Titanic_Survival_Prediction.ipynb

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        "# Titanic Survival Prediction\n",
        "#### This is a classic beginner project with readily available data. that predicts whether a passenger on the Titanic survived or not.\n",
        "#### The dataset typically used for this project contains information about individual passengers, such as their age, gender, ticket class, fare, cabin, and whether or not they survived."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "FGgJdNITrN4m"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "LR = LogisticRegression(solver='lbfgs', max_iter=3000)\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading The Dataset"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0          892         0       3   \n",
              "1          893         1       3   \n",
              "2          894         0       2   \n",
              "3          895         0       3   \n",
              "4          896         1       3   \n",
              "\n",
              "                                           Name     Sex   Age  SibSp  Parch  \\\n",
              "0                              Kelly, Mr. James    male  34.5      0      0   \n",
              "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
              "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
              "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
              "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
              "\n",
              "    Ticket     Fare Cabin Embarked  \n",
              "0   330911   7.8292   NaN        Q  \n",
              "1   363272   7.0000   NaN        S  \n",
              "2   240276   9.6875   NaN        Q  \n",
              "3   315154   8.6625   NaN        S  \n",
              "4  3101298  12.2875   NaN        S  "
            ],
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "data= pd.read_csv('tested.csv')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BR0hiE-rN4n"
      },
      "source": [
        "# Data Preprocessing\n",
        "#### (checking null value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
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        },
        "id": "ahKft9XRrN4o",
        "outputId": "c23ef1e0-314b-49e2-a39f-ee9846012904"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing The Missing Value."
      ],
      "metadata": {
        "id": "Wz1dE5vZsnw3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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nimoiIyNha2sLKysrtGzZEj/99BOsra2lbg6RrPhlTaTFJAog8spAnVdKBwDIyMhAVFQUAgICFOd0dXXh4eGBU6dO5Vl+48aNsXbtWvz9999o0KABbt26hb1796J3794FaqfkAcTEiRPx9OlTVK5cGXp6esjMzMTMmTPRs2dPxT1eXl7o3LkzypYti7i4OPzwww/w9vbGqVOnoKenl6tMpvMmIiJtkVcG6sDAQEybNi3XvY8ePUJmZibs7OyUztvZ2eH69et5lt+jRw88evQITZs2hRACb968wdChQ/HDDz8UqJ2SBxCbNm3C77//jnXr1qFq1aqIjo7GmDFj4ODgoEjn/e233yrur169OmrUqIHy5csjMjISrVq1ylUm03kTEZHGk2gZZ0BAAPz9/ZXOSTlPMDIyErNmzcKvv/6Khg0bIjY2FqNHj8aMGTMwZcqUjy5H8gBi3LhxmDhxoiJIqF69Ov79918EBQUpAoh3lStXDiVLlkRsbGyeAURe/zGtrCtL3XSiT8bhAyLtJbKkmUSZ33BFXkqWLAk9PT0kJSUpnU9KSsp3nuGUKVPQu3dvDBw4EED2czotLQ2DBw/GpEmTcmXLzo/kyzhfvHhRoFTdAPDff//h8ePHKFWqVJ7XDQ0NYW5urnRw+IKIiLSdgYEB6tati4MHDyrOZWVl4eDBg3B3d8/zNfk9pwGgIOmxJO+B8PHxwcyZM+Hk5ISqVaviwoULmD9/Pvr37w8AeP78OaZPn44uXbrA3t4ecXFxGD9+PFxdXeHp6Sl1c4iIiOShplUY/v7+6Nu3L+rVq4cGDRpg4cKFSEtLU6zK6NOnD0qXLo2goCAA2c/p+fPno3bt2oohjClTpsDHxyfPeYj5kTyAWLx4MaZMmYLhw4fjwYMHcHBwwJAhQzB16lQA2VHOpUuXEB4ejpSUFDg4OKBNmzaYMWMG94IgIqKiS01bWXfr1g0PHz7E1KlTkZiYiFq1aiEiIkIxsTIhIUGpx2Hy5MnQ0dHB5MmTcffuXdjY2Ch+/BcE03kTSYjLOEnT8DOZTY503i9CRkpSjsmwxZKUo2rMhUEkIU34oiR6Gz+TMpJoEmVRwQCCSEL8tUeahp9JGWlZMi0GEEQS0povSiLSegwgiIiIpMAeiPc7evQogoODERUVhfv372Pbtm3o2LGj4roQAoGBgVixYgVSUlLQpEkThISEoEKFCgCA+Ph4zJgxA4cOHUJiYiIcHBzQq1cvTJo0CQYGBpK9MSIiYq+YrIrmmoRCK3AAkZaWhpo1a6J///7o3Llzrus///wzFi1ahPDwcJQtWxZTpkyBp6cnrl27BiMjI1y/fh1ZWVlYtmwZXF1dceXKFQwaNAhpaWmYO3euJG+KiIiycQ6EjLSsB+KTlnHq6Ogo9UAIIeDg4IDvv/8eY8eOBQCkpqbCzs4Oq1evVsqB8bbg4GCEhITg1q1bH103l3ESEX0YA4hssizjnD9IknJM/FdIUo6qSToH4vbt20hMTISHh4finIWFBRo2bIhTp07lG0CkpqaiRIkSUjaFiIigGQ9vrcFlnIWXmJgIAHmmFc259q7Y2FgsXrz4vcMXTOdNRFQ47IGQkZp2olQXta7CuHv3Lry8vNC1a1cMGpR/1w/TeVNRwS9rItIWkgYQOalDk5KSlDJrJiUloVatWkr33rt3Dy1atEDjxo2xfPny95bLdN5UVPDhTaTFtGwIQ9J03mXLloW9vb1SWtGnT5/izJkzSmlF7969i+bNm6Nu3bpYtWrVB3OPM503ERFpOpGVJclRVBS4B+L58+eIjY1V/H379m1ER0ejRIkScHJywpgxY/DTTz+hQoUKimWcDg4OipUaOcGDs7Mz5s6di4cPHyrKyunBICIiIs1W4ADi3LlzaNGiheLvnKGFvn37YvXq1Rg/fjzS0tIwePBgpKSkoGnTpoiIiICRkREA4MCBA4iNjUVsbCzKlCmjVHYRTQxKpMA5EERaTMuGMJjOm0hCDCBI0/AzmU2OfSDSfuolSTnFJ6+VpBxVYy4MIglpwhcl0dv4mSRVYQBBRPQZYw+EjLRsCIMBBJGE+GVNpMWK0AoKKTCAIJIQH96kafiZlBF7IN7vQ+m8t27ditDQUERFRSE5ORkXLlzItYlUXFwcxo4di+PHjyM9PR1eXl5YvHhxri2wiYjo07BXjFSlwBtJ5aTzXrp0ab7XmzZtijlz5uR7vU2bNtDR0cGhQ4dw4sQJZGRkwMfHB1la1v1DRESfEZElzVFEFLgHwtvbG97e3vle7927NwAgPj4+z+snTpxAfHw8Lly4AHPz7FwW4eHhsLKywqFDh5QyeRIVNfy1R6TFOIShWunp6dDR0YGhoaHinJGREXR1dXH8+HEGEFSk8eFNmoafSVIVSXNhfIxGjRqhePHimDBhAl68eIG0tDSMHTsWmZmZuH//fp6vSU9Px9OnT5WOIrr/FRERfaaYC0PFbGxssHnzZgwbNgyLFi2Crq4uunfvjjp16uSbVIvpvImICofDajLiEIbqtWnTBnFxcXj06BGKFSsGS0tL2Nvbo1y5cnnez3TeREREmkWt+0CULFkSAHDo0CE8ePAA7du3z/M+Q0NDpTkTAJjOmzQSf+0RaTH2QLzfh9J5JycnIyEhAffu3QMAxMTEAMhO1Z2TrnvVqlVwc3ODjY0NTp06hdGjR8PPzw+VKlWS4j0RqQ0f3qRp+JmUURFagikFydN579y5E/369VNc//bbbwEAgYGBmDZtGoDsoCIgIADJyclwcXHBpEmT4Ofn9ynvg0gjsAeCNA0/k6QqTOdNRPQZYwCRTY503s/98x6GLyjT+TslKUfVmAuDiIhIAoJzIIiosPhrj0iLMYAgosLiw5uItAUDCCIiIikUoV0kpVDgrayPHj0KHx8fODg4QEdHB9u3b1e6Pm3aNFSuXBnFixeHlZUVPDw8cObMmVzl7NmzBw0bNoSxsTGsrKyUUoITEREVOVlCmqOIKHAPRE467/79+6Nz5865rlesWBFLlixBuXLl8PLlSyxYsABt2rRBbGwsbGxsAAB//PEHBg0ahFmzZqFly5Z48+YNrly58unvhoiIlHBYjVTlk5Zx6ujoYNu2be/tPXj69CksLCzw119/oVWrVnjz5g1cXFwwffp0DBgwoLBVcxknaSROoiRNw89kNjmWcT4b6iVJOWahEZKUo2oqnQORkZGB5cuXw8LCAjVr1gQAnD9/Hnfv3oWuri5q166NxMRE1KpVC8HBwahWrZoqm0OkcprwRUlE6lFEt1UqNJWk8969ezdMTU1hZGSEBQsW4MCBA4q8F7du3QKQPVdi8uTJ2L17N6ysrNC8eXMkJyfnWR7TeRMREWkWlQQQLVq0QHR0NE6ePAkvLy988803ePDgAQAg6//PUp00aRK6dOmCunXrYtWqVdDR0cHmzZvzLC8oKAgWFhZKh8h6poqmExERFY6WTaJUSQBRvHhxuLq6olGjRggLC0OxYsUQFhYGAChVqhQAoEqVKor7DQ0NUa5cOSQkJORZXkBAAFJTU5UOHV0zVTSdiIiocBhASC8rKwvp6ekAgLp168LQ0FCRpRMAXr9+jfj4eDg7O+f5ekNDQ5ibmysdTOdNRESkPpKm87a2tsbMmTPRvn17lCpVCo8ePcLSpUtx9+5ddO3aFQBgbm6OoUOHIjAwEI6OjnB2dkZwcDAAKO4hIiIqapgL4wPel847NDQU169fR3h4OB49egRra2vUr18fx44dQ9WqVRWvCQ4ORrFixdC7d2+8fPkSDRs2xKFDh2BlZSXBWyIiIlIDLQsgmM6biIg+e3LsA5Hau5Uk5Vj8dlCSclSNuTCIiD5j3EiKVIUBBBERkQQ4B4KIiIgKjgEEERUWu4tJ0/DzQKpS4ADi6NGjCA4ORlRUFO7fv58rmdZ3332H8PBwpdd4enoiIuJ/yUHat2+P6OhoPHjwQJHye86cOXBwcCj8OyHSAPyyJk3DoFZGWepugLwkT+cNAF5eXli1apXib0NDQ6XrLVq0wA8//IBSpUrh7t27GDt2LL7++mucPHmyoM0h0ij8sibSXpwD8QHe3t7w9vZ+7z2Ghoawt7fP97qfn5/i387Ozpg4cSI6duyI169fQ19fv6BNItIYfHiTpuFnklRFJXMgIiMjYWtrCysrK7Rs2RI//fQTrK2t87w3OTkZv//+Oxo3bszggYo89kCQpuFnUkYcwvg0Xl5e6Ny5M8qWLYu4uDj88MMP8Pb2xqlTp6Cnp6e4b8KECViyZAlevHiBRo0aYffu3VI3hUh2WvNFSR9FEx7eJB9tG8L4pJ0odXR0ck2ifNetW7dQvnx5/PXXX2jV6n+7dD169AjJycn4999/MX36dFhYWGD37t15JslKT09XJOPKYWVdmQm1iIg+QBOCGE0IrOXYiTK5UzNJyimx7Ygk5aiayrNxlitXDiVLllRKwAUAJUuWRMWKFdG6dWts2LABe/fuxenTp/MsIygoCBYWFkqHyHqm6qYTERF9vCyJjiJC5QHEf//9h8ePH6NUqVL53pOVlf1f7N1ehhwBAQFITU1VOnR0zVTSXiIiosIQWdIcRYWk6bxLlCiB6dOno0uXLrC3t0dcXBzGjx8PV1dXeHp6AgDOnDmDs2fPomnTprCyskJcXBymTJmC8uXLw93dPc86DQ0Ncy0F5fAFEdGHacLwgdYoQg9/KUiazjskJASXLl1CeHg4UlJS4ODggDZt2mDGjBmKAMDExARbt25FYGAg0tLSUKpUKXh5eWHy5Mm5ggQiIvo0nANBqsJ03kREnzEGENnkmET5yFuaSZQl/ywakyiZC4NIQvyyJtJiHMIgosLiw5uItAUDCCIiIgkUpRUUUmAAQSQhDmEQaS91BhBLly5FcHAwEhMTUbNmTSxevBgNGjTI9/6UlBRMmjQJW7duRXJyMpydnbFw4UK0bdv2o+uUPJ13fssrf/75Z4wbNw5Adv6LkSNHYteuXdDV1UWXLl3wyy+/wNTUtKDNIdIofHgTkdw2btwIf39/hIaGomHDhli4cCE8PT0RExMDW1vbXPdnZGSgdevWsLW1xZYtW1C6dGn8+++/sLS0LFC9Bd5IKied99KlS/O8fv/+faVj5cqV0NHRQZcuXRT39OzZE1evXsWBAwewe/duHD16FIMHDy5oU4iIiDSGujaSmj9/PgYNGoR+/fqhSpUqCA0NhYmJCVauXJnn/StXrkRycjK2b9+OJk2awMXFBc2aNUPNmjULVK/Kc2F07NgRz549w8GDBwEA//zzD6pUqYKzZ8+iXr16AICIiAi0bdsW//33HxwcHD6qbi7jJCL6MA6rZZNjGWdS8+aSlGO5b1+unZnz2lARyO5NMDExwZYtW5SexX379kVKSgp27NiR6zVt27ZFiRIlYGJigh07dsDGxgY9evTAhAkTlJJefohK50AkJSVhz549CA8PV5w7deoULC0tFcEDAHh4eEBXVxdnzpxBp06dVNkkIpXilzURfaqgoCBMnz5d6VxgYCCmTZuW695Hjx4hMzMTdnZ2Suft7Oxw/fr1PMu/desWDh06hJ49e2Lv3r2IjY3F8OHD8fr1awQGBn50O1UaQISHh8PMzAydO3dWnEtMTMw1JlOsWDGUKFECiYmJqmwOkcrx4U2kvaSaRBkQEKDY5TmHlDs1Z2VlwdbWFsuXL4eenh7q1q2Lu3fvIjg4WHMCiJUrV6Jnz54wMjL6pHLySucthGA+DNI47IEg0l4iS5pnUn7DFXkpWbIk9PT0kJSUpHQ+KSkJ9vb2eb6mVKlS0NfXVxqucHNzQ2JiIjIyMmBgYPBRdassgDh27BhiYmKwceNGpfP29vZ48OCB0rk3b94gOTk53zebV3eOjq4pdPTMpW000Sfiw5tIe6ljGaeBgQHq1q2LgwcPKuZAZGVl4eDBgxgxYkSer2nSpAnWrVuHrKws6Opmr6W4ceMGSpUq9dHBA6DCdN5hYWGoW7durlmd7u7uSElJQVRUlOLcoUOHkJWVhYYNG+ZZFtN5ExER5c3f3x8rVqxAeHg4/vnnHwwbNgxpaWno168fAKBPnz4ICAhQ3D9s2DAkJydj9OjRuHHjBvbs2YNZs2bB19e3QPVKms7byckJAPD06VNs3rwZ8+bNy/V6Nzc3eHl5YdCgQQgNDcXr168xYsQIfPvtt/muwGA6byoqOIRBpL2EUM9zqVu3bnj48CGmTp2KxMRE1KpVCxEREYqJlQkJCYqeBgBwdHTEvn374Ofnhxo1aqB06dIYPXo0JkyYUKB6C7yMMzIyUimdd46+ffti9erVAIDly5djzJgxuH//PiwsLHLdm5ycjBEjRihtJLVo0aICbSTFZZxERB/GoDabHMs4/2vYUpJyypw5JEk5qsZ03kREnzEGENkYQEiPuTCIiD5jmvDw1hZSrcIoKhhAEBERSaBo9ucXnspWYRAREdHniz0QRESfMc6BkA+HMD7gQ+m8nz9/jokTJ2L79u14/PgxypYti1GjRmHo0KEAgPj4eJQtWzbPsjdt2oSuXbsW7p0QEVEu2vLw1gTaFkBIns7b398fERERWLt2Lf755x+MGTMGI0aMwM6dOwFkrz99N+X39OnTYWpqCm9v7097N0RERCSLAvdAeHt7v/dBf/LkSfTt2xfN/39a08GDB2PZsmX4+++/0b59e+jp6eXasnrbtm345ptvCrQPBBERkSbRtkmUks+BaNy4MXbu3In+/fvDwcEBkZGRuHHjBhYsWJDn/VFRUYiOjs63R4OIiAqPcyDko21DGJIHEIsXL8bgwYNRpkwZFCtWDLq6ulixYgW+/PLLPO8PCwuDm5sbGjduLHVTiIiIZKOurazVRSUBxOnTp7Fz5044Ozvj6NGj8PX1hYODAzw8PJTuffnyJdatW4cpU6a8t0ym8yYiItIskgYQL1++xA8//IBt27ahXbt2AIAaNWogOjoac+fOzRVAbNmyBS9evECfPn3eWy7TeVNRwe5iIu2ljnTe6iRpAPH69Wu8fv1aKesXAOjp6SErK/d/2bCwMLRv3x42NjbvLTcgIAD+/v5K56ysK396g4kkxoc3kfbK4hDG+30onXezZs0wbtw4GBsbw9nZGUeOHMGaNWswf/58pXJiY2Nx9OhR7N2794N1Mp03ERGRZilwAHHu3DmldN45PQM56bw3bNiAgIAA9OzZE8nJyXB2dsbMmTMVG0nlWLlyJcqUKYM2bdp84lsg0hwcwiDSXto2iZLpvImIPmMMarPJkc77esW2kpRT+caHe+Y1AZNpERERUYExmRYREZEEimZ/fuExgCAi+oxpwvCBtuBOlERE9NngHAj5aNsyzgLNgQgKCkL9+vVhZmYGW1tbdOzYETExMUr3vHr1Cr6+vrC2toapqSm6dOmCpKSkPMt7/PgxypQpAx0dHaSkpBT6TRAREZG8CtQDceTIEfj6+qJ+/fp48+YNfvjhB7Rp0wbXrl1D8eLFAQB+fn7Ys2cPNm/eDAsLC4wYMQKdO3fGiRMncpU3YMAA1KhRA3fvqn52LJEc+GuPSHtp2zLOAgUQERERSn+vXr0atra2iIqKwpdffonU1FSEhYVh3bp1aNmyJQBg1apVcHNzw+nTp9GoUSPFa0NCQpCSkoKpU6fizz//lOCtEBERqQ8nURZAamoqAKBEiRIAslNzv379WinnReXKleHk5IRTp04pAohr167hxx9/xJkzZ3Dr1q1PaQKRRuGvf9I0/EySqhQ6gMjKysKYMWPQpEkTVKtWDQCQmJgIAwMDWFpaKt1rZ2eHxMREANmZNbt3747g4GA4OTkxgCAiUiEOq8lH2yZRFjqA8PX1xZUrV3D8+PECvS4gIABubm7o1avXR7+G6byJiEjTcQ7ERxgxYgR2796No0ePokyZMorz9vb2yMjIQEpKilIvRFJSEuzt7QEAhw4dwuXLl7FlyxYA2YEAAJQsWRKTJk3KlbYbYDpvKjr4a4+ItEWBcmEIITBy5Ehs27YNkZGRqFChgtL11NRU2NjYYP369ejSpQsAICYmBpUrV1bMgYiLi8PLly8Vrzl79iz69++PkydPonz58rC1tc1Vb149EFbWldkDQUT0AQxqs8mRC+O8YwdJyqlzZ4ck5ahagXogfH19sW7dOuzYsQNmZmaKeQ0WFhYwNjaGhYUFBgwYAH9/f5QoUQLm5uYYOXIk3N3dFRMoy5cvr1Tmo0ePAABubm655k7kYDpvIiLSdJwD8R4hISEAgObNmyudX7VqFb777jsAwIIFC6Crq4suXbogPT0dnp6e+PXXXyVpLBERFYwm/PqnzxPTeRMRfcY4hJFNjiGMs6U7SVJO/bvbJClH1ZgLg0hC/LIm0l4cwiCiQuPDm0h7Fcnu/E9QoGRaRERERAB7IIiIiCTBIYz3CAoKwtatW3H9+nUYGxujcePGmDNnDipVqgQASE5ORmBgIPbv34+EhATY2NigY8eOmDFjBiwsLBTljBo1CidOnMCVK1fg5uaG6OhoSd8UERFl47CafLgT5Xt8KJ33vXv3cO/ePcydOxdVqlTBv//+i6FDh+LevXuKnSdz9O/fH2fOnMGlS5ckfUNE6sRJlKRp+JkkVfmkZZwPHz6Era0tjhw5gi+//DLPezZv3oxevXohLS0NxYopxyvTpk3D9u3bC9UDwWWcREQfxgAimxzLOI/Zfy1JOV8kbvnwTRpA0nTe+d1jbm6eK3gg+hzxy5pIewlwCOOj5JXO+12PHj3CjBkzMHjw4EI3kKgo4cObiLSFytJ5P336FO3atUOVKlUwbdq0wlYDgOm8iYhI82Vp2UYQkqbzzvHs2TN4eXnBzMwM27Ztg76+/ic1kum8iYgKh71i8sniEEb+3k3nXbZs2Vz3PH36FJ6enjA0NMTOnTthZGT0yY0MCAiAv7+/0jkr68qfXC4R0eeO83JIVSRN5/306VO0adMGL168wNq1a/H06VM8ffoUAGBjYwM9PT0AQGxsLJ4/f47ExES8fPlSsQqjSpUqMDAwyFUv03kTEZGm07ZJlAVaxpnfQzsnnXdkZCRatGiR5z23b9+Gi4sLgOx04EeOHHnvPR/CZZykifhrjzQNP5PZ5FjGecCumyTltE7aKEk5qlbgIYz3ad68+QfvAYDIyMiCVEtUZGjCFyURqYe29