Iris Classification

Iris_Classifier_Inference.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "   petal length (cm)  petal width (cm)  sepal length (cm)  sepal width (cm)\n",
       "0                1.5               0.2                5.1               3.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "new_iris_flower = pd.DataFrame([\n",
    "    {\n",
    "        \"sepal length (cm)\": 5.1,\n",
    "        \"sepal width (cm)\": 3.,\n",
    "        \"petal length (cm)\": 1.5,\n",
    "        \"petal width (cm)\": 0.2,\n",
    "    }\n",
    "])\n",
    "\n",
    "new_iris_flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "with open(\"iris_classifier.joblib\", \"rb\") as f:\n",
    "    clf = joblib.load(f)\n",
    "\n",
    "with open(\"iris_classifier_features.joblib\", \"rb\") as f:\n",
    "    features = joblib.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(new_iris_flower[features])"
   ]
  }
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