Linear and Polynomial Regression on Position of Salaries.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d98c14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.preprocessing as sp\n",
    "import sklearn.linear_model as sl\n",
    "import sklearn.metrics as sm\n",
    "import seaborn as sns\n",
    "import math as m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d67cbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CEO</td>\n",
       "      <td>10</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level   Salary\n",
       "0   Business Analyst      1    45000\n",
       "1  Junior Consultant      2    50000\n",
       "2  Senior Consultant      3    60000\n",
       "3            Manager      4    80000\n",
       "4    Country Manager      5   110000\n",
       "5     Region Manager      6   150000\n",
       "6            Partner      7   200000\n",
       "7     Senior Partner      8   300000\n",
       "8            C-level      9   500000\n",
       "9                CEO     10  1000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Position_Salaries[1].csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f7d3ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa9bd3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CEO</td>\n",
       "      <td>10</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level   Salary\n",
       "0   Business Analyst      1    45000\n",
       "1  Junior Consultant      2    50000\n",
       "2  Senior Consultant      3    60000\n",
       "3            Manager      4    80000\n",
       "4    Country Manager      5   110000\n",
       "5     Region Manager      6   150000\n",
       "6            Partner      7   200000\n",
       "7     Senior Partner      8   300000\n",
       "8            C-level      9   500000\n",
       "9                CEO     10  1000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a52aeda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Position  10 non-null     object\n",
      " 1   Level     10 non-null     int64 \n",
      " 2   Salary    10 non-null     int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "081ba1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>249500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>299373.883668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>45000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.25000</td>\n",
       "      <td>65000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>130000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.75000</td>\n",
       "      <td>275000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Level          Salary\n",
       "count  10.00000       10.000000\n",
       "mean    5.50000   249500.000000\n",
       "std     3.02765   299373.883668\n",
       "min     1.00000    45000.000000\n",
       "25%     3.25000    65000.000000\n",
       "50%     5.50000   130000.000000\n",
       "75%     7.75000   275000.000000\n",
       "max    10.00000  1000000.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a03e79a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 7,
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebd24e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Level'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Level',color='red',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16c0a31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Salary'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Salary',color='skyblue',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84f62e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ce5aa60d90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='Salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7506509e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ce5b2ffbb0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='Level',data=df) # no needed "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcdc979",
   "metadata": {},
   "source": [
    "Linear Regression model_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a815a4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Level']]\n",
    "y = df[['Salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "631442a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin = sl.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98d05115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[80878.78787879]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = lin.fit(x,y)\n",
    "s.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0414f3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-195333.33333333])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28009e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6690412331929895"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ac431d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-114454.54545455],\n",
       "       [ -33575.75757576],\n",
       "       [  47303.03030303],\n",
       "       [ 128181.81818182],\n",
       "       [ 209060.60606061],\n",
       "       [ 289939.39393939],\n",
       "       [ 370818.18181818],\n",
       "       [ 451696.96969697],\n",
       "       [ 532575.75757576],\n",
       "       [ 613454.54545455]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4cca2071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26695878787.878788"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.mean_squared_error(y,s.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cea3bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163388.73519272613"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.sqrt(sm.mean_squared_error(y,s.predict(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd79ce39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of level6.5\n",
      "[330378.78787879]\n"
     ]
    }
   ],
   "source": [
    "s1 = float(input('Enter the value of level'))\n",
    "pred_salary = s.coef_[0][0]*s1 + s.intercept_\n",
    "print(pred_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dafef22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "add74123",
   "metadata": {},
