRJLUxAKRJDWxQCRJTSwQSVITC0SS1MQCkSQ1sUB6smb1DEkW9ZCkPnlHwp5s276Tuu3ti1pHzrxoRGkkaeEmXiBJVgHvB44HCthUVe9KcizwIWAN8AXg3Kr68qTzaYFy1OKLbBTrkDRxfWyB7AbeWFV3JnkScEeSW4BXA7dW1aVJLgQuBN7cQz4tRO3lsk2XL2oVGzdcMJJ1SJqsiR8DqaqHq+rO7vk/AfcDK4FzgKu6xa4CXjbpbJKk4fV6ED3JGuBHgM3A8VX1cPfWIwx2cc33mQ1JtiTZMjs7O5GckqTH6q1AknwX8KfAr1bV1+a+V1XF4PjIY1TVpqpaW1VrV6xYMYGkkqT59FIgSR7HoDyurqrru+EvJjmhe/8EYFcf2SRJw5l4gWRwAcPlwP1Vddmct24A1nfP1wMfnXQ2SdLw+jgL6wzgVcDdST7bjV0EXApcm+QCYBtwbg/ZJElDmniBVNX/AQ50GfVZk8wiSWrnVCaSpCYWiCSpiQUiSWpigUiSmlggkqQmFogkqYkFIklqYoFIkppYIJKkJhaIJKmJBSJJamKBSJKaWCAN1qyeIcmiHpK01PUxnfuSt237Tuq2ty9qHTnzohGlkaR+uAUiSWpigUiSmlggkqQmR+QxkDWrZ9i2fWffMaR5XXLJJX1HkIZyRBbIYg+CewBc43Tx+sXd2XnjJz8woiTSwbkLS5LUxAKRJDWxQCRJTSwQSVKTI/Igujo5yhMCJDWzQI5ktZfLNl2+qFVs3HDBiMJII5ajFj3v3Myq1Wx/aNuIAh1+LBBJh6fay2U3P7CoVWw8+5QRhTk8eQxEktRk6gokybokDyTZmuTCvvNIkuY3VQWSZBnwh8CLgFOB85Oc2m8qSdJ8pqpAgNOBrVX1YFV9E7gGOKfnTJKkeaSq+s7wL5K8HFhXVb/QvX4V8KNV9bo5y2wANnQvTwEWd5RsYY4DHp3g9y3UNOeb5mww3fmmORtMd76+sj1aVet6+N6JWnJnYVXVJmBTH9+dZEtVre3ju4cxzfmmORtMd75pzgbTnW+asx0Opm0X1k5g1ZzXM92YJGnKTFuB/C1wcpKTkhwNnAfc0HMmSdI8pmoXVlXtTvI64K+AZcAVVXVvz7Hm6mXX2QJMc75pzgbTnW+as8F055vmbEveVB1ElyQtHdO2C0uStERYIJKkJhZIgySvT/L5JPcm+Z2+88wnyRuTVJLj+s6yT5Lf7f67fS7Jh5McMwWZpnbqnCSrknwsyX3d37U39J1pf0mWJflMkj/vO8v+khyT5Lru79z9SZ7bd6bDjQWyQEleyODq+GdW1dOB3+s50mMkWQWcDTzUd5b93AI8o6p+GPg74C19hlkCU+fsBt5YVacCzwFeO2X5AN4A3N93iAN4F3BTVf0g8EymN+eSZYEs3C8Bl1bVNwCqalfPeebzTuBNwFSdIVFVN1fV7u7l7Qyu8+nTVE+dU1UPV9Wd3fN/YvAP4Mp+U31bkhngxcB7+86yvyRPAZ4PXA5QVd+sqq/0m+rwY4Es3NOAH0uyOcnHkzy770BzJTkH2FlVd/Wd5RB+HvjLnjOsBLbPeb2DKfoHeq4ka4AfATb3m+Rf+X0Gv6js7TvIPE4CZoH3dbvY3pvkiX2HOtxM1XUg0yLJXwP/Zp633srgv9mxDHYpPBu4Nsn31QTPhz5EvosY7L7qxcGyVdVHu2XeymD3zNWTzLZUJfku4E+BX62qr/WdByDJS4BdVXVHkhf0nWcey4HTgNdX1eYk7wIuBH6z31iHFwtkHlX14wd6L8kvAdd3hfHpJHsZTNg223e+JP+WwW9ed3W38pwB7kxyelU90me2fZK8GngJcNYkS/cApn7qnCSPY1AeV1fV9X3nmeMM4KVJfhJ4AvDkJP+7qn6m51z77AB2VNW+LbbrGBSIRshdWAv3EeCFAEmeBhzNlMxEWlV3V9VTq2pNVa1h8D/RaZMqj0NJso7BLo+XVtXX+87DlE+dk8FvAZcD91fVZX3nmauq3lJVM93fs/OA26aoPOj+zm9Psu+etGcB9/UY6bDkFsjCXQFckeQe4JvA+in4TXqp+APg8cAt3RbS7VX1mr7CLIGpc84AXgXcneSz3dhFVXVjj5mWktcDV3e/HDwI/FzPeQ47TmUiSWriLixJUhMLRJLUxAKRJDWxQCRJTSwQSVITC0RHnCQ3TsNMwNJS52m8OmJ0F+alqqZx7iZpyXELREtOkkuTvHbO699O8htJbk1yZ5K7u0klSbKmu9/H+4F7gFVJvrDvPilJPpLkju5+GxvmrPP/JXlbkruS3J7k+G78+O5eJnd1j+d14z+T5NNJPpvkf3VTxUuHNQtES9GHgHPnvD4XuAr4qao6jcFUM/+92+IAOBn4o6p6elVt229dP19VzwLWAr+S5Hu68ScyuFL+mcAngF/sxt8NfLwbPw24N8kPAf8JOKOq/h2wB3jlCH9eaSo5lYmWnKr6TJKnJvleYAXwZeAR4J1Jns9gevGVwPHdR7ZV1e0HWN2vJPmp7vkqBmXzjwymqdl3l707gP/QPT8T+Nkuxx7gq0leBTwL+Nuus74DmMb7xEgjZYFoqfoT4OUMpo7/EIPf+FcAz6qqbyX5AoNZYgH+eb4VdNOQ/zjw3Kr6epK/mfOZb82Z42wPB/9/JcBVVdXrHRalSXMXlpaqDzGYBfblDMrkKQzuT/Gt7rbDJw6xjqcAX+7K4wcZ3OPlUG5lcFfKffcDf0o39vIkT+3Gj00yzPdLS5oFoiWpmzX3SQzuvvgwg5tTrU1yN4NdTJ8fYjU3AcuT3A9cyuA2u4fyBuCF3ffcAZxaVfcBvwHcnORzDO79fsJCfyZpqfE0XklSE7dAJElNLBBJUhMLRJLUxAKRJDWxQCRJTSwQSVITC0SS1OT/A37K354hbN8DAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAFgCAYAAACVLS/VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWcElEQVR4nO3df7BkZX3n8fdnZmCNiYafNUWYuTPEUNQasnHNqPFHLAIJiz8qkARRY+HEIjvJJppkZ6OwJCtSFS0M2TFkN0VqFMK4qxhksSCGRQwYyVouCkYFNawsMszM8mOI8UfiHwrz3T/6DN6MM3O7n7ndp3v6/arqut3nPKf72zVcPvd5zjnPk6pCkqRRrei7AEnSbDJAJElNDBBJUhMDRJLUxACRJDVZ1XcBh+Kss86qW265pe8yJGlf6buASZjpHsjjjz/edwmSNLdmOkAkSf0xQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUZW4AkuTrJY0nuXbTtmCQfTfLl7ufR3fYk+eMk9yf5fJLnjquuebJ+YQ1JhnqsX1jTd7mSZsw41wO5BvivwHsXbbsIuK2qLktyUff6QuBlwMnd4wXAld1PHYLtO3ZRt79jqLY5/eIxVyPpcDO2HkhV3QF8dZ/NZwPbuufbgHMWbX9vDfxv4KgkJ4yrNknSoZv0OZDVVfVw9/wRYHX3/ERgx6J2O7tt3yPJpiR3Jblr9+7d46tUknRQvZ1Er6oCquG4rVW1oao2HH/88WOoTJI0jEkHyKN7h6a6n49123cBaxe1W9NtkyRNqUkHyE3Axu75RuDGRdtf312N9ZPA1xcNdUmSptDYrsJKci1wGnBckp3AJcBlwHVJLgC2A+d1zW8GXg7cD3wLeMO46pIkLY+xBUhVvfYAu87YT9sCfmNctUiSlp93okuSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKAaCArhl58KglrF9b1XbGkno1zQSnNktrDllvvG7r55jNPGWMxkmaBPRBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEANFErF1YR5KhHmsX1vVdrqQhrOq7AM2HnTseYsut9w3VdvOZp4y5GknLwR6IJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKASJKa9BIgSf59ki8kuTfJtUmeluSkJHcmuT/Jnyc5so/aJEnDmXiAJDkR+E1gQ1WdCqwEXgO8E3hXVf0I8A/ABZOuTZI0vL6GsFYB35dkFfB04GHgdOD6bv824JyeapMkDWHiAVJVu4A/BB5iEBxfB+4GvlZVT3TNdgIn7u/4JJuS3JXkrt27d0+iZEnSfvQxhHU0cDZwEvBDwPcDZw17fFVtraoNVbXh+OOPH1OVkqSl9DGE9TPAV6pqd1V9B7gBeDFwVDekBbAG2NVDbZKkIfURIA8BP5nk6UkCnAF8EfgYcG7XZiNwYw+1SZKG1Mc5kDsZnCz/DHBPV8NW4EJgc5L7gWOBqyZdmyRpeKuWbrL8quoS4JJ9Nj8APL+HciRJDbwTXZLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNVvVdgGZUVpCk7yok9cgAUZvaw5Zb7xu6+eYzTxljMZL64BCWJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJr0ESJKjklyf5O+SfCnJC5Mck+SjSb7c/Ty6j9omae3COpIM/Vi7sK7vkiXpKat6+twrgFuq6twkRwJPBy4Gbquqy5JcBFwEXNhTfROxc8dDbLn1vqHbbz7zlDFWI0mjmXgPJMkPAi8FrgKoqm9X1deAs4FtXbNtwDmTrk2SNLw+eiAnAbuBP0vy48DdwG8Bq6vq4a7NI8Dq/R2cZBOwCWBhYWH81U6TrCDJaO1Pv3h89Uiaa30EyCrgucCbqurOJFcwGK56SlVVktrfwVW1FdgKsGHDhv22OWzVnpGHvLZsvWq4tpsuaK1K0pzq4yT6TmBnVd3Zvb6eQaA8muQEgO7nYz3UJkka0sQDpKoeAXYk2XtG+Azgi8BNwMZu20bgxknXJkkaXl9XYb0JeF93BdYDwBsYhNl1SS4AtgPn9VSbJGkIvQRIVX0W2LCfXWdMuhZJUhvvRJckNTFAJElNhgqQJC8eZpskaX4M2wP5L0Num3ujzG8lSbPsoCfRk7wQeBFwfJLNi3Y9E1g5zsJm1SjzWzm3laRZtlQP5EjgBxgEzTMWPb4BnDve0qbDqDPmStK8OGgPpKo+Dnw8yTVVtX1CNU0VZ8yVpP0b9j6Qf5FkK7B+8TFVdfo4ipIkTb9hA+SDwJ8C7wGeHF85kqRZMWyAPFFVV461EknSTBn2Mt6/SPLrSU7olp49JskxY61MknRQSd6W5Hf6+vxheyB7Z8l986JtBfzw8pYjSZoVQwVIVZ007kIkSQeX5PXA7zD4A/7zwP9dtO/fMlit9UjgfuD8qvpWklcBlzA4f/31qnppkh8F/qxruwL4xar68qj1DBUgXdHfo6reO+oHSpJG1/1P//eAF1XV491phN9c1OSGqnp31/b3gQsYzBjyVuDfVNWuJEd1bX8NuKKq9i6r0XRj+LBDWM9b9PxpDKZd/wxggEjSZJwOfLCqHgeoqq/uc/PyqV1wHMXgBvCPdNs/AVyT5Drghm7bJ4HfTbKGQfCM3PuA4Yew3rT4dZdiH2j5QEnSWFwDnFNVn0vyy8BpAFX1a0leALwCuDvJT1TV+5Pc2W27OcmvVtXto35g63Tu/wR4XkSSJud24FVJjgXYz5WwzwAeTnIE8Lq9G5M8q6rurKq3AruBtUl+GHigqv6YwfLh/6qloGHPgfwFg5M2MBgr+5fAdS0fKEkaXVV9IcnbGUwv9STwt8CDi5r8J+BOBiFxJ4NAAbg8yclAgNuAzwEXAucn+Q7wCPCOlpqGPQfyh4uePwFsr6qdLR8oSWpTVduAbQfYdyXwPTd8V9Uv7Kf5Zd3jkAw1hNVNqvh3DBLtaODbh/rBkqTZNuyKhOcBnwJeBZwH3JlkLqZzlyTt37BDWL8LPK+qHgNIcjzwV8D14ypMkjTdhr0Ka8Xe8Oj8/QjHSpIOQ8P2QG5J8hHg2u71q4Gbx1OSJGkWLLUm+o8Aq6vqzUl+AXhJt+uTwPvGXZwkaXotNQz1RwzWP6eqbqiqzVW1GfhQt0+SdBhIclaS+5Lcn+SiYY5ZKkBWV9U9+27stq1vqFGStISsXLUzSS3bY+Wqg963l2Ql8CfAy4BnA69N8uyl6lzqHMhRB9n3fUu9uSSpwZ4nT1x34YcvXa632/7OV16yRJPnA/dX1QMAST4AnA188WAHLdUDuaubY/6fSfIrwN1LHCtJmg0nAjsWvd7ZbTuopXogvw18KMnr+G5gbGCwCMnPNxQpSTpMHDRAqupR4EVJfho4tdv8ly3T/kqSptYuYO2i12u6bQc17HogHwM+1laXJGnKfRo4OclJDILjNcAvLXXQsDcSakwuvXTZzpNJUpOqeiLJGxmsYrgSuLqqvrDUcQZIzy7ZeMbQbTd/4v1jrETS1FixctcQV06N9H5LNamqmxlxhhEDRJKmTD35xJq+axiGEyJKkpoYIJKkJg5h6Sme0Jc0CgNET/GEvqRROIQlSWpigEiSSHJ1kseS3DvsMQaIJE2ZI1ZmWadzP2JlDjqde+ca4KxR6vQciCRNmSf2cGJd8sxlu6oll35jyZsSq+qOJOtHeV97IJo+WUGSoR9rF9b1XbE0l+yBaPrUHrbcet/QzTefecoYi5F0IPZAJElNDBBJUhMDRJJEkmuBTwKnJNmZ5IKljvEciCRNmVUr2DXMlVOjvN9SbarqtSO/b1s5hy7JSuAuYFdVvbJbCesDwLEM1l8/v6q+3Vd908r5qqTD33eerJmYzr3PHshvAV8Cntm9fifwrqr6QJI/BS4AruyruGnlfFWSpkUv50CSrAFeAbynex3gdOD6rsk24Jw+apMkDaevk+h/BLwF2NO9Phb4WlU90b3eCZy4vwOTbEpyV5K7du/ePfIHr19YM9JNapKk/Zv4EFaSVwKPVdXdSU4b9fiq2gpsBdiwYUONevz2Hbuo298xdPucfvGoHyFJc6GPcyAvBn4uycuBpzE4B3IFcFSSVV0vZA0sfdWAJKk/Ex/Cqqr/WFVrqmo98Brg9qp6HfAx4Nyu2UbgxknXJkka3jTdSHghsDnJ/QzOiVzVcz2SpIPo9UbCqvpr4K+75w8Az++zHknS8KapByJJmiEGiCSpiQEiSWpigEiSmhggkqQmTueuZs4MLM03A0TNnBlYmm8OYUmSmhggkqQmBogkqYkBIklq4kn0MfDqpAnLipEW/1qzdoEdD20fY0HSfDBAxmDYq5O8MmmZ1B623Hrf0M03n3nKGIuR5odDWJKkJgaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWrifSCaGG+wlA4vBogmxhsspcOLQ1iSpCYGiNSTtQvrSDL0Y+3Cur5Llv4Zh7Cknuzc8ZBzeGmm2QORJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEM2lYdchf9qRq0Zat3z9wpqpqHsStUiuia65VLe/Y6h2Of3iodvubT9O01SLZA9EktTEAJEkNTFAJElNDBBJUpOJn0RPshZ4L7AaKGBrVV2R5Bjgz4H1wIPAeVX1D5Oub38uvfTSvkuQpKnTx1VYTwD/oao+k+QZwN1JPgr8MnBbVV2W5CLgIuDCHur7HpdsPGPotps/8f4xViJJ02PiQ1hV9XBVfaZ7/k3gS8CJwNnAtq7ZNuCcSdcmSRper/eBJFkP/GvgTmB1VT3c7XqEwRDX/o7ZBGwCWFhYGH+ROvxkxfD3SMTThNKB9BYgSX4A+B/Ab1fVN5I8ta+qKknt77iq2gpsBdiwYcN+20gHVXvYsvWqoZpu3nTBmIuRZlcvf14lOYJBeLyvqm7oNj+a5IRu/wnAY33UJkkazsQDJIOuxlXAl6pqy6JdNwEbu+cbgRsnXZs0r9YvrHGeLY2sjyGsFwPnA/ck+Wy37WLgMuC6JBcA24HzeqhNmkvbd+xyni2NbOIBUlX/C8gBdg9/vawkqVdeYiJJamKASJKaGCCSpCYuKCUdplZmsIKhNC4GiHSYerJGW3lRGpVDWJKkJgaIJKmJASJJamKASJKaGCCSpCYGiCSpiZfxSgczyuJTXXtpXhgg0sGMsPgUuACV5ot/LkmSmtgDkZZTVjh9iOaGASItp9rDllvvG6rp5jNPGXMx0ng5hCVJajJ/PZBRr6qRRnTppZf2XYI0EfMXIF5VozG7ZONwKzNv/sT7x1yJNF4OYUmSmsxfD0SHpVkdNprVukddrGrd2hN58KGdY6xIfTBAdFgYdtgIpmvoaFbrHmWxKnDBqsOVQ1iSpCYGiCSpiQEiSWpigEiSmngSXVNpVq9OGiunll8W6xfWsH3HrqHaevXYwRkgmkqzenXSWHkT7LLYvmPX0FeQefXYwfkniiSpiQEiaez23ng4zGP9wpq+y9WQHMKSNHaj3HjosNHssAciSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKmyij3jHjfSL+8D0TSVHGxqtlhD0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDWZqgBJclaS+5Lcn+SivuuRNP1GnTtLy2dq5sJKshL4E+BngZ3Ap5PcVFVf7LcySdPMubP6M009kOcD91fVA1X1beADwNk91yRJOoBUVd81AJDkXOCsqvqV7vX5wAuq6o37tNsEbOpengLcd4gffRzw+CG+xyzz+/v95/X7j/O7P15VZ43pvafG1AxhDauqtgJbl+v9ktxVVRuW6/1mjd/f7z+v33+ev/tymaYhrF3A2kWv13TbJElTaJoC5NPAyUlOSnIk8Brgpp5rkiQdwNQMYVXVE0neCHwEWAlcXVVfmMBHL9tw2Izy+8+3ef7+8/zdl8XUnESXJM2WaRrCkiTNEANEktTEAAGSvC3JriSf7R4v77umcZv3aWOSPJjknu7f+66+6xm3JFcneSzJvYu2HZPko0m+3P08us8ax+kA33/ufu+XmwHyXe+qqud0j5v7LmacFk0b8zLg2cBrkzy736p68dPdv/c83AtwDbDvjW0XAbdV1cnAbd3rw9U1fO/3hzn6vR8HA2Q+OW3MnKmqO4Cv7rP5bGBb93wbcM5Ei5qgA3x/HSID5LvemOTzXVf3sO3Kd04Edix6vbPbNk8KuDXJ3d30OPNodVU93D1/BFjdZzE9maff+2U3NwGS5K+S3Lufx9nAlcCzgOcADwP/uddiNQkvqarnMhjG+40kL+27oD7V4Hr+ebum39/7QzQ1NxKOW1X9zDDtkrwb+PCYy+nb3E8bU1W7up+PJfkQg2G9O/qtauIeTXJCVT2c5ATgsb4LmqSqenTv8zn5vV92c9MDOZjul2evnwfuPVDbw8RcTxuT5PuTPGPvc+BMDv9/8/25CdjYPd8I3NhjLRM3h7/3y25ueiBL+IMkz2HQhX8Q+NV+yxmvHqeNmRargQ91q9OtAt5fVbf0W9J4JbkWOA04LslO4BLgMuC6JBcA24Hz+qtwvA7w/U+bp9/7cXAqE0lSE4ewJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQzaUkRyX59UM4/uYkRy1nTdKs8TJezZ0kqxjcff/hqjq173qkWWUPRDMtyeu7yfA+l+S/JbkmybmL9v9j9/O0JH+T5CbgiwxuontWtw7E5Rm4vJsf7Z4kr+6OOyHJHV27e5P8VLf9wSTHdXe1/2X3+ffuPU6aB96JrpmV5EeB3wNeVFWPJzkG2HKQQ54LnFpVX0myvnv+nO69fpHBpHo/DhwHfDrJHcAvAR+pqrd366g8fZ/3PAv4f1X1iu59fnDZvqA05eyBaJadDnywqh4HqKql1nv4VFV95QD7XgJcW1VPdpPsfRx4HoN5w96Q5G3Aj1XVN/c57h7gZ5O8M8lPVdXXW7+MNGsMEB1unqD77zrJCuDIRfv+adQ36xYieimD2YqvSfL6ffb/HwY9m3uA30/y1sa6pZljgGiW3Q68KsmxMFjjm8GkeD/R7f854IgDHPtN4BmLXv8N8OokK5MczyA0PpVkHfBoVb0beA+DsHhKkh8CvlVV/x24fN/90uHMcyCaWVX1hSRvBz6e5Engb4ELgRuTfA64hQP0Oqrq75N8Ism9wP8E3gK8EPgcg9lZ31JVjyTZCLw5yXeAfwRev89b/RhweZI9wHeAf7fsX1SaUl7GK0lq4hCWJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmvx/Pwt9StDOSMoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAFgCAYAAACVLS/VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW6ElEQVR4nO3de7BdZ33e8e8jyc6FS2wHxXWtmwFHjQMhEHF1y4CVuuJS7KYOl1JHoQY1FIiJuENaVTMNQwIjIJeBKLaxmLFxDHFqQinYsblMXRAIsDHYODgG3cbGclIuhba2pF//2MuTgyzr7P367L32Oef7mdlz9lp7rb1+S5L9nPdda71vqgpJkka1pO8CJEnzkwEiSWpigEiSmhggkqQmBogkqcmyvgt4KDZs2FCf+MQn+i5Dko6UvguYhHndArnnnnv6LkGSFq15HSCSpP4YIJKkJgaIJKmJASJJajK2AElySZK7k3xtxrqTklyb5JvdzxO79Unyh0luT/LVJE8aV12SpLkxzhbIpcCGI9a9Gbiuqk4HruuWAZ4DnN69NgHvG2NdkqQ5MLYAqarPAn9/xOpzgB3d+x3AuTPWf7AGPg+ckOSUcdUmSXroJn0N5OSqurN7fxdwcvf+VGDvjO32deseIMmmJLuS7Dpw4MD4KpUkHVNvF9FrMBHJyJORVNX2qlpXVeuWL18+hsokScOYdIB85/6uqe7n3d36/cDKGdut6NZJkqbUpAPko8DG7v1G4OoZ63+juxvracD3ZnR1SZKm0NgGU0zyIeBZwKOS7AO2AO8ArkxyAbAbeGG3+ceB5wK3Az8CXjauuiRJc2NsAVJVL3mQj9YfZdsCXjWuWiRJc88n0SX9mJWrVpNk5NfKVav7Ll0TNq/nA5E09/bt3cO2a24beb/NZ68dQzWaZrZAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEmgdWrlpNkpFfK1et7rt0LWDL+i5A0uz27d3DtmtuG3m/zWevHUM10oAtEElSEwNEktTEAJEkNTFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSk15G403yO8DLgQJuBl4GnAJcAfws8CXg/Kq6t4/6pMVu69atfZegeWDiAZLkVOC3gTOq6v8kuRJ4MfBc4N1VdUWS9wMXAO+bdH2SYMvG9SPvs/mGy8dQiaZZX11Yy4CfSrIM+GngTuAs4CPd5zuAc3uqTZI0hIkHSFXtB94F7GEQHN9j0GX13ao62G22Dzj1aPsn2ZRkV5JdBw4cmETJkqSjmHiAJDkROAc4DfjHwMOADcPuX1Xbq2pdVa1bvnz5mKqUJM2mjy6sXwW+VVUHquo+4CrgTOCErksLYAWwv4faJElD6iNA9gBPS/LTSQKsB24BPgWc122zEbi6h9okSUPq4xrITgYXy7/M4BbeJcB24E3A5iS3M7iV9+JJ1yZJGl4vz4FU1RZgyxGr7wCe0kM5kqQGPokuSWpigEiSmhggkqQmBoi0kGUJSUZ6TfJYK1etnrtz1cT1chFd0oTUYbZdc9tIu2w+e+30H0tTwRaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklq4lAm0gK3devWvkvQAmWASAvclo3rR9p+8w2Xj6kSLTR2YUmSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKnJsr4LkDScrVu39l2C9GMMEGme2LJx/cj7bL7h8jFUIg300oWV5IQkH0nyjSS3Jnl6kpOSXJvkm93PE/uoTZI0nL6ugbwX+ERV/RPgCcCtwJuB66rqdOC6blmSNKUmHiBJfgZ4JnAxQFXdW1XfBc4BdnSb7QDOnXRtkqTh9dECOQ04AHwgyVeSXJTkYcDJVXVnt81dwMk91CZJGlIfAbIMeBLwvqp6IvBDjuiuqqoC6mg7J9mUZFeSXQcOHBh7sZKko+sjQPYB+6pqZ7f8EQaB8p0kpwB0P+8+2s5Vtb2q1lXVuuXLl0+kYEnSA008QKrqLmBvkrXdqvXALcBHgY3duo3A1ZOuTZI0vL6eA3kNcFmS44E7gJcxCLMrk1wA7AZe2FNtkqQh9BIgVXUjsO4oH43+pJQkqReOhSVJamKASJKaGCCSpCYGiCSpiQEiTdjKVatJMtJLmkYO5y5N2L69e9h2zW0j7bP57LWzbyRNmC0QSVITA0SS1GSoAEly5jDrJEmLx7DXQP6IwYCHs62TNATnN9dCcMwASfJ04BnA8iSbZ3z0SGDpOAuTFrJR5zd3bnNNo9laIMcDD++2e8SM9d8HzhtXUZKk6XfMAKmqzwCfSXJpVe2eUE2SpHlg2GsgP5FkO7Bm5j5VddY4ipIkTb9hA+TDwPuBi4BD4ytHkjRfDBsgB6vqfWOtRJI0rwz7IOFfJfkPSU5JctL9r7FWJkk6piT/Ocnr+zr+sC2Q++cqf8OMdQU8em7LkSTNF0MFSFWdNu5CJEnHluQ3gNcz+AX+q8DfzvjsFcAmBo9f3A6cX1U/SvLrwBYG16+/V1XPTPKLwAe6bZcA/7qqvjlqPUMFSFf0A1TVB0c9oCRpdN3/9H8XeEZV3dNdRvjtGZtcVVV/1m37X4ALGIwY8p+Af1FV+5Oc0G37W8B7q+qyJMfT+GD4sF1YT57x/ieB9cCXAQNEkibjLODDVXUPQFX9/RFzxTyuC44TGDwA/slu/Q3ApUmuBK7q1n0OeFuSFQyCZ+TWBwzfhfWamctdil3RckBJC5djfPXqUuDcqropyW8CzwKoqt9K8lTgecCXkvxKVV2eZGe37uNJ/n1VXT/qAVsnlPoh4HURST/GMb7G6nrgL5Nsq6q/O8qdsI8A7kxyHPBSYD9AksdU1U5gZ5LnACuT/AxwR1X9YZJVwC913z+SYa+B/BWDizYw6Cv7BeDKUQ8mSWpTVV9P8nsMhpc6BHwF+PaMTf4jsBM40P28f/zCdyY5HQhwHXAT8Cbg/CT3AXcBb2+padgWyLtmvD8I7K6qfS0HlCS1qaodwI4H+ex9wAMe+K6qXzvK5u/oXg/JUA8SdoMqfoNBop0I3PtQDyxJmt+GnZHwhcAXgF8HXsigL83h3CVpERu2C+ttwJOr6m6AJMuBvwY+Mq7CJEnTbdixsJbcHx6dvxthX0nSAjRsC+QTST4JfKhbfhHw8fGUJEmaD2abE/2xwMlV9YYkvwb80+6jzwGXjbs4adqtXLWafXv39F2G1IvZWiDvAd4CUFVX0T0Gn+Tx3Wf/cqzVSVNu3949bLvmtpH22Xz22jFVI7VLsgF4L4Nn/S6qqllv853tOsbJVXXzkSu7dWtaipQkHVuWLtuXpObstXTZMZ/bS7IU+BPgOcAZwEuSnDFbnbO1QE44xmc/NduXS4uB4z9pzh0+dOrqN31szv5h7f7952+ZZZOnALdX1R0ASa4AzgFuOdZOswXIriSvuH+I4PsleTnwpVn2lRYFx3/SAnAqsHfG8j7