software-engineer-salary-prediction.ipynb

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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/salary-data/Salary_Data.csv\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
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   "outputs": [],
   "source": [
    "# Path\n",
    "path = \"/kaggle/input/salary-data/Salary_Data.csv\""
   ]
  },
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       "    .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>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Manager</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Director</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Marketing Analyst</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Product Manager</td>\n",
       "      <td>12.0</td>\n",
       "      <td>120000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Marketing Coordinator</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Scientist</td>\n",
       "      <td>10.0</td>\n",
       "      <td>110000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Education Level              Job Title  Years of Experience  \\\n",
       "0  32.0    Male      Bachelor's      Software Engineer                  5.0   \n",
       "1  28.0  Female        Master's           Data Analyst                  3.0   \n",
       "2  45.0    Male             PhD         Senior Manager                 15.0   \n",
       "3  36.0  Female      Bachelor's        Sales Associate                  7.0   \n",
       "4  52.0    Male        Master's               Director                 20.0   \n",
       "5  29.0    Male      Bachelor's      Marketing Analyst                  2.0   \n",
       "6  42.0  Female        Master's        Product Manager                 12.0   \n",
       "7  31.0    Male      Bachelor's          Sales Manager                  4.0   \n",
       "8  26.0  Female      Bachelor's  Marketing Coordinator                  1.0   \n",
       "9  38.0    Male             PhD       Senior Scientist                 10.0   \n",
       "\n",
       "     Salary  \n",
       "0   90000.0  \n",
       "1   65000.0  \n",
       "2  150000.0  \n",
       "3   60000.0  \n",
       "4  200000.0  \n",
       "5   55000.0  \n",
       "6  120000.0  \n",
       "7   80000.0  \n",
       "8   45000.0  \n",
       "9  110000.0  "
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   "source": [
    "# Loading the dataset\n",
    "data = pd.read_csv(path)\n",
    "\n",
    "# Head\n",
    "data.head(10)"
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6704 entries, 0 to 6703\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Age                  6702 non-null   float64\n",
      " 1   Gender               6702 non-null   object \n",
      " 2   Education Level      6701 non-null   object \n",
      " 3   Job Title            6702 non-null   object \n",
      " 4   Years of Experience  6701 non-null   float64\n",
      " 5   Salary               6699 non-null   float64\n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 314.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Info\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>6702.0</td>\n",
       "      <td>33.620859</td>\n",
       "      <td>7.614633</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years of Experience</th>\n",
       "      <td>6701.0</td>\n",
       "      <td>8.094687</td>\n",
       "      <td>6.059003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>6699.0</td>\n",
       "      <td>115326.964771</td>\n",
       "      <td>52786.183911</td>\n",
       "      <td>350.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                      count           mean           std    min      25%  \\\n",
       "Age                  6702.0      33.620859      7.614633   21.0     28.0   \n",
       "Years of Experience  6701.0       8.094687      6.059003    0.0      3.0   \n",
       "Salary               6699.0  115326.964771  52786.183911  350.0  70000.0   \n",