UAwmRYREREVGHd3IiIikkCWuhsgMwYQREREEtC2AIJDGERERFRgBQoggoKCUL9+fZiZmcHW1hYdO3ZETEyM0j1DhgxB+fLlYWxsDBsbG3To0AHXr19Xuufs2bNo1aoVLC0tYWVlBU9PT1y8ePHT3w0REZGaCOhIchQVkqbzBoC6deuiZ8+ecHJyQnJyMqZNm4Y2bdrg9u3b0NPTw/Pnz+Hl5YX27dvj119/xZs3bxAYGAhPT0/cuXPnk3etJFInLpkj0l5ZRefZLwmVp/O+dOkSatasidjYWJQvXx7nzp1D/fr1kZCQAEdHRwDA5cuXUaNGDdy8eROurq4fVTf3gSAioo8lxz4Qu+y7S1KOT+J6ScpRtU+aA/GhdN5paWlYtWoVypYtqwgWKlWqBGtra4SFhSEjIwMvX75EWFgY3NzcPnoTKSIiIk2TBR1JjqJCJem8f/31V4wfPx5paWmoVKkSDhw4oNii2szMDJGRkejYsSNmzJgBAKhQoQL27duHYsW4KISISEocVpOPliXjLHwPRE467w0bNuS61rNnT1y4cAFHjhxBxYoV8c033+DVq1cAgJcvX2LAgAFo0qQJTp8+jRMnTqBatWpo164dXr58mWdd6enpirwaOccnjLwQERHRJ1JJOm8LCwtYWFigQoUKaNSoEaysrLBt2zZ0794d69atQ3x8PE6dOgVd3ez4Zd26dbCyssKOHTvw7bff5iqP6byJiApHW379awJt2wdC8nTeeb1GCIH09HQAwIsXL6Crq6uUmCvn76ysvP/zM503EVHhcAhDPllaliW6QEMYvr6+WLt2LdatW6dI552TkhsAbt26haCgIERFRSEhIQEnT55E165dYWxsjLZt2wIAWrdujSdPnsDX1xf//PMPrl69in79+qFYsWL5ZvI0NDSEubm50sF03kREpEmEREdRUaAeiJCQEADZWTfflpPO28jICMeOHcPChQvx5MkT2NnZ4csvv8TJkydha2sLAKhcuTJ27dqF6dOnw93dHbq6uqhduzYiIiJQqlQpad4VEREB0J5f/yS/T9oHQp24DwRpInYXk6bhZzKbHPtAbCzVU5Jyut3/XZJyVI3rJokkpAlflERv42dSPtq2EyUDCCKizxh7IEhVmI2TiIhIAurciXLp0qVwcXGBkZERGjZsiL///vujXrdhwwbo6OigY8eOBa6TAQQREZEE1LUKY+PGjfD390dgYCDOnz+PmjVrwtPTEw8ePHjv6+Lj4zF27Fh88UXheogKNIQRFBSErVu34vr16zA2Nkbjxo0xZ84cVKpUSXFP8+bNceTIEaXXDRkyBKGhoYq/81qCuX79+jw3kSIqSthdTERymz9/PgYNGoR+/foBAEJDQ7Fnzx6sXLkSEydOzPM1mZmZ6NmzJ6ZPn45jx44hJSWlwPVKns4bAAYNGoQff/xR8beJiUmuslatWgUvLy/F35aWlgVuPJGm4cObSHtJNYkyPT1dsfliDkNDQxgaGua6NyMjA1FRUQgICFCc09XVhYeHB06dOpVvHT/++CNsbW0xYMAAHDtWuB8+BQogIiIilP5evXo1bG1tERUVpZTO28TEBPb29u8ty9LS8oP3EBERFRVSbWWdV/qGwMBATJs2Lde9jx49QmZmJuzs7JTO29nZ4fr163mWf/z4cYSFhSE6OvqT2vlJqzDyS+f9+++/Y+3atbC3t4ePjw+mTJmSqxfC19cXAwcORLly5TB06FD069ePu0sSEUmMvWJFT17pG/LqfSiMZ8+eoXfv3lixYgVKliz5SWVJns67R48ecHZ2hoODAy5duoQJEyYgJiYGW7duVdzz448/omXLljAxMcH+/fsxfPhwPH/+HKNGjfqkN0NERMo4L0c+Uu3KmN9wRV5KliwJPT09JCUlKZ1PSkrKs5c/Li4O8fHx8PHxUZzLyUNVrFgxxMTEoHz58h9Vd6EDiJx03sePH1c6P3jwYMW/q1evjlKlSqFVq1aIi4tTNGrKlCmKe2rXro20tDQEBwfnG0DkNR4khGCPBWkcflkTaS91bCRlYGCAunXr4uDBg4qlmFlZWTh48CBGjBiR6/7KlSvj8uXLSucmT56MZ8+e4ZdffoGjo+NH112oZZw56bwPHz6cZzrvtzVs2BAAEBsb+957/vvvv1xBQo6goCBFivCcQ2Q9K0zTiYiIVCJLoqOg/P39sWLFCoSHh+Off/7BsGHDkJaWpliV0adPH8UkSyMjI1SrVk3psLS0hJmZGapVqwYDA4OPrlfl6bxzJmm8L1FWdHQ0rKys8u2yYTpvKir465+I5NatWzc8fPgQU6dORWJiImrVqoWIiAjFxMqEhATo6kq/7VOBkmkNHz4c69atw44dO5T2frCwsICxsTHi4uKwbt06tG3bFtbW1rh06RL8/PxQpkwZxd4Qu3btQlJSEho1agQjIyMcOHAAY8eOxdixY3PNOn0fJtMiIvowDqtlkyOZ1rIyvSQpZ8h/ayUpR9UkTedtYGCAv/76CwsXLkRaWhocHR3RpUsXTJ48WXGvvr4+li5dCj8/Pwgh4OrqqtgEg4iIqKgSWjYtr8BDGO/j6OiYaxfKd3l5eSltIEVERERFD7NxEhERSUCqjaSKCgYQREREEtC2AILZOImIiKjA2ANBJCHOeCfSXlLtRFlUMIAgIiKSgDp2olSnAg1hBAUFoX79+jAzM4OtrS06duyImJgYxfX4+Hjo6OjkeWzevBkAcPHiRXTv3h2Ojo4wNjaGm5sbfvnlF2nfFREREalUgXogjhw5Al9fX9SvXx9v3rzBDz/8gDZt2uDatWsoXrw4HB0dcf/+faXXLF++HMHBwfD29gYAREVFwdbWFmvXroWjoyNOnjyJwYMHQ09PL899u4mIiIoCbZtEWaCdKN/18OFD2Nra4siRI/jyyy/zvKd27dqoU6cOwsLC8i3H19cX//zzDw4dOvTRdXMnSiKiD+O8nGxy7EQ5z0manSi/T/gMd6J8V2pqKgCgRIkSeV6PiopCdHQ0li5d+sFy8iuDqCjhlzWR9uIkyo+UlZWFMWPGoEmTJqhWrVqe94SFhcHNzQ2NGzfOt5yTJ09i48aN2LNnT773MJ03FRV8eJOm4WeSVKXQAYSvry+uXLmC48eP53n95cuXWLduHaZMmZJvGVeuXEGHDh0QGBiINm3a5HtfUFBQrkRbOrqm0NEzL1zjiVSEPRCkafiZlA9XYXyEESNGYPfu3Th8+DDKlCmT5z1btmzBixcv0KdPnzyvX7t2Da1atcLgwYOVkm3lJSAgAKmpqUqHjq5ZYZpORESkElkSHUVFgZNpjRw5Etu2bUNkZCTKli2b771hYWFo3749bGxscl27evUqWrZsib59+2LmzJkfrNfQ0BCGhoZK5zh8QZpIW35pEREVKIDw9fXFunXrsGPHDpiZmSExMREAYGFhAWNjY8V9sbGxOHr0KPbu3ZurjCtXrqBly5bw9PSEv7+/ogw9Pb08gw0iIqKiQNsmURZoCCMkJASpqalo3rw5SpUqpTg2btyodN/KlStRpkyZPOc1bNmyBQ8fPsTatWuVyqhfv/6nvRMiIiI1yoKQ5CgqPmkfCHXiPhBERB/GSZTZ5NgHYqZzT0nKmfTv75KUo2rMhUFERCSBojQBUgoMIIiIiCRQJLvzPwEDCCIJsbuYiLQFAwgiCfHhTaS9tG0IQ9J03gCQmJiI3r17w97eHsWLF0edOnXwxx9/KN1z/vx5tG7dGpaWlrC2tsbgwYPx/PnzT383REREapKlI81RVBQogMhJ53369GkcOHAAr1+/Rps2bZCWlqa4p0+fPoiJicHOnTtx+fJldO7cGd988w0uXLgAALh37x48PDzg6uqKM2fOICIiAlevXsV3330n6RsjIiKSk7Yt4yzQEEZERITS36tXr4atrS2ioqIU6bxPnjyJkJAQNGjQAAAwefJkLFiwAFFRUahduzZ2794NfX19LF26FLq62fFLaGgoatSogdjYWLi6ukrxvojUgnMgiEhbSJ7Ou3Hjxti4cSPatWsHS0tLbNq0Ca9evULz5s0BZGfWNDAwUAQPABS7WB4/fpwBBBGRhBhQyqfo9B1IQ/J03ps2bUK3bt1gbW2NYsWKwcTEBNu2bVMEBi1btoS/vz+Cg4MxevRopKWlYeLEiQCA+/fv51kX03kTERUOe8Xkw0mUHyknnfeGDRuUzk+ZMgUpKSn466+/cO7cOfj7++Obb77B5cuXAQBVq1ZFeHg45s2bBxMTE9jb26Ns2bKws7NT6pV4W1BQECwsLJQOkfWssE0nIiKiT1SoraxHjBiBHTt24OjRo0oZOePi4uDq6oorV66gatWqivM5kyZDQ0OVyklKSkLx4sWho6MDc3NzbNiwAV27ds1VX149EFbWldkDQUT0AeyByCbHVtYTXLpLUs6c+PWSlKNqkqbzfvHiBQDk6knQ09NDVlbuzh07OzsA2cm3jIyM0Lp16zzrZTpvKir4ZU2kvTgH4j0+lM67cuXKcHV1xZAhQzB37lxYW1tj+/btOHDgAHbv3q0oZ8mSJWjcuDFMTU1x4MABjBs3DrNnz4alpaWkb45Ibnx4k6bhZ5JUpUBDGPn96l+1apViH4ebN29i4sSJOH78OJ4/fw5XV1eMHTsWvXv3Vtzfp08f7NmzB8+fP0flypVzXf8YzMZJRPRh7BXLJscQxliJhjDmfq5DGB9SoUKFXDtPvmvNmjUFqZaIiEjjFaVNoKTAXBhEEuKvPSLSFgwgiCTEhzeR9tKu/gcGEERERJLQto2kGEAQERFJQGhZH0SBAoiQkBCEhIQgPj4eQPauklOnToW3tzcA4NWrV/j++++xYcMGpKenw9PTE7/++qtiv4ccq1evxvz583Hjxg2Ym5uja9euWLp0qTTviEiNOAeCNA0/D6QqBQogypQpg9mzZ6NChQoQQiA8PBwdOnTAhQsXULVqVfj5+WHPnj3YvHkzLCwsMGLECHTu3BknTpxQlDF//nzMmzcPwcHBaNiwIdLS0hQBCVFRxy9r0jQMauWjbUMYhdrK+m0lSpRAcHAwvv76a9jY2GDdunX4+uuvAQDXr1+Hm5sbTp06hUaNGuHJkycoXbo0du3ahVatWn1Sw7kPBBHRhzGAyCbHPhDDXb6RpJxf4zdJUo6qFXoORGZmJjZv3oy0tDS4u7sjKioKr1+/hoeHh+KeypUrw8nJSRFAHDhwAFlZWbh79y7c3Nzw7NkzNG7cGPPmzYOjo6Mkb4iIiP5HEx7e9HkqcDbOy5cvw9TUFIaGhhg6dCi2bduGKlWqIDExEQYGBrm2o7azs1NseX3r1i1kZWVh1qxZWLhwIbZs2YLk5GS0bt0aGRkZ+daZnp6Op0+fKh2f2HFCREQkKSHRUVQUuAeiUqVKiI6ORmpqKrZs2YK+ffviyJEjH/XarKwsvH79GosWLUKbNm0AAOvXr4e9vT0OHz4MT0/PPF8XFBSE6dOnK53T0TWFjp55QZtPRKRVOIQhH+5E+QEGBgZwdXUFANStWxdnz57FL7/8gm7duiEjIwMpKSlKvRBJSUmwt7cHAJQqVQoAUKVKFcV1GxsblCxZEgkJCfnWGRAQAH9/f6VzVtaVC9p0IiKtoy0Pb5LfJ+8DkZWVhfT0dNStWxf6+vo4ePAgunTpAgCIiYlBQkIC3N3dAQBNmjRRnC9TpgwAIDk5GY8ePYKzs3O+dTCdNxFR4bAHQj7atgqjQAFEQEAAvL294eTkhGfPnmHdunWIjIzEvn37YGFhgQEDBsDf3x8lSpSAubk5Ro4cCXd3dzRq1AgAULFiRXTo0AGjR4/G8uXLYW5ujoCAAFSuXBktWrRQyRskIiKSAzeSeo8HDx6gT58+uH//PiwsLFCjRg3s27cPrVu3BgAsWLAAurq66NKli9JGUm9bs2YN/Pz80K5dO+jq6qJZs2aIiIiAvr6+dO+KiIiIVOqT94FQF+4DQZqI3cWkafiZzCbHPhD9Xb6WpJyV8VskKUfVmAuDSEKa8EVJ9DZ+JuXDIQwiIvpssAdCPpxESUREnw1teXiT/BhAEBF9xtgDIZ+sojmlsNAKtJV1SEgIatSoAXNzc5ibm8Pd3R1//vkngOz9HEaOHIlKlSrB2NgYTk5OGDVqFFJTUxWvf/z4Mby8vODg4ABDQ0M4OjpixIgRePr0qbTvioiISGbcyvo93pfOWwiBe/fuYe7cuahSpQr+/fdfDB06FPfu3cOWLdkzSnV1ddGhQwf89NNPsLGxQWxsLHx9fZGcnIx169ap5A0SERGR9CRL5z1gwIBc1zZv3oxevXohLS0NxYrlHassWrQIwcHBuHPnToHq5TJOIqIP4xBGNjmWcfZw7iRJOev+3SZJOaomWTrvvKSmpsLc3Dzf4OHevXvYunUrmjVrVthmEBHRe2jCw1tbcBnnB1y+fBnu7u549eoVTE1NFem83/Xo0SPMmDEDgwcPznWte/fu2LFjB16+fAkfHx/83//933vrTE9PR3p6utI5IQTzYRARfQB7IEhVCjSJEvhfOu8zZ85g2LBh6Nu3L65du6Z0z9OnT9GuXTtUqVIF06ZNy1XGggULcP78eezYsQNxcXG5Mm2+KygoCBYWFkqHyHpW0KYTERGpTJZER1HxyXMgPDw8UL58eSxbtgwA8OzZM3h6esLExAS7d++GkZHRe19//PhxfPHFF7h3754i3fe78uqBsLKuzB4IIqIPYA9ENjnmQHR17iBJOZv/3SFJOaomWTpvILvnwdPTE4aGhti5c+cHg4ec1wPIFSC8jem8iYgKRxMe3vR5kiyd99OnT9GmTRu8ePECa9euxdOnTxX7O9jY2EBPTw979+5FUlIS6tevD1NTU1y9ehXjxo1DkyZN4OLioor3R0Sk1dgDIR9OonyP96XzjoyMxJkzZwAArq6uSq+7ffs2XFxcYGxsjBUrVsDPzw/p6elwdHRE586dMXHiROneERERkRoUpfkLUmA6byKizxh7ILLJMQeik5OPJOVsS9glSTmqxlwYRESfMU14eJPqLV26FMHBwUhMTETNmjWxePFiNGjQIM97V6xYgTVr1uDKlSsAgLp162LWrFn53p8fBhBEEuKvPdI0/EzKJ0tNcyA2btwIf39/hIaGomHDhli4cCE8PT0RExMDW1vbXPdHRkaie/fuaNy4MYyMjDBnzhy0adMGV69eRenSH9+7zyEMIqLPGAOIbHIMYfg4fSVJOVtu/pFrZWJeqxFzNGzYEPXr18eSJUsAZK9udHR0xMiRIz9qjmFmZiasrKywZMkS9OnT56PbWeCNpIiIiEh18to8MSgoKM97MzIyEBUVBQ8PD8U5XV1deHh44NSpUx9V34sXL/D69WuUKFGiQO0s0BBGSEgIQkJCEB8fDwCoWrUqpk6dCm9vb6X7hBBo27YtIiIisG3bNnTs2FFxLSEhAcOGDcPhw4dhamqKvn37IigoKN98GUREVHia8OtfW0i1jDMgICDXDs359T48evQImZmZsLOzUzpvZ2eH69evf1R9EyZMgIODg1IQ8jEkS+ddtWpVxX0LFy7Mc6OnzMxMtGvXDvb29jh58iTu37+PPn36QF9fH7NmzSpQw4mI6MM4hCEfqeZAvG+4QmqzZ8/Ghg0bEBkZ+VGbP76tQEMYPj4+aNu2LSpUqICKFSti5syZMDU1xenTpxX3REdHY968eVi5cmWu1+/fvx/Xrl3D2rVrUatWLXh7e2PGjBlYunQpMjIyCtRwIiIibVeyZEno6ekhKSlJ6XxSUhLs7e3f+9q5c+di9uzZ2L9/P2rUqFHgugs9ByIzMxMbNmxQSuf94sUL9OjRA0uXLs2z4adOnUL16tWVulo8PT3x9OlTXL16tbBNISIiUjshhCRHQRgYGKBu3bo4ePCg4lxWVhYOHjyoeDbn5eeff8aMGTMQERGBevXqFer9SprO28/PD40bN0aHDnknFElMTMxznCbnWn6YzpuIiDSdunai9Pf3R9++fVGvXj00aNAACxcuRFpaGvr16wcA6NOnD0qXLq2YiDlnzhxMnToV69atg4uLi+L5a2pqClNT04+ut8ABRE4679TUVGzZsgV9+/bFkSNHEBsbi0OHDuHChQsFLfKDgoKCMH36dKVzOrqm0NEzl7wuIiKioqRbt254+PAhpk6disTERNSqVQsRERGKH+gJCQnQ1f3fgENISAgyMjLw9ddfK5UTGBiIadOmfXS9kqXzNjY2xqJFi5QamZmZCV1dXXzxxReIjIzE1KlTsXPnTkRHRyvuuX37NsqVK4fz58+jdu3aedbBdN5UVHDCGmkafiazybEPRBtHL0nK2X8nQpJyVE2ydN7Tp0/HwIEDla5Vr14dCxYsgI9P9v7g7u7umDlzJh48eKDYHevAgQMwNzdXDIPkhem8qajQhC9KorfxMykfde1EqS6SpfO2t7fPc+Kkk5MTypYtCwBo06YNqlSpgt69e+Pnn39GYmIiJk+eDF9fX9mWrBCpEn/tkabhZ1I+RXRj50KTLJ33x9DT08Pu3bsxbNgwuLu7o3jx4ujbty9+/PHHQjWeSNNoyxclERFzYRARfcbYA5FNjjkQLcp83I/pDzn83wFJylE17h9NREQkAam2si4qmEyLiIiICow9EEREnzFNGD7QFllFc0ZAoTGAICL6jHEOhHy0K3wo4BBGSEgIatSoAXNzc5ibm8Pd3R1//vmn4nrz5s2ho6OjdAwdOlRxffXq1bmu5xwPHjyQ7l0RERGRSkmeznvQoEFKyzJNTEwU/+7WrRu8vJR36vruu+/w6tUrxcZSRERERRE3knqPnB0lc8ycORMhISE4ffq0IoAwMTHJN4WosbExjI2NFX8/fPgQhw4dQlhYWEHbTUREpFEYQHykzMxMbN68WSmdNwD8/vvvWLt2Lezt7eHj44MpU6Yo9UK8bc2aNTAxMcmV0IOIiKShLfMPSH6SpvPu0aMHnJ2d4eDggEuXLmHChAmIiYnB1q1b8ywrLCwMPXr0UOqVyAvTeVNRwQlrpGn4mZRPEd2XsdAkS+ddpUoVDB48WHFf9erVUapUKbRq1QpxcXEoX768UjmnTp3CP//8g99+++2DdTKdNxERaTptG8KQLJ33smXLcl1LS0uDqakpIiIi4OnpqXRtwIABOH/+PC5cuPDBOpjOm4iocNgDkU2OrazrO3wpSTln7x2VpBxVkyydd16io6MBAKVKlVI6//z5c2zatAlBQUEfVQfTeRMREWkWydJ5x8XFYd26dWjbti2sra1x6dIl+Pn54csvv0SNGjWUytm4cSPevHmDXr16SfpmiIiI1IVzIN7jfem879y5g7/++gsLFy5EWloaHB0d0aVLF0yePDlXOWFhYejcuTMsLS2leh9ERERqxTkQRQTTeRMRfRjnQGSTYw5EnVJNJSnn/P3jkpSjasyFQUT0GdOEh7e2KKK/xwuNAQSRhPhrjzQNP5Py0bYhDAYQRBLSli9KIiIGEERERBIQWtYDIVk67/j4+HxTdW/evDlXWY8fP0aZMmWgo6ODlJQUSd4MERGRumQJIclRVBQogMhJ5x0VFYVz586hZcuW6NChA65evQpHR0fcv39f6Zg+fTpMTU3h7e2dq6wBAwbk2h+CiIiIioZPXsZZokQJBAcHY8CAAbmu1a5dG3Xq1MmVrjskJAQbN27E1KlT0apVKzx58qTAe0JwGScREX0sOZZxVrVrKEk5V5POSFKOqkmezjtHVFQUoqOjsXTpUqXz165dw48//ogzZ87g1q1bha2eiIhIoxSl4QcpSJrO+21hYWFwc3ND48aNFefS09PRvXt3BAcHw8nJ6aMDCKbzJiIiTadtkyglTeed4+XLl1i3bh2mTJmi9NqAgAC4ubkVOAcG03kTERUO94EgVVFJOu/ffvsNAwYMwN27d2FjY6M4X6tWLVy+fFnRcyCEQFZWFvT09DBp0qRcQUIOpvMmIiocBhDZ5JgDUdGmniTl3Hh4TpJyVE0l6bzDwsLQvn17peABAP744w+8fPlS8ffZs2fRv39/HDt2DOXLl8+3DqbzJiIiTcchjPd4XzrvHLGxsTh69Cj27t2b6/XvBgmPHj0CALi5uTEzJxGRCmjCr3/6PEmWzjvHypUrUaZMGbRp00byxhIRUcFwCEM+2rYKg+m8iSTEL2vSNPxMZpNjDkS5krUlKefWowuSlKNqzIVBJCFN+KIkIpIDAwgiIiIJCJGl7ibIigEEkYTYXUykvbK4CoOICosPbyLSFpKl8waAuLg4dOrUCTY2NjA3N8c333yDpKQkpTJmzpyJxo0bw8TEhEs3iYjosyGEkOQoKiRL552WloY2bdpAR0cHhw4dwokTJ5CRkQEfHx9kZf1vXCgjIwNdu3bFsGHDJH8zRERE6pIFIclRVEiWztvR0RHe3t548uQJzM2zc1SkpqbCysoK+/fvh4eHh9LrVq9ejTFjxiAlJaVQ9XIZJxHRh3FeTjY5lnGWtqoqSTl3n1yVpBxVK1APxNsyMzOxYcMGRTrv9PR06OjoKG05bWRkBF1dXRw/flySxhIREZFmkCydt42NDYoXL44JEyZg1qxZEEJg4sSJyMzMxP379z+pkUznTURUOJrw619baNtOlJKm8968eTOGDRuGRYsWQVdXF927d0edOnWgq1vojg4ATOdNRFRYHMKQD5NpfYCBgQFcXV0BAHXr1sXZs2fxyy+/YNmyZWjTpg3i4uLw6NEjFCtWDJaWlrC3t0e5cuU+qZEBAQHw9/dXOmdlXfmTyiQiIqLCU0k675IlSwIADh06hAcPHqB9+/afVAfTeRMRkaYrSkswpSBpOu9Vq1bBzc0NNjY2OHXqFEaPHg0/Pz9UqlRJUUZCQgKSk5ORkJCAzMxMREdHAwBcXV1hamoq3TsjIiKtGT7QBEVpCaYUJE3nHRMTg4CAACQnJ8PFxQWTJk2Cn5+fUhlTp05FeHi44u/atbOzlx0+fBjNmzf/xLdDRERv4xwIUhWm8yYios+eHPtAlDSvKEk5j57ekKQcVWMuDCKizxh7IOTDZZxERPTZ0JaHN8mPAQQR0WeMPRDyKaIzAgqNAQQREZEEtG0VxidtETl79mzo6OhgzJgxinOvXr2Cr68vrK2tYWpqii5duuRK6X327Fm0atUKlpaWsLKygqenJy5evPgpTSEiIlIrbUvnXegeiLNnz2LZsmWoUaOG0nk/Pz/s2bMHmzdvhoWFBUaMGIHOnTvjxIkTAIDnz5/Dy8sL7du3x6+//oo3b94gMDAQnp6euHPnDvT19T/tHRERkYK2DB+Q/Aq1jPP58+eoU6cOfv31V/z000+oVasWFi5ciNTUVNjY2GDdunX4+uuvAQDXr1+Hm5sbTp06hUaNGuHcuXOoX78+EhIS4OjoCCA7QVeNGjVw8+ZNxTbZH8JlnERE9LHkWMZpalJWknKev7gtSTmqVqgeCF9fX7Rr1w4eHh746aefFOejoqLw+vVreHh4KM5VrlwZTk5OigCiUqVKsLa2RlhYGH744QdkZmYiLCwMbm5ucHFx+eQ3RERE/8NJlPJhMq0P2LBhA86fP4+zZ8/mupaYmAgDAwNYWloqnbezs0NiYiIAwMzMDJGRkejYsSNmzJgBAKhQoQL27duHYsXybg7TeRMRFY62PLxJfgUKIO7cuYPRo0fjwIEDMDIyKlSFL1++xIABA9CkSROsX78emZmZmDt3Ltq1a4ezZ8/C2Ng412uYzpuIqHDYAyEfbiT1HlFRUXjw4AHq1KmjOJeZmYmjR49iyZIl2LdvHzIyMpCSkqLUC5GUlAR7e3sAwLp16xAfH49Tp05BV1dXcc7Kygo7duzAt99+m6tepvOmooJf1kTaqyitoJBCgQKIVq1a4fLly0rn+vXrh8qVK2PChAlwdHSEvr4+Dh48iC5dugDITrCVkJAAd3d3AMCLFy+gq6urNPyQ83dWVlae9TKdNxUVfHiTpuFnklSlQAGEmZkZqlWrpnSuePHisLa2VpwfMGAA/P39UaJECZibm2PkyJFwd3dHo0aNAACtW7fGuHHj4Ovri5EjRyIrKwuzZ89GsWLF0KJFC4neFhERAewVkxMnUX6iBQsWQFdXF126dEF6ejo8PT3x66+/Kq5XrlwZu3btwvTp0+Hu7g5dXV3Url0bERERKFWqlNTNIZIVv6yJtJe2DWEwnTcREX325NgHwsCwjCTlZKT/V+DXLF26FMHBwUhMTETNmjWxePFiNGjQIN/7N2/ejClTpiA+Ph4VKlTAnDlz0LZt2wLVyVwYRESfMfaKyUddv8c3btwIf39/hIaGomHDhli4cCE8PT0RExMDW1vbXPefPHkS3bt3R1BQEL766iusW7cOHTt2xPnz53NNU3gf9kAQSYhf1qRp+JnMJkcPhFTPpbRnt3LtfZTXYoIcDRs2RP369bFkyRIAQFZWFhwdHTFy5EhMnDgx1/3dunVDWloadu/erTjXqFEj1KpVC6GhoR/fUKGlXr16JQIDA8WrV6/YBraBbWAb2AYNbocmtEFOgYGBAoDSERgYmOe96enpQk9PT2zbtk3pfJ8+fUT79u3zfI2jo6NYsGCB0rmpU6eKGjVqFKidWhtApKamCgAiNTWVbWAb2Aa2gW3Q4HZoQhvk9OrVK5Gamqp05Bc83b17VwAQJ0+eVDo/btw40aBBgzxfo6+vL9atW6d0bunSpcLW1rZA7eQcCCIiIg3yvuEKTaKr7gYQERFR4ZQsWRJ6enpISkpSOv/2DtDvsre3L9D9+WEAQUREVEQZGBigbt26OHjwoOJcVlYWDh48qNgB+l3u7u5K9wPAgQMH8r0/P1o7hGFoaIjAwEC1dhOxDWwD28A2aHIbNKUdmtAGTebv74++ffuiXr16aNCgARYuXIi0tDT069cPANCnTx+ULl0aQUFBAIDRo0ejWbNmmDdvHtq1a4cNGzbg3LlzWL58eYHqLbLLOImIiCjbkiVLFBtJ1apVC4sWLULDhg0BAM2bN4eLiwtWr16tuH/z5s2YPHmyYiOpn3/+ucAbSTGAICIiogLjHAgiIiIqMAYQREREVGAMIIiIiKjAGEAQERFRgTGAICKtV65cOTx+/DjX+ZSUFJQrV04NLSLSfAwgSGsdPnw432vLli2TrR0ZGRmIiYnBmzdvZKszLw8ePMCxY8dw7NgxPHjwQK1tkVt8fDwyMzNznU9PT8fdu6rP4khUFGntRlJy6ty580ffu3XrVhW2JH8pKSmwtLSUvd7Y2FjExcXhyy+/hLGxMYQQ0NHRkaVuLy8vjBo1CrNmzYK+vj4A4NGjR+jXrx+OHz+OIUOGqLT+Fy9eYOTIkQgPDwcA3LhxA+XKlcPIkSNRunTpPNPwqsKzZ88wfPhwbNiwQfEQ1dPTQ7du3bB06VJYWFjI0o4cGRkZePDgAbKyspTOOzk5SV7Xzp07Ff/et2+f0nvNzMzEwYMH4eLiInm9HxIXF4dVq1YhLi4Ov/zyC2xtbfHnn3/CyckJVatWVWndmZmZWL16NQ4ePJjn/w6HDh1SWd2XLl366Htr1KihsnbQx/nsAwh/f/+Pvnf+/PkqacPbX0pCCGzbtg0WFhaoV68eACAqKgopKSkFCjQ+xZw5c+Di4oJu3boBAL755hv88ccfsLe3x969e1GzZk2Vt+Hx48fo1q0bDh06BB0dHdy8eRPlypXDgAEDYGVlhXnz5qm8DYcPH0afPn1w4MABrFu3Drdv38aAAQNQqVIlREdHq7z+gIAAXLx4EZGRkfDy8lKc9/DwwLRp02QLIAYOHIgLFy5g9+7diq1sT506hdGjR2PIkCHYsGGDLO24efMm+vfvj5MnTyqdzwkq8+oh+FQdO3YEAOjo6KBv375K1/T19eHi4iLLZ/FtR44cgbe3N5o0aYKjR49i5syZsLW1xcWLFxEWFoYtW7aotP7Ro0dj9erVaNeuHapVqyZbQA8AtWrVgo6Ozkf9kFDF54EKqEC5O4ug5s2bKx3m5ubCxMRE1K5dW9SuXVsUL15cmJubixYtWsjSnvHjx4uBAweKN2/eKM69efNGDB48WIwdO1aWNri4uIgTJ04IIYTYv3+/sLS0FPv27RMDBgwQrVu3lqUNvXv3Fp6enuLOnTvC1NRUxMXFCSGEiIiIEFWqVJGlDUII8ezZM9GzZ09haGgo9PX1xezZs0VWVpYsdTs5OYlTp04JIYTSf4ObN28KMzMzWdoghBAmJibi2LFjuc4fPXpUmJiYyNaOxo0biy+//FLs3btXXLhwQURHRysdquTi4iIePnyo0jo+VqNGjcS8efOEEMqfizNnzojSpUurvH5ra2uxZ88eldeTl/j4eMWxbds2Ub58eREaGiouXrwoLl68KEJDQ0WFChXEtm3b1NI+UvbZBxBvmzdvnvDx8RHJycmKc8nJyaJDhw5i7ty5srShZMmS4vr167nOX79+XZQoUUKWNhgZGYmEhAQhhBCjRo0SgwcPFkIIERMTIywtLWVpg52dneKh8PaXZFxcnChevLgsbRBCiKioKFGpUiVRvnx5YWxsLPr16yeeP38uS93GxsaK9/32f4Po6Ghhbm4uSxuEEMLR0VFcunQp1/mLFy/K8sDKYWJiIv755x/Z6svPy5cv1Vp/8eLFxa1bt4QQyp+L27dvC0NDQ5XXX6pUKRETE6Pyej6kfv36eQYye/bsEXXq1FFDi+hdWjWJct68eQgKCoKVlZXinJWVFX766SfZuinfvHmD69ev5zp//fr1XGONqmJlZYU7d+4AACIiIuDh4QEgu6tYrm7BtLQ0mJiY5DqfnJwsW8Kc2bNnw93dHa1bt8aVK1fw999/48KFC6hRowZOnTql8vrr1auHPXv2KP7O6bL9v//7vwJnxfsUkydPhr+/PxITExXnEhMTMW7cOEyZMkW2dlSpUgWPHj2Srb63ZWVlYcaMGShdujRMTU1x69YtAMCUKVMQFhYma1ssLS1x//79XOcvXLiA0qVLq7z+77//Hr/88guEmrMcXL58GWXLls11vmzZsrh27ZoaWkS5qDuCkZOpqak4fPhwrvOHDh0SpqamsrTBz89PWFtbi3nz5oljx46JY8eOiblz54qSJUsKPz8/Wdrg6+srnJ2dhYeHh7C2thbPnj0TQgixfv16Ubt2bVna4O3tLSZPniyEyP7f5datWyIzM1N07dpVdOnSRZY22Nvbi7179yqdy8jIEGPHjhUGBgYqr//YsWPC1NRUDB06VBgZGYnRo0eL1q1bi+LFi4tz586pvP4ctWrVEqampkJfX1+UL19elC9fXujr6wtTU1PFUF/OIbXU1FTFcfDgQeHu7i4OHz4sHj16pHQtNTVV8rrfNn36dFGuXDmxdu1apZ6hDRs2iEaNGqm07nd9//33omnTpuL+/fvCzMxM3Lx5Uxw/flyUK1dOTJs2TeX1d+zYUVhYWIiyZcuKr776SnTq1EnpkEvt2rVF7969RXp6uuJcenq66N27t2zfU/R+WpVMq0+fPjh27BjmzZuHBg0aAADOnDmDcePG4YsvvlDMhlelrKwszJ07F7/88oviV0apUqUwevRofP/999DT01N5G16/fo1ffvkFd+7cwXfffYfatWsDABYsWAAzMzMMHDhQ5W24cuUKWrVqhTp16uDQoUNo3749rl69iuTkZJw4cQLly5dXeRsePXqEkiVL5nntyJEjaNasmcrbEBcXh9mzZ+PixYt4/vw56tSpgwkTJqB69eoqrzvH9OnTP/rewMBASevW1dVVmiwn8pg8J1Q4iTKHq6srli1bhlatWsHMzAwXL15EuXLlcP36dbi7u+PJkycqq/tdGRkZ8PX1xerVq5GZmYlixYohMzMTPXr0wOrVq1X+HZGTAjo/q1atUmn9Of7++2/4+PhACKFYcXHp0iXo6Ohg165diu9wUh+tCiBevHiBsWPHYuXKlXj9+jUAoFixYhgwYACCg4NRvHhxWdvz9OlTAIC5ubms9WqK1NRULFmyROnh6evri1KlSsnWhpSUFGzZsgVxcXEYN24cSpQogfPnz8POzk6W7mJtd+TIkY++V5UBnbGxMa5fvw5nZ2elAOLatWto0KABnj9/rrK63yaEwJ07d2BjY4NHjx7h8uXLeP78OWrXro0KFSrI0gZNkpaWht9//10x7Ovm5oYePXrI/l1NedOqACJHWloa4uLiAADly5eX/cP45s0bREZGIi4uDj169ICZmRnu3bsHc3NzmJqaqrz+8PBwlCxZEu3atQMAjB8/HsuXL0eVKlWwfv16ODs7q7wNmuDSpUvw8PCAhYUF4uPjERMTg3LlymHy5MlISEjAmjVrVFp/TgD5Lh0dHRgaGsLAwECl9efl1atX2LhxI9LS0tC6dWuteWjVrVsXfn5+6NWrl1IA8eOPP+LAgQM4duyYLO3IysqCkZERrl69qjX/7akIU9fYibaKj48XlStXFiYmJkJPT08x1jpq1CgxZMgQWdpQsWJFcfDgQSGEECdPnhQmJiZi2bJlwsfHR9YxzpcvX4ozZ86IXbt2iR07digdcmjZsqUYN26cEEJ5tvuJEyeEs7OzyuvX0dERurq6+R5OTk5i6tSpIjMzUyX1+/n5iREjRij+Tk9PFzVr1hT6+vrCwsJCFC9eXLHcVw4rV64UmzZtynV+06ZNYvXq1Sqte/v27cLCwkLMnj1bmJiYiODgYDFw4EBhYGAg9u/fr9K631WlShXF8l651K5dW7E6rVatWrnmvqhyHsz7rFmzRjRp0kSUKlVKxMfHCyGEmD9/vti+fbus7aC8ffYbSQEfvxOkHLtAjh49GvXq1cPFixdhbW2tON+pUycMGjRI5fUDwJ07d+Dq6goA2L59O7p06YLBgwejSZMmaN68uSxtiIiIQJ8+ffKcda/q8e4c586dw/Lly3OdL126tNKKBFVZvXo1Jk2ahO+++04xnvv3338jPDwckydPxsOHDzF37lwYGhrihx9+kLz+/fv3Y9asWYq/f//9dyQkJODmzZtwcnJC//79MXPmTKWVIqoUFBSU5xbitra2GDx4cK6NnqTUoUMH7Nq1Cz/++COKFy+OqVOnok6dOti1axdat26tsnrzMnv2bIwbNw4hISGoVq2aLHV26NBBsfopZ3MtdQsJCcHUqVMxZswY/PTTT4rvBCsrKyxcuBAdOnRQcwtJKwIIubfifZ9jx47h5MmTubqnXVxcZNtz39TUFI8fP4aTkxP279+v2K3TyMgIL1++lKUNI0eORNeuXTF16lTY2dnJUue7DA0N8xxGuHHjBmxsbFRef3h4OObNm4dvvvlGcc7HxwfVq1fHsmXLcPDgQTg5OWHmzJkqCSASEhJQpUoVxd/79+/H119/rRjCGj16NNq2bSt5ve9rT17L9pydnZGQkKDSuv/77z988cUXOHDgQK5rp0+fRqNGjVRa/9v69OmDFy9eoGbNmjAwMICxsbHS9eTkZMnrfHtyrNQTZQtr8eLFWLFiBTp27IjZs2crzterVw9jx45VY8soh1YEEHLNGv4YWVlZef66/u+//2BmZiZLG1q3bo2BAweidu3auHHjhuIhcfXqVdn2/U9KSoK/v7/aggcAaN++PX788Uds2rQJQHbPR0JCAiZMmIAuXbqovP6TJ08