Insurance Buying model -Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de62483d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58848fab",
   "metadata": {},
   "source": [
    "# READING FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5d599f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  bought_insurance\n",
       "0   22                 0\n",
       "1   43                 1\n",
       "2   45                 1\n",
       "3   34                 0\n",
       "4   53                 1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"insurance.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da190f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22850838690>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.age,df.bought_insurance,marker='*', color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25fee1e6",
   "metadata": {},
   "source": [
    "# Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3228d002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7fd17c",
   "metadata": {},
   "source": [
    "# Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86bda534",
   "metadata": {},
   "outputs": [],
   "source": [
    "#[['age']] is multi- dimensional array thats why it have two sq brackets for this if it is one dimensional - we have to use the one sq brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31d6fb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4948d0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d270caff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f8596b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age\n",
       "3   34\n",
       "4   53\n",
       "1   43\n",
       "0   22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca0acee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age\n",
       "2   45"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfaf8add",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6c13e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3350e88",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14ece0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age\n",
       "2   45"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5457c8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916806ff",
   "metadata": {},
   "source": [
    "# PREDICTION MODEL OF BUYING INSURANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "abe7d480",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\surya\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[51]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5490f9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\surya\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[31]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdf4fd39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02093699, 0.97906301]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test) #probablity ke basis pr result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "470cb1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test) #accuracy of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed88507b",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}