scikit_learn_diabetes_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - Age\\n      - Sex\\n      - Body mass index\\n      - Average blood pressure\\n      - S1\\n      - S2\\n      - S3\\n      - S4\\n      - S5\\n      - S6\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990842, -0.01764613],\n",
       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "        -0.06832974, -0.09220405],\n",
       "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "         0.00286377, -0.02593034],\n",
       "       ...,\n",
       "       [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "        -0.04687948,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452837, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00421986,  0.00306441]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(diabetes.data,columns = diabetes.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.634285e-16</td>\n",
       "      <td>1.308343e-16</td>\n",
       "      <td>-8.045349e-16</td>\n",
       "      <td>1.281655e-16</td>\n",
       "      <td>-8.835316e-17</td>\n",
       "      <td>1.327024e-16</td>\n",
       "      <td>-4.574646e-16</td>\n",
       "      <td>3.777301e-16</td>\n",
       "      <td>-3.830854e-16</td>\n",
       "      <td>-3.412882e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.072256e-01</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-9.027530e-02</td>\n",
       "      <td>-1.123996e-01</td>\n",
       "      <td>-1.267807e-01</td>\n",
       "      <td>-1.156131e-01</td>\n",
       "      <td>-1.023071e-01</td>\n",
       "      <td>-7.639450e-02</td>\n",
       "      <td>-1.260974e-01</td>\n",
       "      <td>-1.377672e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.729927e-02</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-3.422907e-02</td>\n",
       "      <td>-3.665645e-02</td>\n",
       "      <td>-3.424784e-02</td>\n",
       "      <td>-3.035840e-02</td>\n",
       "      <td>-3.511716e-02</td>\n",
       "      <td>-3.949338e-02</td>\n",
       "      <td>-3.324879e-02</td>\n",
       "      <td>-3.317903e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.383060e-03</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-7.283766e-03</td>\n",
       "      <td>-5.670611e-03</td>\n",
       "      <td>-4.320866e-03</td>\n",
       "      <td>-3.819065e-03</td>\n",
       "      <td>-6.584468e-03</td>\n",
       "      <td>-2.592262e-03</td>\n",
       "      <td>-1.947634e-03</td>\n",
       "      <td>-1.077698e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.807591e-02</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>3.124802e-02</td>\n",
       "      <td>3.564384e-02</td>\n",
       "      <td>2.835801e-02</td>\n",
       "      <td>2.984439e-02</td>\n",
       "      <td>2.931150e-02</td>\n",
       "      <td>3.430886e-02</td>\n",
       "      <td>3.243323e-02</td>\n",
       "      <td>2.791705e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.107267e-01</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>1.705552e-01</td>\n",
       "      <td>1.320442e-01</td>\n",
       "      <td>1.539137e-01</td>\n",
       "      <td>1.987880e-01</td>\n",
