hr_scaler.ipynb

{
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   "source": [
    "# Import our dependencies\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satisfaction_Level</th>\n",
       "      <th>Num_Projects</th>\n",
       "      <th>Time_Spent</th>\n",
       "      <th>Num_Promotions</th>\n",
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       "   Satisfaction_Level  Num_Projects  Time_Spent  Num_Promotions\n",
       "0                0.30             1         253               2\n",
       "1                0.25             1         200               0\n",
       "2                0.90             4        2880               5\n",
       "3                0.65             3        1450               3\n",
       "4                0.50             2         785               2"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in our dataset\n",
    "hr_df = pd.read_csv(\"Resources/hr_dataset.csv\")\n",
    "hr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Satisfaction_Level    float64\n",
       "Num_Projects            int64\n",
       "Time_Spent              int64\n",
       "Num_Promotions          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the StandardScaler\n",
    "scaler.fit(hr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "scaled_data = scaler.transform(hr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satisfaction_Level</th>\n",
       "      <th>Num_Projects</th>\n",
       "      <th>Time_Spent</th>\n",
       "      <th>Num_Promotions</th>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>1.187080</td>\n",
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       "   Satisfaction_Level  Num_Projects  Time_Spent  Num_Promotions\n",
       "0           -1.303615     -1.162476   -1.049481       -0.558656\n",
       "1           -1.512945     -1.162476   -1.094603       -1.804887\n",
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       "4           -0.466299     -0.488240   -0.596549       -0.558656"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the scaled data\n",
    "transformed_scaled_data = pd.DataFrame(scaled_data, columns=hr_df.columns)\n",
    "transformed_scaled_data.head()"
   ]
  },
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