iNDQ01/natWsr9qFo2rSpyh6eurq6Smv9T58+rbTvg6WlpayrD2xtbXHp0qVcn8F3e+tUoU2bNjh+/DhKlCihdP7EiRNo164dUlJSVFr/2xYuXChbXe9z7tw5/PPPPwCy9+ioW7eurPXfvn1bsULsbYaGhkhLS5O1LZQPdY+hyCUjI0Po6emJy5cvq7Ud33zzjRg0aJAQ4n/7Hzx79ky0bNlSfPfdd7K04cmTJ8LX11e0b99e/Pnnn4rzU6dOFT/99JMsbejXr5/4v//7P1nqyk9KSorw8PAQlpaWQk9PTzg6OopixYqJL774QpbdKCtUqCAmTJiQ6/yECRNExYoVhRBCnD17Vjg4OKik/re3TL5y5YrQ1dVV7IAohBCRkZGyzAXJMX78eOHs7CwOHTok3rx5I968eSMOHjwonJ2dxffff6/Suvv16yfq1q0rnj59qjh35MgRYW5uLubPn6/SujXNnTt3RNOmTYWOjo6wsrISVlZWQkdHRzRp0kTcuXNHtna4ubkp5jq8PUdp0aJF3AdCQ2hNACGEEGXLllX5nvofcufOHVGlShXh5uYmihUrJho1aiSsra1FpUqVRFJSklrbJqe0tDTRtm1b0bdvXzF37lzxyy+/KB1yOnbsmFi6dKmYM2eO+Ouvv2Srd8eOHcLAwEDUqFFDDBgwQAwYMEDUrFlTGBgYiF27dgkhhPj1119VtsHY1q1bhYGBgWjZsqWws7MTX331ldL18ePHi65du6qk7rykp6eLb775Rujo6Ah9fX2hr68v9PT0RL9+/ZQ2E1KFzMxM0alTJ9GsWTPx6tUrxeZyCxcuVGm9H/Ly5UtZN9QSQghPT0/RsGFDpS33r1+/Ltzd3YWnp6fK68+xYsUKUbp0abFhwwZRvHhxsX79evHTTz8p/k3qp1XLOMPCwrB161b89ttvuboq5fTmzRts2LABly5dUux/0LNnz1xjnar24sULJCQkICMjQ+m8HGlyw8LCMHToUBgZGcHa2lpp8yAdHR3FVsKqcOrUKTx+/BhfffWV4lx4eDgCAwPx4sULdOzYEYsXL5ZlS+34+HiEhobixo0bAIBKlSphyJAheP78uSwT6A4ePIjdu3fD3t4eI0eOVNpefPr06WjWrJksE2vFW/sf/Pfff4iOjoaxsTGqV68u27LijIwMtGvXDi9evMClS5cQFBSEESNGyFL329LS0jBhwgRs2rQJjx8/znVd1ROMjY2NcfLkyVzDB1FRUfjiiy/w4sULldb/tt9//x3Tpk1TLLt3cHDA9OnTMWDAANnaQO+h5gBGVjlb9hoaGoqKFSuqZXmSuhP1CCHEgwcPRNu2bfNdPigHOzs7MXPmTJUtUXwfLy8vMXv2bMXfly5dEvr6+mLgwIFi3rx5wt7eXgQGBsrertTUVBEaGioaNGgg2/8OmiIzM1Po6+uLGzduyFZnTobHt4/jx48LR0dHMXToUKXzcho+fLhwc3MTW7ZsEcbGxmLlypVixowZokyZMmLt2rUqr79ChQrizJkzuc6fOXNGlC9fXuX153i7tyUtLU2ph/bmzZuytYPypxWTKHNowvIkW1tbdOrUCb169UKrVq2gqyt/PrMxY8YgNTUVZ86cQfPmzbFt2zYkJSXJmlQsIyMD3bp1U8v7j46OxowZMxR/b9iwAQ0aNMCKFSsAAI6OjggMDMS0adNkac/Ro0cRFhaGP/74Aw4ODujcuTOWLFkiS905njx5grCwMMWkOTc3N/Tv31+2njpdXV1UqFABjx8/lm0DpVq1akFHR0dpImnO38uWLcPy5ctl2Ub7Xbt27cKaNWvQvHlz9OvXD1988QVcXV3h7OyM33//HT179lRp/cHBwRg5ciSWLl2KevXqAcieUDl69GjMnTtXpXW/rV27dvjrr79gaGgIExMTRe9YTEwMWrVqhf/++0+2tlA+1BzAaJ2tW7eKr7/+WhgbGwt7e3sxevRocfbsWVnbYG9vr/iFYWZmpkjdu2PHDtGkSRNZ2jBmzBgxc+ZMWep6l6GhoSKduRBCNGnSRGny6O3bt1WeXO3+/fsiKChIuLq6CltbWzFixAhRrFgxcfXqVZXWm5ecyYKOjo6KhElOTk7C3NxcHDlyRLZ27Ny5UzRt2lS2ic7x8fEffcipePHi4t9//xVCCFG6dGnF/6/eunVLZanuLS0tFRMmrayshIGBgdDV1RUGBgZK/7ayslJJ/Xnx8vIS3t7e4vXr14pz165dE/b29mLUqFGytYPyp1U9EID6cx906tQJnTp1wrNnz7BlyxasX78ejRo1Qrly5dCrVy9MnTpV5W1IS0uDra0tgOxNWR4+fIiKFSuievXqOH/+vMrrB7LHcX/++Wfs27cPNWrUgL6+vtL1+fPnq6xuOzs73L59G46OjsjIyMD58+eVEko9e/YsV3uk5OPjg6NHj6Jdu3ZYuHAhvLy8oKenl+eSTjn4+vqiW7duCAkJUSRqyszMxPDhw+Hr64vLly/L0g659z/Q1C3by5Urh9u3b8PJyQmVK1fGpk2b0KBBA+zatQuWlpYqqVNTlo6+bevWrfDw8EDPnj2xYcMGXL16Fa1atULPnj1V+v1AH0+rJlGqO/dBfq5du4aePXvi0qVLsnSV1q9fHz/99BM8PT3Rvn17WFpaIigoCIsWLVIEV6rWokWLfK/p6Ojg0KFDKqt72LBhuHjxIubMmYPt27cjPDwc9+7dU2zu9fvvv2PhwoU4e/asSuovVqwYRo0ahWHDhil11+vr6+PixYtKmzvJwdjYGNHR0ahUqZLS+ZiYGNSqVUu2zcU+lA1XlTtRBgUFwc7ODv3791c6v3LlSjx8+BATJkxQWd05bt26BRcXF/zyyy/Q09PDqFGj8NdffykyUr5+/Rrz58/H6NGjVd4WTZGSkoLmzZujQoUKOHr0KPr06YPg4GB1N4tyqLkHRFatWrVSa+6Dt718+VJs3LhRdOjQQRgaGgonJ6c89wRQhd9++02sWrVKCCHEuXPnRMmSJYWurq4wMjISGzZskKUN6vTw4UPxxRdfCB0dHWFmZia2bt2qdL1ly5bihx9+UFn9p06dEgMHDhRmZmaiQYMGYvHixeLhw4dqG8Jo3Lix2LZtW67z27ZtEw0bNpS9Perg7OycZ96P06dPCxcXF1naoKurqzRR8JtvvhGJiYkiPj5e/PHHH7JP5hRC/mWk79aVmpoqrl+/LhwdHcWwYcNkXc5KH6ZVAYS5ubmIjY0VQigHEPHx8cLQ0FCWNkRERIg+ffoIc3NzUaJECTF48GBZx5nzkpaWJqKiosTDhw/V2g65paSkiDdv3uQ6//jxY5XvOyCEEM+fPxdhYWGiSZMmQl9fX+jq6oqFCxcqbWakKm+vMNiwYYNwcnISwcHB4tixY+LYsWMiODhYuLi4qC2glPvBZWhoqLSJVo64uDjZvht0dHSUAoi3v6Pk9Pz5c+Hr6ytsbGxkX6WVX4I5HR0dxbWc/0vqp1VzINSd+wDIngPx1VdfYc2aNWjbtq1Kx9o/lomJCerUqSN7vefOncOmTZvy3ItCjsRm+eVIkWvlQfHixdG/f3/0798fMTExCAsLw+zZszFx4kS0bt0aO3fuVFndea1AGD9+fK77evTogW7duqmsHW9T5/4Hjo6OOHHiRK5cHCdOnICDg4PK6tVE48ePx+HDhxESEoLevXtj6dKluHv3LpYtW6aUk0IVDh8+rNLySVpaFUCoO/cBkJ0DQq6cF2/LSZj1MeSYoLRhwwb06dMHnp6e2L9/P9q0aYMbN24gKSkJnTp1Unn9mqZSpUr4+eefERQUhF27dmHlypUqre/27dsqLb8w1PngGjRoEMaMGYPXr1+jZcuWALI32Ro/fjy+//57ldadQ0dHR2lDtZxzclPnMtJmzZoByN5sb9asWejfvz/KlCmjsvro02jVJMrU1FR8/fXXOHfuHJ49ewYHBwckJibC3d0de/fuRfHixVVS79OnT2Fubq749/vk3Ce1901afJuqJzDmqFGjBoYMGQJfX1+YmZnh4sWLKFu2LIYMGYJSpUoprYog7eDk5KR4cJmbm+P8+fNwdXXFb7/9hvXr12Pv3r0qq1sIgYkTJ2LRokWK3jAjIyNMmDBBlpVRQPZeGN7e3oodUHft2oWWLVvm+l5Sde+cqakprl27BicnJ5QpUwZbt25FgwYNcPv2bVSvXh3Pnz9Xaf05zMzMcPnyZdkS/FHBaVUPhIWFBQ4cOIDjx48rbSPt4eGh0nqtrKxw//592NrawtLSMs9fFULFG9ZoWtdgXFwc2rVrBwAwMDBAWloadHR04Ofnh5YtWzKAULGdO3fC29sb+vr6Hxwqad++vSxtSk5ORrly5QBkB9I5yzabNm2KYcOGqbRuHR0dzJkzB1OmTME///wDY2NjVKhQQZbtzHO8u8qkV69estX9NnUsI81Ly5YtceTIEQYQGkyrAogcTZs2RdOmTWWr79ChQ4px9UOHDqmlW/JtqampyMzMzDXWn5ycjGLFiqmsF+RtVlZWePbsGQCgdOnSuHLlCqpXr46UlBRZ99rXVh07dkRiYiJsbW3fu0OrnLswasKDy9TUFPXr15elrnetWrVKLfW+q1+/frh48SKaNWuGiRMnwsfHB0uWLEFGRgYWLFggWzu8vb0xceJEXL58GXXr1s3VEyNXYEv506ohjEWLFuV5XkdHB0ZGRnB1dcWXX36p2Eznc+Xt7Q0fHx8MHz5c6XxoaCh27typ0q7iHD169EC9evXg7++PGTNmYPHixejQoQMOHDiAOnXqyDKJkjTLggULZN3/oHPnzli9ejXMzc3RuXPn996rzZ/Hf//9F1FRUahQoQKqV68uW73v2+Ze7u3FKW9a1QOxYMECPHz4EC9evICVlRWA7BwAJiYmMDU1xYMHD1CuXDkcPnwYjo6OKmlDhQoV0LNnT/Ts2VO2Pf/fdebMmTwnSjZv3hyTJk2SpQ1LlizBq1evAACTJk2Cvr4+Tp48iS5dumDy5MmytEHb5ZWVdM2aNQgMDERaWppsWUmzsrIQHByMnTt3IiMjA/fu3UNgYCCuX7+OqKgouLq6qiRDrIWFhaI3ML8VOdrk0KFDGDFiBE6fPq3UC+ns7AxLS0s0btwYoaGh+OKLL2RpT1ZWliz10CdQ3wpS+a1bt040b95csReEENlZ3Vq2bCk2bNgg7ty5I5o0aSK6dOmisjbMnz9f1KtXT+jq6op69eqJhQsXivv376usvryYmJiIS5cu5Tp/6dIlYWxsLGtbSH3yykparFgx2bOS/vjjj0JXV1e0adNGdOjQQRgZGYl+/fqpvF4hhJg+fbpIS0uTpS5N5+PjI+bPn5/v9V9++UV07NhRxhaRptOqAKJcuXLiwoULuc6fP39elC1bVgiRvSulvb29ytsSExMjpk6dKipUqCCKFSsmWrduLcLDw1VerxBCNG/eXIwYMSLX+eHDh4umTZuqtO78Nop5+9DT01NpGyibvb29UiK3H374QSmZ2qZNm4Sbm5vK2+Hq6ipCQ0MVfx84cEAYGBjIkur93d0ftZmTk5O4du1avtf/+ecf4ejoKGOLsje12rNnjwgJCRG//PKL0kHqp1VzIExMTHD06FFFitocZ8+eRbNmzfDixQvEx8ejWrVqsi1VAoDTp09j2LBhsuXCOHHiBDw8PFC/fn20atUKQPaa97Nnz2L//v0q7aLcsWNHvtdOnTqFRYsWISsrSzG8QapjZGSEmzdvKobrmjZtCm9vb8UwVnx8PKpXr66Y7KoqhoaGiI2NVRo2NDIyQmxsrMr3ANDV1VVMJtV2RkZGuHLlClxdXfO8Hhsbi+rVq8uWG+XChQto27YtXrx4gbS0NJQoUQKPHj2CiYkJbG