gqbPtNFuAvJbB4F0v5R8CYx2DSUj+VUORkqQF4pgBUlXfAZ6R5NnA47rV/61l2F9J0tTaD6ycsbyiW3dMw84H8ingU211SZKm3BeB05OcxiA4Xgz8m9l2ap0PRJK0QFTVwSSvZjCL4VLgkqr6+mz7GSCSNG2WLN0/xJ1TI33fbJtU1ccZcYQRA0SSpkwdOrii7xqG4YCIkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJIkklyS5O8nXht3HAJGkKXPc0szpcO7HLc0xh3PvXApsGKVOnwORpClz8DCn1pZHztlw7tn6/VkfSqyqzyZZM8r32gKRJDUxQCRJTXoLkCRLk3wlyce65dOS7Exye5I/T3J8X7VJkmbXZwvkQuDWGcu/D7y7qh4L/C/ggl6qkiQNpZcASbICeB5wUbcc4CzgI90mO4Bz+6hNkhajJB8CPgesTbIvyay/xPd1F9Z7gDcCj+iWfxb4blUd7Jb3MZij9wGSbAI2AaxatWrMZUrS5C1bwv5h7pwa5ftm26aqXjLq9068BZLk+cDdVfWlWTc+iqraXlXrqmrd8uXL57g6SerffYdqRVVlrl73HaqxDA/fRwvkTOAFSZ4L/CTwSOC9wAlJlnWtkKHm45Uk9WfiLZCqektVraiqNQzm3b2+ql7KYM7187rNNgJXT7o2SdLwpuk5kDcBm5PczuCayMU91yNJOoZehzKpqk8Dn+7e3wE8pc96JEnDm6YWiCRpHjFAJElNDBBJUhMDRJLUxACRJDUxQCRJTQyQRWzlqtUkGfm1ctXqvkufc61/FnqIssR/f/OYU9ouYvv27mHbNbeNvN/ms9eOoZp++WfRkzo88p+7f+bTwxaIJKmJASJJamKASJKaGCCSpCYGiCSpiQEiSWpigEiSmhggkqQmBogkqYkBIklqYoBIkpoYIJKkJgaIJKmJASJJamKASJKaGCCSpCYGyALRMqOeJD0Uzki4QLTMqOfMbpIeClsgkqQmBogkqYkBIklqYoBIkpoYIJpqa1atGPnusjWrVvRdtrQoeBeWptruvfup698+0j45661jqkbSTLZAJElNbIFI6tXWrVv7LkGNDBBJvdqycf1I22++4fIxVaJR2YUlSWpigEiSmhggkqQmBogkqYkX0bXgLA0OVy9NgAGiBedQ4cOH0gTYhSVJamILRKPLkpG7iJYuO45DB+8bU0FzwwfapNEYIBpdHW6a/XDUbiWYbNfSqA+0gQ+1aXGzC0uS1MQWyCI3sW6bLGlrTcTfcaRpZYAschPrtqnDbNt+8ejH2nTB6MeSNBH+eidJamKASJKaGCCSpCYGiCSpycQDJMnKJJ9KckuSrye5sFt/UpJrk3yz+3nipGuTJA2vjxbIQeB1VXUG8DTgVUnOAN4MXFdVpwPXdcuSpCk18QCpqjur6svd+x8AtwKnAucAO7rNdgDnTro2SdLwer0GkmQN8ERgJ3ByVd3ZfXQXcPKD7LMpya4kuw4cODCROiVJD9RbgCR5OPAXwGur6vszP6uqAupo+1XV9qpaV1Xrli9fPoFKJUlH00uAJDmOQXhcVlVXdau/k+SU7vNTgLv7qE2SNJyJD2WSwTjgFwO3VtW2GR99FNgIvKP7efWka9MC0TrulqSR9DEW1pnA+cDNSW7s1r2VQXBcmeQCYDfwwh5q00LQMO6WY25Jo5t4gFTV/wAebDai0Uf2kyT1wtF4Nd3sjtKRGmbEBFixchV79+weQ0GLlwGi6WZ3lI7UMCMmDGbF1NxyLCxJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ18TkQNdm6dWvfJUjqmQGiJls2jjbqzOYbLh9TJZL6YheWJKmJASJJamKASJKaGCCSpCYGyBRauWo1SUZ6SdKkeRfWFNq3d8/Iw1U7VLWkSbMFIklqYoBIkpoYIJKkJgaIJKmJATJGLXdTeUeVpPnCu7DGqOVuKvCOKknzgy0QSVKTRRcga1ataOpWWrNqRd+lS9JUWXRdWLv37qeuf/vI++Wst46hGkmavxZdC0SSNDcWXQtkIXOWQEmTZIAsIM4SKGmS7MKSJDUxQCRJTQwQSVITA0SS1MSL6MPKEsepkqaEdxxOBwNkWHXYWQKlKTHqHYfgXYfjYBeWJKmJLZAR2GyW5rGGbugVK1exd8/uMRU0/xkgI/BBPWkesxt6ztmFJUlqYgtkzFq7vewukzTtDJAxa71bxO4ySdPOLixJUhMDRJLUxACRJDUxQCRJTQwQSVITA0SS1MQAkSQ1MUAkSU0MEElSEwNEktRkqoYySbIBeC+wFLioqt4x9wdZQs5665x/raTp5xhzc2tqAiTJUuBPgH8O7AO+mOSjVXXLnB6oDrNt+8Uj77Z50wVzWoakyXOMubk1TV1YTwFur6o7qupe4ArgnJ5rkiQ9iFRV3zUAkOQ8YENVvbxbPh94alW9+ojtNgGbusW1wGgzxLR5FHDPBI4zLRbb+cLiO+fFdr4w2XO+p6o2TOhYvZmaLqxhVdV2YPskj5lkV1Wtm+Qx+7TYzhcW3zkvtvOFxXnO4zZNXVj7gZUzlld06yRJU2iaAuSLwOlJTktyPPBi4KM91yRJehBT04VVVQeTvBr4JIPbeC+pqq/3XNb9JtplNgUW2/nC4jvnxXa+sDjPeaym5iK6JGl+maYuLEnSPGKASJKaGCAjSPKaJN9I8vUkf9B3PZOQ5HVJKsmj+q5l3JK8s/v7/WqSv0xyQt81jUOSDUluS3J7kjf3Xc84JVmZ5FNJbun+u72w75oWEgNkSEmezeDJ+CdU1S8C7+q5pLFLshI4G9jTdy0Tci3wuKr6JeBvgLf0XM+cmzFk0HOAM4CXJDmj36rG6iDwuqo6A3ga8KoFfr4TZYAM75XAO6rq/wFU1d091zMJ7wbeCCyKOy2q6pqqOtgtfp7Bs0gLzaIaMqiq7qyqL3fvfwDcCpzab1ULhwEyvJ8H/lmSnUk+k+TJfRc0TknOAfZX1U1919KTfwf8976LGINTgb0zlvexSP6HmmQN8ERgZ7+VLBxT8xzINEjy18A/OspHb2PwZ3USg2bwk4Erkzy65vF90LOc71sZdF8tKMc656q6utvmbQy6Pi6bZG0anyQPB/4CeG1Vfb/vehYKA2SGqvrVB/ssySuBq7rA+EKSwwwGZzswqfrm2oOdb5LHA6cBNyWBQVfOl5M8parummCJc+5Yf8cASX4TeD6wfj7/cnAMi27IoCTHMQiPy6rqqr7rWUjswhrefwWeDZDk54HjWaCjmVbVzVX1c1W1pqrWMOjmeNJ8D4/ZdBOavRF4QVX9qO96xmRRDRmUwW9AFwO3VtW2vutZaAyQ4V0CPDrJ1xhceNy4QH9DXcz+GHgEcG2SG5O8v++C5lp3k8D9QwbdClw5RUMGjcOZwPnAWd3f6Y1Jntt3UQuFQ5lIkprYApEkNTFAJElNDBBJUhMDRJLUxACRJDUxQLSoJDnXwfSkuWGAaLE5l8EotA+QxJEZpBEYIJr3kvzbJF/oHhL70yRLk/zvJL+X5KYkn09ycpJnAC8A3tlt+5gkn07yniS7gAuTrE/ylSQ3J7kkyU90x/h2kj/o1n8hyWOTPCLJt7qhMkjyyJnL0kJngGheS/ILwIuAM6vql4FDwEuBhwGfr6onAJ8FXlFV/5PBsB1vqKpfrqq/7b7m+Kpax2CejEuBF1XV4xmMFffKGYf7Xrf+j4H3dMODfxp4Xvf5ixmMl3bf2E5YmiIGiOa79cCvAF9McmO3/GjgXuBj3TZfAtYc4zv+vPu5FvhWVf1Nt7wDeOaM7T404+fTu/cXAS/r3r8M+EDTWUjzkH2+mu8C7KiqH5s9MMnrZ4xVdohj/1v/4ZDHqiPfV9UNSdYkeRawtKq+NuR3SfOeLRDNd9cB5yX5OYAkJyVZfYztf8BgwMSjuQ1Yk+Sx3fL5wGdmfP6iGT8/N2P9B4HLsfWhRcYA0bxWVbcAvwtck+SrDOY1P+UYu1wBvKG7UP6YI77r/zLohvpwkpuBw8DMEXlP7I5xIfA7M9ZfBpzIP3RxSYuCo/FKQ0jybWBdVT1gDpgk5wHnVNX5Ey9M6pHXQKSHIMkfAc8BnGNCi44tEElSE6+BSJKaGCCSpCYGiCSpiQEiSWpigEiSmvx/hVzaQxIAFTYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like gaussian distribution"
      ],
      "metadata": {
        "id": "fY3-me2FsKNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "Mheats5rgLhZ",
        "outputId": "6eaffedb-35e5-496c-dfb1-920ed5738ed0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f83f4967410>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that entropy has very less correlation with the class. So if we want we can drop that column. But I will keep it for now and see the results."
      ],
      "metadata": {
        "id": "t72HcJmdgmG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Normalizer:\n",
        "  def __init__(self,mean,stdev):\n",
        "    self.mean=mean\n",
        "    self.stdev=stdev\n",
        "  def normalize(self,t):\n",
        "    return (t-self.mean)/(self.stdev)"
      ],
      "metadata": {
        "id": "SrUDNwh0Wy_R"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalizing the data"
      ],
      "metadata": {
        "id": "2CT3sFEwsOjx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for c in list(data.columns)[:4]:\n",
        "  normalizer=Normalizer(data[c].mean(),data[c].std())\n",
        "  data[c]=data[c].apply(normalizer.normalize)"
      ],
      "metadata": {
        "id": "kGTVA-aMeCR-"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train-test split(70-30)"
      ],
      "metadata": {
        "id": "dOuYhOdGsQoR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_set=data.sample(frac=0.7, replace=False)\n",
        "test_set=data.drop(train_set.index)\n",
        "\n",
        "train_x=train_set.drop(columns=[\"class\"])\n",
        "train_y=train_set[\"class\"]\n",
        "\n",
        "test_x=test_set.drop(columns=[\"class\"])\n",
        "test_y=test_set[\"class\"]\n",
        "\n",
        "train_x=train_x.to_numpy()\n",
        "train_y=train_y.to_numpy()\n",
        "test_x=test_x.to_numpy()\n",
        "test_y=test_y.to_numpy()"
      ],
      "metadata": {
        "id": "z0ko0J18g8JN"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding the column for the constant term w0 in the X matrix"
      ],
      "metadata": {
        "id": "mbkLv0c_sUTJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_x_final=np.insert(train_x,0,1,axis=1)\n",
        "test_x_final=np.insert(test_x,0,1,axis=1)"
      ],
      "metadata": {
        "id": "4GQB91qvlp_J"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we build the models\n",
        "\n",
        "#1. Logistic Regression"
      ],
      "metadata": {
        "id": "j9Eo4NeksQya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(w,x):\n",
        "  t=((w.T)@x)\n",
        "  sig=1/(1+np.exp(-t))\n",
        "  return sig"
      ],
      "metadata": {
