ML_Linear_Regression.ipynb

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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
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          "name": "stdout",
          "text": [
            "Saving diabetes (1).csv to diabetes (1).csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "upload=files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "from sklearn import linear_model\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "data = pd.read_csv(\"diabetes (1).csv\")\n",
        "\n",
        "data = data[[\"Pregnancies\",\"BloodPressure\", \"SkinThickness\", \"BMI\", \"DiabetesPedigreeFunction\", \"Age\", \"Outcome\"]]\n",
        "\n",
        "predict = \"Age\"\n",
        "\n",
        "X = np.array(data.drop([predict], axis=1))\n",
        "y = np.array(data[predict])\n",
        "\n",
        "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1)\n",
        "\n",
        "linear = linear_model.LinearRegression()\n",
        "\n",
        "linear.fit(x_train, y_train)\n",
        "acc = linear.score(x_test, y_test)\n",
        "print(acc)\n",
        "print(x_test)\n",
        "print(y_test)\n",
        "print('Coefficient: \\n', linear.coef_)\n",
        "print('Intercept: \\n', linear.intercept_)\n",
        "\n",
        "predictions = linear.predict(x_test)\n",
        "print(predictions)\n",
        "\n",
        "for x in range(len(predictions)):\n",
        "    print(predictions[x], x_test[x], y_test[x])"
      ],
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      "execution_count": 10,
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            " [1.000e+00 6.000e+01 0.000e+00 3.010e+01 3.490e-01 1.000e+00]\n",
            " [1.000e+00 7.400e+01 2.100e+01 2.590e+01 6.730e-01 0.000e+00]\n",
            " [0.000e+00 6.400e+01 1.800e+01 3.490e+01 7.250e-01 0.000e+00]\n",
            " [0.000e+00 6.600e+01 3.200e+01 4.130e+01 5.020e-01 1.000e+00]\n",
            " [1.000e+00 7.200e+01 3.000e+01 3.440e+01 5.280e-01 0.000e+00]\n",
            " [4.000e+00 6.200e+01 0.000e+00 3.400e+01 3.910e-01 0.000e+00]\n",
            " [8.000e+00 9.600e+01 0.000e+00 0.000e+00 2.320e-01 1.000e+00]\n",
            " [6.000e+00 0.000e+00 0.000e+00 0.000e+00 1.890e-01 0.000e+00]]\n",
            "[23 51 24 70 52 41 45 38 22 33 31 28 41 28 58 22 23 26 25 29 25 30 22 34\n",
            " 22 41 28 22 24 29 22 26 25 41 29 43 31 21 38 29 24 25 28 32 23 30 23 55\n",
            " 21 31 24 22 21 39 25 34 24 27 27 28 29 31 26 33 24 43 37 59 24 47 36 23\n",
            " 28 25 25 54 26]\n",
            "Coefficient: \n",
            " [ 1.72173553  0.11084408 -0.09353678 -0.02064818  1.78277751  2.5510871 ]\n",
            "Intercept: \n",
            " 19.976423998069688\n",
            "[25.79262505 38.41568269 33.5852849  36.56789263 49.85938098 40.04650895\n",
            " 47.71876776 31.84874006 24.5396908  33.60329561 27.84997723 29.83711666\n",
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            " 33.97838459 26.27221644 38.67945022 34.3804917  32.57231984 28.62912303\n",
            " 37.09932192 37.1547533  24.98977778 30.01462129 25.24215612 33.8295767\n",
            " 25.17134188 24.86719326 34.45440084 29.65461701 28.76433668 49.90706871\n",
            " 34.15584592 35.05356679 28.36724443 35.16015968 29.05822418 26.35608921\n",
            " 36.70379076 27.6587802  33.2877129  31.65390387 26.37277671 41.91123514\n",
            " 36.7808038  28.83658071 27.70052626 30.90057066 28.60137048 25.95867532\n",
            " 26.89222787 27.103839   33.73072706 47.35603147 30.64378212]\n",
            "25.79262505284257 [ 0.    82.    31.    38.2    0.233  0.   ] 23\n",
            "38.415682692360264 [ 7.    74.    26.    26.     0.647  0.   ] 51\n",
            "33.58528489573828 [ 4.    64.     0.    28.9    0.126  0.   ] 24\n",
            "36.567892632151576 [ 4.    82.    18.    32.5    0.235  1.   ] 70\n",
            "49.85938097547742 [13.    70.     0.    34.2    0.251  0.   ] 52\n",
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            "34.35065643419247 [ 5.    62.    41.    35.8    0.514  1.   ] 25\n",
            "36.10621753229634 [ 5.    68.     0.    26.     0.292  0.   ] 30\n",
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            "37.09932192099723 [ 6.    90.    39.    35.7    0.674  0.   ] 28\n",
            "37.154753300026684 [ 4.    75.     0.    48.3    0.238  1.   ] 32\n",
            "24.989777781906632 [ 3.     0.     0.    23.5    0.187  0.   ] 23\n",
            "30.01462128677486 [ 3.    70.    30.    42.9    0.452  0.   ] 30\n",
            "25.242156120807905 [ 1.    54.    25.    25.2    0.234  0.   ] 23\n",
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            "33.73072706473905 [ 4.    62.     0.    34.     0.391  0.   ] 25\n",
            "47.356031467732706 [ 8.    96.     0.     0.     0.232  1.   ] 54\n",
            "30.643782115645486 [6.    0.    0.    0.    0.189 0.   ] 26\n"
          ]
        }
      ]
    }
  ]
}