LinearRegressionHousing.ipynb - machinelearning

LinearRegressionHousing.ipynb (226310B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "code",
      5    "execution_count": 69,
      6    "metadata": {},
      7    "outputs": [],
      8    "source": [
      9     "# This is my first attempt prior to following the book's guide.\n",
     10     "\n",
     11     "# 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",
     12     "# When looking at histograms of this set it seemed generally representative, but still this is probably not good practice.\n",
     13     "# 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",
     14     "# Additionally, I guessed about how the ocean_proximity values should be parameterized to use integers instead of strings.\n",
     15     "\n",
     16     "# Overall, this was a good learning experience, but the model had the following error rates:\n",
     17     "\n",
     18     "# RMSE: 69192.4363439793\n",
     19     "# MAE: 50417.739384587694\n",
     20     "# 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",
     21     "# Make sure to remove the data that was capped at the top end to ensure the training data is better. \n",
     22     "\n",
     23     "from pathlib import Path\n",
     24     "import pandas as pd\n",
     25     "import tarfile\n",
     26     "import requests\n",
     27     "import matplotlib.pyplot as plt\n",
     28     "from sklearn.linear_model import LinearRegression\n",
     29     "\n",
     30     "# Download tar file and write it out to a csv then read that back into a pandas dataframe\n",
     31     "def load_data():\n",
     32     "    filePath = Path(\"../datasets/housing.tgz\")\n",
     33     "    if not filePath.is_file():\n",
     34     "        Path(\"../datasets\").mkdir(parents=True, exist_ok=True)\n",
     35     "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
     36     "        req = requests.get(url)\n",
     37     "        with open('../datasets/housing.tgz', 'wb') as f:\n",
     38     "            f.write(req.content)\n",
     39     "\n",
     40     "        with tarfile.open(filePath) as housingFile:\n",
     41     "            housingFile.extractall(path=\"../datasets\")\n",
     42     "    return pd.read_csv(Path(\"../datasets/housing/housing.csv\"))\n",
     43     "\n",
     44     "housing = load_data()"
     45    ]
     46   },
     47   {
     48    "cell_type": "code",
     49    "execution_count": 70,
     50    "metadata": {},
     51    "outputs": [
     52     {
     53      "data": {
     54       "text/html": [
     55        "<div>\n",
     56        "<style scoped>\n",
     57        "    .dataframe tbody tr th:only-of-type {\n",
     58        "        vertical-align: middle;\n",
     59        "    }\n",
     60        "\n",
     61        "    .dataframe tbody tr th {\n",
     62        "        vertical-align: top;\n",
     63        "    }\n",
     64        "\n",
     65        "    .dataframe thead th {\n",
     66        "        text-align: right;\n",
     67        "    }\n",
     68        "</style>\n",
     69        "<table border=\"1\" class=\"dataframe\">\n",
     70        "  <thead>\n",
     71        "    <tr style=\"text-align: right;\">\n",
     72        "      <th></th>\n",
     73        "      <th>longitude</th>\n",
     74        "      <th>latitude</th>\n",
     75        "      <th>housing_median_age</th>\n",
     76        "      <th>total_rooms</th>\n",
     77        "      <th>total_bedrooms</th>\n",
     78        "      <th>population</th>\n",
     79        "      <th>households</th>\n",
     80        "      <th>median_income</th>\n",
     81        "      <th>median_house_value</th>\n",
     82        "      <th>ocean_proximity</th>\n",
     83        "    </tr>\n",
     84        "  </thead>\n",
     85        "  <tbody>\n",
     86        "    <tr>\n",
     87        "      <th>0</th>\n",
     88        "      <td>-122.23</td>\n",
     89        "      <td>37.88</td>\n",
     90        "      <td>41.0</td>\n",
     91        "      <td>880.0</td>\n",
     92        "      <td>129.0</td>\n",
     93        "      <td>322.0</td>\n",
     94        "      <td>126.0</td>\n",
     95        "      <td>8.3252</td>\n",
     96        "      <td>452600.0</td>\n",
     97        "      <td>NEAR BAY</td>\n",
     98        "    </tr>\n",
     99        "    <tr>\n",
    100        "      <th>1</th>\n",
