LOGISITIC REGRESSION.ipynb

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   "execution_count": 6,
   "id": "54193598",
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   "source": [
    "import pandas as pd #to create dataframe\n",
    "from sklearn.linear_model import LogisticRegression # to use logisitc reg \n",
    "from sklearn.model_selection import train_test_split # 2 more methods cross validation & end sembble techniques.TO TRAIN THE ML MODEL TO DIVIDE THE DATA INTO TESTING AND TRAINING\n",
    "import matplotlib.pyplot as plt # to plot graph of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fb17002",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('insurance_data.csv')"
   ]
  },
  {
   "cell_type": "code",
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   "id": "d5acb78d",
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       "(27, 2)"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
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   "execution_count": 9,
   "id": "bd02902c",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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"
      ]
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     "execution_count": 9,
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   ],
   "source": [
    "df.head() # give values of the top 5 rows"
   ]
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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"
      ]
     },
     "execution_count": 10,
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   "source": [
    "df.head(9)"
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   "execution_count": 11,
   "id": "ac17b9bf",
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bought_insurance</th>\n",
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       "      <td>45</td>\n",
       "      <td>1</td>\n",
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       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "    age  bought_insurance\n",
       "22   40                 1\n",
       "23   45                 1\n",
       "24   50                 1\n",
       "25   54                 1\n",
       "26   23                 0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "df.tail() #display the bottom 5 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e97f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bought_insurance'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64a89740",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate the input features and target features .age is input feature and bought_insurance is target feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59f69d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[['age']] #input feature always in 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc4fca05",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   age\n",
       "0   22\n",
       "1   25\n",
       "2   47\n",
       "3   52\n",
       "4   46"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "source": [
    "x.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa1466e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.bought_insurance #or y=df['bought_insurance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3d5f5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: bought_insurance, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c3a1f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2) #return 4 values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec43ae10",
   "metadata": {},
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       "(21, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "x_train.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "id": "2b86eee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6243ed81",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92740304",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a615dbdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5117f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3bf72d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "252a26ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae55229f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     1\n",
       "11    0\n",
       "25    1\n",
       "21    0\n",
       "1     0\n",
       "10    0\n",
       "Name: bought_insurance, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7543df24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_predict,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df9ad98b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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