12_Save_N_Load_ML_Models.ipynb

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      "source": [
        "#Pima-Indian-Diabetes Dataset\n",
        "from pandas import read_csv\n",
        "url = 'https://raw.githubusercontent.com/erojaso/MLMasteryEndToEnd/master/data/pima-indians-diabetes.data.csv' #Load CSV using Pandas\n",
        "column_names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] #Define column names\n",
        "data = read_csv(url, names=column_names) #Load CSV using Pandas\n",
        "array = data.values #Convert to NumPy array\n",
        "X = array[:,0:8] #Split into input column\n",
        "Y = array[:,8] #Split into output column"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=7)\n",
        "model = LogisticRegression(solver='liblinear')\n",
        "model.fit(X_train, Y_train)"
      ],
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            ]
          },
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    {
      "cell_type": "code",
      "source": [
        "from pickle import dump\n",
        "from pickle import load\n",
        "# save the model to disk\n",
        "filename = '.\\models\\lgrg_pickle_model.sav'\n",
        "dump(model, open(filename, 'wb'))\n",
        "# load the model from disk\n",
        "loaded_model = load(open(filename, 'rb'))\n",
        "result = loaded_model.score(X_test, Y_test)\n",
        "print(result)"
      ],
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