    101        "      <td>-122.22</td>\n",
    102        "      <td>37.86</td>\n",
    103        "      <td>21.0</td>\n",
    104        "      <td>7099.0</td>\n",
    105        "      <td>1106.0</td>\n",
    106        "      <td>2401.0</td>\n",
    107        "      <td>1138.0</td>\n",
    108        "      <td>8.3014</td>\n",
    109        "      <td>358500.0</td>\n",
    110        "      <td>NEAR BAY</td>\n",
    111        "    </tr>\n",
    112        "    <tr>\n",
    113        "      <th>2</th>\n",
    114        "      <td>-122.24</td>\n",
    115        "      <td>37.85</td>\n",
    116        "      <td>52.0</td>\n",
    117        "      <td>1467.0</td>\n",
    118        "      <td>190.0</td>\n",
    119        "      <td>496.0</td>\n",
    120        "      <td>177.0</td>\n",
    121        "      <td>7.2574</td>\n",
    122        "      <td>352100.0</td>\n",
    123        "      <td>NEAR BAY</td>\n",
    124        "    </tr>\n",
    125        "    <tr>\n",
    126        "      <th>3</th>\n",
    127        "      <td>-122.25</td>\n",
    128        "      <td>37.85</td>\n",
    129        "      <td>52.0</td>\n",
    130        "      <td>1274.0</td>\n",
    131        "      <td>235.0</td>\n",
    132        "      <td>558.0</td>\n",
    133        "      <td>219.0</td>\n",
    134        "      <td>5.6431</td>\n",
    135        "      <td>341300.0</td>\n",
    136        "      <td>NEAR BAY</td>\n",
    137        "    </tr>\n",
    138        "    <tr>\n",
    139        "      <th>4</th>\n",
    140        "      <td>-122.25</td>\n",
    141        "      <td>37.85</td>\n",
    142        "      <td>52.0</td>\n",
    143        "      <td>1627.0</td>\n",
    144        "      <td>280.0</td>\n",
    145        "      <td>565.0</td>\n",
    146        "      <td>259.0</td>\n",
    147        "      <td>3.8462</td>\n",
    148        "      <td>342200.0</td>\n",
    149        "      <td>NEAR BAY</td>\n",
    150        "    </tr>\n",
    151        "  </tbody>\n",
    152        "</table>\n",
    153        "</div>"
    154       ],
    155       "text/plain": [
    156        "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
    157        "0    -122.23     37.88                41.0        880.0           129.0   \n",
    158        "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
    159        "2    -122.24     37.85                52.0       1467.0           190.0   \n",
    160        "3    -122.25     37.85                52.0       1274.0           235.0   \n",
    161        "4    -122.25     37.85                52.0       1627.0           280.0   \n",
    162        "\n",
    163        "   population  households  median_income  median_house_value ocean_proximity  \n",
    164        "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
    165        "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
    166        "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
    167        "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
    168        "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
    169       ]
    170      },
    171      "execution_count": 70,
    172      "metadata": {},
    173      "output_type": "execute_result"
    174     }
    175    ],
    176    "source": [
    177     "housing.head()"
    178    ]
    179   },
    180   {
    181    "cell_type": "code",
    182    "execution_count": 71,
    183    "metadata": {},
    184    "outputs": [
    185     {
    186      "data": {
    187       "text/plain": [
    188        "ocean_proximity\n",
    189        "<1H OCEAN     9136\n",
    190        "INLAND        6551\n",
    191        "NEAR OCEAN    2658\n",
    192        "NEAR BAY      2290\n",
    193        "ISLAND           5\n",
    194        "Name: count, dtype: int64"
    195       ]
    196      },
    197      "execution_count": 71,
    198      "metadata": {},
    199      "output_type": "execute_result"
    200     }
    201    ],
    202    "source": [
    203     "housing[\"ocean_proximity\"].value_counts()"
    204    ]
    205   },
    206   {
    207    "cell_type": "code",
    208    "execution_count": 72,
    209    "metadata": {},
    210    "outputs": [
    211     {
    212      "data": {
    213       "image/png": 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",
    214       "text/plain": [
    215        "<Figure size 1200x800 with 9 Axes>"
    216       ]
    217      },
