Hands-On Machine Learning/02_End_to_End_Machine_Learning_Project.ipynb

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      "name": "Hands On Machine Learning Chapter 2.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPPTOMrkoz9g"
      },
      "source": [
        "In this chapter, you will go through an example project end to end, pretending to be a recently hired data scientist in a real estate company. Here are the main steps you will go through:\n",
        "1. Look at the big picture.\n",
        "2. Get the data.\n",
        "3. Discover and visualize the data to gain insights.\n",
        "4. Prepare the data for Machine Learning algorithms.\n",
        "5. Select a model and train it.\n",
        "6. Fine-tune your model.\n",
        "7. Present your solution.\n",
        "8. Launch, monitor, and maintain your system."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VB-a08Ir4_X4"
      },
      "source": [
        "The first task you are asked toperform is to build a model of housing prices in California using the California census data. \n",
        "\n",
        "This data has metrics such as the population, median income, median housing\n",
        "price, and so on for each block group in California. \n",
        "\n",
        "Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a blockgroup typically has a population of 600 to 3,000 people). We will just call them “districts”\n",
        "\n",
        "Your model should learn from this data and be able to predict the median housing price in any district, given all the other metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-l4eSoRo7AI"
      },
      "source": [
        "# Get the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uuy8YFo2wSQ"
      },
      "source": [
        "Load the Data with Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPoiqWXt302F"
      },
      "source": [
        "import pandas as pd\n",
        "csv_path = 'https://github.com/lucaslokchan/ml-journal/raw/master/Learning%20Notes/Chapter%202:%20End-to-End%20Machine%20Learning%20Project/housing.csv'\n",
        "housing = pd.read_csv(csv_path)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ao00a6lTpFfe"
      },
      "source": [
        "## Take a Quick Look at the Data Structure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3DQo9L4DGuQ"
      },
      "source": [
        "There are 20,640 instances in the dataset\n",
        "\n",
        "Notice that the total_bed_rooms attribute has only 20,433 non-null values, meaning that 207 districts are missing this feature\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdZq_ZsnChbo",
        "outputId": "d1cd38b8-ca2b-4437-fbeb-10fe4e096657"
      },
      "source": [
        "housing.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           20640 non-null  float64\n",
            " 1   latitude            20640 non-null  float64\n",
            " 2   housing_median_age  20640 non-null  float64\n",
            " 3   total_rooms         20640 non-null  float64\n",
            " 4   total_bedrooms      20433 non-null  float64\n",
            " 5   population          20640 non-null  float64\n",
            " 6   households          20640 non-null  float64\n",
            " 7   median_income       20640 non-null  float64\n",
            " 8   median_house_value  20640 non-null  float64\n",
            " 9   ocean_proximity     20640 non-null  object \n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJWTMcMAENcX"
      },
      "source": [
        "Notice that the total_bed_rooms attribute has only 20,433 non-null values, meaning that 207 districts are missing this feature\n",
        "\n",
        "The values in that column were repetitive, which means that it is\n",
        "probably a categorical attribute\n",
        "\n",
