commit 4f396342c919ebe7a530e1ff99f2c4cff78b18b4
parent d42d1adbddbf6a11f444c3ad25782836b7bf7266
Author: Andrew <andrewlaack1@gmail.com>
Date: Wed, 15 May 2024 10:18:00 -0500
Implemented linear regression on a dataset with many features to do univariate inference for house prices.
Diffstat:
1 file changed, 742 insertions(+), 43 deletions(-)
diff --git a/linearRegression/LinearRegressionHousing.ipynb b/linearRegression/LinearRegressionHousing.ipynb
@@ -2,14 +2,30 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 278,
"metadata": {},
"outputs": [],
"source": [
+ "# This is my first attempt prior to following the book's guide.\n",
+ "\n",
+ "# A few issues I know off the bat are that I used the first 1000 samples as the validation set instead of random data. \n",
+ "# When looking at histograms of this set it seemed generally representative, but still this is probably not good practice.\n",
+ "# Also, there was a cap for certain data parameters that limits the accuracy of the model given that there is a max house price.\n",
+ "# Additionally, I guessed about how the ocean_proximity values should be parameterized to use integers instead of strings.\n",
+ "\n",
+ "# Overall, this was a good learning experience, but the model had the following error rates:\n",
+ "\n",
+ "# RMSE: 69192.4363439793\n",
+ "# MAE: 50417.739384587694\n",
+ "# This means it was off by about $50,000 each time which is not great, but could be worse I guess. When I do this again, I will\n",
+ "# Make sure to remove the data that was capped at the top end to ensure the training data is better. \n",
+ "\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"import tarfile\n",
"import requests\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.linear_model import LinearRegression\n",
"\n",
"# Download tar file and write it out to a csv then read that back into a pandas dataframe\n",
"def load_data():\n",
@@ -30,58 +46,741 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 279,
+ "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>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>housing_median_age</th>\n",
+ " <th>total_rooms</th>\n",
+ " <th>total_bedrooms</th>\n",
+ " <th>population</th>\n",
+ " <th>households</th>\n",
+ " <th>median_income</th>\n",
+ " <th>median_house_value</th>\n",
+ " <th>ocean_proximity</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>-122.23</td>\n",
+ " <td>37.88</td>\n",
+ " <td>41.0</td>\n",
+ " <td>880.0</td>\n",
+ " <td>129.0</td>\n",
+ " <td>322.0</td>\n",
+ " <td>126.0</td>\n",
+ " <td>8.3252</td>\n",
+ " <td>452600.0</td>\n",
+ " <td>NEAR BAY</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>-122.22</td>\n",
+ " <td>37.86</td>\n",
+ " <td>21.0</td>\n",
+ " <td>7099.0</td>\n",
+ " <td>1106.0</td>\n",
+ " <td>2401.0</td>\n",
+ " <td>1138.0</td>\n",
+ " <td>8.3014</td>\n",
+ " <td>358500.0</td>\n",
+ " <td>NEAR BAY</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>-122.24</td>\n",
+ " <td>37.85</td>\n",
+ " <td>52.0</td>\n",
+ " <td>1467.0</td>\n",
+ " <td>190.0</td>\n",
+ " <td>496.0</td>\n",
+ " <td>177.0</td>\n",
+ " <td>7.2574</td>\n",
+ " <td>352100.0</td>\n",
+ " <td>NEAR BAY</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>-122.25</td>\n",
+ " <td>37.85</td>\n",
+ " <td>52.0</td>\n",
+ " <td>1274.0</td>\n",
+ " <td>235.0</td>\n",
+ " <td>558.0</td>\n",
+ " <td>219.0</td>\n",
+ " <td>5.6431</td>\n",
+ " <td>341300.0</td>\n",
+ " <td>NEAR BAY</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>-122.25</td>\n",
+ " <td>37.85</td>\n",
+ " <td>52.0</td>\n",
+ " <td>1627.0</td>\n",
+ " <td>280.0</td>\n",
+ " <td>565.0</td>\n",
+ " <td>259.0</td>\n",
+ " <td>3.8462</td>\n",
+ " <td>342200.0</td>\n",
+ " <td>NEAR BAY</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
+ "0 -122.23 37.88 41.0 880.0 129.0 \n",
+ "1 -122.22 37.86 21.0 7099.0 1106.0 \n",
+ "2 -122.24 37.85 52.0 1467.0 190.0 \n",
+ "3 -122.25 37.85 52.0 1274.0 235.0 \n",
+ "4 -122.25 37.85 52.0 1627.0 280.0 \n",
+ "\n",
+ " population households median_income median_house_value ocean_proximity \n",
+ "0 322.0 126.0 8.3252 452600.0 NEAR BAY \n",
+ "1 2401.0 1138.0 8.3014 358500.0 NEAR BAY \n",
+ "2 496.0 177.0 7.2574 352100.0 NEAR BAY \n",
+ "3 558.0 219.0 5.6431 341300.0 NEAR BAY \n",
+ "4 565.0 259.0 3.8462 342200.0 NEAR BAY "
+ ]
+ },
+ "execution_count": 279,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "housing.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 280,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ocean_proximity\n",
+ "<1H OCEAN 9136\n",
+ "INLAND 6551\n",
+ "NEAR OCEAN 2658\n",
+ "NEAR BAY 2290\n",
+ "ISLAND 5\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 280,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "housing[\"ocean_proximity\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x800 with 9 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "housing.hist(bins=50, figsize=(12,8))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 284,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2 1 0 3 4]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Convert Strings to Ints\n",