    218      "metadata": {},
    219      "output_type": "display_data"
    220     }
    221    ],
    222    "source": [
    223     "housing.hist(bins=50, figsize=(12,8))\n",
    224     "plt.show()"
    225    ]
    226   },
    227   {
    228    "cell_type": "code",
    229    "execution_count": 73,
    230    "metadata": {},
    231    "outputs": [
    232     {
    233      "name": "stdout",
    234      "output_type": "stream",
    235      "text": [
    236       "[2 1 0 3 4]\n"
    237      ]
    238     },
    239     {
    240      "name": "stderr",
    241      "output_type": "stream",
    242      "text": [
    243       "/tmp/ipykernel_705292/3607618268.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
    244       "  housing = housing.replace({'INLAND' : 0, '<1H OCEAN' : 1, 'NEAR BAY' : 2, 'NEAR OCEAN' : 3 , 'ISLAND' : 4})\n"
    245      ]
    246     }
    247    ],
    248    "source": [
    249     "# Convert Strings to Ints\n",
    250     "housing = housing.replace({'INLAND' : 0, '<1H OCEAN' : 1, 'NEAR BAY' : 2, 'NEAR OCEAN' : 3 , 'ISLAND' : 4})\n",
    251     "print(housing['ocean_proximity'].unique())\n"
    252    ]
    253   },
    254   {
    255    "cell_type": "code",
    256    "execution_count": 74,
    257    "metadata": {},
    258    "outputs": [
    259     {
    260      "name": "stdout",
    261      "output_type": "stream",
    262      "text": [
    263       "19640\n",
    264       "1000\n"
    265      ]
    266     }
    267    ],
    268    "source": [
    269     "# Split training and validation sets\n",
    270     "\n",
    271     "trn = []\n",
    272     "tst = []\n",
    273     "count = 0\n",
    274     "\n",
    275     "for i in housing.values.tolist():\n",
    276     "    if count < 1000:\n",
    277     "        tst.append(i)\n",
    278     "    else:\n",
    279     "        trn.append(i)\n",
    280     "    count += 1\n",
    281     "\n",
    282     "\n",
    283     "training = pd.DataFrame(trn, columns=housing.columns)\n",
    284     "print(len(training))\n",
    285     "\n",
    286     "validation = pd.DataFrame(tst, columns=housing.columns)\n",
    287     "print(len(validation))"
    288    ]
    289   },
    290   {
    291    "cell_type": "code",
    292    "execution_count": 75,
    293    "metadata": {},
    294    "outputs": [
    295     {
    296      "data": {
    297       "text/html": [
    298        "<style>#sk-container-id-7 {\n",
    299        "  /* Definition of color scheme common for light and dark mode */\n",
    300        "  --sklearn-color-text: black;\n",
    301        "  --sklearn-color-line: gray;\n",
    302        "  /* Definition of color scheme for unfitted estimators */\n",
    303        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
    304        "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
    305        "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
    306        "  --sklearn-color-unfitted-level-3: chocolate;\n",
    307        "  /* Definition of color scheme for fitted estimators */\n",
    308        "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
    309        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
    310        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
    311        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
    312        "\n",
    313        "  /* Specific color for light theme */\n",
    314        "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
    315        "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
    316        "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
    317        "  --sklearn-color-icon: #696969;\n",
    318        "\n",
    319        "  @media (prefers-color-scheme: dark) {\n",
    320        "    /* Redefinition of color scheme for dark theme */\n",
    321        "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
    322        "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
    323        "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
    324        "    --sklearn-color-icon: #878787;\n",
    325        "  }\n",
    326        "}\n",
    327        "\n",
    328        "#sk-container-id-7 {\n",
    329        "  color: var(--sklearn-color-text);\n",
    330        "}\n",
    331        "\n",
    332        "#sk-container-id-7 pre {\n",
    333        "  padding: 0;\n",
    334        "}\n",
    335        "\n",