       "\n",
       "                          50%       75%       max  \n",
       "Age                      32.0      38.0      62.0  \n",
       "Years of Experience       7.0      12.0      34.0  \n",
       "Salary               115000.0  160000.0  250000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Basic stats (Numerical)\n",
    "data.describe().T"
   ]
  },
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>6702</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education Level</th>\n",
       "      <td>6701</td>\n",
       "      <td>7</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Job Title</th>\n",
       "      <td>6702</td>\n",
       "      <td>193</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>518</td>\n",
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       "                count unique                top  freq\n",
       "Gender           6702      3               Male  3674\n",
       "Education Level  6701      7  Bachelor's Degree  2267\n",
       "Job Title        6702    193  Software Engineer   518"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic stats (Categorical)\n",
    "data.describe(include=\"object\").T"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Age'}>,\n",
       "        <Axes: title={'center': 'Years of Experience'}>],\n",
       "       [<Axes: title={'center': 'Salary'}>, <Axes: >]], dtype=object)"
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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of quantitive data\n",
    "data.hist(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6af647b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:15.346150Z",
     "iopub.status.busy": "2023-12-25T14:04:15.345696Z",
     "iopub.status.idle": "2023-12-25T14:04:16.169259Z",
     "shell.execute_reply": "2023-12-25T14:04:16.167977Z"
    },
    "papermill": {
     "duration": 0.841289,
     "end_time": "2023-12-25T14:04:16.172111",
     "exception": false,
     "start_time": "2023-12-25T14:04:15.330822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x79b92fae0910>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of qualitative data\n",
    "seaborn.catplot(data=data,x = \"Gender\", kind=\"count\")\n",
    "seaborn.catplot(data=data,y = \"Education Level\", kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c09d7b85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:16.205045Z",
     "iopub.status.busy": "2023-12-25T14:04:16.204544Z",
     "iopub.status.idle": "2023-12-25T14:04:16.213389Z",
     "shell.execute_reply": "2023-12-25T14:04:16.212433Z"
    },
    "papermill": {
     "duration": 0.029034,
     "end_time": "2023-12-25T14:04:16.215840",
     "exception": false,
     "start_time": "2023-12-25T14:04:16.186806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Bachelor's\", \"Master's\", 'PhD', nan, \"Bachelor's Degree\",\n",
       "       \"Master's Degree\", 'High School', 'phD'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique Values in Education Level column\n",
    "data[\"Education Level\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20887fb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:16.248803Z",
     "iopub.status.busy": "2023-12-25T14:04:16.247990Z",
     "iopub.status.idle": "2023-12-25T14:04:16.259373Z",
     "shell.execute_reply": "2023-12-25T14:04:16.258454Z"
    },
    "papermill": {
     "duration": 0.03061,
     "end_time": "2023-12-25T14:04:16.262005",
     "exception": false,
     "start_time": "2023-12-25T14:04:16.231395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    2\n",
       "Gender                 2\n",
       "Education Level        3\n",
       "Job Title              2\n",
       "Years of Experience    3\n",
       "Salary                 5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum of missing/NAN values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94b4798b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:16.296171Z",
     "iopub.status.busy": "2023-12-25T14:04:16.295207Z",
