Linear Regression Multiple Variables (1).ipynb

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    "# Sample problem of predicting home price in monroe, new jersey (USA)\n",
    "#Below is the table containing home prices in monroe twp, NJ. Here price depends on area (square feet), bed rooms and age of the home (in years). Given these prices we have to predict prices of new homes based on area, bed rooms and age."
   ]
  },
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   "id": "970dcc8f-2662-468d-9d32-a0ca8b20cbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a7326f0-c828-454f-a242-ea9eb62648af",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
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       "      <th>3</th>\n",
       "      <td>3600</td>\n",
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       "      <td>30</td>\n",
       "      <td>595000</td>\n",
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       "      <th>4</th>\n",
       "      <td>4000</td>\n",
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       "      <td>8</td>\n",
       "      <td>760000</td>\n",
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       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
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      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       NaN   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv('homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e2a8f7-afd3-44c4-9121-aaab58e419dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data Preprocessing: Fill NA values with median value of a column"
   ]
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   "id": "2d9396d6-438a-416f-b095-cf66dd2c138d",
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       "4.0"
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   "source": [
    "df.bedrooms.median()"
   ]
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   "execution_count": 9,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
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       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
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      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       4.0   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
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     "metadata": {},
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   ],
   "source": [
    "df.bedrooms = df.bedrooms.fillna(df.bedrooms.median())\n",
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "1d5f4308-23bc-4263-841c-098f4650071d",
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       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df.drop('price',axis='columns'),df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e30d0a5-0589-450a-b394-20dab74cbf7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  112.06244194, 23388.88007794, -3231.71790863])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "725939d1-86a2-4271-80e2-053c44751e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221323.0018654043"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb28d9b2-0433-4794-aff8-6c4eefc0b21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find price of home with 3000 sqr ft area, 3 bedrooms, 40 year old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea20da4f-21ac-4b6c-b42a-ea96f45895f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thris\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([498408.25158031])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3000, 3, 40]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00fbce8f-7785-4bc7-8992-0d473e97e1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498408.25157402386"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "112.06244194*3000 + 23388.88007794*3 + -3231.71790863*40 + 221323.00186540384"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20f23c83-9a28-4763-82ac-736cb61d505c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find price of home with 2500 sqr ft area, 4 bedrooms, 5 year old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0dbc019-c6fa-4ff4-a2f4-3cccf3850881",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thris\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([578876.03748933])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2500, 4, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cb5b315-661d-460f-907e-42ee5e45f046",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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