    336        "#sk-container-id-7 input.sk-hidden--visually {\n",
    337        "  border: 0;\n",
    338        "  clip: rect(1px 1px 1px 1px);\n",
    339        "  clip: rect(1px, 1px, 1px, 1px);\n",
    340        "  height: 1px;\n",
    341        "  margin: -1px;\n",
    342        "  overflow: hidden;\n",
    343        "  padding: 0;\n",
    344        "  position: absolute;\n",
    345        "  width: 1px;\n",
    346        "}\n",
    347        "\n",
    348        "#sk-container-id-7 div.sk-dashed-wrapped {\n",
    349        "  border: 1px dashed var(--sklearn-color-line);\n",
    350        "  margin: 0 0.4em 0.5em 0.4em;\n",
    351        "  box-sizing: border-box;\n",
    352        "  padding-bottom: 0.4em;\n",
    353        "  background-color: var(--sklearn-color-background);\n",
    354        "}\n",
    355        "\n",
    356        "#sk-container-id-7 div.sk-container {\n",
    357        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
    358        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
    359        "     so we also need the `!important` here to be able to override the\n",
    360        "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
    361        "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
    362        "  display: inline-block !important;\n",
    363        "  position: relative;\n",
    364        "}\n",
    365        "\n",
    366        "#sk-container-id-7 div.sk-text-repr-fallback {\n",
    367        "  display: none;\n",
    368        "}\n",
    369        "\n",
    370        "div.sk-parallel-item,\n",
    371        "div.sk-serial,\n",
    372        "div.sk-item {\n",
    373        "  /* draw centered vertical line to link estimators */\n",
    374        "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
    375        "  background-size: 2px 100%;\n",
    376        "  background-repeat: no-repeat;\n",
    377        "  background-position: center center;\n",
    378        "}\n",
    379        "\n",
    380        "/* Parallel-specific style estimator block */\n",
    381        "\n",
    382        "#sk-container-id-7 div.sk-parallel-item::after {\n",
    383        "  content: \"\";\n",
    384        "  width: 100%;\n",
    385        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
    386        "  flex-grow: 1;\n",
    387        "}\n",
    388        "\n",
    389        "#sk-container-id-7 div.sk-parallel {\n",
    390        "  display: flex;\n",
    391        "  align-items: stretch;\n",
    392        "  justify-content: center;\n",
    393        "  background-color: var(--sklearn-color-background);\n",
    394        "  position: relative;\n",
    395        "}\n",
    396        "\n",
    397        "#sk-container-id-7 div.sk-parallel-item {\n",
    398        "  display: flex;\n",
    399        "  flex-direction: column;\n",
    400        "}\n",
    401        "\n",
    402        "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
    403        "  align-self: flex-end;\n",
    404        "  width: 50%;\n",
    405        "}\n",
    406        "\n",
    407        "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
    408        "  align-self: flex-start;\n",
    409        "  width: 50%;\n",
    410        "}\n",
    411        "\n",
    412        "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
    413        "  width: 0;\n",
    414        "}\n",
    415        "\n",
    416        "/* Serial-specific style estimator block */\n",
    417        "\n",
    418        "#sk-container-id-7 div.sk-serial {\n",
    419        "  display: flex;\n",
    420        "  flex-direction: column;\n",
    421        "  align-items: center;\n",
    422        "  background-color: var(--sklearn-color-background);\n",
    423        "  padding-right: 1em;\n",
    424        "  padding-left: 1em;\n",
    425        "}\n",
    426        "\n",
    427        "\n",
    428        "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
    429        "clickable and can be expanded/collapsed.\n",
    430        "- Pipeline and ColumnTransformer use this feature and define the default style\n",
    431        "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
    432        "*/\n",
    433        "\n",
    434        "/* Pipeline and ColumnTransformer style (default) */\n",
    435        "\n",
    436        "#sk-container-id-7 div.sk-toggleable {\n",