     "iopub.status.idle": "2023-12-25T14:04:16.311490Z",
     "shell.execute_reply": "2023-12-25T14:04:16.310240Z"
    },
    "papermill": {
     "duration": 0.035613,
     "end_time": "2023-12-25T14:04:16.314393",
     "exception": false,
     "start_time": "2023-12-25T14:04:16.278780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    0\n",
       "Gender                 0\n",
       "Education Level        0\n",
       "Job Title              0\n",
       "Years of Experience    0\n",
       "Salary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows with missing/NAN values\n",
    "data = data.dropna()\n",
    "\n",
    "# Recheck\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4893cab",
   "metadata": {
    "papermill": {
     "duration": 0.01448,
     "end_time": "2023-12-25T14:04:16.344244",
     "exception": false,
     "start_time": "2023-12-25T14:04:16.329764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **Synopsis**: The higher the experience, the better the pay gets with respect to the field/job title. The line graph below doesn't take job/field into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92aba593",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:16.376684Z",
     "iopub.status.busy": "2023-12-25T14:04:16.376244Z",
     "iopub.status.idle": "2023-12-25T14:04:18.051002Z",
     "shell.execute_reply": "2023-12-25T14:04:18.049759Z"
    },
    "papermill": {
     "duration": 1.69418,
     "end_time": "2023-12-25T14:04:18.053566",
     "exception": false,
     "start_time": "2023-12-25T14:04:16.359386",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Years of Experience', ylabel='Salary'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data = data,x = \"Years of Experience\",y = \"Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629e8201",
   "metadata": {
    "papermill": {
     "duration": 0.015349,
     "end_time": "2023-12-25T14:04:18.084897",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.069548",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> Selecting Rows with 'Software Engineer' as title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b6ecb10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:18.119347Z",
     "iopub.status.busy": "2023-12-25T14:04:18.118915Z",
     "iopub.status.idle": "2023-12-25T14:04:18.137570Z",
     "shell.execute_reply": "2023-12-25T14:04:18.136533Z"
    },
    "papermill": {
     "duration": 0.038759,
     "end_time": "2023-12-25T14:04:18.139960",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.101201",
     "status": "completed"
    },
    "tags": []
   },
   "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>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>3.0</td>\n",
       "      <td>125000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>6.0</td>\n",
       "      <td>155000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender Education Level          Job Title  Years of Experience  \\\n",
       "0    32.0    Male      Bachelor's  Software Engineer                  5.0   \n",
       "376  28.0  Female      Bachelor's  Software Engineer                  3.0   \n",
       "380  29.0  Female      Bachelor's  Software Engineer                  4.0   \n",
       "384  31.0  Female      Bachelor's  Software Engineer                  6.0   \n",
       "388  29.0  Female      Bachelor's  Software Engineer                  4.0   \n",
       "\n",
       "       Salary  \n",
       "0     90000.0  \n",
       "376  125000.0  \n",
       "380  140000.0  \n",
       "384  155000.0  \n",
       "388  140000.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering Data\n",
    "swedata =  data.loc[data[\"Job Title\"] == 'Software Engineer']\n",
    "\n",
    "# Peeking in swedata\n",
    "swedata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dfbeac0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:18.173973Z",
     "iopub.status.busy": "2023-12-25T14:04:18.173239Z",
     "iopub.status.idle": "2023-12-25T14:04:18.181235Z",