        "id": "BKyp6UDWsOLm"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gradientDescent(w,x,y,n,alpha):\n",
        "  for i in range(n):\n",
        "    s=[0]*len(x)\n",
        "    for j in range(len(x)):\n",
        "      s[j]=(sigmoid(w,x[j]))\n",
        "    e=y-s\n",
        "    delJ=(x.T@(-e))\n",
        "    w=w-alpha*delJ\n",
        "  return w"
      ],
      "metadata": {
        "id": "8Kxa0XtmvUaz"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Multivariate"
      ],
      "metadata": {
        "id": "FwEL7hhvsmLM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the weights for multivariate logistic regression"
      ],
      "metadata": {
        "id": "tDAkwQ2TscUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "W=gradientDescent(np.array([1]*len(train_x_final[0])),train_x_final,train_y,4000,0.1)"
      ],
      "metadata": {
        "id": "4jMGrV671F_7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(w,x):\n",
        "  yp=[]\n",
        "  for i in range(len(x)):\n",
        "    yp.append(sigmoid(w,x[i]))\n",
        "  return yp\n",
        "\n",
        "def findAccuracy(yp,y):\n",
        "  correct=0\n",
        "  wrong=0\n",
        "  tp=0\n",
        "  fp=0\n",
        "  tn=0\n",
        "  fn=0\n",
        "  for i in range(len(yp)):\n",
        "    if(round(yp[i])==y[i]):\n",
        "      correct+=1\n",
        "      if(y[i]==1):\n",
        "        tp+=1\n",
        "      else:\n",
        "        tn+=1\n",
        "    else:\n",
        "      wrong+=1\n",
        "      if(y[i]==1):\n",
        "        fn+=1\n",
        "      else:\n",
        "        fp+=1\n",
        "  percentageAccuracy=correct*100/len(yp)\n",
        "  #pr=tp/(tp+fp)\n",
        "  #rec=tp/(tp+fn)\n",
        "  f1=tp/(tp+(fp+fn)/2)\n",
        "  return correct, wrong, percentageAccuracy, [[tp,fn],[fp,tn]], f1"
      ],
      "metadata": {
        "id": "OzKm5Oe838za"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(W,train_x_final),train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NshlaKA5dkK",
        "outputId": "df9ebdb4-e9a7-4d46-b13b-a0cf2e77c1c1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(906, 3, 99.66996699669967, [[419, 1], [2, 487]], 0.9964328180737217)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(W,test_x_final),test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32D2UW-k6w5I",
        "outputId": "c62be6db-9508-49dc-80e0-495d8d9b4465"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(387, 3, 99.23076923076923, [[156, 2], [1, 231]], 0.9904761904761905)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(train_set,x=\"class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "eOaJL9E87WkS",
        "outputId": "06b8ee0c-2787-4791-a0ef-646b1a322aa1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f83f4c3d450>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(test_set,x=\"class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "AGchOVmG7oZR",
        "outputId": "7ab4db32-ce7e-4801-ba7f-086e8813c7bb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f83f4c3c250>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Naive Bayes\n",
        "Univariate"
      ],
      "metadata": {
        "id": "oNs4kCdFVoKI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will split the training data according to the classes and get mean and variance for each column wrt each class"
      ],
      "metadata": {
        "id": "1zowl7_9sw7y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating the prior probabilities according to the training data given\n",
        "p1=np.count_nonzero(train_y)/len(train_y)\n",
        "p0=1-p1\n",
        "\n",
        "# to find mean and variance of each class, we need to divide the dataset appropriately first\n",
        "train_x0=[]\n",
        "train_x1=[]\n",
        "for i in range(len(train_x)):\n",
        "  if(train_y[i]==0):\n",
        "    train_x0.append(train_x[i])\n",
        "  else:\n",
        "    train_x1.append(train_x[i])\n",
        "train_x0=np.array(train_x0)\n",
        "train_x1=np.array(train_x1)\n",
        "\n",
        "# mean and variance (of each column)\n",
        "m0=np.mean(train_x0,axis=0,dtype=np.float64)\n",
        "m1=np.mean(train_x1,axis=0,dtype=np.float64)\n",
        "v0=np.var(train_x0,axis=0,dtype=np.float64)\n",
        "v1=np.var(train_x1,axis=0,dtype=np.float64)\n",
        "\n",
        "def N(m,v,x):\n",
        "  return math.exp(-((x-m)**2)/(2*v))/(math.sqrt(2*math.pi*v))"
      ],
      "metadata": {
        "id": "mYOMBj3ZVxCb"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predictNB(m0,v0,m1,v1,x):\n",
        "  gp0=N(m0,v0,x)\n",
        "  gp1=N(m1,v1,x)\n",
        "  if(gp0*p0>gp1*p1):\n",
        "    return 0\n",
        "  else:\n",
        "    return 1\n",
        "\n",
        "colToIndex={\n",
        "    \"variance\":0,\n",
        "    \"skewness\":1,\n",
        "    \"curtosis\":2,\n",
        "    \"entropy\":3\n",
        "}\n",
        "\n",
        "def testModel(feature,x,y):\n",
        "  ftind=colToIndex[feature]\n",
        "  correct=wrong=0\n",
        "  tp=0\n",
        "  fp=0\n",
        "  tn=0\n",
        "  fn=0\n",
        "  for i in range(len(x)):\n",
        "    ypi=predictNB(m0[ftind],v0[ftind],m1[ftind],v1[ftind],x[i][ftind])\n",
        "    if(ypi==y[i]):\n",
        "      correct+=1\n",
        "      if(y[i]==1):\n",
        "        tp+=1\n",
        "      else:\n",
        "        tn+=1\n",
        "    else:\n",
        "      wrong+=1\n",
        "      if(y[i]==1):\n",
        "        fn+=1\n",
        "      else:\n",
        "        fp+=1\n",
        "  #pr=tp/(tp+fp)\n",
        "  #rec=tp/(tp+fn)\n",
        "  f1=tp/(tp+(fp+fn)/2)\n",
        "  #returns correct guesses, wrong guesses, acurracy %, confusion matrix, and f1 score\n",
        "  return correct,wrong,correct*100/len(y), [[tp,fn],[fp,tn]], f1\n"
      ],
      "metadata": {
        "id": "XmtRyyF5bg6v"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import norm\n",
        "def plotGaussian(m0,s0,m1,s1,title):\n",
        "  x0=np.linspace(m0-3*s0,m0+3*s0,1000)\n",
        "  x1=np.linspace(m1-3*s1,m1+3*s1,1000)\n",
        "  plt.plot(x0,norm.pdf(x0,m0,s0))\n",
        "  plt.plot(x0,norm.pdf(x1,m1,s1))\n",
        "  plt.title(title)\n",
        "  plt.xlabel(\"x\")\n",
        "  plt.ylabel(\"p(x)\")\n",
        "  plt.legend(['0','1'])"
      ],
      "metadata": {
        "id": "9FUefjFwl_h9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will test using every column/featue one by one"
      ],
      "metadata": {
        "id": "LzlyO-Nod8le"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I can choose to just do this for a few columns/features with high correlation with output, but lets try to build a model with each one and see the if the correlation trend follows in the performances."