    437        "  /* Default theme specific background. It is overwritten whether we have a\n",
    438        "  specific estimator or a Pipeline/ColumnTransformer */\n",
    439        "  background-color: var(--sklearn-color-background);\n",
    440        "}\n",
    441        "\n",
    442        "/* Toggleable label */\n",
    443        "#sk-container-id-7 label.sk-toggleable__label {\n",
    444        "  cursor: pointer;\n",
    445        "  display: block;\n",
    446        "  width: 100%;\n",
    447        "  margin-bottom: 0;\n",
    448        "  padding: 0.5em;\n",
    449        "  box-sizing: border-box;\n",
    450        "  text-align: center;\n",
    451        "}\n",
    452        "\n",
    453        "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
    454        "  /* Arrow on the left of the label */\n",
    455        "  content: \"▸\";\n",
    456        "  float: left;\n",
    457        "  margin-right: 0.25em;\n",
    458        "  color: var(--sklearn-color-icon);\n",
    459        "}\n",
    460        "\n",
    461        "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
    462        "  color: var(--sklearn-color-text);\n",
    463        "}\n",
    464        "\n",
    465        "/* Toggleable content - dropdown */\n",
    466        "\n",
    467        "#sk-container-id-7 div.sk-toggleable__content {\n",
    468        "  max-height: 0;\n",
    469        "  max-width: 0;\n",
    470        "  overflow: hidden;\n",
    471        "  text-align: left;\n",
    472        "  /* unfitted */\n",
    473        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    474        "}\n",
    475        "\n",
    476        "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
    477        "  /* fitted */\n",
    478        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    479        "}\n",
    480        "\n",
    481        "#sk-container-id-7 div.sk-toggleable__content pre {\n",
    482        "  margin: 0.2em;\n",
    483        "  border-radius: 0.25em;\n",
    484        "  color: var(--sklearn-color-text);\n",
    485        "  /* unfitted */\n",
    486        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    487        "}\n",
    488        "\n",
    489        "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
    490        "  /* unfitted */\n",
    491        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    492        "}\n",
    493        "\n",
    494        "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
    495        "  /* Expand drop-down */\n",
    496        "  max-height: 200px;\n",
    497        "  max-width: 100%;\n",
    498        "  overflow: auto;\n",
    499        "}\n",
    500        "\n",
    501        "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
    502        "  content: \"▾\";\n",
    503        "}\n",
    504        "\n",
    505        "/* Pipeline/ColumnTransformer-specific style */\n",
    506        "\n",
    507        "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    508        "  color: var(--sklearn-color-text);\n",
    509        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    510        "}\n",
    511        "\n",
    512        "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    513        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    514        "}\n",
    515        "\n",
    516        "/* Estimator-specific style */\n",
    517        "\n",
    518        "/* Colorize estimator box */\n",
    519        "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    520        "  /* unfitted */\n",
    521        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    522        "}\n",
    523        "\n",
    524        "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    525        "  /* fitted */\n",
    526        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    527        "}\n",
    528        "\n",
    529        "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
    530        "#sk-container-id-7 div.sk-label label {\n",
    531        "  /* The background is the default theme color */\n",
    532        "  color: var(--sklearn-color-text-on-default-background);\n",
    533        "}\n",
    534        "\n",
    535        "/* On hover, darken the color of the background */\n",
    536        "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
    537        "  color: var(--sklearn-color-text);\n",