     "shell.execute_reply": "2023-12-25T14:04:18.179841Z"
    },
    "papermill": {
     "duration": 0.02788,
     "end_time": "2023-12-25T14:04:18.183837",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.155957",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(518, 6)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of the new dataframe\n",
    "swedata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b588b67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:18.219375Z",
     "iopub.status.busy": "2023-12-25T14:04:18.218957Z",
     "iopub.status.idle": "2023-12-25T14:04:18.249361Z",
     "shell.execute_reply": "2023-12-25T14:04:18.248005Z"
    },
    "papermill": {
     "duration": 0.051411,
     "end_time": "2023-12-25T14:04:18.251947",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.200536",
     "status": "completed"
    },
    "tags": []
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>518.0</td>\n",
       "      <td>28.318533</td>\n",
       "      <td>2.617617</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years of Experience</th>\n",
       "      <td>518.0</td>\n",
       "      <td>4.449807</td>\n",
       "      <td>2.219221</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>518.0</td>\n",
       "      <td>113243.243243</td>\n",
       "      <td>48031.287179</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>197000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count           mean           std      min      25%  \\\n",
       "Age                  518.0      28.318533      2.617617     22.0     27.0   \n",
       "Years of Experience  518.0       4.449807      2.219221      1.0      3.0   \n",
       "Salary               518.0  113243.243243  48031.287179  50000.0  70000.0   \n",
       "\n",
       "                         50%       75%       max  \n",
       "Age                     28.0      30.0      42.0  \n",
       "Years of Experience      4.0       5.0      13.0  \n",
       "Salary               90000.0  160000.0  197000.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic stats (Numerical)\n",
    "swedata.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82033836",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:18.286481Z",
     "iopub.status.busy": "2023-12-25T14:04:18.286064Z",
     "iopub.status.idle": "2023-12-25T14:04:18.304391Z",
     "shell.execute_reply": "2023-12-25T14:04:18.303093Z"
    },
    "papermill": {
     "duration": 0.038886,
     "end_time": "2023-12-25T14:04:18.307280",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.268394",
     "status": "completed"
    },
    "tags": []
   },
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>518</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education Level</th>\n",
       "      <td>518</td>\n",
       "      <td>4</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Job Title</th>\n",
       "      <td>518</td>\n",
       "      <td>1</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count unique                top freq\n",
       "Gender            518      2               Male  325\n",
       "Education Level   518      4  Bachelor's Degree  251\n",
       "Job Title         518      1  Software Engineer  518"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic stats (Categorical)\n",
    "swedata.describe(include = \"object\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d314834",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:18.343460Z",
     "iopub.status.busy": "2023-12-25T14:04:18.343000Z",
     "iopub.status.idle": "2023-12-25T14:04:19.333392Z",
     "shell.execute_reply": "2023-12-25T14:04:19.331965Z"
    },
    "papermill": {
     "duration": 1.011878,
     "end_time": "2023-12-25T14:04:19.336325",
     "exception": false,
     "start_time": "2023-12-25T14:04:18.324447",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Age'}>,\n",
       "        <Axes: title={'center': 'Years of Experience'}>],\n",