1tcevWLVnaQfnLf5bKZ6hFixYYMmQILly4oDh34cIFDBs2TLF5zOXLl3PtRqcqf//9N8aMGYNOnTrhxo0b6Nq1qyz1NmnSBKdPn4ajoyM2bdqEXbt2wdXVFZcuXVL5+GaHDh1yHZUrV8bq1asxd+5cdO3aFTExMSptA2XLyUoKQJGVtFGjRorrqs5KmuPNmzcwMjJSOqevr4/Xr1+rvG5APZs1aaKc1VD5uXTpEkqVKiVbe/z8/ODj44MnT57A2NgYp0+fxr///ou6deti7ty5srWD8qdVkyjDwsLQu3dv1K1bV/HF+ObNG7Rq1QphYWEAspdxzZs3T2VtuHHjBn7//XesX78et2/fRsuWLTFnzhx07twZpqamKqsXyD1ZrWXLlvi///u/XGmT5ZIzWS48PByenp6Ijo5GtWrV1NIWbdS2bVtMnDhRkZXUxMREKYC8dOkSypcvr/J2CCHw3XffKU3WfPXqFYYOHaq0dE9VKyEqVqz4wSBC6lTimqht27aYMmUKvLy8cgV0L1++RGBgoNKEW1WLjo7GsmXLoKurCz09PaSnp6NcuXL4+eef0bdv3w+unCHV06oAwt7eHgcOHMD169dx48YNANlbCL+dxvhjd2wsrMqVK6N+/frw9fXFt99+Czs7O5XW97aZM2di2rRp8PDwgLGxMRYtWoSHDx+qfNvkd6WmpmLWrFlYvHgxatWqhYMHD8o2s5v+Z8aMGejcuTOaNWsGU1NThIeHK1KaA9mprNu0aaPyduSVplvOTZSmT5/OVRgAJk+ejK1bt6JixYoYMWKE4nvx+vXrWLp0KTIzM2VbpQVk90LlLOW0tbVFQkIC3NzcYGFhgTt37sjWDnoPNc/B0Cpv3rwRy5cvF8nJyWqpX52T1XLMmTNHlChRQlSpUkVs375dtnopf+rOSqpO72bA1Hbx8fHC29s7VwZMb2/vPLOVqlLr1q3F77//LoQQYuDAgaJBgwZi7dq1wtPTUzRo0EDWtlDetGoSZWZmJlavXo2DBw/iwYMHudYZy5EDwsjICP/8849s8yzeps7Jajl0dXVhbGwMDw+P927Ypc0b95B89PT0FNvM0/88efIEsbGxEEKgQoUKin1z5JSTs6hFixZ48OAB+vTpg5MnT6JChQpYuXIlatasKXubSJlWDWGMHj0aq1evRrt27VCtWjW1TJ6qVq0abt26pZYAQt2T1QCgT58+nLRGGkOLfj8ViJWVldq29M7x9mo5W1tbREREqLE1lBet6oEoWbIk1qxZg7Zt26qtDREREQgICMCMGTPy3N9dlXko3s32B+Sd8Y+//olIUzx48ECxMqty5cqwsbFRc4soh1YFEA4ODoiMjETFihXV1oa393d/+5e4UHE2TiA7Sc7H0JSkPkSkvZ49e4bhw4djw4YNiu9FPT09dOvWDUuXLuXEVw2gVQHEvHnzcOvWLSxZskRt3ehHjhx57/VmzZrJ1BIiIs3VrVs3XLhwAYsXL4a7uzuA7M3mRo8ejVq1amHDhg1qbiFpVQDRqVMnHD58GCVKlEDVqlVzbZLDrnsiIs1QvHhx7Nu3D02bNlU6f+zYMXh5eSEtLU1NLaMcWjWJ0tLSEp06dVJrG44ePfre619++aVMLSEi0lzW1tZ5DlNYWFioZVUI5aZVPRCaIK8c928PpzDHPRERsHz5cmzevBm//fYb7O3tAQCJiYmKXSiHDBmi5haSVvVAANlLGSMjIxEXF4cePXrAzMwM9+7dg7m5ucq3kgay11e/7fXr17hw4QKmTJmCmTNnqrx+IiJNVbt2baUfVDdv3oSTkxOcnJwAAAkJCTA0NMTDhw8ZQGgArQog/v33X3h5eSEhIQHp6elo3bo1zMzMMGfOHKSnpyM0NFTlbcirS65169YwMDCAv78/oqKiVN4GIiJN1LFjR3U3gQpAq4YwOnbsCDMzM4SFhcHa2hoXL15EuXLlEBkZiUGDBuHmzZtqa9v169dRr149WdOIExERFZZW9UAcO3YMJ0+eVEoYBAAuLi64e/euLG24dOmS0t9CCNy/fx+zZ89GrVq1ZGkDEVFR8vz581ypB1S56R59HK0KILKysvKcpPjff//BzMxMljbUqlULOjo6ubbQbdSokexZMYmINNXt27cxYsQIREZG4tWrV4rzcmy6Rx9HqwKINm3aYOHChVi+fDmA7NUPz58/R2BgoGzbW9++fVvpb11dXdjY2OTKUUFEpM169eoFIQRWrlwJOzs75tDRQFo1B+K///6Dp6cnhBC4efMm6tWrh5s3b6JkyZI4evSoSjPynTp1Co8fP8ZXX32lOLdmzRoEBgYiLS0NHTt2xOLFi5XyVBARaStTU1NERUWhUqVK6m4K5SP3pgSfsTJlyuDixYuYNGkS/Pz8ULt2bcyePRsXLlxQeTrfH3/8EVevXlX8ffnyZQwYMAAeHh6YOHEidu3ahaCgIJW2gYioqKhfvz7u3Lmj7mbQe2hVD4Q6lSpVCrt27VKkqJ00aRKOHDmC48ePAwA2b96MwMBAXLt2TZ3NJCLSCHFxcRg6dCh69eqFatWq5Uo9UKNGDTW1jHJo1RyI8PBwlCxZEu3atQMAjB8/HsuXL0eVKlWwfv16ODs7q6zuJ0+ewM7OTvH3kSNH4O3trfib0TYR0f88fPgQcXFxSlmEcyagcxKlZtCqIYxZs2bB2NgYQPachCVLluDnn39GyZIl4efnp9K67ezsFBMoMzIycP78eTRq1Ehx/dmzZ7kibCIibdW/f3/Url0bp06dwq1bt3D79m2l/0vqp1U9EHfu3IGrqysAYPv27fj6668xePBgNGnSBM2bN1dp3W3btsXEiRMxZ84cbN++HSYmJvjiiy8U1y9duoTy5curtA1EREXFv//+i507dyq+s0nzaFUPhKmpKR4/fgwA2L9/P1q3bg0AMDIywsuXL1Va94wZM1CsWDE0a9YMK1aswIoVK5Q2tFq5ciXatGmj0jYQERUVLVu2xMWLF9XdDHoPreqBaN26NQYOHIjatWvjxo0bir0frl69ChcXF5XWnbNUNDU1FaamptDT01O6vnnzZlmSeRERFQU+Pj7w8/PD5cuXUb169VxDvO3bt1dTyyiHVq3CSElJweTJk3Hnzh0MGzYMXl5eAIDAwEAYGBhg0qRJam4hEREB2Zvs5YeTKDWDVgUQREREJA2tmgMRERGh2HcBAJYuXYpatWqhR48eePLkiRpbRkREQPaE89TUVMXfs2fPRkpKiuLvx48fo0qVKmpoGb1LqwKIcePG4enTpwCyd4L8/vvv0bZtW9y+fRv+/v5qbh0REe3btw/p6emKv2fNmoXk5GTF32/evEFMTIw6mkbv0KpJlLdv31ZErn/88Qe++uorzJo1C+fPn5ctmRYREeXv3VF1jrJrLq3qgTAwMMCLFy8AAH/99Zdi2WSJEiUUPRNERET0YVrVA9G0aVP4+/ujSZMm+Pvvv7Fx40YAwI0bN1CmTBk1t46IiHR0dHKl7mYqb82kVQHEkiVLMHz4cGzZsgUhISEoXbo0AODPP/9ULOkkIiL1EULgu+++g6GhIQDg1atXGDp0KIoXLw4ASvMjSL24jJOIiDTG28mz3mfVqlUqbgl9iNYGEK9evUJGRobSOXNzczW1hoiIqGjRqkmUaWlpGDFiBGxtbVG8eHFYWVkpHURERPRxtCqAGD9+PA4dOoSQkBAYGhri//7v/zB9+nQ4ODhgzZo16m4eERFRkaFVQxhOTk5Ys2YNmjdvDnNzc5w/fx6urq747bffsH79euzdu1fdTSQiIioStKoHIjk5GeXKlQOQPd8hZ3ezpk2b4ujRo+psGhERUZGiVQFEuXLlcPv2bQBA5cqVsWnTJgDArl27YGlpqcaWERERFS1aNYSxYMEC6OnpYdSoUfjrr7/g4+MDIQRev36N+fPnY/To0epuIhERUZGgFRtJZWVlITg4GDt37kRGRgbu3buHwMBAXL9+HVFRUXB1dUWNGjXU3UwiIqIiQyt6IGbMmIFp06bBw8MDxsbG2LdvH7p3746VK1equ2lERERFklYEEBUqVMDYsWMxZMgQANmJtNq1a4eXL19CV1erpoEQERFJQisCCENDQ8TGxsLR0VFxzsjICLGxsUyiRUREVAha8fP7zZs3MDIyUjqnr6+P169fq6lFRERERZtWTKJ8N7sbkDvDGwBs3bpVHc0jIiIqcrQigOjbt2+uc7169VJDS4iIiD4PWjEHgoiIiKSlFXMgiIiISFoMIIiIiKjAGEAQERFRgTGAICIiogJjAEFEREQFxgCCiIiICowBBBERERXY/wOoin21dHGSXwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(data.isna())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rD2I6ZV3rN4o"
      },
      "source": [
        "Filling the missing values for 'Age' and 'Fare', but won't fill 'Cabin' due to over 30% missing data. Features with >30% missing data will be dropped from the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filling the missing value by its mean"
      ],
      "metadata": {
        "id": "6uv6ulOiuHfH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "N3UxVashrN4o"
      },
      "outputs": [],
      "source": [
        "data['Age'].fillna(data['Age'].mean(), inplace=True)\n",
        "data['Fare'].fillna(data['Fare'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Verify Missing Value\n",
        "\n"
      ],
      "metadata": {
        "id": "BW4HOhEouSwg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "tZUeJ7WvrN4p",
        "outputId": "2d888ca8-cbea-45e6-be33-6ce335b7b402"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(data.isna())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking Data Type Of Features"
      ],
      "metadata": {
        "id": "LBzyLRMSuaUL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMmYp3hRrN4p",
        "outputId": "b0dbc460-40bb-4c88-a2f3-47ff237ac227"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njZY35DNrN4p",
        "outputId": "d9fc77d6-53fb-4d12-93f8-db41f90baf41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 13 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Survived     418 non-null    int64  \n",