    538        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    539        "}\n",
    540        "\n",
    541        "/* Label box, darken color on hover, fitted */\n",
    542        "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
    543        "  color: var(--sklearn-color-text);\n",
    544        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    545        "}\n",
    546        "\n",
    547        "/* Estimator label */\n",
    548        "\n",
    549        "#sk-container-id-7 div.sk-label label {\n",
    550        "  font-family: monospace;\n",
    551        "  font-weight: bold;\n",
    552        "  display: inline-block;\n",
    553        "  line-height: 1.2em;\n",
    554        "}\n",
    555        "\n",
    556        "#sk-container-id-7 div.sk-label-container {\n",
    557        "  text-align: center;\n",
    558        "}\n",
    559        "\n",
    560        "/* Estimator-specific */\n",
    561        "#sk-container-id-7 div.sk-estimator {\n",
    562        "  font-family: monospace;\n",
    563        "  border: 1px dotted var(--sklearn-color-border-box);\n",
    564        "  border-radius: 0.25em;\n",
    565        "  box-sizing: border-box;\n",
    566        "  margin-bottom: 0.5em;\n",
    567        "  /* unfitted */\n",
    568        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    569        "}\n",
    570        "\n",
    571        "#sk-container-id-7 div.sk-estimator.fitted {\n",
    572        "  /* fitted */\n",
    573        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    574        "}\n",
    575        "\n",
    576        "/* on hover */\n",
    577        "#sk-container-id-7 div.sk-estimator:hover {\n",
    578        "  /* unfitted */\n",
    579        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    580        "}\n",
    581        "\n",
    582        "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
    583        "  /* fitted */\n",
    584        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    585        "}\n",
    586        "\n",
    587        "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
    588        "\n",
    589        "/* Common style for \"i\" and \"?\" */\n",
    590        "\n",
    591        ".sk-estimator-doc-link,\n",
    592        "a:link.sk-estimator-doc-link,\n",
    593        "a:visited.sk-estimator-doc-link {\n",
    594        "  float: right;\n",
    595        "  font-size: smaller;\n",
    596        "  line-height: 1em;\n",
    597        "  font-family: monospace;\n",
    598        "  background-color: var(--sklearn-color-background);\n",
    599        "  border-radius: 1em;\n",
    600        "  height: 1em;\n",
    601        "  width: 1em;\n",
    602        "  text-decoration: none !important;\n",
    603        "  margin-left: 1ex;\n",
    604        "  /* unfitted */\n",
    605        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
    606        "  color: var(--sklearn-color-unfitted-level-1);\n",
    607        "}\n",
    608        "\n",
    609        ".sk-estimator-doc-link.fitted,\n",
    610        "a:link.sk-estimator-doc-link.fitted,\n",
    611        "a:visited.sk-estimator-doc-link.fitted {\n",
    612        "  /* fitted */\n",
    613        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
    614        "  color: var(--sklearn-color-fitted-level-1);\n",
    615        "}\n",
    616        "\n",
    617        "/* On hover */\n",
    618        "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
    619        ".sk-estimator-doc-link:hover,\n",
    620        "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
    621        ".sk-estimator-doc-link:hover {\n",
    622        "  /* unfitted */\n",
    623        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
    624        "  color: var(--sklearn-color-background);\n",
    625        "  text-decoration: none;\n",
    626        "}\n",
    627        "\n",
    628        "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
    629        ".sk-estimator-doc-link.fitted:hover,\n",
    630        "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
    631        ".sk-estimator-doc-link.fitted:hover {\n",
    632        "  /* fitted */\n",
    633        "  background-color: var(--sklearn-color-fitted-level-3);\n",
    634        "  color: var(--sklearn-color-background);\n",
    635        "  text-decoration: none;\n",