       "       [<Axes: title={'center': 'Salary'}>, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Q8/W0z3khZTV7VnNH9M7s3f06uG3btujXr1/3ngQAgEwpShH255o9e3bMnDkz97izszO2bNkSAwYMiJKS3vN/1tva2mLYsGHxxz/+MWpra4s9zocqq9mzmjtC9ixmz2ruiOxm/6C5U0qxbdu2qK+v78bpAADIoqIUYQMHDoyysrJoaWnJ297S0hJDhgzZZ31VVVVUVVXlbaurq+vOEYuqtrY2U98ovVNWs2c1d4TsWcye1dwR2c3+QXK7EwwAgO5QlF+WX1lZGWPHjo1ly5bltnV2dsayZcuioaGhGCMBAAAA0MsV7UcjZ86cGeeff36MGzcuxo8fHzfddFO89dZbMW3atGKNBAAAAEAvVrQi7Oyzz47//d//je9973uxadOmOOGEE2Lx4sX7/AL9LKmqqoqrrrpqnx8DzYKsZs9q7gjZs5g9q7kjsps9q7kBADh0lSTvTQ4AAABABhTld4QBAAAAwIdNEQYAAABAJijCAAAAAMgERRgAAAAAmaAIK4DXXnstvvGNb8SAAQOiT58+MWbMmHjqqady+1NK8b3vfS8+8pGPRJ8+faKxsTFefvnlvGNs2bIlpk6dGrW1tVFXVxcXXnhhbN++PW/Nc889F3/5l38Z1dXVMWzYsLj++uv3mWXRokUxcuTIqK6ujjFjxsRDDz3ULZn37NkTV155ZYwYMSL69OkTH/vYx+L73/9+vPO9F3pL7pUrV8aXv/zlqK+vj5KSkrjnnnvy9h9KObsySyFyd3R0xOWXXx5jxoyJww47LOrr6+Nv//Zv4/XXX+/xuQ+U/d0uvvjiKCkpiZtuuqnHZ+9K7hdffDHOPPPM6NevXxx22GFx8sknx8aNG3P733777WhqaooBAwZE3759Y8qUKdHS0pJ3jI0bN8bkyZOjpqYmBg0aFLNmzYrdu3fnrXnkkUfipJNOiqqqqjjmmGPijjvu2GeW+fPnx9FHHx3V1dUxYcKEeOKJJz5Q7q5k3759e0yfPj2GDh0affr0ieOOOy5uvfXWvDU9MfucOXPi5JNPjsMPPzwGDRoUX/3qV2PdunWHbK6uzAIAAO8rcVC2bNmSjjrqqHTBBRekxx9/PK1fvz4tWbIkvfLKK7k1c+fOTf369Uv33HNPevbZZ9OZZ56ZRowYkXbu3Jlbc8YZZ6Tjjz8+rV69Oj366KPpmGOOSeecc05uf2traxo8eHCaOnVqWrt2bfrFL36R+vTpk2677bbcmt/85jeprKwsXX/99emFF15I//zP/5wqKirS888/X/Dc1157bRowYEB64IEH0oYNG9KiRYtS3759049//ONel/uhhx5KV1xxRbrrrrtSRKS77747b/+hlLMrsxQi99atW1NjY2P65S9/mX7/+9+nVatWpfHjx6exY8fmHaMn5j5Q9ne666670vHHH5/q6+vTjTfe2OOzHyj3K6+8kvr3759mzZqVnn766fTKK6+ke++9N7W0tOTWXHzxxWnYsGFp2bJl6amnnkqnnHJKOvXUU3P7d+/enUaPHp0aGxvTM888kx566KE0cODANHv27Nya9evXp5qamjRz5sz0wgsvpHnz5qWysrK0ePHi3Jo777wzVVZWpp/97Gfpd7/7XbroootSXV1d3iyFzH7RRRelj33sY2n58uVpw4YN6bbbbktlZWXp3nvv7dHZJ02alBYsWJDWrl2b1qxZk/7qr/4qDR8+PG3fvv2QzHWgWQAA4EAUYQfp8ssvT5/+9Kffc39nZ2caMmRI+rd/+7fctq1bt6aqqqr0i1/8IqWU0gsvvJAiIj355JO5Nb/+9a9TSUlJeu2111JKKd18883piCOOSO3t7XnnPvbYY3OPv/71r6fJkyfnnX/ChAnpH/7hHw4u5H5Mnjw5/d3f/V3etrPOOitNnTo1pdR7c7/7G+RDKWdXZilU7v154oknUkSkV199NaXUO3Kn9N7Z/+d//icdeeSRae3atemoo47KK8J6Q/b95T777LPTN77xjff8b7Zu3ZoqKirSokWLcttefPHFFBFp1apVKaX/XziVlpamTZs25dbccsstqba2Nvex+M53vpNGjRq1z7knTZqUezx+/PjU1NSUe7xnz55UX1+f5syZ8+eHfZf9ZR81alT6l3/5l7xtJ510UrriiitSSr0n++bNm1NEpBUrVhxyuboyCwAAHIgfjTxI9913X4wbNy7++q//OgYNGhQnnnhi/PSnP83t37BhQ2zatCkaGxtz2/r16xcTJkyIVatWRUTEqlWroq6uLsaNG5db09jYGKWlpfH444/n1px++ulRWVmZWzNp0qRYt25d/N///V9uzTvPs3fN3vMU0qmnnhrLli2Ll156KSIinn322XjsscfiS1/6Uq/O/W6HUs6uzNKdWltbo6SkJOrq6nLz9tbcnZ2dcd5558WsWbNi1KhR++zvjdk7OzvjwQcfjE984hMxadKkGDRoUEyYMCHvRwibm5ujo6Mjb56RI0fG8OHD854PY8aMicGDB+dlamtri9/97nddyr1r165obm7OW1NaWhqNjY3d9jk/9dRT47777ovXXnstUkqxfPnyeOmll2LixIm9Kntra2tERPTv3/+Qy9WVWQAA4EAUYQdp/fr1ccstt8THP/7xWLJkSVxyySXxzW9+M/7jP/4jIiI2bdoUEZH3DcLex3v3bdq0KQYNGpS3v7y8PPr375+3Zn/HeOc53mvN3v2F9N3vfjf+5m/+JkaOHBkVFRVx4oknxowZM2Lq1Kl5M/W23O92KOXsyizd5e23347LL788zjnnnKitrc3N01tzX3fddVFeXh7f/OY397u/N2bfvHlzbN++PebOnRtnnHFG/Nd//Vd87Wtfi7POOitWrFiRm6eysjJXhr7XzB80d1tbW+zcuTP+9Kc/xZ49ez7Uz/m8efPiuOOOi6FDh0ZlZWWcccYZMX/+/Dj99NNzM/f07J2dnTFjxow47bTTYvTo0Ydcrq7MAgAAB1Je7AF6us7Ozhg3blz867/+a0REnHjiibF27dq49dZb4/zzzy/ydN3nV7/6Vfz85z+PhQsXxqhRo2LNmjUxY8aMqK+v79W52VdHR0d8/etfj5RS3HLLLcUep9s1NzfHj3/843j66aejpKSk2ON8aDo7OyMi4itf+UpcdtllERFxwgknxG9/+9u49dZb4zOf+Uwxx+t28+bNi9WrV8d9990XRx11VKxcuTKampqivr5+nzudeqqmpqZYu3ZtPPbYY8UeBQAAuo07wg7SRz7ykTjuuOPytn3yk5/MvYvakCFDIiL2eVerlpaW3L4hQ4bE5s2b8/bv3r07tmzZkrdmf8d45znea83e/YU0a9as3F1hY8aMifPOOy8uu+yymDNnTt5MvS33ux1KObsyS6HtLcFeffXVWLp0ae5usL3z9Mbcjz76aGzevDmGDx8e5eXlUV5eHq+++mp8+9vfjqOPPjo3U2/LPnDgwCgvLz/g692uXbti69at7zvzB81dW1sbffr0iYEDB0ZZWdmH9jnfuXNn/NM//VPccMMN8eUvfzk+9alPxfTp0+Pss8+OH/7wh7mZe3L26dOnxwMPPBDLly+PoUOH5rYfSrm6MgsAAByIIuwgnXbaafu81fxLL70URx11VEREjBgxIoYMGRLLli3L7W9ra4vHH388GhoaIiKioaEhtm7dGs3Nzbk1Dz/8cHR2dsaECRNya1auXBkdHR25NUuXLo1jjz02jjjiiNyad55n75q95ymkHTt2RGlp/uVTVlaWu2ukt+Z+t0MpZ1dmKaS9JdjLL78c//3f/x0DBgzI299bc5933nnx3HPPxZo1a3J/6uvrY9asWbFkyZJem72ysjJOPvnk9329Gzt2bFRUVOTNs27duti4cWPe8+H555/PKwr3lqh7S7YD5a6srIyxY8fmrens7Ixly5Z127Xe0dHxvq95PTV7SimmT58ed999dzz88MMxYsSIvP2HUq6uzAIAAAdU5F/W3+M98cQTqby8PF177bXp5ZdfTj//+c9TTU1N+s///M/cmrlz56a6urp07733pueeey595StfSSNGjEg7d+7MrTnjjDPSiSeemB5//PH02GOPpY9//OPpnHPOye3funVrGjx4cDrvvPPS2rVr05133plqamrSbbfdllvzm9/8JpWXl6cf/vCH6cUXX0xXXXVVqqioSM8//3zBc59//vnpyCOPTA888EDasGFDuuuuu9LAgQPTd77znV6Xe9u2bemZZ55JzzzzTIqIdMMNN6Rnnnkm9+6Ih1LOrsxSiNy7du1KZ555Zho6dGhas2ZNeuONN3J/3vkuiD0x94Gy78+73zWyp2Y/UO677rorVVRUpJ/85Cfp5ZdfTvPmzUtlZWXp0UcfzR3j4osvTsOHD08PP/xweuqpp1JDQ0NqaGjI7d+9e3caPXp0mjhxYlqzZk1avHhx+ou/+Is0e/bs3Jr169enmpqaNGvWrPTiiy+m+fPnp7KysrR48eLcmjvvvDNVVVWlO+64I73wwgvp7//+71NdXV3eOxcWMvtnPvOZNGrUqLR8+fK0fv36tGDBglRdXZ1uvvnmHp39kksuSf369UuPPPJI3vN4x44dh2SuA80CAAAHoggrgPvvvz+NHj06VVVVpZEjR6af/OQnefs7OzvTlVdemQYPHpyqqqrSF77whbRu3bq8NW+++WY655xzUt++fVNtbW2aNm1a2rZtW96aZ599Nn36059OVVVV6cgjj0xz587dZ5Zf/epX6ROf+ESqrKxMo0aNSg8++GDhA6eU2tra0re+9a00fPjwVF1dnT760Y+mK664Iq8E6S25ly9fniJinz/nn3/+IZezK7MUIveGDRv2uy8i0vLly3t07gNl35/9FWE9MXtXct9+++3pmGOOSdXV1en4449P99xzT94xdu7cmf7xH/8xHXHEEammpiZ97WtfS2+88Ubemj/84Q/pS1/6UurTp08aOHBg+va3v506Ojr2meWEE05IlZWV6aMf/WhasGDBPvPOmzcvDR8+PFVWVqbx48en1atXf6DcXcn+xhtvpAsuuCDV19en6urqdOyxx6Yf/ehHqbOzs0dnf6/n8TvPeSjl6sosAADwfkpSSqm77jYDAAAAgEOF3xEGAAAAQCYowgAAAADIBEUYAAAAAJmgCAMAAAAgExRhAAAAAGSCIgwAAACATFCEAQAAAJAJijAAAAAAMkERBgAAAEAmKMIAAAAAyARFGAAAAACZoAgDAAAAIBP+H00WRsmsVUnhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution\n",
    "swedata.hist(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30b86780",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:19.374417Z",
     "iopub.status.busy": "2023-12-25T14:04:19.373971Z",
     "iopub.status.idle": "2023-12-25T14:04:20.479552Z",
     "shell.execute_reply": "2023-12-25T14:04:20.478256Z"
    },
    "papermill": {
     "duration": 1.127371,
     "end_time": "2023-12-25T14:04:20.482203",
     "exception": false,
     "start_time": "2023-12-25T14:04:19.354832",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x79b92fac38b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Experience (x) vs Salary (y) plot\n",
    "seaborn.relplot(swedata, x = 'Years of Experience', y = 'Salary')\n",
    "seaborn.relplot(swedata, x = 'Years of Experience', y = 'Salary', kind = 'line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f22df338",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:20.587757Z",
     "iopub.status.busy": "2023-12-25T14:04:20.587341Z",
     "iopub.status.idle": "2023-12-25T14:04:21.106782Z",
     "shell.execute_reply": "2023-12-25T14:04:21.105683Z"
    },
    "papermill": {
     "duration": 0.608629,
     "end_time": "2023-12-25T14:04:21.109550",
     "exception": false,
     "start_time": "2023-12-25T14:04:20.500921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Years of Experience', ylabel='Salary'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Plot\n",
    "seaborn.regplot(swedata, x = 'Years of Experience', y = 'Salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab5b6d9",
   "metadata": {
    "papermill": {
     "duration": 0.021024,
     "end_time": "2023-12-25T14:04:21.151340",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.130316",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Simple Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02312030",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.197986Z",
     "iopub.status.busy": "2023-12-25T14:04:21.197481Z",
     "iopub.status.idle": "2023-12-25T14:04:21.208590Z",
     "shell.execute_reply": "2023-12-25T14:04:21.206852Z"
    },
    "papermill": {
     "duration": 0.036937,
     "end_time": "2023-12-25T14:04:21.211622",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.174685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the data for model training\n",
    "# features = swedata.drop(['Salary'], axis=1).values\n",
    "features = swedata[['Years of Experience']]\n",
    "label = swedata['Salary']\n",
    "Ftrain, Ftest, Ltrain, Ltest = train_test_split(features, label, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6c07227",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.257082Z",
     "iopub.status.busy": "2023-12-25T14:04:21.256616Z",
