8.4 分类问题 信用卡欺诈数据集.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7ffbeb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "078d918c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7028ef15",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/credit-a.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "def26f33",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b355e217",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "# from sklearn.linear_model._logistic import LogisticRegression\n",
    "# from sklearn.linear_model.logistic import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fcf8cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[data.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20f92f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[15].replace(-1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e01103a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aefb1c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f461f3af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3cfedc70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)  # 返回分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d97ef242",
   "metadata": {},
   "outputs": [
    {
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       "       [1.16908139e-01, 8.83091861e-01],\n",
       "       [2.03066721e-01, 7.96933279e-01],\n",
       "       [8.56061038e-01, 1.43938962e-01],\n",
       "       [9.95836941e-01, 4.16305867e-03],\n",
       "       [2.93070764e-04, 9.99706929e-01],\n",
       "       [1.00000000e+00, 3.30189002e-16],\n",
       "       [9.83297981e-01, 1.67020194e-02],\n",
       "       [1.86251489e-01, 8.13748511e-01],\n",
       "       [5.35926548e-01, 4.64073452e-01],\n",
       "       [7.55667682e-01, 2.44332318e-01],\n",
       "       [6.43859743e-01, 3.56140257e-01],\n",
       "       [2.79667316e-01, 7.20332684e-01],\n",
       "       [9.63765556e-01, 3.62344443e-02],\n",
       "       [2.64071641e-02, 9.73592836e-01],\n",
       "       [4.19146429e-02, 9.58085357e-01],\n",
       "       [1.79191699e-01, 8.20808301e-01],\n",
       "       [1.93460409e-01, 8.06539591e-01],\n",
       "       [9.64036616e-02, 9.03596338e-01],\n",
       "       [1.21332162e-01, 8.78667838e-01],\n",
       "       [2.48143536e-01, 7.51856464e-01],\n",
       "       [2.20133110e-03, 9.97798669e-01],\n",
       "       [4.08915180e-01, 5.91084820e-01],\n",
       "       [7.65807010e-02, 9.23419299e-01],\n",
       "       [1.44802213e-01, 8.55197787e-01],\n",
       "       [9.98846818e-01, 1.15318205e-03],\n",
       "       [1.97074786e-01, 8.02925214e-01],\n",
       "       [2.83473824e-01, 7.16526176e-01],\n",
       "       [9.08388473e-01, 9.16115268e-02],\n",
       "       [8.25108746e-01, 1.74891254e-01],\n",
       "       [8.77065777e-01, 1.22934223e-01],\n",
       "       [7.83165840e-01, 2.16834160e-01],\n",
       "       [8.31404589e-01, 1.68595411e-01],\n",
       "       [1.56427134e-02, 9.84357287e-01],\n",
       "       [8.29387297e-01, 1.70612703e-01],\n",
       "       [1.06507496e-01, 8.93492504e-01],\n",
       "       [9.99995813e-01, 4.18677113e-06],\n",
       "       [2.20942896e-01, 7.79057104e-01],\n",
       "       [6.89460457e-03, 9.93105395e-01],\n",
       "       [3.55591481e-01, 6.44408519e-01],\n",
       "       [6.68914006e-01, 3.31085994e-01],\n",
       "       [4.95366015e-01, 5.04633985e-01],\n",
       "       [8.32531498e-01, 1.67468502e-01],\n",
       "       [1.00000000e+00, 2.44663902e-11],\n",
       "       [3.90869392e-01, 6.09130608e-01],\n",
       "       [4.88321272e-01, 5.11678728e-01],\n",
       "       [9.42062404e-01, 5.79375961e-02],\n",
       "       [1.56880504e-01, 8.43119496e-01],\n",
       "       [8.49206525e-01, 1.50793475e-01],\n",
       "       [1.94623394e-01, 8.05376606e-01],\n",
       "       [8.25040460e-01, 1.74959540e-01],\n",
       "       [8.45659998e-01, 1.54340002e-01],\n",
       "       [1.66965523e-01, 8.33034477e-01],\n",
       "       [9.94559601e-01, 5.44039916e-03],\n",
       "       [1.38836763e-01, 8.61163237e-01],\n",
       "       [3.42777589e-01, 6.57222411e-01],\n",
       "       [7.28376159e-01, 2.71623841e-01],\n",
       "       [1.30228900e-02, 9.86977110e-01],\n",
       "       [1.43409431e-01, 8.56590569e-01],\n",
       "       [2.82580320e-01, 7.17419680e-01],\n",
       "       [1.76323337e-01, 8.23676663e-01],\n",
       "       [5.37400050e-01, 4.62599950e-01],\n",
       "       [1.71094087e-01, 8.28905913e-01],\n",
       "       [4.95141183e-01, 5.04858817e-01],\n",
       "       [3.38614440e-02, 9.66138556e-01],\n",
       "       [8.16965131e-01, 1.83034869e-01],\n",
       "       [1.07113710e-01, 8.92886290e-01],\n",
       "       [1.77590609e-01, 8.22409391e-01],\n",
       "       [1.04104620e-01, 8.95895380e-01],\n",
