ML for insurance data. LOgistic algorithm.ipynb

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   "source": [
    "import pandas as pd\n",
    "#import matplotlib.pyplot as plt\n",
    "#matplotlib.inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bought_insurance</th>\n",
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      "text/plain": [
       "    age  bought_insurance\n",
       "0    22                 0\n",
       "1    25                 0\n",
       "2    47                 1\n",
       "3    52                 0\n",
       "4    46                 1\n",
       "5    56                 1\n",
       "6    55                 0\n",
       "7    60                 1\n",
       "8    62                 1\n",
       "9    61                 1\n",
       "10   18                 0\n",
       "11   28                 0\n",
       "12   27                 0\n",
       "13   29                 0\n",
       "14   49                 1\n",
       "15   55                 1\n",
       "16   25                 1\n",
       "17   58                 1\n",
       "18   19                 0\n",
       "19   18                 0\n",
       "20   21                 0\n",
       "21   26                 0\n",
       "22   40                 1\n",
       "23   45                 1\n",
       "24   50                 1\n",
       "25   54                 1\n",
       "26   23                 0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.read_csv('insurance_data.csv')\n",
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(a[['age']],a.bought_insurance,train_size=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>9</th>\n",
       "      <td>61</td>\n",
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       "      <td>18</td>\n",
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       "      <td>60</td>\n",
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      "text/plain": [
       "    age\n",
       "9    61\n",
       "10   18\n",
       "7    60"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>age</th>\n",
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       "    age\n",
       "9    61\n",
       "10   18\n",
       "7    60"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.19770555, 0.80229445],\n",
       "       [0.67347453, 0.32652547],\n",
       "       [0.20566009, 0.79433991]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 64,
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       "1.0"
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     "execution_count": 64,
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    "model.score(X_test,y_test)"
   ]
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       "    age\n",
       "9    61\n",
       "10   18\n",
       "7    60"
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     "metadata": {},
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   "source": [
    "X_test"
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   "cell_type": "code",
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     "data": {
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   "source": [
    "model.predict(X_test)"
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