     "iopub.status.idle": "2023-12-25T14:04:21.282836Z",
     "shell.execute_reply": "2023-12-25T14:04:21.281647Z"
    },
    "papermill": {
     "duration": 0.052209,
     "end_time": "2023-12-25T14:04:21.285733",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.233524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Fitting the data\n",
    "model.fit(Ftrain, Ltrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fb0cefe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.331870Z",
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     "exception": false,
     "start_time": "2023-12-25T14:04:21.306948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5841261202324304"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation \n",
    "model.score(Ftest, Ltest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abbdb97",
   "metadata": {
    "papermill": {
     "duration": 0.021545,
     "end_time": "2023-12-25T14:04:21.388756",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.367211",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> Very poor model because the relationship between x & y doesnt appear to be linear"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc10fbe0",
   "metadata": {
    "papermill": {
     "duration": 0.020376,
     "end_time": "2023-12-25T14:04:21.430171",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.409795",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Multiple Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b092985",
   "metadata": {
    "papermill": {
     "duration": 0.020356,
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     "exception": false,
     "start_time": "2023-12-25T14:04:21.451044",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Now I will try to estimate the salary while taking the experience and education label into account. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e787aefb",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-25T14:04:21.492086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Bachelor's\", \"Bachelor's Degree\", \"Master's\", \"Master's Degree\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values of Education Level\n",
    "swedata['Education Level'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4b5ff5",
   "metadata": {
    "papermill": {
     "duration": 0.021137,
     "end_time": "2023-12-25T14:04:21.568224",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.547087",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> While examining the education label column, I noticed that some column had the degree as i.e. \"Bachelor's\" while some other had \"Bachelor's degree\". What does this imply? I checked the data page on kaggle but it doesn't address anything about it. What I think that the rows with education as 'Degree_Name' could imply the person was either a student or dropped out in the middle while 'Degree Name' actually completed the degree. I could be wrong. But to keep everything simple, I will assume that everyone on this data finished their degree and this is just random formatting issue. So, I will change the formats into a singular one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f9426bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.612600Z",
     "iopub.status.busy": "2023-12-25T14:04:21.612202Z",
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     "shell.execute_reply": "2023-12-25T14:04:21.622553Z"
    },
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     "duration": 0.036728,
     "end_time": "2023-12-25T14:04:21.626239",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.589511",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelors', 'Masters'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swedata.loc[swedata['Education Level'].str.contains(\"Bachelor\"), 'Education Level'] = 'Bachelors'\n",
    "swedata.loc[swedata['Education Level'].str.contains(\"Master\"), 'Education Level'] = 'Masters'\n",
    "\n",
    "# Unique values now\n",