      ],
      "metadata": {
        "id": "S3yGe684uevq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Variance"
      ],
      "metadata": {
        "id": "0wNxWVv_hTRm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"variance\",train_x,train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_UmdpPleEZd",
        "outputId": "c98a52db-807c-4770-acaa-1c2d40e44aff"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(787, 122, 86.57865786578658, [[357, 63], [59, 430]], 0.854066985645933)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotGaussian(m0[0],math.sqrt(v0[0]),m1[0],math.sqrt(v1[0]),\"variance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Q4uW0itznbhJ",
        "outputId": "c4981f1a-c35f-46d0-baae-97079e5fe74d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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0ppQu+qc1ClLqCmkhUJXL6omQedha+/e8VcHW7T3G9F+SuLtTfbo2CrYp4OXp27I2g9rW5a2fd7F1//Fzd7q5Qc9nIWMv/PqhPQFVpaCFQFUeOSfhl7esVhLhcefsys4r4C9zN1G3WhX+2repTQGvzPMDW1DDz4snZm8kN/+8U0RRvaBeZ1j6GuRl2xNQOT0tBKryWDsNTqfDtU/+YdcbC3eRmJbJKze3sv1+gctV3deLVwa3YsfBk7yzePe5O0WsGUQnD8CGj+wJqJyeFgJVOeRmWaOBhj0h7NyJEZtTjjN5aSJDYsLo1tg5Tgmdr3fzWtzYpi7vLE5gz5HzVitr0A3CO8Dy/+m1AnVFtBCoymHddMg6At3ObSVRWGh45qst1PD14ukBzW0KVzb+PqAZ3u5u/P2rLefe0CQC3f4Cx5Nh0yz7AiqnpYVAOb+807DiDYjoaq3oVczs+GQ2Jmfwt/5NqVbl4ktTVnQhAT480acxy3Yd4dvzVzaL6g11o2HZf6x7DJS6DA4tBCLSV0R2ishuERlfwv4RIpImIhuK/tzvyDyqklo/A04d+kNjuYysXF79fgexETUYHB1qU7iydVenCFqGBvDC19s4mZ33+44zo4L0PbBlrm35lHNyWCEQEXdgAtAPaA4ME5GSxuafGmPaFv2Z6qg8qpLKz4EV/4N6nawRQTGv/biTE9n5vDCoJSIV88axy+XuJrx8UyvSTuXwxk+7zt3ZuB+EtLBmEBUW2BNQOSVHjgjigN3GmERjTC4wCxjkwPdTrmjTp3AiFbr9+Zz7BrakHufj1fu4q2N9mtUJsDFg2WsTXp0h7cP5YOUekopfOHZzs34OR3fBti9ty6ecjyMLQSiQXOxxStG2890iIptEZK6IhDswj6psCgthxZtQu/U5dxEbY3jxm20E+nrxp+sa2xjQcZ7o0xhPdzf+77vt5+5oPgiCoqwZRE7WR0zZx+6LxV8DEcaY1sAC4IOSDhKR0SISLyLxFbnrnypnv31vffq95tFzRgMLtx9mddIxHuvdyOkvEF9ISIAPY7s35Ieth1iZcPT3HW7u0PlhOLgJkpbYF1A5FUcWglSg+Cf8sKJtZxljjhpjcooeTgXal/RCxpjJxpgYY0xMcLBzzgNXDrDiDahWD5rfdHZTfkEhr3y3nciafgyNq2djOMe7v2skdav58NK32ygoLPbpv/VQ8AuxRktKlYIjC8FaoJGINBARL2AoMK/4ASJSp9jDgcB541ylLmDfKkheBZ3HnbPewKfxySSkZfJkv6Z4uts94HUsH093nuzXlK37T/D5+pTfd3j6QIcxkLAQDm6xL6ByGg77l2KMyQfGAT9g/YKfbYzZKiIviMjAosMeEZGtIrIReAQY4ag8qpJZ8aa13kD0nWc3ncrJ5/UFvxEbUYM+zWvZGK783Ni6Lm3Cq/PajzvJzis2Uyj2PvD0s+62VuoSHPqRyRgz3xjT2BjT0BjzctG2Z40x84q+fsoY08IY08YY08MYs8OReVQlkfabtR5x7Cjw8ju7efKSBI6cyuVv/ZtVmumil+LmJjzVrymHTuTw4co9v++oUgPa3W3dU3A85UJPVwqw/2KxUpdv5Vvg4Q1xo89uOnwimynLkrihdR2i6/1xofrKrGNkEF0b1eSdxQnn3mTWaaw1c2jVu/aFU05BC4FyLicPwsZZ0PYO8P994sA7ixPILSjkz32a2BjOPn+9vikZWXlMWZb0+8bq9aDlzdaKbaczbMumKj4tBMq5rJkCBXnQ6aGzm/ZnnGbm6n3c1j6MiJp+F3ly5dUqrBr9WtZm2rJEjp7K+X1H50cg95TVlE+pC9BCoJxHXrb1C61JfwhqeHbzWz/vxmAY1zPKxnD2e6JPY07nFfDO4mLLWtZpDZE9YPUkq4AqVQItBMp5bP0cso5Ch9+vDew7msWc+GSGxdUjrIavjeHsFxVSlVvahTFj1V72Z5z+fUfHsdbCNdu+si+cqtC0ECjnYIz1qTa4KTS49uzmNxbuwt1NeKiHa48Gzni0dyMw8PaiYiuZRfW22k7oRWN1AVoIlHNIWQsHNkDcqLPtJBLSTvHFrync1bE+tQJ8bA5YMYTV8OW2mDDmxCf/Pipwc4MOD0BqPCSvtTegqpC0ECjnsHoSeFez2icUeeOnXfh4uvNA94YXeaLrGVs0Onq3+LWCNsOsn99qHRWoP9JCoCq+kwettsrRd4C3PwC7Dp3k6037uadzBDX9vW0OWLGEVq/Cre3D+HRtMgePZ1sbvf2h3V2w9Us4nnrxF1AuRwuBqvjip1sLrcT+voDdO4sT8PFwZ1TXSBuDVVxju0dRaAwTlxQbFcSNBgys1fWf1Lm0EKiKLT/XmjLa6LqzU0b3Hs1k3sb93NmxHoF+XjYHrJjCA325uV0on6zZx+ETRaOCGvWh6QDr55mbZW9AVaFoIVAV27avrPWI48ac3TRxSQLubqKjgUt4qEcU+YWGSUsTf9/Y4UE4nQ6bZ9sXTFU4WghUxbZmEgQ2hIY9AThw/DRz16Vwe0w4ITpT6KLqB/lxU9tQPl69l7STRXcb1+9srei2aqKuYKbO0kKgKq7U9da00bjR1hRIYNKSRIyBMdfqaKA0xvWMIje/kMlLi64ViEDHByFtOyQutjWbqji0EKiKa81k8PKHtsMBSDuZwydr9jE4OtTl7yIurQY1/RjYpi4fr95HRlautbHlLeAXrDeYqbO0EKiK6VQabPnMmv/uEwDAtOVJ5BUU8qDeN3BZHujekKzcAj5cudfa4OENMffBrh/haMLFn6xcghYCVTGtfx8Kcs+uOZCRlcuMlXsY0LoukcH+tkZzNk1rB9CraQjTVySRlZtvbWw/wlroPv49W7OpikELgap4CvJg7XtW18zgxgC8/8seMnMLeKiHjgauxIPdG5Kelcena5OtDQF1oNlA+HUG5GbaG07ZTguBqnh2fAMn91sLsAOZOflMX7GH65rXomntAJvDOaeYiEBiI2owZWkieQWF1sa40ZB9HDbPsTecsp0WAlXxrJlira7VqA8An65N5vjpPMbqtYGrMrZ7FPuPZzNvw35rQ72OUKsVrJmqU0ldnBYCVbEc3AJ7V1gL07u5k1dQyLTlScRFBLrcWsRlrXuTYJrWrsrEJQkUFhprKmnc/XBoM+xbZXc8ZSMtBKpiWTMJPKpA9J0AzN98gNSM03rfQBkQER7s3pBdh0+xcMdha2Or28CnmjVVV7ksLQSq4sg6BpvmQOsh4BuIMYaJSxJpFOJPjyYhdqerFAa0qkN4YBXeWbwbYwx4+UH0XbB9Hpw4YHc8ZROHFgIR6SsiO0Vkt4iMv8hxt4iIEZEYR+ZRFdyvMyD/9NmLxMt2HWH7gROM6haJm5vYHK5y8HB3Y3S3hvy6L4PVScesjTEjre6u6963NZuyj8MKgYi4AxOAfkBzYJiINC/huKrAo8BqR2VRTqCwwGqPXL8L1GoBwOSlidQK8GZQ27o2h6tcbmsfRk1/r98XuQ9qaHV3XTfd6vaqXI4jRwRxwG5jTKIxJheYBQwq4bgXgVeBbAdmURXdb99Dxr6zC9NvST3O8t1HGHlNA7w93G0OV7n4eLpz7zUNWPpbGtsPnLA2xo6yurzu+NrecMoWjiwEoUBysccpRdvOEpF2QLgx5tuLvZCIjBaReBGJT0tLK/ukyn6rJ0FAGDQZAMCkpYn4e3swrEM9m4NVTnd0qIevlztTlyVZG6J6Q40Ia+qucjm2XSwWETfgv8ATlzrWGDPZGBNjjIkJDg52fDhVvg7vgKQlEDsS3D1IPpbF/M0HuKNDPQJ8PO1OVylV9/ViSEw48zamWstZurlZo4J9K+HAJrvjqXLmyEKQCoQXexxWtO2MqkBLYLGI7AE6AvP0grELWjMZ3L2h3QjAai7nJnDvNQ3szVXJ3delAQWFhum/FI0Kou+wpu6u1VGBq3FkIVgLNBKRBiLiBQwF5p3ZaYw5boypaYyJMMZEAKuAgcaYeAdmUhVN9nHYOAta3Qp+QaRn5vLp2mQGtQ2ldjVdeMaRwgN96d+qDjNX7eNkdh5UqQGtb7Om8J5OtzueKkcOKwTGmHxgHPADsB2YbYzZKiIviMhAR72vcjK/fgx5mWe7jM5YtZfTeQWM7qY3kJWH0d0iOZmT/3szuthR1hTeXz+2N5gqVw69RmCMmW+MaWyMaWiMeblo27PGmHklHNtdRwMuprDQOg0R3gHqtiU7r4D3f9lDz6YhNK5V1e50LqF1WHU6NAhk+oo9VjO6Oq2hXifr76Ww0O54qpzoncXKPrt/gmOJZ0cDc9alcCwzlzE6GihXo7tFkppxmvmbi+4sjhsF6Xusvx/lErQQKPusmQT+taH5IAoKDVOXJdI2vDpxDQLtTuZSejQJoWGwH1OWJVptJ5reaP29aP8hl6GFQNnjyG7rE2fMSHD35IetB9l7NIsx3SIR0XYS5cnNTRjVNZItqSdYmXAUPLysFcx2L9ClLF2EFgJlj7VTwM0T2o/AGMOkpYnUD/KlT4vadidzSTdFh1LT35vJyxKtDe1HgJuH1fZDVXpaCFT5yzlpzUppMRiq1mLtnnQ2Jmdwf9dI3LW5nC18PN0Z0bk+i3emsfPgyWJLWX4MOafsjqccTAuBKn8bZ0HuybNdRicvTaSGrye3tguzOZhru6NDfap4ujP1zKigwxjIOQ6bPrU3mHI4LQSqfBljXYSsGw2h7UlIO8VP2w9xV6cIqnhpczk71fDzYkhMGF9uSOXwiWxrWm/t1lb/IV3KslLTQqDKV+IiOPIbxI0BEaYuS8LLw427O9W3O5kCRha1nXj/lz3WUpYdxkDadtizzO5oyoG0EKjytXoy+NaEljdz5FQOn61P4ZZ2YdT097Y7mQLqB/nRt2VtPlq1l8ycfGh5C1QJtLrDqkpLC4EqP+l7rHUH2o8AD28+XLmXvIJC7u+qzeUqklFdIzmRXdR2wrMKtLsbds631otQlZIWAlV+1k4FcYOYkZzOLWDGyj30blaLhsH+didTxUTXq0FsRA2mLU8iv6AQYu+zdqydZm8w5TBaCFT5yM2C9TOg2Y1QLZS565JJz8rT5nIV1OhuDa22E1sOQvV60KQ/rP8Q8k7bHU05gBYCVT42fQrZGdBhjNVOYnkSbcOrE1O/ht3JVAl6NQ0hMtiPKUuL2k50GAOnj8GWz+yOphzgsgqBiPgVLUqvVOkZY11srG11tlywzWonMVrbSVRYZ9pObE49zsrEoxDRFYKbWX+POpW00rloIRARNxEZLiLfishhYAdwQES2ici/RSSqfGIqp5a01JqC2OEBEGHy0kTCA6twvbaTqNAGF7WdmLQk0ZpKGjcKDm6C5NV2R1Nl7FIjgkVAQ+ApoLYxJtwYEwJ0wVpR7FURudPBGZWzWz0JfIOg5S2s23uM9fsyuL+LtpOo6M60nVjyWxo7Dp6A1reDdzXtSloJXaoQ9DbGvGiM2WSMObtKhTHmmDHmM2PMLYDef64uLH2PNfWw/b3g6cPkpYlUq+LJbTHaTsIZ3NmxPr5e7kxemgje/hB9J2z7Ck4csDuaKkMXLQTGmDwAEel9/j4Ruaf4MUqVaM0Ua8po7H0kHcnkx22HuKtjfXy9POxOpkqhuq8Xt8eGM2/Dfg4cPw1x90NhAaybbnc0VYZKe7H4WRF5t+hicS0R+Rq40ZHBVCWQc8qaMtp8EATUZdryRDzd3Li7s7aTcCb3dWmAAd5bngSBkdCoD8RPh/xcu6OpMlLaQnAtkABsAJYDM40xtzoslaocNn1qda/s8ABHT+UwJz6FwdGhhFT1sTuZugxhNXwZ0KoOn6xJ5kR2nrW0aOZh6xSRqhRKWwhqAHFYxSAHqC86709dzJkpo3XaQngcM1btJSe/kFHdtJ2EMxrdLZJTOfnMXL0PGvaEwIbWUqOqUihtIVgFfG+M6QvEAnWBFQ5LpZxf4mI4shM6PEB2fiEfrtxLr6YhRIVUtTuZugItQ6vRJaom01ckkVNorFFBylpIXW93NFUGSlsIehtj3gMwxpw2xjwCjHdcLOX0Vk8Cv2BoeTOfrU/hWGYuo7SdhFMb3S2SQydy+GrDfmg7HLz8dSppJXGpG8oiAIwxf2g7aIxZKpYLznnrfywAABy9SURBVAMUkb4islNEdovIHwqHiDwgIptFZIOILBeR5pf/LagK51hiUZfReylw82LasiRah1WjQ4NAu5Opq9C1UU2a1QlgytJECr2qQpthVsuJU4ftjqau0qVGBP8Wkc9E5G4RaSEiISJST0R6isiLWKeHmpX0xKJWFBOAfkBzYFgJv+hnGmNaGWPaAv8C/nt1346qENZMBTd3iBnJgm0HSTySyaiu2k7C2YkIY7pFsuvwKRb/dti6U7wgV7uSVgKXuo/gNuDvQBOsX+pLga+A+4GdQE9jzIILPD0O2G2MSTTG5AKzgEHnvf6JYg/9AG1i4uxyTsGvM6D5TZiqtXl3cQL1g3zp11LbSVQGA1rXoW41HyYuSYSaUdC4n9VeXLuSOrVLXiMwxmwDXgK+BrYDScBaYK4xJvsiTw0Fkos9Tinadg4ReUhEErBGBI+U9EIiMlpE4kUkPi0t7VKRlZ02zIScE9DhAVYmHGVjynFGd4vEw10b3VYGnu5ujOzSgDVJx9iQnAGdHoKsI7Bptt3R1FUo7b/OD7BOAb0JvIV1qufDsghgjJlgjGkIPAk8c4FjJhtjYowxMcHBwWXxtsoRCgtg1QQIi4PwWN5dkkBNf29uaaftJCqToXH1CPDxYPLSBIjoArVbwap3tCupEyttIWhpjLnfGLOo6M8ooOUlnpMKhBd7HFa07UJmATeVMo+qiHZ8a/UW6jyOLanHWbbrCPd1aYCPp3Yur0z8vT24s2N9vt9ykD1Hs6DTOEjbAQkL7Y6mrlBpC8F6Eel45oGIdADiL/GctUAjEWkgIl7AUGBe8QNEpFGxhwOAXaXMoyqilW9D9frQ9AbeXZJAVW8P7uhYz+5UygFGdI7Aw82NqcsTocXN4F8bVk6wO5a6QqUtBO2BX0Rkj4jsAVYCsUVTPzeV9ARjTD4wDvgB69rCbGPMVhF5QUQGFh02TkS2isgG4HHgnqv5ZpSNktdafeo7jiXpWDbfbT7AnZ3qE+DjaXcy5QAhAT4Mjg5lTnwKR7INdBgNCT/DoW12R1NXoLQtIPteyYsbY+YD88/b9myxrx+9ktdVFdDKt8GnGkTfyeRvE/Fwd+PeayLsTqUcaMy1kcxZl8y05Uk82e1eWPqadY1okI4MnE2pRgTGmL0X++PokKqCS98D2+dB+3s5nOPBZ+tSuLV9mDaXq+Qig/3p36oOM1bu5ThFN5htmqM3mDkhndOnrt6qidaaAx3GMG1FEvmFhYzuqu0kXMHY7lGcysnnw5V7oONYKMjRG8yckBYCdXVOZ1g3kLW8heOewXy8ah/9W9Uhoqaf3clUOWheN4BeTUN4b0USmVUj9AYzJ6WFQF2d9R9A7inoNI6PVu3lVE4+D1zb0O5UqhyN7RFFelYen6zZV+wGM13B1ploIVBXriDP6jLaoBung1rw3vIkujaqScvQanYnU+Woff0adIoMYvLSRHLCOllrUPzylnWDoXIKWgjUlds8F06kQqeH+Xj1Xo5m5vJIr0aXfp6qdMb1jOLwyRzmrk+FLo/B0d2w4xu7Y6lS0kKgrkxhISx/HUJakB3Rk0lLE+kUGURshLaadkWdGwbRJrw6E5ckkN/4Bmtt4+Wva9sJJ6GFQF2ZnfOtFci6/IlP41NIO5nDw72i7E6lbCIijOsRRfKx03y95RBc8yjs/xWSltgdTZWCFgJ1+YyB5f+F6vXJaTqQiUsSiI2wzhMr19WraQhNa1flnUUJFLYaarWdWP663bFUKWghUJdvzzJIXQfXPMLcXw9y4Hg2D/dspAvPuDg3N+HB7g3ZdfgUP+xMh05jrbWrdV3jCk8Lgbp8y18HvxDyWg/nnUUJtA2vTtdGNe1OpSqAG1rXpUFNP95YuIvCdiPAuxqs+J/dsdQlaCFQl2f/r1ZzsY4P8sWmo6RmnObRXjoaUBZ3N+GRXlHsOHiSH3ZnQdz9sG0eHNltdzR1EVoI1OVZ/j/wDiC/3b28vWg3rUKr0b2JLhakfjewTSiRwX7876ddFMaOAQ9v+OUNu2Opi9BCoErvyG7Y9hXE3se8nZnsO5bFwz2jdDSgzuHuJjzaqxE7D51k/p4CiL4TNs6C4xdbl0rZSQuBKr3l/wUPb/JiH+DNhbtoVieA65rXsjuVqoBuaF2XqBB/3vhpFwWdHgFTqNcKKjAtBKp0jiVan+piRvL5b7nsOZrF49c11tGAKtGZUcGuw6f4NtkT2t4B696HE/vtjqZKoIVAlc7S/4C7JzkdxvHmwt20Ca9O72YhdqdSFdiAVnVoXMufN376jYIuj1ujguU6KqiItBCoSzuWCBs/gfb3Mmt7HqkZp/lzHx0NqItzcxMe692YhLRMvt7raS1cs+59OHHA7mjqPFoI1KUts0YDpzs8zNuLdtOhQSBdovS+AXVpfVvUpmntqry5cBf51zwOpgBW6AyiikYLgbq4Y0mw4RNoP4IPN2eTdjKHP1/fREcDqlTc3IQ/XdeYxCOZfJbkAW2GwrrpcPKg3dFUMVoI1MUt+w+4eXAq5iHeXZLAtY2DtcOouix9mtciul51Xl+wi5xOj1vrWOiooELRQqAuLH1P0bWBEUzdmE1GVh5P9GlsdyrlZESEJ/s25eCJbKZvxxoVxL8HJw/ZHU0VcWghEJG+IrJTRHaLyPgS9j8uIttEZJOILBSR+o7Moy7T4ldB3EmPHsvUZUn0aV6L1mHV7U6lnFDHyCC6NwnmnUW7ORn7mDUqWPYfu2OpIg4rBCLiDkwA+gHNgWEi0vy8w34FYowxrYG5wL8clUddpsPbrdFAh9G8sTaT03kF/LVvE7tTKSf21+ubcjInnwmbCqHd3daoIH2P3bEUjh0RxAG7jTGJxphcYBYwqPgBxphFxpisooergDAH5lGX4+eXwLsqe5uN4aNVe7k9NpyokKp2p1JOrHndAAa1qcv0FUkcbvcYuLnDolfsjqVwbCEIBZKLPU4p2nYh9wHfOTCPKq3ktdZ6s50f4dWlh/HycOOx3roWsbp6T/RpQqExvL76BHQYA5s+hUNb7Y7l8irExWIRuROIAf59gf2jRSReROLT0tLKN5yrMQYWPg9+wfwaOoz5mw8yulskIVV97E6mKoHwQF/u6FCf2fEpJDYZBd4BsPBFu2O5PEcWglQgvNjjsKJt5xCR3sDTwEBjTE5JL2SMmWyMiTHGxAQHa8tjh0r4GfYsw3T9My8t2EdwVW9GdY20O5WqRMb1jMLX052Xfz4IXR6F376DfavsjuXSHFkI1gKNRKSBiHgBQ4F5xQ8QkWhgElYROOzALKo0Cgth4QtQrR4/VunPur3p/Kl3Y/y8PexOpiqRmv7ejOsZxcIdh1kedCv4hcBPz1ujUWULhxUCY0w+MA74AdgOzDbGbBWRF0RkYNFh/wb8gTkiskFE5l3g5VR52DIXDmwg/9qn+L8FiUSF+DMkRq/fq7I34poI6gf58vz3eyjo9hfY9wv89oPdsVyWQz/qGWPmA/PP2/Zssa97O/L91WXIzYKfnoM6bXnvZCxJR35j+r2xeLhXiMtIqpLx9nDnb/2bMWbGOmbm9+CuoCj48RmI6gXunnbHczn6r1xZVk6AE6mkd32ONxYm0KtpCD2aaJtp5Th9mteiU2QQ/1mYROa1z8PRXbB2mt2xXJIWAmW1BV7+OjS7kZe2BJJXYPj7Deff+6dU2RIRnr2xOSdO5/HangiI7A6LX4GsYzYncz1aCBQsegkKctna4s98tj6F+7o2IKKmn92plAtoVieA22PrMWPVPvbFPg05J2DJq3bHcjlaCFzdgY3w68eYuDH8bfEpagV4M65HlN2plAt5ok9jfL3ceXJZIabdPbB2KqT9Zncsl6KFwJUZA98/BVVq8GXAcDamHGd8v6Y6XVSVq5r+3vy1b1NWJh7l+5CR4OkLPz5tdyyXooXAlW2aDXtXkNn1GV74KZXYiBrc1PZiXUCUcoxhcfVoE1aNvy84zOlOj8OuH2HH/Es/UZUJLQSu6nSGNV0vtD3PJkdzKieffw5upSuPKVu4uwkvD27FscwcXs3oAcFN4bsnITfT7mguQQuBq1r0T8hMY2Prv/PZrwcY060hjWppd1Fln5ah1bi7UwQfrE4lIe5FOL4Plr5mdyyXoIXAFR3YCGunkN9+JI8tEyKCfBnXUy8QK/s93qcxNf29+dNqXwpbD4Nf3oK0nXbHqvS0ELiawkL49gmoEshE92EkHcnk5cGt8PF0tzuZUgT4ePKPG5uzKeU4M6reB15+1v+v2ofIobQQuJq1UyFlLQc6PMMbK9K4OTqUa6Jq2p1KqbMGtKrD9S1q8fKSI6R1GA97llkTG5TDaCFwJel74afnKGzYi9Ebowjw8eTpAc3sTqXUOUSEF29qSRVPdx7c3goTGgvfj4dT2qDYUbQQuApj4JvHQIQPAh9j8/4TvHRTS4L8ve1OptQfhFT14R83Nid+33G+CB8Puadg/p/tjlVpaSFwFRtmQsLPHIh9kpdXnGJgm7r0a1XH7lRKXdDg6FB6Ng3hbyvySI97ArZ9BVu/tDtWpaSFwBWcPAg/PEVheEdGbmlNDT8vnh/Ywu5USl2UiPDPwa3wdHdjTMI1mDptrVFB5lG7o1U6WggqO2Ng3sOQn8N7QY+z/VAmrwxuRQ0/L7uTKXVJtav58OKglqzZd4JP6oy3boT87q92x6p0tBBUdmunwq4fSYx+kpdX5zMkJozezWvZnUqpUrspOpRBbevy91WG/W0ftlbS2zzX7liVihaCyixtJ/z4DLkNejJsQysa1PTjOT0lpJzQC4NaUjvAhzt3XENBaBx88ydrFpwqE1oIKqv8XPh8FMbTl6fyx5Celc9bw6Lx9dLOosr5VKviyeu3t2VPeg6v+j5ubfx8NBTk2xusktBCUFktehkObGRRk7/z2a4CnurflBZ1q9mdSqkrFtcgkHE9opi8uZDVzf4Gyatg2X/sjlUpaCGojH77AVb8jyNNhvHA2jr0bBrCiM4RdqdS6qo92rsxnRsGcXd8BBlRg63VzPautDuW09NCUNmk74XPR5Mf0pJbkwYSEuDNf4e00fbSqlJwdxPeHBZNdV9Phu+/lYLq9WDOCDh5yO5oTk0LQWWSnwNz7sGYQp7gcQ5kCRPvbE91X50qqiqPmv7eTBjejt8y3HjJ9ylM9nGYO1KvF1wFhxYCEekrIjtFZLeIjC9hfzcRWS8i+SJyqyOzuIQf/gb7f2Vu+N/4ap8Pr9zcipahel1AVT4xEYGM79eU6Qn+/NjwKdi7HBY+Z3csp+WwQiAi7sAEoB/QHBgmIs3PO2wfMAKY6agcLmPdB7B2KrsajuAvW+pxT6f63NwuzO5USjnMfV0aMDg6lDEbo9gTOdxau2DrF3bHckqOHBHEAbuNMYnGmFxgFjCo+AHGmD3GmE1AoQNzVH5Jy+Dbx8mo05Ubd/SmU2QQTw84v+YqVbmICK/c3Ir29Wtw467+ZIa0gy8ehNT1dkdzOo4sBKFAcrHHKUXbLpuIjBaReBGJT0tLK5NwlcbRBJh9F7nVIrjhwH2EBlZl4p3t8fLQyz+q8vPxdGfSXe0J8PPj1vSHKPCtCZ8MheMpdkdzKk7x28IYM9kYE2OMiQkODrY7TsVxOgM+GUqhgXuyn+C0uz/TR8RRzdfT7mRKlZua/t5MGxHDvhx/xponMblZMPN2yDlpdzSn4chCkAqEF3scVrRNlYW80zBrOOZYIn/3Hs+6kzWYfHcM9YJ87U6mVLlrWjuAiXe15+djQbzsNx5zeHvRTKI8u6M5BUcWgrVAIxFpICJewFBgngPfz3UU5MPckZi9v/B2wJ+ZlVaPt4ZF075+DbuTKWWbro2Cef32tkw72IAZgQ/Drh/hq4esdbrVRTms8YwxJl9ExgE/AO7Ae8aYrSLyAhBvjJknIrHAF0AN4EYRed4Yo13RLsYY+PpR2DmfmUHj+E9qa16/vTXXt6htdzKlbHdD67pkZOXxzJcQFH4/AzZNBe+q0P810JsqL8ihHciMMfOB+edte7bY12uxThmp0jDGuldgw0d8F3QPT6d25sVBLRgcrT9Cpc64s2N90jNzeWiBwS80k+5rp4JPNej17KWf7KK0FaWzMAa+fwpWv8vP1W/hwdQ+/LVvE+7qFGF3MqUqnId7NaLAGEb8dAOf1D5Jp2X/AXcvuPZJHRmUQAuBMzAGvh8PqyeyIOBmRh28maf7N2dUt0i7kylVYT3WuzFuItyxYCizauUTt/gVa5JF7+e0GJxHC0FFV1gA3z4B66Yz3/8Wxh6+mRcHtdSRgFKl8EivRrgJ3P7jnUwP9qT7iv9Bfjb0/T8tBsVoIajI8rLh8/th+9fMrXIbfzl6E/+6tQ1DYsIv/VylFADjejbC39uDe78R3qzhzo2rJ0LOKbjxf+Cu99yAFoKK63QGzBoOe1fwhudIJp66nsl3RXOdrjes1GUbcU0Dgqv68KdP3Uj38+buDR/BiVQY8oF1IdnFaSGoiI7shlnDKDyWxJM8yiLTjU/HxNA6rLrdyZRyWgNa1yHI34tRH7qzxy2QZ5Km4PZeXxg+G6q79ijbKVpMuJRdP2Gm9OD08cMMzx7PhoBefDG2sxYBpcpAx8ggvhh7DYt9+zIi96/kHtuHmdrL5Vc500JQURQWwoo3MDNvI8UEc92p5wls0YMvHrqG8EBtG6FUWYkK8efLcdfg2agnAzKf5UiuB+b9AbBygjVDzwVpIagITqXBzCGw4FmWuHei36lnGNG/GxOGt8PfW8/eKVXWAnw8mXJ3DP16dKfniedZ7h5r3aw55x7IPmF3vHKnv2XslvAz5vMxFGRl8GL+vfzodQNT74+mY2SQ3cmUqtTc3ITH+zQhrkEQj39anZsLvuCv22chqeuRm96FBl3tjlhudERgl+wT1v0BMwaTnO3DgOwXyGh5D98/dq0WAaXKUZdGNfn+T9eyO2okt2Q/y4FThfDBDdad/Hmn7Y5XLnREYIed32G++ROcPMgHBX15lzt5Zmh7bmxT1+5kSrmkQD8vptzdnjnxtRj8bQMeLvyIO1e9g/ntB2TAa9Cwp90RHUoLQXk6lohZ8Cyy/WsSpR5P5DxPZPS1fNu/GTX9ve1Op5RLExGGxIbTvWkwz88L57ut7Xk1/X3CZgzGtBiMXP9PCKicH9bEONlV8piYGBMfH293jMuTfRyWvkbhqonkGuGt3EH8VGMIzw1uR6eGehpIqYpo4fZDvPr1Bq4/PpuHvebh4e6B2zUPQ6dx4BNgd7zLJiLrjDExJe7TQuBAOSdhzWTyV7yFR3Y6c/K7Mc37Lu64rgNDY8PxdNdLNEpVZLn5hcxYtZe5Py1jXMFHDHBfTb5PIB7X/gViRoKnj90RS00LQXnLPg6rJ5P/y9t45GSwqKAN78hQunS7jvu7NsBPp4Qq5VTSM3OZtDSRX1f+xMPMpIvbVvKqBOPZeSzE3ucUbSq0EJSXtJ0UrJqE2TATj4LT/FQQzXSPIXToch13d6pPdV8vuxMqpa5CemYu05Ynse2XbxhR+AXd3DeT5+GHW+y9uMfeD4EN7I54QVoIHCk3C3bOJ2v1+/imLCMHT74u6MR3fjdxTZeeDI0Lx9dLRwBKVSbHs/KYHZ/Mql8WclPmXPq7r8GdQrLCu+Hb8T5o0h88KtYHPy0EZa0gH/auIHPtx3j+9g1eBZmkmiA+KejNwajbGdi5NV2iauLmpv3OlarMCgoNi3Yc5uvl8UTs+5wh7osIlaNke1ansOkN+EbfBvW7gLv9Hwa1EJSF7OMU7lpIxoavqLJ3EVXyj3PSVOG7gjg2BfWlQcz13NgmlJAA57l4pJQqO4dPZDPv12T2rf2a9scX0NttHX6SQ5ZnIDkN+1K9dT8ksrttM460EFyJ3EwK963h2LaFFCQtJyh9Mx7kc8z4s6gwmsTArgS06k+/6IbUC9KmcEqp3+06dJJFW/aQsXE+LdIX0s1tE1XlNAW4cyQwGo9GvQhs1g2p2w68yuf3hxaCSynIwxzeTnpCPKf2rMPz0EaCT27DgwLyjRtbTAQbPVqTEdaTiOgedGtcmxp+Fev8n1KqYko7mcPibakc2LqUgJQlxOavo4XbXgDycSfNvyl5dWKpFhVHtYhoqNnIISunaSE4IzeT3MO7OLZ3K5n7d2CO7KLKiUSCTyfiRR4Ap4wP2019dvm0JLtuJwKbdaNdVDjhgVUQXeNUKXUVjDHsPZrF+h0JHNuxHL/D8URmb6WNJOAj1u+gPDxJq9KAzOpNcAtpQrXQptQIb4Z7zYbgWeWK39u2QiAifYE3AHdgqjHm/87b7w18CLQHjgK3G2P2XOw1r7QQbJj1PG13/Pfs40Ij7CeIfVKXw1WiyA5uhW+9doQ1akXTOtV0po9Sqlxk5eazPeUoyb9tJCt5I1XSt1MrazdRZi8hknHOsZvaPEvrwU9c0ftcrBA47LediLgDE4DrgBRgrYjMM8ZsK3bYfUC6MSZKRIYCrwK3OyJPfmgc3xy5DxMUhW+dJgTWa079WkF08vXUT/pKKdv4ennQPrIW7SP7AH0Aa+RwLDOX+NSDHN23jZxDu3BLTyKsbnuHZHDYiEBEOgHPGWOuL3r8FIAx5pVix/xQdMxKEfEADgLB5iKhKsT0UaWUcjIXGxE4stlNKJBc7HFK0bYSjzHG5APHgT90YROR0SISLyLxaWlpDoqrlFKuySm6nhljJhtjYowxMcHBwXbHUUqpSsWRhSAVCC/2OKxoW4nHFJ0aqoZ10VgppVQ5cWQhWAs0EpEGIuIFDAXmnXfMPOCeoq9vBX6+2PUBpZRSZc9hs4aMMfkiMg74AWv66HvGmK0i8gIQb4yZB0wDZojIbuAYVrFQSilVjhw6Wd4YMx+Yf962Z4t9nQ3c5sgMSimlLs4pLhYrpZRyHC0ESinl4pyu15CIpAF77c5RCjWBI3aHcAL6cyo9/VmVjv6cSlbfGFPi/HunKwTOQkTiL3QXn/qd/pxKT39WpaM/p8unp4aUUsrFaSFQSikXp4XAcSbbHcBJ6M+p9PRnVTr6c7pMeo1AKaVcnI4IlFLKxWkhUEopF6eFwIFE5DYR2SoihSKi09nOIyJ9RWSniOwWkfF256moROQ9ETksIlvszlKRiUi4iCwSkW1F/+4etTuTs9BC4FhbgJuBpXYHqWiKLWXaD2gODBOR5vamqrDeB/raHcIJ5ANPGGOaAx2Bh/T/qdLRQuBAxpjtxpiddueooOKA3caYRGNMLjALGGRzpgrJGLMUqzuvughjzAFjzPqir08C2/njqoiqBFoIlF1Ks5SpUldERCKAaGC1vUmcg0PbULsCEfkJqF3CrqeNMV+Vdx6lXJ2I+AOfAY8ZY07YnccZaCG4SsaY3nZncFKlWcpUqcsiIp5YReBjY8zndudxFnpqSNmlNEuZKlVqIiJYqx5uN8b81+48zkQLgQOJyGARSQE6Ad+KyA92Z6oojDH5wJmlTLcDs40xW+1NVTGJyCfASqCJiKSIyH12Z6qgrgHuAnqKyIaiP/3tDuUMtMWEUkq5OB0RKKWUi9NCoJRSLk4LgVJKuTgtBEop5eK0ECillIvTQqCUUi5OC4FSSrk4LQRKXSURiRWRTSLiIyJ+Rb3wW9qdS6nS0hvKlCoDIvIS4ANUAVKMMa/YHEmpUtNCoFQZKOqXtBbIBjobYwpsjqRUqempIaXKRhDgD1TFGhko5TR0RKBUGRCReVirrDUA6hhjxtkcSalS0/UIlLpKInI3kGeMmVm0FvMvItLTGPOz3dmUKg0dESillIvTawRKKeXitBAopZSL00KglFIuTguBUkq5OC0ESinl4rQQKKWUi9NCoJRSLu7/AVR/X596//TjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"variance\",test_x,test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnyuXT97hNCm",
        "outputId": "3d78fc60-2c3b-4c29-9b49-d8930eff29c1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(336, 54, 86.15384615384616, [[141, 17], [37, 195]], 0.8392857142857143)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Skewness"
      ],
      "metadata": {
        "id": "7N_En2uxheM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"skewness\",train_x,train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHonIvErhgBu",
        "outputId": "d8d8da38-1c8c-47fb-f46e-9275fcaef60a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595, 314, 65.45654565456546, [[236, 184], [130, 359]], 0.6005089058524173)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotGaussian(m0[1],math.sqrt(v0[1]),m1[1],math.sqrt(v1[1]),\"skewness\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OZoICPBmpM2u",
        "outputId": "29661178-2751-4fdd-dfd8-94976c6db789"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"skewness\",test_x,test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDvTM6cwhjW_",
        "outputId": "8368928f-7847-4c01-9e7c-40a33037e686"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(255, 135, 65.38461538461539, [[91, 67], [68, 164]], 0.5741324921135647)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Curtosis"
      ],
      "metadata": {
        "id": "4Yqsg9jchzrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"curtosis\",train_x,train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bdbd9ad-420a-4904-e972-7e8a0ecc7638",
        "id": "PLQYFJiVhzrN"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(562, 347, 61.826182618261825, [[131, 289], [58, 431]], 0.4302134646962233)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotGaussian(m0[2],math.sqrt(v0[2]),m1[2],math.sqrt(v1[2]),\"curtosis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "CRhiqMsipRJ9",
        "outputId": "4515d05a-a56c-4f25-c34f-6575578f38d5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"curtosis\",test_x,test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9da4d89-fb2c-4d0d-c5e3-4a8ebcf1c2ff",
        "id": "fMONd6BIhzrN"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(253, 137, 64.87179487179488, [[49, 109], [28, 204]], 0.41702127659574467)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Entropy"
      ],
      "metadata": {
        "id": "Yk0dv_h-h-wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"entropy\",train_x,train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "840fe19d-683b-4853-e0c5-bf51b2e7d729",
        "id": "H4ZIMXTmh-wj"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(489, 420, 53.79537953795379, [[0, 420], [0, 489]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we saw earlier, entropy had very less correlation with class, it is now evident with the poor perfomance of our model."
      ],
      "metadata": {
        "id": "4lZsUArJtu4S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model only predicts 0 here."
      ],
      "metadata": {
        "id": "miw4c9UWKhNo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plotGaussian(m0[3],math.sqrt(v0[3]),m1[3],math.sqrt(v1[3]),\"entropy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2dbjf_3EpZnn",
