L3HW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from word2number import w2n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>six</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score  interview_score  salary\n",
       "0        NaN           7                4     500\n",
       "1        NaN           5                5     450\n",
       "2       five           6                3     650\n",
       "3        two           5                7     625\n",
       "4      seven          10                3     850\n",
       "5      three           7                8     800\n",
       "6        ten           8                6    1000\n",
       "7     eleven           9                5    1050\n",
       "8        six           6                6     800"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"l3_data.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zero</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>zero</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>six</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score  interview_score  salary\n",
       "0       zero           7                4     500\n",
       "1       zero           5                5     450\n",
       "2       five           6                3     650\n",
       "3        two           5                7     625\n",
       "4      seven          10                3     850\n",
       "5      three           7                8     800\n",
       "6        ten           8                6    1000\n",
       "7     eleven           9                5    1050\n",
       "8        six           6                6     800"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.experience = data.experience.fillna(\"zero\")\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score  salary\n",
       "0           0           7                4     500\n",
       "1           0           5                5     450\n",
       "2           5           6                3     650\n",
       "3           2           5                7     625\n",
       "4           7          10                3     850\n",
       "5           3           7                8     800\n",
       "6          10           8                6    1000\n",
       "7          11           9                5    1050\n",
       "8           6           6                6     800"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.experience = data.experience.apply(w2n.word_to_num)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1dd23a6bd10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel(\"para\")\n",
    "plt.ylabel(\"salary\")\n",
    "plt.scatter(data.experience + data.test_score + data.interview_score, data.salary, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(data[['experience','test_score','interview_score']], data.salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([731.10318509])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4456.89018717])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[12,10,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40.2518946 , 34.79108795, 39.31976321])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([511.4350441])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[0,5,6]])"
   ]
  }
 ],
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