+ "housing = housing.replace({'INLAND' : 0, '<1H OCEAN' : 1, 'NEAR BAY' : 2, 'NEAR OCEAN' : 3 , 'ISLAND' : 4})\n",
+ "print(housing['ocean_proximity'].unique())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 287,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "19640\n",
+ "1000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Split training and validation sets\n",
+ "\n",
+ "trn = []\n",
+ "tst = []\n",
+ "count = 0\n",
+ "\n",
+ "for i in housing.values.tolist():\n",
+ " if count < 1000:\n",
+ " tst.append(i)\n",
+ " else:\n",
+ " trn.append(i)\n",
+ " count += 1\n",
+ "\n",
+ "\n",
+ "training = pd.DataFrame(trn, columns=housing.columns)\n",
+ "print(len(training))\n",
+ "\n",
+ "validation = pd.DataFrame(tst, columns=housing.columns)\n",
+ "print(len(validation))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 288,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-15 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-15 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"▸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"▾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-15 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-15 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-15 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-15 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-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",
+ "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-15 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-15 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-15 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-15 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-15\" 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-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/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": 288,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Training\n",
+ "model = LinearRegression()\n",
+ "# Non-destructive\n",
+ "trainingNoNA = training.dropna()\n",
+ "x = trainingNoNA[['median_income', 'longitude' , 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity']]\n",
+ "y = trainingNoNA[['median_house_value']]\n",
+ "\n",
+ "model.fit(X=x , y=y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 289,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Predictions\n",
+ "validationNoNA = validation.dropna()\n",
+ "\n",
+ "x = trainingNoNA[['median_income', 'longitude' , 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity']]\n",
+ "actual = trainingNoNA['median_house_value'].values.tolist()\n",
+ "predicted = model.predict(x).tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 292,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "69192.4363439793\n"
+ ]
+ }
+ ],
+ "source": [
+ "# RMSE Calculation\n",
+ "import math\n",
+ "count = 0\n",
+ "total = 0\n",
+ "\n",
+ "\n",
+ "while count < len(predicted):\n",
+ " total += (predicted[count][0] - actual[count]) ** 2\n",
+ " count += 1\n",
+ "\n",
+ "total = total / len(predicted)\n",
+ "\n",
+ "total = math.sqrt(total)\n",
+ "print(total)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 293,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
- "0 -122.23 37.88 41.0 880.0 129.0 \n",
- "1 -122.22 37.86 21.0 7099.0 1106.0 \n",
- "2 -122.24 37.85 52.0 1467.0 190.0 \n",
- "3 -122.25 37.85 52.0 1274.0 235.0 \n",
- "4 -122.25 37.85 52.0 1627.0 280.0 \n",
- "... ... ... ... ... ... \n",
- "20635 -121.09 39.48 25.0 1665.0 374.0 \n",
- "20636 -121.21 39.49 18.0 697.0 150.0 \n",
- "20637 -121.22 39.43 17.0 2254.0 485.0 \n",
- "20638 -121.32 39.43 18.0 1860.0 409.0 \n",
- "20639 -121.24 39.37 16.0 2785.0 616.0 \n",
- "\n",
- " population households median_income median_house_value \\\n",
- "0 322.0 126.0 8.3252 452600.0 \n",
- "1 2401.0 1138.0 8.3014 358500.0 \n",
- "2 496.0 177.0 7.2574 352100.0 \n",
- "3 558.0 219.0 5.6431 341300.0 \n",
- "4 565.0 259.0 3.8462 342200.0 \n",
- "... ... ... ... ... \n",
- "20635 845.0 330.0 1.5603 78100.0 \n",
- "20636 356.0 114.0 2.5568 77100.0 \n",
- "20637 1007.0 433.0 1.7000 92300.0 \n",
- "20638 741.0 349.0 1.8672 84700.0 \n",
- "20639 1387.0 530.0 2.3886 89400.0 \n",
- "\n",
- " ocean_proximity \n",
- "0 NEAR BAY \n",
- "1 NEAR BAY \n",
- "2 NEAR BAY \n",
- "3 NEAR BAY \n",
- "4 NEAR BAY \n",
- "... ... \n",
- "20635 INLAND \n",
- "20636 INLAND \n",
- "20637 INLAND \n",
- "20638 INLAND \n",
- "20639 INLAND \n",
- "\n",
- "[20640 rows x 10 columns]\n"
+ "50417.739384587694\n"
]
}
],
"source": [
- "print(housing)"
+ "# MAE Calculation\n",
+ "total = 0\n",
+ "count = 0\n",
+ "\n",
+ "while count < len(predicted):\n",
+ " total += abs((predicted[count][0] - actual[count]))\n",
+ " count += 1\n",
+ "\n",
+ "total = total/len(predicted)\n",
+ "print(total)"
]
}
],