   "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>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>pred_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "      <td>-114454.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "      <td>-33575.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "      <td>47303.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "      <td>128181.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "      <td>209060.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "      <td>289939.393939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "      <td>370818.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "      <td>451696.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "      <td>532575.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CEO</td>\n",
       "      <td>10</td>\n",
       "      <td>1000000</td>\n",
       "      <td>613454.545455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level   Salary    pred_salary\n",
       "0   Business Analyst      1    45000 -114454.545455\n",
       "1  Junior Consultant      2    50000  -33575.757576\n",
       "2  Senior Consultant      3    60000   47303.030303\n",
       "3            Manager      4    80000  128181.818182\n",
       "4    Country Manager      5   110000  209060.606061\n",
       "5     Region Manager      6   150000  289939.393939\n",
       "6            Partner      7   200000  370818.181818\n",
       "7     Senior Partner      8   300000  451696.969697\n",
       "8            C-level      9   500000  532575.757576\n",
       "9                CEO     10  1000000  613454.545455"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['pred_salary']=s.predict(x)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2f499a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ce5b575580>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color='blue')\n",
    "plt.plot(x.values.reshape(-1,1),s.predict(x),color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c164d6fd",
   "metadata": {},
   "source": [
    "The above model is liner regression which 66.90% is accurate and rmse is 163388.73519272613. And also check for level value 6.5 the predicted salary is 330378.7878787"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "867df6e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "05926600",
   "metadata": {},
   "source": [
    "Polynomial Regression Model_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb55dafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Level']]\n",
    "y = df[['Salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "362569b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00],\n",
       "       [1.000e+00, 2.000e+00, 4.000e+00, 8.000e+00, 1.600e+01],\n",
       "       [1.000e+00, 3.000e+00, 9.000e+00, 2.700e+01, 8.100e+01],\n",
       "       [1.000e+00, 4.000e+00, 1.600e+01, 6.400e+01, 2.560e+02],\n",
       "       [1.000e+00, 5.000e+00, 2.500e+01, 1.250e+02, 6.250e+02],\n",
       "       [1.000e+00, 6.000e+00, 3.600e+01, 2.160e+02, 1.296e+03],\n",
       "       [1.000e+00, 7.000e+00, 4.900e+01, 3.430e+02, 2.401e+03],\n",
       "       [1.000e+00, 8.000e+00, 6.400e+01, 5.120e+02, 4.096e+03],\n",
       "       [1.000e+00, 9.000e+00, 8.100e+01, 7.290e+02, 6.561e+03],\n",
       "       [1.000e+00, 1.000e+01, 1.000e+02, 1.000e+03, 1.000e+04]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly=sp.PolynomialFeatures(degree=4)\n",
    "x1 = poly.fit_transform(x)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa1e8c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      0.        , -211002.33100266,   94765.44289054,\n",
       "         -15463.2867133 ,     890.15151515]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin2 = sl.LinearRegression()\n",
    "sa = lin2.fit(x1,y)\n",
    "sa.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f40e284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([184166.66666707])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3a734d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973922891706614"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sa.score(x1,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "127b4f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 53356.64335681],\n",
       "       [ 31759.90675996],\n",
       "       [ 58642.19114221],\n",
       "       [ 94632.86713289],\n",
       "       [121724.94172498],\n",
       "       [143275.05827508],\n",
       "       [184003.49650349],\n",
       "       [289994.17249411],\n",
       "       [528694.63869452],\n",
       "       [988916.08391594]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.predict(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "108e6e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210343822.8438184"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.mean_squared_error(y,sa.predict(x1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "859f1c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14503.234909626832"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.sqrt(sm.mean_squared_error(y,sa.predict(x1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1eec9c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Level 6.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[162939.8974359]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lev = float(input('Enter the Level '))\n",
    "a = pd.DataFrame({'level':[lev]})\n",
    "a1 = sp.PolynomialFeatures(degree=4).fit_transform(a)\n",
    "sa.predict(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "84e512f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ce5b4c9610>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color='blue')\n",
    "plt.plot(x.values.reshape(-1,1),sa.predict(x1),color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c831cfab",
   "metadata": {},
   "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>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>pred_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "      <td>53356.643357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "      <td>31759.906760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "      <td>58642.191142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "      <td>94632.867133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "      <td>121724.941725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "      <td>143275.058275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "      <td>184003.496503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "      <td>289994.172494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "      <td>528694.638695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CEO</td>\n",