        "You can find out what categories exist and how many districts belong to each category by using the method value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHXxCRtWElVg",
        "outputId": "17564249-7b81-4f9a-a802-8546c65e93db"
      },
      "source": [
        "housing['ocean_proximity'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1H OCEAN     9136\n",
              "INLAND        6551\n",
              "NEAR OCEAN    2658\n",
              "NEAR BAY      2290\n",
              "ISLAND           5\n",
              "Name: ocean_proximity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0fW-m1ZFItO"
      },
      "source": [
        "Let’s look at the other fields. The method shows a summary of the describe() numerical attributes\n",
        "\n",
        "Note that the null values are\n",
        "ignored\n",
        "\n",
        "The 25%, 50%, and 75% rows show the corresponding percentiles: a percentile indicates the value below which a given percentage of observations in a group of observations falls\n",
        "\n",
        "For example, 25% of the districts have a lower than housing_median_age 18, while 50% are lower than 29 and 75% are lower than 37. These are often called the 25th percentile (or 1st quartile), the median, and the 75th percentile (or 3rd quartile)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "Ouic_rpaFNee",
        "outputId": "8b1300c3-c8c3-4f1c-e001-2778a02e3ad8"
      },
      "source": [
        "housing.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20433.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.569704</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>2635.763081</td>\n",
              "      <td>537.870553</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>499.539680</td>\n",
              "      <td>3.870671</td>\n",
              "      <td>206855.816909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003532</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2181.615252</td>\n",
              "      <td>421.385070</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>382.329753</td>\n",
              "      <td>1.899822</td>\n",
              "      <td>115395.615874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1447.750000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.563400</td>\n",
              "      <td>119600.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.534800</td>\n",
              "      <td>179700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>4.743250</td>\n",
              "      <td>264725.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39320.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  20640.000000  20640.000000  ...   20640.000000        20640.000000\n",
              "mean    -119.569704     35.631861  ...       3.870671       206855.816909\n",
              "std        2.003532      2.135952  ...       1.899822       115395.615874\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.800000     33.930000  ...       2.563400       119600.000000\n",
              "50%     -118.490000     34.260000  ...       3.534800       179700.000000\n",
              "75%     -118.010000     37.710000  ...       4.743250       264725.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 6
        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYI75uYXGXd-"
      },
      "source": [
        "Another quick way to get a feel of the type of data you are dealing with is to plot a histogram for each numerical attribute\n",
        "\n",
        "A histogram shows the number of instances (on the vertical axis) that have a given value range (on the horizontal axis).\n",
        "\n",
        "you can call the method on the hist() whole dataset, and it will plot a histogram for each numerical attribute\n",
        "\n",