        "outputId": "2062cdd0-4303-4813-d5a4-aa770bdaa06e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testModel(\"entropy\",test_x,test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08926f23-7dab-4d44-c2d5-71ff01025525",
        "id": "zhdOpM0uh-wj"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(232, 158, 59.48717948717949, [[0, 158], [0, 232]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So its clear that with decreasing correlation with output, model performance decreases."
      ],
      "metadata": {
        "id": "Uq4gFzxYuwqb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multivariate Gaussian"
      ],
      "metadata": {
        "id": "eOdDwdfUvBmq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will not implement this but try to come with a few combinations of features that could give good results with multivariate gaussian."
      ],
      "metadata": {
        "id": "EZ102OoHvId6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We try to have the features as mutually independent as possible"
      ],
      "metadata": {
        "id": "UqJiQ5e40J8y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "So we should have those features together that have less correlation among themselves."
      ],
      "metadata": {
        "id": "DCRf0u5IvTSy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "Ik7tYmj10Rcj",
        "outputId": "7bc40a12-5493-43de-fca8-e22c1727fe40"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f83f50e5710>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So some good choices will be(for two features lets say):\n",
        "* variance and entropy\n",
        "* variance and skewness (would be best because corr with output is high)\n",
        "* curtosis and entropy <br>\n",
        "etc. <br>\n",
        "Choosing skewness and curtosis would be a bad idea. <br>\n",
        "For 3 features:\n",
        "* variance, entropy and curtosis\n",
        "* variance, skewness and entropy"
      ],
      "metadata": {
        "id": "V2EZ1jet1Jxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Univariate Logistic regression"
      ],
      "metadata": {
        "id": "M5Q9yFarb1UK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the individual feature matrices"
      ],
      "metadata": {
        "id": "3MtPsBAcuDz6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_x1=np.array([train_x_final[:,0], train_x_final[:,1]]).T\n",
        "train_x2=np.array([train_x_final[:,0], train_x_final[:,2]]).T\n",
        "train_x3=np.array([train_x_final[:,0], train_x_final[:,3]]).T\n",
        "train_x4=np.array([train_x_final[:,0], train_x_final[:,4]]).T\n",
        "\n",
        "test_x1=np.array([test_x_final[:,0], test_x_final[:,1]]).T\n",
        "test_x2=np.array([test_x_final[:,0], test_x_final[:,2]]).T\n",
        "test_x3=np.array([test_x_final[:,0], test_x_final[:,3]]).T\n",
        "test_x4=np.array([test_x_final[:,0], test_x_final[:,4]]).T"
      ],
      "metadata": {
        "id": "m-RMQzFgekPc"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variance"
      ],
      "metadata": {
        "id": "aqxLZup9csbz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Wgd=gradientDescent(np.array([1]*2),train_x1,train_y,4000,0.1)\n",
        "findAccuracy(predict(Wgd,train_x1),train_y)"
      ],
      "metadata": {
        "id": "jw2D5BkGb4Qw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6990234c-bd21-4008-f002-9552d69a04b5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 125, 86.24862486248625, [[353, 67], [58, 431]], 0.8495788206979543)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(Wgd,test_x1),test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-2YI-dddYqN",
        "outputId": "82674a62-86f6-4f36-82ea-f428379da45d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(335, 55, 85.8974358974359, [[139, 19], [36, 196]], 0.8348348348348348)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Skewness"
      ],
      "metadata": {
        "id": "H-QiuztbiwxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Wgd=gradientDescent(np.array([1]*2),train_x2,train_y,4000,0.1)\n",
        "findAccuracy(predict(Wgd,train_x2),train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc9f8a20-1d1f-4d77-c6a6-284b6c8d7936",
        "id": "VxZPGaEYiwxW"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(314, 595, 34.54345434543454, [[98, 322], [273, 216]], 0.24778761061946902)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(Wgd,test_x2),test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eda348f8-8e02-421f-9c0e-671c41eb3624",
        "id": "bYFmIQtciwxa"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(126, 264, 32.30769230769231, [[33, 125], [139, 93]], 0.2)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Curtosis"
      ],
      "metadata": {
        "id": "AKfPVIuDixon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Wgd=gradientDescent(np.array([1]*2),train_x3,train_y,4000,0.1)\n",
        "findAccuracy(predict(Wgd,train_x3),train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4758ba2b-9eed-4388-8529-bf402222c7db",
        "id": "cb6ZsDKBixon"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(489, 420, 53.79537953795379, [[0, 420], [0, 489]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(Wgd,test_x3),test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95208263-ed3f-4d6c-953e-2b44e2f4b043",
        "id": "TbiOUoQmixoo"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(232, 158, 59.48717948717949, [[0, 158], [0, 232]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entropy"
      ],
      "metadata": {
        "id": "n44T2WiXiyBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Wgd=gradientDescent(np.array([1]*2),train_x4,train_y,4000,0.1)\n",
        "findAccuracy(predict(Wgd,train_x4),train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8f3a70a-5a1e-49c1-b4d6-b7b69b4174db",
        "id": "pkYGc50giyBy"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(489, 420, 53.79537953795379, [[0, 420], [0, 489]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "findAccuracy(predict(Wgd,test_x4),test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11e27371-dad5-4f61-947b-4e6ab6347ff3",
        "id": "P0uP4Y2ZiyBz"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(232, 158, 59.48717948717949, [[0, 158], [0, 232]], 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Very low f1 score shows how bad this model is."
      ],
      "metadata": {
        "id": "rt38XR7SU2Vc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that with lesser correlation with output, our model for that column/feature performs more and more poorly."
      ],
      "metadata": {
        "id": "Wn8dj88uuRqB"
      }
    }
  ]
}