       "      <td>1.811791e-01</td>\n",
       "      <td>1.852344e-01</td>\n",
       "      <td>1.335990e-01</td>\n",
       "      <td>1.356118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age           sex           bmi            bp            s1  \\\n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
       "mean  -3.634285e-16  1.308343e-16 -8.045349e-16  1.281655e-16 -8.835316e-17   \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
       "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
       "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
       "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
       "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
       "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
       "\n",
       "                 s2            s3            s4            s5            s6  \n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
       "mean   1.327024e-16 -4.574646e-16  3.777301e-16 -3.830854e-16 -3.412882e-16  \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
       "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
       "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
       "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
       "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
       "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target_col'] = diabetes.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('target_col',axis = 1)\n",
    "y = df['target_col']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = tts(x,y,test_size = 0.2,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "rmse = np.sqrt(mean_squared_error(y_test,y_pred))\n",
    "R2 = round(lr.score(x_test,y_test),3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.60389611984443"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.527"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.22183644503602"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2.72195846, -255.94592688,  522.83461574,  353.10273364,\n",
       "       -827.58494078,  543.32591808,  115.93459912,  214.68877404,\n",
       "        694.94194778,   32.73088487])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gpTVKRJQtM6pp7kcqqy0sFHcgh3wv1BjLLEnTPVTroHzlelVdBYc6F0sqA6ukExTcRxXjP3aoQRLKT4obNoTbXJNKk5h0gpplxrG8HE++cuiDZ8rK9/rXw4svrt5eTR8iq6hZpi5l+cohXiGH0gFcpuwq+KWSWweo01JkLN0L7nUH4lCzPmLI7Cg6WapvQ6QW3QruTQTiUK+QYw2SMfZtiASoO8G9aq70aQJxqFfIIQfJwW9PH//4hZ/37s2OkzotRaZTNrpplkvjI1SLRnXWdXefOkctLi25b9x44T02blw9anOcUZ0hjgAddixiHnErMmNUjFBtPbD7LIJ7WQCuY/h4XdMBLC25r1u3umzr1194rxSmHhh2LPqPSYgnJ5GAKLiX3Y+zruBYRxCqCnq9k08Kc5tUHYuiYzPLE5lOJhKZquDejTz3smHja9fCffeF0Z5bdbOO3hQHKUyBUHYsBq1dW3zLwaby3UMfFyBSQHnuZZ2LoQR2qM5i6b0WawZMv6JjMWhurvxesk11VIea9SQyoW4E9xiGje/bV3xjjvXrL2S4hJwBM6qiY/Gxj60+NvPzxb/f1Iks1KwnkUmVtdfMcoliPvdZqDtbJmaz7jxOoT9DOofOt7lLnGY5l4/a3CVCanOXTIhz4FSZ5b1HY2i6ExnDJW0XQGZk8Mq0N/UCKID1LC5qX0gydOXeFcoGEekUBfeuKMv6OHEi/OYZERmbgntXVKUQhjBFsYjUSsE9VYOdp7t2lQ8eaqt5JrYOXpGIKLinqGje+vvuy6bSLTPrwTqh3uREJBHKc09R2fwtvVGfZa/N8h6lVWXUvVJFRqI89xRVNWlUDaUPZQoDDfcXaZSCe4yGNWlUTTAWymCdFCZBEwmYgnuMhuWsD7s6n+XIzzKhfIMQSdTQ4G5mB8zsjJkd6Vu3wcweNLOn8scr8vVmZl8ys+NmdtjMtjdW8i5nWgxr0gjl6rxKDGUUidjQDlUzuwV4BfiGu/9Bvu5zwEvu/lkzuxu4wt3/2sx2AX8J7ALeAXzR3d8xrBBjd6h2fZIndUaKCFN2qLr7T4CXBlbfBtyXP78PeH/f+m/ks1H+FLjczLZMVuwKXR9KryYNERli0jb3K939NED++OZ8/VXAs33bncrXrWJme8xsxcxWzp49O95f73qmhZo0RGSIujtUrWBdYbuPu+93953uvnPz5s3j/RVlWoTRKSoiwZo0uD/fa27JH8/k608BW/u2uxp4bvLilVCzhIhIpUmD+0GgN5Z9N/BA3/qP5FkzNwEv95pvaqVmCRGRSkNv1mFm3wLeBWwys1PA3wKfBe43s48CJ4EP5Zv/kCxT5jhwDviLBsqc0Y0VRERKDQ3u7v7hkpfeU7CtA3dOWygREZmORqiKiCRIwV1EJEEK7tDcVAZdniIhdjp2Ermhbe7JG5zKoDfDIkzXYdvU+0rzdOwkAbpZR1PztGj+l3jp2EkkdLOOKk1NZdD1KRJipmMnCVBwb2oqA02REC8dO0lAd4J7WQdZHVMZFL23pkiIl46dpMDdW1927NjhjVpacp+bc89uSpctc3PZ+t7r8/PuZtljb/207z3N+45rln+rC7Q/JQLAipfE1W50qDbZQTbJey8vZ3PPnzyZfdXft6/ezBzo1s1LRDqqqkO1G8F9zZrsmnqQWTZl7izfu4lArOwOkU5StkyTHWTjvncTd5FSdoeIDOhGcG+yg2zc924iECu7Q0QGdCO4Nzn/+7jvPW0gVmaOiIygG23uIZmmzb3qd6HeTloRCZ46VEMzabaMOk5FpI+CeyqazPoRkegoWyYV6jgVkREpuMdEHaciMiIF95g0mfUjIknRzTpis7ioYC4iQ+nKfZBuryYiCVBw79fLIz9xIstK6d1erSzA60QgIoFScO83zrwv454IRERmSMG93zjzvjQxAZiISE0U3PuNk0eumRhFJGAK7v3GySPXgCIRCZiCe79x8shnPaBInbciMgbluQ8aNY+8t80sZmIcnA2y13nbXw4RkT6aOCwGmg1SRApo4rDYqfNWRMak4B4Ddd6KyJimCu5m9oyZPWFmh8xsJV+3wcweNLOn8scr6ilqA2LppNRskCIypjqu3N/t7jf0tfvcDTzk7tcBD+U/hyemEaaaDVJExjRVh6qZPQPsdPcX+tYdA97l7qfNbAvwY3d/a9X7tNKhqk5KEYlckx2qDvzIzB4zszw3jyvd/TRA/vjmkkLtMbMVM1s5e/bslMWYgDopRSRh0wb3d7r7duBW4E4zu2XUX3T3/e6+0913bt68ecpiTECdlCKSsKmCu7s/lz+eAX4A3Ag8nzfHkD+embaQjVAnZbFYOplFpNLEwd3M3mBmb+w9B94LHAEOArvzzXYDD0xbyEaok3K1mDqZRaTSxB2qZnYt2dU6ZNMY/LO77zOzjcD9wDbgJPAhd3+p6r00QjUQ6mQWiUpVh+rEc8u4+/8Cf1Sw/kXgPZO+r7RIncwiydAIVblAncwiyVBwlwvUySySDAV3uUCdzCLJ0HzucrFR57MXkaDpyl1EJEEK7iIiCVJwFxFJkIK7iEiCFNxFRBIUxA2yzewsUDDuvRWbgBeGbhW+VOoB6dQllXpAOnWJvR7z7l44rW4QwT0kZrZSNldDTFKpB6RTl1TqAenUJZV6FFGzjIhIghTcRUQSpOC+2v62C1CTVOoB6dQllXpAOnVJpR6rqM1dRCRBunIXEUmQgruISII6HdzN7Bkze8LMDpnZSr5ug5k9aGZP5Y9XtF3OImZ2wMzOmNmRvnWFZbfMl8zsuJkdNrPt7ZX8YiX1uMfMfpEfl0Nmtqvvtc/k9ThmZu9rp9TFzGyrmT1sZkfN7EkzuytfH9VxqahHdMfFzF5nZo+a2eN5Xf4uX3+NmT2SH5PvmNn6fP2l+c/H89cX2iz/VNy9swvwDLBpYN3ngLvz53cDf992OUvKfguwHTgyrOzALuDfAANuAh5pu/xD6nEP8OmCba8HHgcuBa4BngbWtl2HvvJtAbbnz98I/E9e5qiOS0U9ojsu+b69LH++Dngk39f3A7fn678KfCx//nHgq/nz24HvtF2HSZdOX7mXuA24L39+H/D+FstSyt1/AgzeeLys7LcB3/DMT4HLzWzLbEparaQeZW4Dvu3uv3H3nwPHgRsbK9yY3P20u/8sf/5r4ChwFZEdl4p6lAn2uOT79pX8x3X54sAfA9/N1w8ek96x+i7wHjOzGRW3Vl0P7g78yMweM7M9+bor3f00ZB9y4M2tlW58ZWW/Cni2b7tTVP+zhuATeVPFgb6msWjqkX+dfzvZlWK0x2WgHhDhcTGztWZ2CDgDPEj2zeKX7v67fJP+8p6vS/76y8DG2Za4Hl0P7u909+3ArcCdZnZL2wVqSNGVR8g5sF8B3gLcAJwGPp+vj6IeZnYZ8D3gk+7+q6pNC9YFU5+CekR5XNz9VXe/Abia7BvF24o2yx+Drss4Oh3c3f25/PEM8AOyA/9876tx/nimvRKOrazsp4CtfdtdDTw347KNzN2fz/8hXwO+xoWv+MHXw8zWkQXEZXf/fr46uuNSVI+YjwuAu/8S+DFZm/vlZta7zWh/ec/XJX/99xi92TAonQ3uZvYGM3tj7znwXuAIcBDYnW+2G3ignRJOpKzsB4GP5NkZNwEv95oJQjTQ7vwBsuMCWT1uzzMargGuAx6ddfnK5G2z9wJH3f0LfS9FdVzK6hHjcTGzzWZ2ef789cCfkPUhPAx8MN9s8Jj0jtUHgf/wvHc1Om336La1ANeS9fA/DjwJ7M3XbwQeAp7KHze0XdaS8n+L7Kvxb8muNj5aVnayr5pfJmtrfALY2Xb5h9Tjm3k5D5P9s23p235vXo9jwK1tl3+gLjeTfYU/DBzKl12xHZeKekR3XIA/BP4rL/MR4G/y9deSnYCOA/8CXJqvf13+8/H89WvbrsOki6YfEBFJUGebZUREUqbgLiKSIAV3EZEEKbiLiCRIwV1EJEEK7iIiCVJwFxFJ0P8DW3S6BAgCqu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred,color='Red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}