            " 2   Pclass       418 non-null    int64  \n",
            " 3   Name         418 non-null    object \n",
            " 4   Sex          418 non-null    object \n",
            " 5   Age          418 non-null    float64\n",
            " 6   SibSp        418 non-null    int64  \n",
            " 7   Parch        418 non-null    int64  \n",
            " 8   Ticket       418 non-null    object \n",
            " 9   Fare         418 non-null    float64\n",
            " 10  Cabin        91 non-null     object \n",
            " 11  Embarked     418 non-null    object \n",
            " 12  Gender       418 non-null    uint8  \n",
            "dtypes: float64(2), int64(5), object(5), uint8(1)\n",
            "memory usage: 39.7+ KB\n"
          ]
        }
      ],
      "source": [
        "gender=pd.get_dummies(data['Sex'],drop_first=True)\n",
        "\n",
        "data['Gender']=gender\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dropping irrelevant columns and verifying changes"
      ],
      "metadata": {
        "id": "D19K8o33upyQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "meTJc24VrN4q",
        "outputId": "3ece8a4c-c7b6-4dbf-f6fa-1faf660ec89c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Gender\n",
              "0          892         0       3  34.5      0      0   7.8292       1\n",
              "1          893         1       3  47.0      1      0   7.0000       0\n",
              "2          894         0       2  62.0      0      0   9.6875       1\n",
              "3          895         0       3  27.0      0      0   8.6625       1\n",
              "4          896         1       3  22.0      1      1  12.2875       0"
            ],
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              "\n",
              "  <div id=\"df-35ce97f1-7a55-41c4-96c7-535f20aba6be\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th>PassengerId</th>\n",
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              "      <td>896</td>\n",
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              "      <td>22.0</td>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-35ce97f1-7a55-41c4-96c7-535f20aba6be button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-35ce97f1-7a55-41c4-96c7-535f20aba6be');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f3041bf9-7ab5-48fc-a0db-7f43cde4a054\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3041bf9-7ab5-48fc-a0db-7f43cde4a054')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3041bf9-7ab5-48fc-a0db-7f43cde4a054 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "data.drop(['Name','Sex','Ticket','Embarked','Cabin'], axis=1, inplace=True)\n",
        "\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnfjS3oQrN4q"
      },
      "source": [
        "# Data Modeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "-Ks29HI-rN4q"
      },
      "outputs": [],
      "source": [
        "X = data.drop('Survived', axis=1)\n",
        "y = data['Survived']\n",
        "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Fitting in Logistic Regression"
      ],
      "metadata": {
        "id": "tzFXQiGku5kV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "QhVUhuNFrN4q",
        "outputId": "6ba7df67-186d-46a8-cc14-da608d7b31d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=3000)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=3000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=3000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "LR.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making predictions using confusion matrix"
      ],
      "metadata": {
        "id": "iFNzTFvEvA23"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "OgBAth-mrN4q",
        "outputId": "ab4329fc-ad66-4dd6-e975-f31bd5971db1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Predicted No  Predicted Yes\n",
              "Actual No             50              0\n",
              "Actual Yes             0             34"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-632d1781-d2a1-4401-8b9c-60f1a196979b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted No</th>\n",
              "      <th>Predicted Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual No</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Yes</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-632d1781-d2a1-4401-8b9c-60f1a196979b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-632d1781-d2a1-4401-8b9c-60f1a196979b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-632d1781-d2a1-4401-8b9c-60f1a196979b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c11aa61b-aa97-4a2f-909e-19cea0a37feb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c11aa61b-aa97-4a2f-909e-19cea0a37feb')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c11aa61b-aa97-4a2f-909e-19cea0a37feb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "y_pred=LR.predict(X_test)\n",
        "\n",
        "pd.DataFrame(confusion_matrix(y_test,y_pred),columns=['Predicted No','Predicted Yes'],index=['Actual No', 'Actual Yes'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the model"
      ],
      "metadata": {
        "id": "Xk_ge2flvGrH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dA4uUDGwrN4r",
        "outputId": "37af6f48-07df-4c9a-db9c-d0fbd492823e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       1.00      1.00      1.00        34\n",
            "\n",
            "    accuracy                           1.00        84\n",
            "   macro avg       1.00      1.00      1.00        84\n",
            "weighted avg       1.00      1.00      1.00        84\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
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  "nbformat_minor": 0
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