linear regration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in ./common/.virtualenv/python3/lib/python3.5/site-packages (2.7.0)\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.5/dist-packages (from plotly) (2016.4)\n",
      "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from plotly) (1.10.0)\n",
      "Requirement already satisfied: decorator>=4.0.6 in /usr/local/lib/python3.5/dist-packages (from plotly) (4.0.10)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.5/dist-packages (from plotly) (2.11.0)\n",
      "Requirement already satisfied: nbformat>=4.2 in ./common/.virtualenv/python3/lib/python3.5/site-packages (from plotly) (4.4.0)\n",
      "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.5/dist-packages (from nbformat>=4.2->plotly) (4.1.0)\n",
      "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.5/dist-packages (from nbformat>=4.2->plotly) (0.1.0)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.5/dist-packages (from nbformat>=4.2->plotly) (2.5.1)\n",
      "Requirement already satisfied: traitlets>=4.1 in /usr/local/lib/python3.5/dist-packages (from nbformat>=4.2->plotly) (4.2.2)\n",
      "\u001b[31mtensorflow 1.2.1 has requirement html5lib==0.9999999, but you'll have html5lib 0.999 which is incompatible.\u001b[0m\n",
      "\u001b[31mtensorflow 1.2.1 has requirement markdown>=2.6.8, but you'll have markdown 2.6.6 which is incompatible.\u001b[0m\n",
      "\u001b[31mtensorflow 1.2.1 has requirement protobuf>=3.2.0, but you'll have protobuf 3.0.0b2 which is incompatible.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!pip install plotly\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pylab import rcParams\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.cluster import DBSCAN\n",
    "from collections import Counter \n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "plotly.tools.set_credentials_file(username='iremoze', api_key='xxxxxxx')\n",
    "\n",
    "\n",
    "### this is a test with modifictaion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2646529\n",
       "b    2646529\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/resources/data/last.txt')\n",
    "data.columns=('a','b')\n",
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2287705</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2287706</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2287707</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2287708</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2287709</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2287710</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2287711</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2287712</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2287713</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2287714</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2287715</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2287716</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2287717</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2287718</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2287719</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2287720</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2287721</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2287722</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2287723</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2287724</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2287725</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2287726</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2287727</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2287728</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2287729</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2287730</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2287731</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2287732</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2287733</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2287734</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646499</th>\n",
       "      <td>5011170</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646500</th>\n",
       "      <td>5011171</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646501</th>\n",
       "      <td>5011172</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646502</th>\n",
       "      <td>5011173</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646503</th>\n",
       "      <td>5011174</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646504</th>\n",
       "      <td>5011175</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646505</th>\n",
       "      <td>5011176</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646506</th>\n",
       "      <td>5011177</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646507</th>\n",
       "      <td>5011178</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646508</th>\n",
       "      <td>5011179</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646509</th>\n",
       "      <td>5011180</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646510</th>\n",
       "      <td>5011181</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646511</th>\n",
       "      <td>5011182</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646512</th>\n",
       "      <td>5011183</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646513</th>\n",
       "      <td>5011184</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646514</th>\n",
       "      <td>5011185</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646515</th>\n",
       "      <td>5011186</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646516</th>\n",
       "      <td>5011187</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646517</th>\n",
