Linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=datasets.load_iris()\n",
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['class']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "100                6.3               3.3                6.0               2.5   \n",
       "101                5.8               2.7                5.1               1.9   \n",
       "102                7.1               3.0                5.9               2.1   \n",
       "103                6.3               2.9                5.6               1.8   \n",
       "104                6.5               3.0                5.8               2.2   \n",
       "105                7.6               3.0                6.6               2.1   \n",
       "106                4.9               2.5                4.5               1.7   \n",
       "107                7.3               2.9                6.3               1.8   \n",
       "108                6.7               2.5                5.8               1.8   \n",
       "109                7.2               3.6                6.1               2.5   \n",
       "110                6.5               3.2                5.1               2.0   \n",
       "111                6.4               2.7                5.3               1.9   \n",
       "112                6.8               3.0                5.5               2.1   \n",
       "113                5.7               2.5                5.0               2.0   \n",
       "114                5.8               2.8                5.1               2.4   \n",
       "115                6.4               3.2                5.3               2.3   \n",
       "116                6.5               3.0                5.5               1.8   \n",
       "117                7.7               3.8                6.7               2.2   \n",
       "118                7.7               2.6                6.9               2.3   \n",
       "119                6.0               2.2                5.0               1.5   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "121                5.6               2.8                4.9               2.0   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "123                6.3               2.7                4.9               1.8   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "126                6.2               2.8                4.8               1.8   \n",
       "127                6.1               3.0                4.9               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     class  \n",
       "100      2  \n",
       "101      2  \n",
       "102      2  \n",
       "103      2  \n",
       "104      2  \n",
       "105      2  \n",
       "106      2  \n",
       "107      2  \n",
       "108      2  \n",
       "109      2  \n",
       "110      2  \n",
       "111      2  \n",
       "112      2  \n",
       "113      2  \n",
       "114      2  \n",
       "115      2  \n",
       "116      2  \n",
       "117      2  \n",
       "118      2  \n",
       "119      2  \n",
       "120      2  \n",
       "121      2  \n",
       "122      2  \n",
       "123      2  \n",
       "124      2  \n",
       "125      2  \n",
       "126      2  \n",
       "127      2  \n",
       "128      2  \n",
       "129      2  \n",
       "130      2  \n",
       "131      2  \n",
       "132      2  \n",
       "133      2  \n",
       "134      2  \n",
       "135      2  \n",
       "136      2  \n",
       "137      2  \n",
       "138      2  \n",
       "139      2  \n",
       "140      2  \n",
       "141      2  \n",
       "142      2  \n",
       "143      2  \n",
       "144      2  \n",
       "145      2  \n",
       "146      2  \n",
       "147      2  \n",
       "148      2  \n",
       "149      2  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['class']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 4)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38,)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X[['petal width (cm)']],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=regressor.predict(X_test[['petal width (cm)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.scatter(X_test['petal width (cm)'], y_test,  color='black')\n",
    "plt.plot(X_test['petal width (cm)'], prediction, color='blue', linewidth=3)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: \n",
      "Iris Setosa\n"
     ]
    }
   ],
   "source": [
    "#sl = input('Enter sepal length (cm): ')\n",
    "#sw = input('Enter sepal width (cm): ')\n",
    "dataClass = regressor.predict([[0.2]])\n",
    "print('Prediction: ') \n",
    "if round(int(dataClass)) == 0:\n",
    "    print('Iris Setosa')\n",
    "elif round(int(dataClass) )== 1:\n",
    "    print('Iris Versicolour')\n",
    "else:\n",
    "    print (round(int(dataClass)))\n",
    "    print('Iris Virginica')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [1.02807056]\n",
      "Mean squared error: 0.04\n",
      "Variance score: 0.94\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "print('Coefficients: \\n', regressor.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test,prediction))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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