        "For example, you can see that slightly over 800 districts have a equal to about $500,000 median_house_value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "2nH7PRrHGtvV",
        "outputId": "ca525379-3578-4b7a-e344-2a90ddd0ea4e"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "housing.hist(bins=50, figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjtkOM2IWfhk"
      },
      "source": [
        "Notice a few things in these histograms:\n",
        "\n",
        "1. First, the median income attribute does not look like it is expressed in US dollars\n",
        "(USD). After checking with the team that collected the data, you are told that the\n",
        "data has been scaled and capped at 15 (actually 15.0001) for higher median\n",
        "incomes, and at 0.5 (actually 0.4999) for lower median incomes. Working with\n",
        "preprocessed attributes is common in Machine Learning, and it is not necessarily\n",
        "a problem, but you should try to understand how the data was computed\n",
        "\n",
        "2. The housing median age and the median house value were also capped. The lat\n",
        "ter\n",
        "may be a serious problem since it is your target attribute (your labels). Your\n",
        "Machine Learning algorithms may learn that prices never go beyond that limit.\n",
        "You need to check with your client team (the team that will use your system’s out\n",
        "put)\n",
        "to see if this is a problem or not. If they tell you that they need precise pre\n",
        "dictions\n",
        "even beyond 500,000, then you have mainly two options:\n",
        "\n",
        "  a. Collect proper labels for the districts whose labels were capped.\n",
        "\n",
        "  b. Remove those districts from the training set (and also from the test set, since\n",
        "your system should not be evaluated poorly if it predicts values beyond\n",
        "500,000).\n",
        "\n",
        "3. These attributes have very different scales. We will discuss this later in this chap\n",
        "ter\n",
        "when we explore feature scaling\n",
        "\n",
        "4. Finally, many histograms are tail heavy: they extend much farther to the right of\n",
        "the median than to the left. This may make it a bit harder for some Machine\n",
        "Learning algorithms to detect patterns. We will try transforming these attributes\n",
        "later on to have more bell-shaped distributions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCDsTUJWpOZ-"
      },
      "source": [
        "## Create a Test Set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChZ9riwLInkE"
      },
      "source": [
        "Creating a test set is theoretically quite simple: just pick some instances randomly, typically 20% of the dataset, and set them aside\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKc7KjjQImvF",
        "outputId": "06d2f90e-a4c0-4227-d67d-9e82af248f8c"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def split_train_test(data, test_ratio):\n",
        "  shuffled_indices = np.random.permutation(len(data))\n",
        "  test_set_size = int(len(data) * test_ratio)\n",
        "  test_indices = shuffled_indices[:test_set_size]\n",
        "  train_indices = shuffled_indices[test_set_size:]\n",
        "  return data.iloc[train_indices], data.iloc[test_indices]\n",
        "\n",
        "train_set, test_set = split_train_test(housing, 0.2)\n",
        "print(str(len(train_set)) + ' train + ' + str(len(test_set)) + ' test')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16512 train + 4128 test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpJsVdXrQcB4"
      },
      "source": [
        "If you run the program again, it will generate a different test set! Over time, you (or your Machine Learning algorithms) will get to see the whole dataset, which is what you want to avoid\n",
