Rishav - Salary prediction ML model (multivariate regression).ipynb

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     "text": [
      "Collecting word2number\n",
      "  Downloading word2number-1.1.zip (9.7 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Building wheels for collected packages: word2number\n",
      "  Building wheel for word2number (setup.py): started\n",
      "  Building wheel for word2number (setup.py): finished with status 'done'\n",
      "  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5590 sha256=e0f657b42001548e838a5e6fc238187134cfbbba72b99012c97c270714e6c8e4\n",
      "  Stored in directory: c:\\users\\souga\\appdata\\local\\pip\\cache\\wheels\\cd\\ef\\ae\\073b491b14d25e2efafcffca9e16b2ee6d114ec5c643ba4f06\n",
      "Successfully built word2number\n",
      "Installing collected packages: word2number\n",
      "Successfully installed word2number-1.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
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   "source": [
    "pip install word2number"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e11355ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from word2number import w2n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "541deccf",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
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       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
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       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv(\"C:/Users/souga/Downloads/hiring.csv\")\n",
    "d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8d15c51",
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    {
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       "0      zero\n",
       "1      zero\n",
       "2      five\n",
       "3       two\n",
       "4     seven\n",
       "5     three\n",
       "6       ten\n",
       "7    eleven\n",
       "Name: experience, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.experience = d.experience.fillna(\"zero\")\n",
    "d.experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e660b2e2",
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       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0                    8.0                           9      50000\n",
       "1           0                    8.0                           6      45000\n",
       "2           5                    6.0                           7      60000\n",
       "3           2                   10.0                          10      65000\n",
       "4           7                    9.0                           6      70000\n",
       "5           3                    7.0                          10      62000\n",
       "6          10                    NaN                           7      72000\n",
       "7          11                    7.0                           8      80000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.experience = d.experience.apply(w2n.word_to_num)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "329319dc",
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       "7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "median_test_score = math.floor(d['test_score(out of 10)'].mean())\n",
    "median_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63627f68",
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   "outputs": [
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       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0                    8.0                           9      50000\n",
       "1           0                    8.0                           6      45000\n",
       "2           5                    6.0                           7      60000\n",
       "3           2                   10.0                          10      65000\n",
       "4           7                    9.0                           6      70000\n",
       "5           3                    7.0                          10      62000\n",
       "6          10                    7.0                           7      72000\n",
       "7          11                    7.0                           8      80000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['test_score(out of 10)'] = d['test_score(out of 10)'].fillna(median_test_score)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5339e84",
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(d[['experience','test_score(out of 10)','interview_score(out of 10)']], d['salary($)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1144a45b",
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\souga\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
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      ]
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "reg.predict([[0,9,9]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9a260b7",
   "metadata": {},
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     "text": [
      "C:\\Users\\souga\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
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     "data": {
      "text/plain": [
       "array([64525.1061363])"
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     "execution_count": 18,
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   ],
   "source": [
    "reg.predict([[2,10,10]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97248f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.score(d[['experience','test_score(out of 10)','interview_score(out of 10)']], d['salary($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b58c162e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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