       "      <td>5011188</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646518</th>\n",
       "      <td>5011189</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646519</th>\n",
       "      <td>5011190</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646520</th>\n",
       "      <td>5011191</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646521</th>\n",
       "      <td>5011192</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646522</th>\n",
       "      <td>5011193</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646523</th>\n",
       "      <td>5011194</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646524</th>\n",
       "      <td>5011195</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646525</th>\n",
       "      <td>5011196</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646526</th>\n",
       "      <td>5011197</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646527</th>\n",
       "      <td>5011198</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646528</th>\n",
       "      <td>5011199</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2646529 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               a   b\n",
       "0        2287705  15\n",
       "1        2287706  19\n",
       "2        2287707  25\n",
       "3        2287708  18\n",
       "4        2287709  18\n",
       "5        2287710  23\n",
       "6        2287711  21\n",
       "7        2287712  29\n",
       "8        2287713  16\n",
       "9        2287714  18\n",
       "10       2287715  25\n",
       "11       2287716  23\n",
       "12       2287717  21\n",
       "13       2287718  19\n",
       "14       2287719  15\n",
       "15       2287720  20\n",
       "16       2287721  25\n",
       "17       2287722  16\n",
       "18       2287723  27\n",
       "19       2287724  17\n",
       "20       2287725  21\n",
       "21       2287726  21\n",
       "22       2287727  14\n",
       "23       2287728  16\n",
       "24       2287729  15\n",
       "25       2287730  18\n",
       "26       2287731  18\n",
       "27       2287732  20\n",
       "28       2287733  21\n",
       "29       2287734  17\n",
       "...          ...  ..\n",
       "2646499  5011170   7\n",
       "2646500  5011171   6\n",
       "2646501  5011172   5\n",
       "2646502  5011173  11\n",
       "2646503  5011174  10\n",
       "2646504  5011175   8\n",
       "2646505  5011176  13\n",
       "2646506  5011177  13\n",
       "2646507  5011178   7\n",
       "2646508  5011179   9\n",
       "2646509  5011180   7\n",
       "2646510  5011181  10\n",
       "2646511  5011182  11\n",
       "2646512  5011183   7\n",
       "2646513  5011184   8\n",
       "2646514  5011185  11\n",
       "2646515  5011186   8\n",
       "2646516  5011187   8\n",
       "2646517  5011188   9\n",
       "2646518  5011189  15\n",
       "2646519  5011190   8\n",
       "2646520  5011191   6\n",
       "2646521  5011192   7\n",
       "2646522  5011193   9\n",
       "2646523  5011194   8\n",
       "2646524  5011195  11\n",
       "2646525  5011196  10\n",
       "2646526  5011197   6\n",
       "2646527  5011198  10\n",
       "2646528  5011199  12\n",
       "\n",
       "[2646529 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= data.drop_duplicates('a')\n",
    "#df.insert(0, 'co', range(1, 1 + len(df)))\n",
    "#df.head()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f117fbe6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['a']\n",
    "y= df['b']\n",
    "plt.scatter(x,y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2287705],\n",
       "       [2287706],\n",
       "       [2287707],\n",
       "       ..., \n",
       "       [5011197],\n",
       "       [5011198],\n",
       "       [5011199]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y =  df.iloc[:, :-1].values #Şayet X = dataset.iloc[:,0] deseydik yine de X’e ilk sütun olan Kidem sütununu atamış olurduk ancak veri yapısı vektör olurdu.\n",
    "X = df.iloc[:, 1].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#değişkenleri eğitim ve test olmak üzere ikiye bölme\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#modeli eğitme\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit () metdou ile eğitme yapılacak\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3676261.12571943],\n",
       "       [ 3681550.49016457],\n",
       "       [ 3678150.18444983],\n",
       "       ..., \n",
       "       [ 3678150.18444983],\n",
       "       [ 3678905.807942  ],\n",
       "       [ 3677016.74921159]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.reshape(-1,1)\n",
    "y_pred = regressor.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3109135],\n",
       "       [4357129],\n",
       "       [2738413],\n",
       "       ..., \n",
       "       [2828197],\n",
       "       [3970985],\n",
       "       [4526409]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b7df6ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter( X_train, y_train, color = 'red')\n",
    "plt.title('DNS')\n",
    "plt.xlabel('b')\n",
    "plt.ylabel('a')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1194efad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "modelin_tahmin_ettigi_y = regressor.predict(X_train)\n",
    "plt.scatter(X_train, modelin_tahmin_ettigi_y, color = 'blue')\n",
    "plt.title('Kıdeme Göre Maaş Tahmini Regresyon Modeli')\n",
    "plt.xlabel('Kıdem')\n",
    "plt.ylabel('Maaş')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#http://www.datascience.istanbul/2017/06/13/basit-regresyon-python-ile-uygulama/"
   ]
  }
 ],
 "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": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}