       "       [6.88476144e-01, 3.11523856e-01],\n",
       "       [4.25696584e-02, 9.57430342e-01],\n",
       "       [7.16955909e-01, 2.83044091e-01],\n",
       "       [1.08485083e-01, 8.91514917e-01],\n",
       "       [6.71159064e-02, 9.32884094e-01],\n",
       "       [2.54821601e-02, 9.74517840e-01],\n",
       "       [9.60269763e-02, 9.03973024e-01],\n",
       "       [5.91442414e-01, 4.08557586e-01],\n",
       "       [1.52634801e-01, 8.47365199e-01],\n",
       "       [9.90429568e-01, 9.57043153e-03],\n",
       "       [6.38957493e-01, 3.61042507e-01],\n",
       "       [8.60182469e-01, 1.39817531e-01],\n",
       "       [6.07033553e-01, 3.92966447e-01],\n",
       "       [8.42531416e-02, 9.15746858e-01],\n",
       "       [4.46158080e-01, 5.53841920e-01],\n",
       "       [7.76886228e-01, 2.23113772e-01],\n",
       "       [8.64070117e-02, 9.13592988e-01],\n",
       "       [1.00736932e-02, 9.89926307e-01],\n",
       "       [1.48243067e-01, 8.51756933e-01],\n",
       "       [1.62073639e-01, 8.37926361e-01],\n",
       "       [5.46812011e-01, 4.53187989e-01],\n",
       "       [8.07066910e-01, 1.92933090e-01],\n",
       "       [1.73334950e-01, 8.26665050e-01],\n",
       "       [5.30791168e-02, 9.46920883e-01],\n",
       "       [3.51361385e-01, 6.48638615e-01],\n",
       "       [1.31771479e-01, 8.68228521e-01],\n",
       "       [6.74049876e-02, 9.32595012e-01],\n",
       "       [9.16807700e-01, 8.31923005e-02],\n",
       "       [9.99999104e-01, 8.95557855e-07],\n",
       "       [9.66401604e-01, 3.35983962e-02],\n",
       "       [4.82663509e-01, 5.17336491e-01],\n",
       "       [1.57841163e-02, 9.84215884e-01],\n",
       "       [9.41811072e-01, 5.81889280e-02],\n",
       "       [9.82444528e-01, 1.75554718e-02],\n",
       "       [6.46206097e-02, 9.35379390e-01],\n",
       "       [1.06196025e-01, 8.93803975e-01],\n",
       "       [6.13190015e-01, 3.86809985e-01],\n",
       "       [8.62913171e-01, 1.37086829e-01],\n",
       "       [9.72545008e-01, 2.74549917e-02],\n",
       "       [9.88004128e-01, 1.19958718e-02],\n",
       "       [9.99465727e-01, 5.34273145e-04],\n",
       "       [1.16548591e-01, 8.83451409e-01],\n",
       "       [2.51884231e-01, 7.48115769e-01],\n",
       "       [2.26399929e-01, 7.73600071e-01],\n",
       "       [4.83522417e-01, 5.16477583e-01],\n",
       "       [8.37031421e-01, 1.62968579e-01],\n",
       "       [6.13359088e-03, 9.93866409e-01],\n",
       "       [2.71283831e-01, 7.28716169e-01],\n",
       "       [9.53814562e-01, 4.61854379e-02],\n",
       "       [9.52414282e-01, 4.75857182e-02],\n",
       "       [8.43110707e-01, 1.56889293e-01],\n",
       "       [1.35242228e-01, 8.64757772e-01],\n",
       "       [3.36992363e-01, 6.63007637e-01],\n",
       "       [8.18726543e-01, 1.81273457e-01],\n",
       "       [2.29354989e-01, 7.70645011e-01],\n",
       "       [1.88943642e-01, 8.11056358e-01],\n",
       "       [1.46145773e-01, 8.53854227e-01],\n",
       "       [2.30817213e-01, 7.69182787e-01],\n",
       "       [8.66472491e-02, 9.13352751e-01],\n",
       "       [8.12998267e-01, 1.87001733e-01],\n",
       "       [8.03897904e-01, 1.96102096e-01],\n",
       "       [8.30199720e-02, 9.16980028e-01],\n",
       "       [9.00751049e-01, 9.92489506e-02],\n",
       "       [1.06650273e-01, 8.93349727e-01],\n",
       "       [7.65257470e-02, 9.23474253e-01],\n",
       "       [9.57502919e-01, 4.24970810e-02],\n",
       "       [7.60105651e-01, 2.39894349e-01],\n",
       "       [2.66443940e-01, 7.33556060e-01],\n",
       "       [7.94193465e-01, 2.05806535e-01],\n",
       "       [4.82328179e-01, 5.17671821e-01],\n",
       "       [1.47014979e-01, 8.52985021e-01],\n",
       "       [1.94260076e-01, 8.05739924e-01],\n",
       "       [1.38559212e-01, 8.61440788e-01],\n",
       "       [9.82125609e-02, 9.01787439e-01],\n",
       "       [2.92974865e-02, 9.70702514e-01],\n",
       "       [2.10696581e-01, 7.89303419e-01],\n",
       "       [6.69251981e-01, 3.30748019e-01],\n",
       "       [2.79784723e-01, 7.20215277e-01],\n",
       "       [1.99942546e-01, 8.00057454e-01],\n",
       "       [1.95397982e-01, 8.04602018e-01],\n",
       "       [1.26122756e-03, 9.98738772e-01],\n",
       "       [8.68284726e-01, 1.31715274e-01]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(x_test)  # 返回分类概率值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f758064",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c7ee09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8475609756097561"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(model.predict(x_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9790ff1",
   "metadata": {},
   "source": [
    "提高正确率：做标准化，使特征在同一量级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "372d0b58",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}