       "      <td>10</td>\n",
       "      <td>1000000</td>\n",
       "      <td>988916.083916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level   Salary    pred_salary\n",
       "0   Business Analyst      1    45000   53356.643357\n",
       "1  Junior Consultant      2    50000   31759.906760\n",
       "2  Senior Consultant      3    60000   58642.191142\n",
       "3            Manager      4    80000   94632.867133\n",
       "4    Country Manager      5   110000  121724.941725\n",
       "5     Region Manager      6   150000  143275.058275\n",
       "6            Partner      7   200000  184003.496503\n",
       "7     Senior Partner      8   300000  289994.172494\n",
       "8            C-level      9   500000  528694.638695\n",
       "9                CEO     10  1000000  988916.083916"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['pred_salary']=sa.predict(x1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0227b4b3",
   "metadata": {},
   "source": [
    "The above model is polynomial regression which 99.73% is accurate and rmse is 14503.234909626832. And also check for level value 6.5 the predicted salary is 158862.45265153"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ea1fc4",
   "metadata": {},
   "source": [
    "remove outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816ac5fc",
   "metadata": {},
   "source": [
    "Linear Regression model_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "90c5d743",
   "metadata": {},
   "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>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000\n",
       "5     Region Manager      6  150000\n",
       "6            Partner      7  200000\n",
       "7     Senior Partner      8  300000\n",
       "8            C-level      9  500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df.copy()\n",
    "df4.drop(index=9,inplace=True)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ca6d483a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[48666.66666667]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df4[['Level']]\n",
    "y = df4[['Salary']]\n",
    "linr = sl.LinearRegression()\n",
    "sr = linr.fit(x,y)\n",
    "sr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ad051472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-77222.22222222])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ee4ebf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7860365066683056"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4a35ea47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4298024691.358024"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.mean_squared_error(y,sr.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1479323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65559.32192570347"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.sqrt(sm.mean_squared_error(y,sr.predict(x)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cdff5029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ce5af02880>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x.values.reshape(-1,1),sr.predict(x),color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7ec23306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of level6.5\n",
      "[239111.11111111]\n"
     ]
    }
   ],
   "source": [
    "s1 = float(input('Enter the value of level'))\n",
    "pred_salary = sr.coef_[0][0]*s1 + sr.intercept_\n",
    "print(pred_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb3c572",
   "metadata": {},
   "source": [
    "The above model is linear regression which 78.68% is accurate and rmse is 65559.32192570347. And also check for level value 6.5 the predicted salary is 239111.11111111. which is closer to the true value than model_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8b79584",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb4b4a7c",
   "metadata": {},
   "source": [
    "Polynomial Regression Model_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3dc226da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[     0.        , -82716.26521636,  40115.57886561,\n",
       "         -6739.83423984,    428.32167832]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df4[['Level']]\n",
    "y = df4[['Salary']]\n",
    "po = sp.PolynomialFeatures(degree=4)\n",
    "x2 = po.fit_transform(x)\n",
    "mq = sl.LinearRegression()\n",
    "mr = mq.fit(x2,y)\n",
    "mr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "276c8068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([96111.1111112])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "61e2c533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9991623519986866"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr.score(x2,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "709ff438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16826383.493049692"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sm.mean_squared_error(y,mr.predict(x2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ab6fbcfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4101.997500370971"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.sqrt(sm.mean_squared_error(y,mr.predict(x2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2cdfa15a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Level 6.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[166992.58207071]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lev = float(input('Enter the Level '))\n",
    "a = pd.DataFrame({'level':[lev]})\n",
    "a1 = sp.PolynomialFeatures(degree=4).fit_transform(a)\n",
    "mr.predict(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "116fc5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ce5b378400>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x.values.reshape(-1,1),mr.predict(x2),color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0295ed66",
   "metadata": {},
   "source": [
    "The above model is polynomial regression which 99.91% is accurate and rmse is 4101.997500370971. And also check for level value 6.5 the predicted salary is 166992.58207071. which is closer to the true value than model_2 ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8deb9e56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}