predict-with-pipeline.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30686900",
   "metadata": {},
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   "source": [
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d96fa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = pickle.load(open('pipe.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30510787",
   "metadata": {},
   "outputs": [],
   "source": [
    "#asume user input\n",
    "test_input2 = np.array([3,'male',24.0,0,0,7.8,'S'],dtype=object).reshape(1,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebf62b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(test_input2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52a4a4d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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