Asteroid Classification.ipynb

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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e9651de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39136873",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"nasa.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c80067e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb31614",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Neo Reference ID\", \"Name\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8754573",
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(column):\n",
    "    le = LabelEncoder().fit(column)\n",
    "    print(column.name, le.classes_)\n",
    "    return le.transform(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2df0991b",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_cols = [\"Close Approach Date\", \"Orbiting Body\", \"Orbit Determination Date\", \"Equinox\", \"Hazardous\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb232c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in date_cols:\n",
    "    df[col] = df[col].astype(\"|S\")\n",
    "    df[col] = label_encoder(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df26805d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Est Dia in KM(min)\", \"Est Dia in KM(max)\", \"Est Dia in M(min)\", \"Est Dia in M(max)\",\n",
    "              \"Est Dia in Miles(min)\", \"Est Dia in Miles(max)\", \"Est Dia in Feet(min)\", \"Est Dia in Feet(max)\",\n",
    "              \"Relative Velocity km per sec\", \"Relative Velocity km per hr\", \"Miles per hour\", \"Semi Major Axis\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aebf86d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7069f8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Hazardous\", axis=1)\n",
    "y = df[\"Hazardous\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93f3fa1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b5dfae0",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "GaussianNB()"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb_model = GaussianNB()\n",
    "gnb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f95baadc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gnb_model.predict(X_test)"
   ]
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   "id": "64a7d120",
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    "gnb_model.score(X_test, y_test)"
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   "cell_type": "code",
   "execution_count": 21,
   "id": "e48b4a09",
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   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f769163",
   "metadata": {},
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   "source": [
    "plt.figure()\n",
    "sns.heatmap(cm, annot=True, fmt=\".0f\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06eeb3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(gnb_model, open(\"gnb.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2093d330",
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