    636        "}\n",
    637        "\n",
    638        "/* Span, style for the box shown on hovering the info icon */\n",
    639        ".sk-estimator-doc-link span {\n",
    640        "  display: none;\n",
    641        "  z-index: 9999;\n",
    642        "  position: relative;\n",
    643        "  font-weight: normal;\n",
    644        "  right: .2ex;\n",
    645        "  padding: .5ex;\n",
    646        "  margin: .5ex;\n",
    647        "  width: min-content;\n",
    648        "  min-width: 20ex;\n",
    649        "  max-width: 50ex;\n",
    650        "  color: var(--sklearn-color-text);\n",
    651        "  box-shadow: 2pt 2pt 4pt #999;\n",
    652        "  /* unfitted */\n",
    653        "  background: var(--sklearn-color-unfitted-level-0);\n",
    654        "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
    655        "}\n",
    656        "\n",
    657        ".sk-estimator-doc-link.fitted span {\n",
    658        "  /* fitted */\n",
    659        "  background: var(--sklearn-color-fitted-level-0);\n",
    660        "  border: var(--sklearn-color-fitted-level-3);\n",
    661        "}\n",
    662        "\n",
    663        ".sk-estimator-doc-link:hover span {\n",
    664        "  display: block;\n",
    665        "}\n",
    666        "\n",
    667        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
    668        "\n",
    669        "#sk-container-id-7 a.estimator_doc_link {\n",
    670        "  float: right;\n",
    671        "  font-size: 1rem;\n",
    672        "  line-height: 1em;\n",
    673        "  font-family: monospace;\n",
    674        "  background-color: var(--sklearn-color-background);\n",
    675        "  border-radius: 1rem;\n",
    676        "  height: 1rem;\n",
    677        "  width: 1rem;\n",
    678        "  text-decoration: none;\n",
    679        "  /* unfitted */\n",
    680        "  color: var(--sklearn-color-unfitted-level-1);\n",
    681        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
    682        "}\n",
    683        "\n",
    684        "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
    685        "  /* fitted */\n",
    686        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
    687        "  color: var(--sklearn-color-fitted-level-1);\n",
    688        "}\n",
    689        "\n",
    690        "/* On hover */\n",
    691        "#sk-container-id-7 a.estimator_doc_link:hover {\n",
    692        "  /* unfitted */\n",
    693        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
    694        "  color: var(--sklearn-color-background);\n",
    695        "  text-decoration: none;\n",
    696        "}\n",
    697        "\n",
    698        "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
    699        "  /* fitted */\n",
    700        "  background-color: var(--sklearn-color-fitted-level-3);\n",
    701        "}\n",
    702        "</style><div id=\"sk-container-id-7\" 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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" 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/1.4/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>"
    703       ],
    704       "text/plain": [
    705        "LinearRegression()"
    706       ]
    707      },
    708      "execution_count": 75,
    709      "metadata": {},
    710      "output_type": "execute_result"
    711     }
    712    ],
    713    "source": [
    714     "# Training\n",
    715     "model = LinearRegression()\n",
    716     "# Non-destructive\n",
    717     "trainingNoNA = training.dropna()\n",
    718     "x = trainingNoNA[['median_income', 'longitude' , 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity']]\n",
    719     "y = trainingNoNA[['median_house_value']]\n",
    720     "\n",
    721     "model.fit(X=x , y=y)"
    722    ]
    723   },
    724   {
    725    "cell_type": "code",
    726    "execution_count": 76,
    727    "metadata": {},
    728    "outputs": [],
    729    "source": [
    730     "# Predictions\n",
    731     "validationNoNA = validation.dropna()\n",
    732     "\n",
    733     "x = trainingNoNA[['median_income', 'longitude' , 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity']]\n",
    734     "actual = trainingNoNA['median_house_value'].values.tolist()\n",
    735     "predicted = model.predict(x).tolist()"
    736    ]
    737   },
    738   {
    739    "cell_type": "code",
