DiabetesPrediction1.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the diabetes dataset\n",
    "diabetes_dataset = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "# Separating data and labels\n",
    "X = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
    "Y = diabetes_dataset['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing the data\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify=Y, random_state=2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# Training the model\n",
    "classifier = svm.SVC(kernel='linear')\n",
    "classifier.fit(X_train, Y_train)"
   ]
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      "Accuracy score of the training data :  0.7866449511400652\n",
      "Accuracy score of the test data :  0.7727272727272727\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "X_train_prediction = classifier.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
    "print('Accuracy score of the training data : ', training_data_accuracy)\n",
    "\n",
    "X_test_prediction = classifier.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print('Accuracy score of the test data : ', test_data_accuracy)"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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       "['svm_diabetes_model.joblib']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# Saving the trained SVM classifier to a file\n",
    "model_filename = 'svm_diabetes_model.joblib'\n",
    "joblib.dump(classifier, model_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "The patient is diabetic\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\haade\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# New patient's data\n",
    "new_patient_data = np.array([[5,166,72,19,175,25.8,0.587,51]])\n",
    "\n",
    "# Standardizing the new data\n",
    "new_patient_data_standardized = scaler.transform(new_patient_data)\n",
    "\n",
    "# Making a prediction\n",
    "new_patient_prediction = classifier.predict(new_patient_data_standardized)\n",
    "\n",
    "# Printing the prediction\n",
    "if (new_patient_prediction[0] == 0):\n",
    "   print('The patient is not diabetic')\n",
    "else:\n",
    "   print('The patient is diabetic')\n"
   ]
  }
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