    "swedata['Education Level'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d00cf4",
   "metadata": {
    "papermill": {
     "duration": 0.020482,
     "end_time": "2023-12-25T14:04:21.667821",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.647339",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> Since the education level is ordinal data, I want to see the model performs in one hot encoding and label encoding technique. So...."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2b12c7",
   "metadata": {
    "papermill": {
     "duration": 0.020667,
     "end_time": "2023-12-25T14:04:21.709707",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.689040",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c8f2eee4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.755304Z",
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    },
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     "duration": 0.048403,
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     "exception": false,
     "start_time": "2023-12-25T14:04:21.731812",
     "status": "completed"
    },
    "tags": []
   },
   "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>Gender</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Education Level_Bachelors</th>\n",
       "      <th>Education Level_Masters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>3.0</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>6.0</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender          Job Title  Years of Experience    Salary  \\\n",
       "0    32.0    Male  Software Engineer                  5.0   90000.0   \n",
       "376  28.0  Female  Software Engineer                  3.0  125000.0   \n",
       "380  29.0  Female  Software Engineer                  4.0  140000.0   \n",
       "384  31.0  Female  Software Engineer                  6.0  155000.0   \n",
       "388  29.0  Female  Software Engineer                  4.0  140000.0   \n",
       "\n",
       "     Education Level_Bachelors  Education Level_Masters  \n",
       "0                            1                        0  \n",
       "376                          1                        0  \n",
       "380                          1                        0  \n",
       "384                          1                        0  \n",
       "388                          1                        0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the education label: One-Hot-Encoding\n",
    "HotOne = swedata.copy()\n",
    "ohencoded = pd.get_dummies(HotOne, columns = ['Education Level'], dtype=int)\n",
    "ohencoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf4c6543",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-25T14:04:21.825886Z",
     "iopub.status.busy": "2023-12-25T14:04:21.825482Z",
     "iopub.status.idle": "2023-12-25T14:04:21.835392Z",
     "shell.execute_reply": "2023-12-25T14:04:21.834239Z"
    },
    "papermill": {
     "duration": 0.036304,
     "end_time": "2023-12-25T14:04:21.838106",
     "exception": false,
     "start_time": "2023-12-25T14:04:21.801802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Preparing & splitting dataset\n",
    "ohefeatures = ohencoded[['Education Level_Masters', 'Education Level_Bachelors', 'Years of Experience']]\n",
    "ohelabel = ohencoded[['Salary']]\n",
    "ohf_train, ohf_test, ohl_train, ohl_test = train_test_split(ohefeatures, ohelabel, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0236462f",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model_2 = LinearRegression()\n",
    "model_2.fit(ohf_train, ohl_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2208bcbc",
   "metadata": {
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6618221976950922"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation\n",
    "model_2.score(ohf_test, ohl_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6de03faa",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": []
  }
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