    740    "execution_count": 77,
    741    "metadata": {},
    742    "outputs": [
    743     {
    744      "name": "stdout",
    745      "output_type": "stream",
    746      "text": [
    747       "69192.4363439793\n"
    748      ]
    749     }
    750    ],
    751    "source": [
    752     "# RMSE Calculation\n",
    753     "import math\n",
    754     "count = 0\n",
    755     "total = 0\n",
    756     "\n",
    757     "\n",
    758     "while count < len(predicted):\n",
    759     "    total += (predicted[count][0] - actual[count]) ** 2\n",
    760     "    count += 1\n",
    761     "\n",
    762     "total = total / len(predicted)\n",
    763     "\n",
    764     "total = math.sqrt(total)\n",
    765     "print(total)"
    766    ]
    767   },
    768   {
    769    "cell_type": "code",
    770    "execution_count": 78,
    771    "metadata": {},
    772    "outputs": [
    773     {
    774      "name": "stdout",
    775      "output_type": "stream",
    776      "text": [
    777       "50417.739384587694\n"
    778      ]
    779     }
    780    ],
    781    "source": [
    782     "# MAE Calculation\n",
    783     "total = 0\n",
    784     "count = 0\n",
    785     "\n",
    786     "while count < len(predicted):\n",
    787     "    total += abs((predicted[count][0] - actual[count]))\n",
    788     "    count += 1\n",
    789     "\n",
    790     "total = total/len(predicted)\n",
    791     "print(total)"
    792    ]
    793   },
    794   {
    795    "cell_type": "code",
    796    "execution_count": 79,
    797    "metadata": {},
    798    "outputs": [
    799     {
    800      "data": {
    801       "text/plain": [
    802        "array([[<Axes: title={'center': 'longitude'}>,\n",
    803        "        <Axes: title={'center': 'latitude'}>,\n",
    804        "        <Axes: title={'center': 'housing_median_age'}>],\n",
    805        "       [<Axes: title={'center': 'total_rooms'}>,\n",
    806        "        <Axes: title={'center': 'total_bedrooms'}>,\n",
    807        "        <Axes: title={'center': 'population'}>],\n",
    808        "       [<Axes: title={'center': 'households'}>,\n",
    809        "        <Axes: title={'center': 'median_income'}>,\n",
    810        "        <Axes: title={'center': 'median_house_value'}>],\n",
    811        "       [<Axes: title={'center': 'ocean_proximity'}>, <Axes: >, <Axes: >]],\n",
    812        "      dtype=object)"
    813       ]
    814      },
    815      "execution_count": 79,
    816      "metadata": {},
    817      "output_type": "execute_result"
    818     },
    819     {
    820      "data": {
    821       "image/png": 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",
    822       "text/plain": [
    823        "<Figure size 640x480 with 12 Axes>"
    824       ]
    825      },
    826      "metadata": {},
    827      "output_type": "display_data"
    828     },
    829     {
    830      "data": {
    831       "image/png": 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",
    832       "text/plain": [
    833        "<Figure size 640x480 with 12 Axes>"
    834       ]
    835      },
    836      "metadata": {},
    837      "output_type": "display_data"
    838     }
    839    ],
    840    "source": [
    841     "# As I am going through my V2 implementation I got curious about the distribution of my test and validation sets\n",
    842     "# So here it is. Not great but not terrible either. With a larger dataset the differences would be negligible and with a smaller\n",
    843     "# one it would be more prominent.\n",
    844     "\n",
    845     "trainingNoNA.hist()\n",
    846     "validationNoNA.hist()"
    847    ]
    848   }
    849  ],
    850  "metadata": {
    851   "kernelspec": {
    852    "display_name": "notebook",
    853    "language": "python",
    854    "name": "notebook"
    855   },
    856   "language_info": {
    857    "codemirror_mode": {
    858     "name": "ipython",
    859     "version": 3
    860    },
    861    "file_extension": ".py",
    862    "mimetype": "text/x-python",
    863    "name": "python",
    864    "nbconvert_exporter": "python",
    865    "pygments_lexer": "ipython3",
    866    "version": "3.11.2"
    867   }
    868  },
    869  "nbformat": 4,
    870  "nbformat_minor": 2
    871 }
	machinelearning Machine learning code
	git clone git://git.laack.co/machinelearning.git
	Log \| Files \| Refs