Untitled.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.ipynb_checkpoints', 'Data-Preproceesing', 'Decision Tree', 'KNN', 'LinearRegression', 'MachineLearning', 'MachineLearning.zip', 'Naive_Bayes', 'PortFolioProject', 'pythonBasics', 'SVM', 'telecust1.csv', 'Untitled Folder', 'Untitled.ipynb']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.listdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./telecust1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   region   1000 non-null   int64  \n",
      " 1   age      998 non-null    float64\n",
      " 2   marital  1000 non-null   int64  \n",
      " 3   address  1000 non-null   int64  \n",
      " 4   income   1000 non-null   int64  \n",
      " 5   ed       1000 non-null   int64  \n",
      " 6   retire   1000 non-null   int64  \n",
      " 7   gender   1000 non-null   int64  \n",
      " 8   reside   1000 non-null   int64  \n",
      " 9   custcat  1000 non-null   int64  \n",
      "dtypes: float64(1), int64(9)\n",
      "memory usage: 78.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region   age  marital  address  income  ed  retire  gender  reside  custcat\n",
       "0       2   NaN        1        9      64   4       0       0       2        1\n",
       "1       3   NaN        1        7     136   5       0       0       6        4\n",
       "2       3  52.0        1       24     116   1       0       1       2        3\n",
       "3       2  33.0        0       12      33   2       0       1       1        1\n",
       "4       2  30.0        1        9      30   1       0       0       4        3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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