Logistic_Regresssion_Test.ipynb

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   "source": [
    "### Initialization"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from Logistic_Regression import LogisticRegression\n",
    "from sklearn.datasets import make_classification\n"
   ]
  },
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   "attachments": {},
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   "metadata": {},
   "source": [
    "### Preparing data"
   ]
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   "source": [
    "X, y = n_samples = make_classification(\n",
    "    n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, random_state=42, class_sep=10)\n"
   ]
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   "source": [
    "### Training model"
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X, y)\n"
   ]
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   "source": [
    "### Making prediction"
   ]
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   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
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     "execution_count": 15,
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   "source": [
    "model.predict(X)\n"
   ]
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.score(X, y)\n"
   ]
  }
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