week_2_make_test_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('orange_small_churn_data.txt', sep=',')\n",
    "labels = pd.read_csv('orange_small_churn_labels.txt', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "отток:  7.44%\n",
      "не отток:  92.56%\n"
     ]
    }
   ],
   "source": [
    "counts = pd.value_counts(labels[0])\n",
    "print \"отток: \", str(100.*counts[1]/(counts[1]+counts[-1])) + \"%\"\n",
    "print \"не отток: \", str(100.*counts[-1]/(counts[1]+counts[-1])) + \"%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_data = data\n",
    "full_data['labels'] = labels\n",
    "full_data.to_csv(\"Train_full.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data,labels, test_size=0.25, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "отток:  7.43333333333%\n",
      "не отток:  92.5666666667%\n"
     ]
    }
   ],
   "source": [
    "counts = pd.value_counts(y_train[0])\n",
    "print \"отток: \", str(100.*counts[1]/(counts[1]+counts[-1])) + \"%\"\n",
    "print \"не отток: \", str(100.*counts[-1]/(counts[1]+counts[-1])) + \"%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "отток:  7.46%\n",
      "не отток:  92.54%\n"
     ]
    }
   ],
   "source": [
    "counts = pd.value_counts(y_test[0])\n",
    "print \"отток: \", str(100.*counts[1]/(counts[1]+counts[-1])) + \"%\"\n",
    "print \"не отток: \", str(100.*counts[-1]/(counts[1]+counts[-1])) + \"%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['labels']= y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['labels']= y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test.to_csv(\"Hold-out Dataset.csv\",index = False)\n",
    "X_train.to_csv(\"Train.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_test = pd.read_csv('Hold-out Dataset.csv', sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "   Var1  Var2  Var3  Var4  Var5    Var6  Var7  Var8  Var9  Var10   ...    \\\n",
       "0   NaN   NaN   NaN   NaN   NaN  4634.0   7.0   NaN   NaN    NaN   ...     \n",
       "1   NaN   NaN   NaN   NaN   NaN   966.0   7.0   NaN   NaN    NaN   ...     \n",
       "2   NaN   NaN   NaN   NaN   NaN   259.0   0.0   NaN   NaN    NaN   ...     \n",
       "3   0.0   NaN   NaN   NaN   NaN     NaN   NaN   NaN   0.0    NaN   ...     \n",
       "4   NaN   NaN   NaN   NaN   NaN   154.0   0.0   NaN   NaN    NaN   ...     \n",
       "\n",
       "    Var222      Var223  Var224  Var225  Var226   Var227         Var228  \\\n",
       "0  QqVuch3  LM8l689qOp     NaN    kG3k    5Acm  02N6s8f  xwM2aC7IdeMC0   \n",
       "1  3B1QowC  LM8l689qOp     NaN     NaN    Qu4f     RAYp  F2FyR07IdsN7I   \n",
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       "3  NnYzgmc         NaN     NaN     NaN    Qu4f     RAYp  F2FyR07IdsN7I   \n",
       "4  _aspRtK  LM8l689qOp     NaN     NaN    Qcbd     RAYp  F2FyR07IdsN7I   \n",
       "\n",
       "   Var229  Var230  labels  \n",
       "0     NaN     NaN      -1  \n",
       "1     NaN     NaN      -1  \n",
       "2     NaN     NaN       1  \n",
       "3     NaN     NaN      -1  \n",
       "4     NaN     NaN      -1  \n",
       "\n",
       "[5 rows x 231 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>MHP8mHO</td>\n",
       "      <td>LM8l689qOp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELof</td>\n",
       "      <td>7P5s</td>\n",
       "      <td>nIGXDli</td>\n",
       "      <td>F2FyR07IdsN7I</td>\n",
       "      <td>mj86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
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       "</table>\n",
       "<p>5 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var1  Var2  Var3  Var4  Var5    Var6  Var7  Var8  Var9  Var10   ...    \\\n",
       "0   NaN   NaN   NaN   NaN   NaN  1176.0   7.0   NaN   NaN    NaN   ...     \n",
       "1   NaN   NaN   NaN   NaN   NaN  1813.0  14.0   NaN   NaN    NaN   ...     \n",
       "2   NaN   NaN   NaN   NaN   NaN   777.0   7.0   NaN   NaN    NaN   ...     \n",
       "3   NaN   NaN   NaN   NaN   NaN  1554.0   7.0   NaN   NaN    NaN   ...     \n",
       "4   NaN   NaN   NaN   NaN   NaN   980.0   0.0   NaN   NaN    NaN   ...     \n",
       "\n",
       "    Var222      Var223  Var224  Var225  Var226   Var227         Var228  \\\n",
       "0  VjJAgOW  LM8l689qOp     NaN     NaN    xb3V     RAYp  F2FyR07IdsN7I   \n",
       "1  catzS2D  LM8l689qOp     NaN    ELof    3Cy4     ZI9m  ib5G6X1eUxUn6   \n",
       "2  jjOYyXX  LM8l689qOp     NaN     NaN    Qu4f     RAYp  F2FyR07IdsN7I   \n",
       "3  MHP8mHO  LM8l689qOp     NaN    ELof    7P5s  nIGXDli  F2FyR07IdsN7I   \n",
       "4  C6OGkwT  LM8l689qOp     NaN     NaN    Xa3G  02N6s8f  xwM2aC7IdeMC0   \n",
       "\n",
       "   Var229  Var230  labels  \n",
       "0     NaN     NaN      -1  \n",
       "1    mj86     NaN      -1  \n",
       "2     NaN     NaN      -1  \n",
       "3    mj86     NaN       1  \n",
       "4     NaN     NaN      -1  \n",
       "\n",
       "[5 rows x 231 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv('Train.csv', sep=',')\n",
    "data_train.head()"
   ]
  }
 ],
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