        "\n",
        "You could compute a hash of each instance’s identifier, keep only the last byte of the hash, and put the instance in the test set if this value is lower or equal to 51 (~20% of 256). \n",
        "\n",
        "This ensures that the test set will remain consistent across multiple runs, even if you refresh the dataset. The new test set will contain 20% of the new instances, but it will not contain any instance that was previously in the training set. Here is a possible implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAVwORzkQgbv"
      },
      "source": [
        "import hashlib\n",
        "\n",
        "def test_set_check(identifier, test_ratio, hash):\n",
        "  return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
        "\n",
        "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
        "  ids = data[id_column]\n",
        "  in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
        "  return data.loc[~in_test_set], data.loc[in_test_set]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQQln-HwsIOh"
      },
      "source": [
        "Unfortunately, the housing dataset does not have an identifier column. The simplest solution is to use the row index as the ID:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxqE2vxfpgNU"
      },
      "source": [
        "housing_with_id = housing.reset_index()\n",
        "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwExxU_HfC6L"
      },
      "source": [
        "If you use the row index as a unique identifier, you need to make sure that new data gets appended to the end of the dataset, and no row ever gets deleted. If this is not possible, then you can try to use the most stable features to build a unique identifier. For example, a district’s latitude and longitude are guaranteed to be stable for a few million years, so you could combine them into an ID like so:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42H3x4lQfDaC"
      },
      "source": [
        "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']\n",
        "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGFwPmkIfmiq"
      },
      "source": [
        "Scikit-Learn provides a few functions to split datasets into multiple subsets in various ways. The simplest function is train_test_split, which does pretty much the same thing as the function split_train_test defined earlier, with a couple of additional features. First there is a random_state parameter that allows you to set the random generator seed as explained previously, and second you can pass it multiple datasets with an identical number of rows, and it will split them on the same indices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRFGo4GvfmH7"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3XzdxkcnNYq"
      },
      "source": [
        "So far we have considered purely random sampling methods. This is generally fine if your dataset is large enough (especially relative to the number of attributes), but if it is not, you run the risk of introducing a significant sampling bias. When a survey company decides to call 1,000 people to ask them a few questions, they don’t just pick 1,000 people randomly in a phone booth. They try to ensure that these 1,000 people are representative of the whole population. For example, the US population is composed of 51.3% female and 48.7% male, so a well-conducted survey in the US would try to maintain this ratio in the sample: 513 female and 487 male. This is called stratified sampling: the population is divided into homogeneous subgroups called strata, and the right number of instances is sampled from each stratum to guarantee that the test set is representative of the overall population. If they used purely random sampling, there would be about 12% chance of sampling a skewed test set with either less than 49% female or more than 54% female. Either way, the survey results would be significantly biased.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpCqD2fupdbB"
