COVID_19_risk_calculator_with_cleaned_data_.ipynb

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      "name": "COVID-19 risk calculator with cleaned data..",
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        "<a href=\"https://colab.research.google.com/github/e-aditi/hello-world/blob/master/COVID_19_risk_calculator_with_cleaned_data_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "path='/content/foo.csv'\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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        "id": "IqmJ6Ltoz1DY",
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      "source": [
        "first_data= pd.read_csv(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "first_data.head()"
      ],
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          "data": {
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              "      <th>asthma</th>\n",
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              "   Unnamed: 0  C_pos  C_sym  C_con  asthma  lung_d  diabetes   hw  risk  male\n",
              "0           0    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   1.0\n",
              "1           1    0.0    0.0    0.0     0.0     1.0       1.0  0.0   0.0   1.0\n",
              "2           2    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   0.0\n",
              "3           3    0.0    0.0    0.0     0.0     0.0       1.0  0.0   0.0   1.0\n",
              "4           4    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   0.0"
            ]
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      "source": [
        "first_data.drop([\"Unnamed: 0\"],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "first_data.head()"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>C_pos</th>\n",
              "      <th>C_sym</th>\n",
              "      <th>C_con</th>\n",
              "      <th>asthma</th>\n",
              "      <th>lung_d</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hw</th>\n",
              "      <th>risk</th>\n",
              "      <th>male</th>\n",
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            "text/plain": [
              "   C_pos  C_sym  C_con  asthma  lung_d  diabetes   hw  risk  male\n",
              "0    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   1.0\n",
              "1    0.0    0.0    0.0     0.0     1.0       1.0  0.0   0.0   1.0\n",
              "2    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   0.0\n",
              "3    0.0    0.0    0.0     0.0     0.0       1.0  0.0   0.0   1.0\n",
              "4    0.0    0.0    0.0     0.0     0.0       0.0  0.0   0.0   0.0"
            ]
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        }
      ]
    },
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      "metadata": {
        "id": "9sTjw75gnLWj",
        "colab_type": "code",
        "outputId": "04db1994-0056-4d17-bee3-c820000c0747",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "first_data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C_pos       0\n",
              "C_sym       0\n",
              "C_con       0\n",
              "asthma      0\n",
              "lung_d      0\n",
              "diabetes    0\n",
              "hw          0\n",
              "risk        0\n",
              "male        1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcjOOO52S-M3",
        "colab_type": "code",
        "outputId": "9d534d27-fd05-4381-a772-f4b854a97a36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "zer=0\n",
        "first_data['male'].fillna(zer,inplace=True)\n",
        "first_data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C_pos       0\n",
              "C_sym       0\n",
              "C_con       0\n",
              "asthma      0\n",
              "lung_d      0\n",
              "diabetes    0\n",
              "hw          0\n",
              "risk        0\n",
              "male        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQ3nWuQH05gl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = first_data[[\"male\",\"C_pos\",\"C_con\",\"asthma\",\"lung_d\",\"diabetes\",\"hw\"]]\n",
        "y = first_data['risk']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZxhxspG1iMB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size = 0.25)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_5mW-4a1uK5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA4dHj2w13ZH",
        "colab_type": "code",
        "outputId": "7aa71483-27f1-48e0-bbc1-43648d9d2c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "#### training of model (fitting the model)\n",
        "model.fit(xtrain,ytrain)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvL4Fugc18zN",
        "colab_type": "code",
        "outputId": "7b5f105b-d8dc-445a-8ed9-0bdc8740341f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#ytest = actual data, ypred = \n",
        "ypred = model.predict(xtest)\n",
        "ypred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8py-YpD6S2P",
        "colab_type": "code",
        "outputId": "2c791c19-e793-4b26-beed-d98a326fcba8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(ytest,ypred)\n",
        "cm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[47361,   756],\n",
              "       [ 8666,  1438]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XX8aO-P86Zkr",
        "colab_type": "code",
        "outputId": "9e4a2a44-a87a-4a21-d6d9-04af8eb89eac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc = accuracy_score(ytest,ypred)\n",
        "acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8381683584960753"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qCBitovT3OT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}