2.Linear Regression with Multiple Variables.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "596e2460",
   "metadata": {},
   "source": [
    "# Question: What is the Risk for given data? where seed=160 km/h,car_age=10 years,experience=5 yearrs.."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea810ff",
   "metadata": {},
   "source": [
    "# y=mx+c,,,\n",
    "M=Coefficients..\n",
    "Y =DV..\n",
    "speed,car_age,experience= IV..\n",
    "C = Intercept..\n",
    "y=m1X1 + M2X2 + M3X3 +....+ MnXn + C..\n",
    "..risk = M1*speed+ M2*car_age + M3*experience + intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f52f5062",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ef86454",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>car_age</th>\n",
       "      <th>experience</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200</td>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>115</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   speed  car_age  experience  risk\n",
       "0    200       15         5.0    85\n",
       "1     90       17        13.0    20\n",
       "2    165       12         4.0    93\n",
       "3    110       20         NaN    60\n",
       "4    140        5         3.0    82\n",
       "5    115        2         8.0    10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv('2.car age experience risk multiple regression.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6e4dd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1    13.0\n",
       "2     4.0\n",
       "3     NaN\n",
       "4     3.0\n",
       "5     8.0\n",
       "Name: experience, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.experience  #find missing value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b2211c",
   "metadata": {},
   "source": [
    "# There are no value in row 3,,so find out the mean and set there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dee1ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.experience.mean() # finding mean without calculate null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "779fdea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.experience.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4c03f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exn_fit= df.experience.median()\n",
    "exn_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "495f7502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1    13.0\n",
       "2     4.0\n",
       "3     5.0\n",
       "4     3.0\n",
       "5     8.0\n",
       "Name: experience, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.experience = df.experience.fillna(exn_fit)\n",
    "df.experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f24e4e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>car_age</th>\n",
       "      <th>experience</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200</td>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>115</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   speed  car_age  experience  risk\n",
       "0    200       15         5.0    85\n",
       "1     90       17        13.0    20\n",
       "2    165       12         4.0    93\n",
       "3    110       20         5.0    60\n",
       "4    140        5         3.0    82\n",
       "5    115        2         8.0    10"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d336200c",
   "metadata": {},
   "source": [
    "# Pre processing complete and linear regression model start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa093a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6deed0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(df[['speed','car_age','experience']],df.risk) #Two dimension for input 1 dimen for output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6474253e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([71.37146872])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[160,10,5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d1ff4c",
   "metadata": {},
   "source": [
    "# Answer =array([71.37146872])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0812d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.33059217,  1.61053246, -6.20772074])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e29da323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.410000910435855"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2b3c104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.37146901043586"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y=m1X1 + M2X2 + M3X3 +....+ MnXn + C.. ..\n",
    "#risk = M1speed+ M2car_age + M3*experience + intercept\n",
    "y=0.33059217*160 + 1.61053246*10 + 5*-6.20772074 + 33.410000910435855\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f9a05db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Congratulation ,,predict value with risk is exactly same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cefdae33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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