      },
      "source": [
        "The following code creates an income category attribute by dividing the median income by 1.5 (to limit the number of income cate gories), and rounding up using (to have discrete categories), and then merging ceil all the categories greater than 5 into category 5:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKQd0L5joNtQ"
      },
      "source": [
        "housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)\n",
        "housing['income_cat'].where(housing['income_cat'] < 5, 5.0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLpEAL8wU3QN"
      },
      "source": [
        "You may want to ensure that the test set is representative of the various categories of incomes in the whole dataset. Since the median income is a continuous numerical attribute, you first need to create an income category attribute. Let’s look at the median income histogram more closely.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "79o6aFXqU3nd",
        "outputId": "5fc6884a-e5f3-4c3b-be35-e5053f934c7c"
      },
      "source": [
        "housing['income_cat'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb6039b2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYA0lEQVR4nO3df2zcd33H8eeLpqVVzOJ0ZV6UZEslIlAhoyRWGsSEzkSkaTs1ldZVRR11qkzZj2xjWqY1Reqy9YcWtBZGC5RZJCKFgIkCXbJQ6Ky03tQ/WtpAV/cHXUxJRawsHnVqZpoxhb33x308DmP7fnx950s/r4dk+b6f7+d7n/f3c+fX3X3v6ztFBGZmloc3zXcBZmbWOg59M7OMOPTNzDLi0Dczy4hD38wsIwvmu4DZXHLJJbFixYqGt//xj3/MwoUL566gOeK66uO66uO66vNGrOvo0aM/jIi3TrsyItr2Z82aNVHEY489Vmj7ZnFd9XFd9XFd9Xkj1gU8HTPkqg/vmJllxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llpK0/hsGsnQ2NjLN5x9dbPu7xXde0fEx74/AzfTOzjDj0zcwyUjX0Jb1d0jMVPz+S9GeSLpY0IOlY+r049Zek+yQNS3pW0uqK6+pN/Y9J6m3mjpmZ2S+qGvoR8VJEXB4RlwNrgNeBh4AdwJGIWAkcScsAVwEr089W4AEASRcDO4ErgLXAzskHCjMza416D++sB74XEa8Am4C9qX0vcF26vAl4MH3C5xNAp6QlwJXAQESMRcRpYADYWHgPzMysZip/9HKNnaU9wLcj4lOSXouIztQu4HREdEo6DOyKiMfTuiPArUAJuDAi7krttwNnIuKeKWNspfwKga6urjX9/f0N79zExAQdHR0Nb98srqs+7VrX6Ng4p860ftxVSxfNur5d58t11adIXT09PUcjonu6dTWfsinpAuBa4Lap6yIiJNX+6DGLiOgD+gC6u7ujVCo1fF2Dg4MU2b5ZXFd92rWu+/cd5N6h1p/1fPym0qzr23W+XFd9mlVXPYd3rqL8LP9UWj6VDtuQfo+m9hFgecV2y1LbTO1mZtYi9YT+h4AvVywfAibPwOkFDla035zO4lkHjEfESeARYIOkxekN3A2pzczMWqSm16aSFgIfBH6/onkXsF/SFuAV4IbU/jBwNTBM+UyfWwAiYkzSncBTqd8dETFWeA/MzKxmNYV+RPwY+OUpba9SPptnat8Ats1wPXuAPfWXaWZmc8H/kWtmlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRvzNWW8gKwp+i9P2VWcb/iYof5uT2bnBz/TNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDJSU+hL6pR0QNJ3Jb0o6b2SLpY0IOlY+r049ZWk+yQNS3pW0uqK6+lN/Y9J6m3WTpmZ2fRqfab/SeCbEfEO4N3Ai8AO4EhErASOpGWAq4CV6Wcr8ACApIuBncAVwFpg5+QDhZmZtUbV0Je0CHg/sBsgIv4nIl4DNgF7U7e9wHXp8ibgwSh7AuiUtAS4EhiIiLGIOA0MABvndG/MzGxWiojZO0iXA33AC5Sf5R8FPgKMRERn6iPgdER0SjoM7IqIx9O6I8CtQAm4MCLuSu23A2ci4p4p422l/AqBrq6uNf39/Q3v3MTEBB0dHQ1v3yzNqmtoZLzQ9l0XwakzjW27aumiQmPPpl1vx9Gx8Ybnq4hqc92u8+W66lOkrp6enqMR0T3dulq+OWsBsBr4k4h4UtIn+dmhHAAiIiTN/uhRo4joo/wgQ3d3d5RKpYava3BwkCLbN0uz6mr0W68mbV91lnuHGvsyteM3lQqNPZt2vR3v33ew4fkqotpct+t8ua76NKuuWo7pnwBORMSTafkA5QeBU+mwDen3aFo/Aiyv2H5Zapup3czMWqRq6EfEfwA/kPT21LSe8qGeQ8DkGTi9wMF0+RBwczqLZx0wHhEngUeADZIWpzdwN6Q2MzNrkVpfm/4JsE/SBcDLwC2UHzD2S9oCvALckPo+DFwNDAOvp75ExJikO4GnUr87ImJsTvbCzMxqUlPoR8QzwHRvCqyfpm8A22a4nj3AnnoKNDOzueP/yDUzy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDJSU+hLOi5pSNIzkp5ObRdLGpB0LP1enNol6T5Jw5KelbS64np6U/9jknqbs0tmZjaTep7p90TE5RHRnZZ3AEciYiVwJC0DXAWsTD9bgQeg/CAB7ASuANYCOycfKMzMrDWKHN7ZBOxNl/cC11W0PxhlTwCdkpYAVwIDETEWEaeBAWBjgfHNzKxOiojqnaTvA6eBAP4hIvokvRYRnWm9gNMR0SnpMLArIh5P644AtwIl4MKIuCu13w6ciYh7poy1lfIrBLq6utb09/c3vHMTExN0dHQ0vH2zNKuuoZHxQtt3XQSnzjS27aqliwqNPZt2vR1Hx8Ybnq8iqs11u86