logistic_regression.ipynb

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    "#Logistic Regression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/Users/mohammadreza/Desktop/python'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "        \n",
    "data = pd.read_csv('dataset.csv', encoding ='latin1')\n",
    "data.info()\n",
    "\n",
    "data = data.drop(['ID'],axis=1)\n",
    "data.head(10000)\n",
    "\n",
    "y = data['Y'].values\n",
    "y = y.reshape(-1,1)\n",
    "x_data = data.drop(['Y'],axis = 1)\n",
    "print(x_data)\n",
    "\n",
    "x = (x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values\n",
    "x.head(20000)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.5,random_state=100)\n",
    "\n",
    "y_train = y_train.reshape(-1,1)\n",
    "y_test = y_test.reshape(-1,1)\n",
    "\n",
    "print(\"x_train: \",x_train.shape)\n",
    "print(\"x_test: \",x_test.shape)\n",
    "print(\"y_train: \",y_train.shape)\n",
    "print(\"y_test: \",y_test.shape)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.5,random_state=100)\n",
    "\n",
    "y_train = y_train.reshape(-1,1)\n",
    "y_test = y_test.reshape(-1,1)\n",
    "\n",
    "print(\"x_train: \",x_train.shape)\n",
    "print(\"x_test: \",x_test.shape)\n",
    "print(\"y_train: \",y_train.shape)\n",
    "print(\"y_test: \",y_test.shape)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(solver='lbfgs')\n",
    "lr.fit(x_train, y_train.ravel())\n",
    "print(\"accuracy : \", lr.score(x_test, y_test))"
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   "execution_count": null,
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