X66pPkbp6enqOVhyV+TkLaryO34yIEUm/AgxI+m7lyogISdUfPWoQEX1AH0B3d3eUSqWGr2twcJAi2zdLs+ravOPrhbbfvuos9w7Vepf4ecdvKhUaezbtejvev+9gw/NVRLW5btf5cl31aVZdNR3eiYiR9HsUeIjyMflT6bAN6fdo6j4CLK/YfFlqm6ndzMxapGroS1oo6S2Tl4ENwHPAIWDyDJxe4GC6fAi4OZ3Fsw4Yj4iTwCPABkmL0xu4G1KbmZm1SC2vTbuAh8qH7VkAfCkivinpKWC/pC3AK8ANqf/DwNXAMPA6cAtARIxJuhN4KvW7IyLG5mxPzMysqqqhHxEvA++epv1VYP007QFsm+G69gB76i/TzMzmgv8j18wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDJSc+hLOk/SdyQdTsuXSnpS0rCkr0i6ILW/OS0Pp/UrKq7jttT+kqQr53pnzMxsdvU80/8I8GLF8seAT0TE24DTwJbUvgU4ndo/kfoh6TLgRuCdwEbgM5LOK1a+mZnVo6bQl7QMuAb4XFoW8AHgQOqyF7guXd6Ulknr16f+m4D+iPhJRHwfGAbWzsVOmJlZbRQR1TtJB4C/Bd4C/AWwGXgiPZtH0nLgGxHxLknPARsj4kRa9z3gCuCv0zZfTO270zYHpoy1FdgK0NXVtaa/v7/hnZuYmKCjo6Ph7ZulWXUNjYwX2r7rIjh1prFtVy1dVGjs2bTr7Tg6Nt7wfBVRba7bdb5cV32K1NXT03M0IrqnW7eg2saSfgsYjYijkkoNVVCHiOgD+gC6u7ujVGp8yMHBQYps3yzNqmvzjq8X2n77qrPcO1T1LjGt4zeVCo09m3a9He/fd7Dh+Sqi2ly363y5rvo0q65a7rHvA66VdDVwIfBLwCeBTkkLIuIssAwYSf1HgOXACUkLgEXAqxXtkyq3MTOzFqh6TD8ibouIZRGxgvIbsY9GxE3AY8D1qVsvcDBdPpSWSesfjfIxpEPAjensnkuBlcC35mxPzMysqiKvTW8F+iXdBXwH2J3adwNfkDQMjFF+oCAinpe0H3gBOAtsi4ifFhjfzMzqVFfoR8QgMJguv8w0Z99ExH8DvzPD9ncDd9dbpJmZzQ3/R66ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUZa/++EZnbOWlHgv763rzrb8H+NH991TcPj2s/zM30zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMlI19CVdKOlbkv5N0vOS/ia1XyrpSUnDkr4i6YLU/ua0PJzWr6i4rttS+0uSrmzWTpmZ2fRqeab/E+ADEfFu4HJgo6R1wMeAT0TE24DTwJbUfwtwOrV/IvVD0mXAjcA7gY3AZySdN5c7Y2Zms6sa+lE2kRbPTz8BfAA4kNr3Ately5vSMmn9eklK7f0R8ZOI+D4wDKydk70wM7OaKCKqdyo/Iz8KvA34NPB3wBPp2TySlgPfiIh3SXoO2BgRJ9K67wFXAH+dtvliat+dtjkwZaytwFaArq6uNf39/Q3v3MTEBB0dHQ1v3yzNqmtoZLzQ9l0XwakzjW27aumiQmPPpl1vx9Gx8Ybnq4hqc93M+SpyH/P9qz5F6urp6TkaEd3Travp6xIj4qfA5ZI6gYeAdzRUSW1j9QF9AN3d3VEqlRq+rsHBQYps3yzNqqvRr6KbtH3VWe4dauwbNI/fVCo09mza9Xa8f9/BhueriGpz3cz5KnIf8/2rPs2qq66zdyLiNeAx4L1Ap6TJW3AZMJIujwDLAdL6RcCrle3TbGNmZi1Qy9k7b03P8JF0EfBB4EXK4X996tYLHEyXD6Vl0vpHo3wM6RBwYzq751JgJfCtudoRMzOrrpbXWkuAvem4/puA/RFxWNILQL+ku4DvALtT/93AFyQNA2OUz9ghIp6XtB94ATgLbEuHjczMrEWqhn5EPAu8Z5r2l5nm7JuI+G/gd2a4rruBu+sv08zM5oL/I9fMLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8tI678BwszsHLGi4BcTFfH5jQubcr1+pm9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRqqGvqTlkh6T9IKk5yV9JLVfLGlA0rH0e3Fql6T7JA1LelbS6orr6k39j0nqbd5umZnZdGp5pn8W2B4RlwHrgG2SLgN2AEciYiVwJC0DXAWsTD9bgQeg/CAB7ASuoPyF6jsnHyjMzKw1qoZ+RJyMiG+ny/8FvAgsBTYBe1O3vcB16fIm4MEoewLolLQEuBIYiIixiDgNDAAb53RvzMxsVnUd05e0AngP8CTQFREn06r/ALrS5aXADyo2O5HaZmo3M7MWUUTU1lHqAP4FuDsivibptYjorFh/OiIWSzoM7IqIx1P7EeBWoARcGBF3pfbbgTMRcc+UcbZSPixEV1fXmv7+/oZ3bmJigo6Ojoa3b5Zm1TU0Ml5o+66L4NSZxrZdtXRRobFn06634+jYeMPzVUS1uW7mfBW5j52L96+if1NFXLrovIZvx56enqMR0T3dupo+e0fS+cBXgX0R8bXUfErSkog4mQ7fjKb2EWB5xebLUtsI5eCvbB+cOlZE9AF9AN3d3VEqlaZ2qdng4CBFtm+WZtW1ueDnhGxfdZZ7hxr7OKbjN5UKjT2bdr0d7993sOH5KqLaXDdzvorcx87F+1fRv6kiPr9xYVNux1rO3hGwG3gxIj5eseoQMHkGTi9wsKL95nQWzzpgPB0GegTYIGlxegN3Q2ozM7MWqeVh933Ah4EhSc+kto8Cu4D9krYArwA3pHUPA1cDw8DrwC0AETEm6U7gqdTvjogYm5O9MDOzmlQN/XRsXjOsXj9N/wC2zXBde4A99RRoZmZzx/+Ra2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZaRq6EvaI2lU0nMVbRdLGpB0LP1enNol6T5Jw5KelbS6Ypve1P+YpN7m7I6Zmc2mlmf6nwc2TmnbARyJiJXAkbQMcBWwMv1sBR6A8oMEsBO4AlgL7Jx8oDAzs9apGvoR8a/A2JTmTcDedHkvcF1F+4NR9gTQKWkJcCUwEBFjEXEaGOAXH0jMzKzJFBHVO0krgMMR8a60/FpEdKbLAk5HRKekw8CuiHg8rTsC3AqUgAsj4q7UfjtwJiLumWasrZRfJdDV1bWmv7+/4Z2bmJigo6Oj4e2bpVl1DY2MF9q+6yI4daaxbVctXVRo7Nm06+04Ojbe8HwVUW2umzlfRe5j5+L9q+jfVBGXLjqv4duxp6fnaER0T7duQaGqgIgISdUfOWq/vj6gD6C7uztKpVLD1zU4OEiR7ZulWXVt3vH1QttvX3WWe4cau0scv6lUaOzZtOvteP++gw3PVxHV5rqZ81XkPnYu3r+K/k0V8fmNC5tyOzZ69s6pdNiG9Hs0tY8Ayyv6LUttM7WbmVkLNRr6h4DJM3B6gYMV7Tens3jWAeMRcRJ4BNggaXF6A3dDajMzsxaq+lpL0pcpH5O/RNIJymfh7AL2S9oCvALckLo/DFwNDAOvA7cARMSYpDuBp1K/OyJi6pvDZmbWZFVDPyI+NMOq9dP0DWDbDNezB9hTV3UFDY2Mz8sxueO7rmn5mGZmtfB/5JqZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlpeehL2ijpJUnDkna0enwzs5y1NPQlnQd8GrgKuAz4kKTLWlmDmVnOWv1Mfy0wHBEvR8T/AP3AphbXYGaWLUVE6waTrgc2RsTvpeUPA1dExB9X9NkKbE2LbwdeKjDkJcAPC2zfLK6rPq6rPq6rPm/Eun49It463YoFjdfTHBHRB/TNxXVJejoiuufiuuaS66qP66qP66pPbnW1+vDOCLC8YnlZajMzsxZodeg/BayUdKmkC4AbgUMtrsHMLFstPbwTEWcl/THwCHAesCcinm/ikHNymKgJXFd9XFd9XFd9sqqrpW/kmpnZ/PJ/5JqZZcShb2aWkXM+9CXtkTQq6bkZ1kvSfeljH56VtLpN6ipJGpf0TPr5qxbUtFzSY5JekPS8pI9M06fl81VjXS2frzTuhZK+JenfUm1/M02fN0v6SpqzJyWtaJO6Nkv6z4o5+71m15XGPU/SdyQdnmZdy+eqxrrmZa7S2MclDaVxn55m/dz+TUbEOf0DvB9YDTw3w/qrgW8AAtYBT7ZJXSXgcIvnagmwOl1+C/DvwGXzPV811tXy+UrjCuhIl88HngTWTenzR8Bn0+Ubga+0SV2bgU/Nw5z9OfCl6W6v+ZirGuual7lKYx8HLpll/Zz+TZ7zz/Qj4l+BsVm6bAIejLIngE5JS9qgrpaLiJMR8e10+b+AF4GlU7q1fL5qrGtepHmYSIvnp5+pZz9sAvamyweA9ZLUBnW1nKRlwDXA52bo0vK5qrGudjanf5PnfOjXYCnwg4rlE7RJoADvTS/PvyHpna0cOL2sfg/lZ4iV5nW+ZqkL5mm+0mGBZ4BRYCAiZpyziDgLjAO/3AZ1Afx2OiRwQNLyadbPtb8H/hL43xnWz8tc1VAXtH6uJgXwz5KOqvwxNFPN6d9kDqHfrr5N+fMx3g3cD/xjqwaW1AF8FfiziPhRq8atpkpd8zZfEfHTiLic8n+Qr5X0rlaNPZsa6vonYEVE/AYwwM+eYTeFpN8CRiPiaDPHqVeNdbV0rqb4zYhYTfnTh7dJen8zB8sh9Nvyox8i4keTL88j4mHgfEmXNHtcSedTDtZ9EfG1abrMy3xVq2u+5mtKDa8BjwEbp6z6/zmTtABYBLw633VFxKsR8ZO0+DlgTZNLeR9wraTjlD9B9wOSvjilz3zMVdW65mGuKsceSb9HgYcofxpxpTn9m8wh9A8BN6d3wNcB4xFxcr6LkvSrk8cyJa2lfFs09c6fxtsNvBgRH5+hW8vnq5a65mO+0lhvldSZLl8EfBD47pRuh4DedPl64NFI78DNZ11TjvteS/m9kqaJiNsiYllErKD8Ju2jEfG7U7q1fK5qqavVc1Ux7kJJb5m8DGwApp7xN6d/k233KZv1kvRlymd2XCLpBLCT8ptaRMRngYcpv/s9DLwO3NImdV0P/KGks8AZ4MZm3/kpP+P5MDCUjgUDfBT4tYq65mO+aqlrPuYLymcW7VX5C4DeBOyPiMOS7gCejohDlB+wviBpmPKb9ze2SV1/Kula4Gyqa3ML6voFbTBXtdQ1X3PVBTyUns8sAL4UEd+U9AfQnL9JfwyDmVlGcji8Y2ZmiUPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4z8H2bWVmc2N0DJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGPsQ7wLU991"
      },
      "source": [
        "Most median income values are clustered around 2–5 (tens of thousands of dollars), but some median incomes go far beyond 6. It is important to have a sufficient number of instances in your dataset for each stratum, or else the estimate of the stratum’s importance may be biased. This means that you should not have too many strata, and each stratum should be large enough."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kaLnJFxqVcR"
      },
      "source": [
        "Now you are ready to do stratified sampling based on the income category. For this you can use Scikit-Learn’s StratifiedShuffleSplit class:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_l91hvk0qVOC"
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
        "  strat_train_set = housing.loc[train_index]\n",
        "  strat_test_set = housing.loc[test_index]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-g-wmuTWq4aA"
      },
      "source": [
        "Let’s see if this worked as expected. You can start by looking at the income category proportions in the full housing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-chgtrt9q7QJ",
        "outputId": "a1048b19-67a9-4401-aaeb-31071ea25fd9"
      },
      "source": [
        "housing['income_cat'].value_counts() / len(housing)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0    0.350581\n",
              "2.0    0.318847\n",
              "4.0    0.176308\n",
              "5.0    0.114438\n",
              "1.0    0.039826\n",
              "Name: income_cat, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGuJ9NF4mILH"
      },
      "source": [
        "Now you should remove the attribute so the data is back to its original income_cat state"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GB_K5GhCmCmL"
      },
      "source": [
        "for set in (strat_train_set, strat_test_set):\n",
        "  set.drop([\"income_cat\"], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01I93s6LmnxV"
      },
      "source": [
        "First, make sure you have put the test set aside and you are only exploring the train ing set. Also, if the training set is very large, you may want to sample an exploration set, to make manipulations easy and fast. In our case, the set is quite small so you can just work directly on the full set. Let’s create a copy so you can play with it without harming the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1SnJfU7rRlZ"
      },
      "source": [
        "housing = strat_train_set.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5xQxdXJpURp"
      },
      "source": [
        "# Discover and Visualize the Data to Gain Insights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8upXRAvEqFjn"
      },
      "source": [
        "## Visualizing Geographical Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6vq2TummiW8"
      },
      "source": [
        "Since there is geographical information (latitude and longitude), it is a good idea to create a scatterplot of all districts to visualize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yhi9E3qEkkAO",
        "outputId": "691aa12e-efbc-47b9-c87d-f322c110d485"
      },
      "source": [
        "housing.plot(kind='scatter', x='longitude', y='latitude')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb4f8a22d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ByE44CxnFZJ"
      },
      "source": [
        "This looks like California all right, but other than that it is hard to see any particular pattern. Setting the option to makes it much easier to visualize the places alpha 0.1 where there is a high density of data points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "7U6AaP8vnGAN",
        "outputId": "4d46871a-fb98-4dd6-d610-61ebd3eb25e5"
      },
      "source": [
        "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb4f801910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiLQgZKGnlZI"
      },
      "source": [
        "Now that’s much better: you can clearly see the high-density areas, namely the Bay Area and around Los Angeles and San Diego, plus a long line of fairly high density in the Central Valley, in particular around Sacramento and Fresno"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_4ouKvbnp5W"
      },
      "source": [
        "The radius of each circle represents the district’s population (option s), and the color represents the price (option c). We will use a predefined color map (option cmap) called jet, which ranges from blue (low values) to red (high prices)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "Q4beY6WEnvQs",
        "outputId": "b89ebe7b-89bc-45f9-d81c-a6b5805b59a8"
      },
      "source": [
        "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4,\n",
        "             s=housing['population']/100, label='population',\n",
        "             c='median_house_value', cmap=plt.get_cmap('jet'), colorbar=True\n",
        "             )\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbb4f784850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1PU0wytol0y"
      },
      "source": [
        "This image tells you that the housing prices are very much related to the location (e.g., close to the ocean) and to the population density"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OOVbzl3owFv"
      },
      "source": [
        "It will probably be useful to use a clustering algorithm to detect the main clusters, and add new features that measure the proximity to the cluster centers. The ocean prox imity attribute may be useful as well, although in Northern California the housing prices in coastal districts are not too high, so it is not a simple rule"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2HWwKQfqKHe"
      },
      "source": [
        "## Looking for Correlations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FajfRhRqqL8P"
      },
      "source": [
        "Since the dataset is not too large, you can easily compute the standard correlation coefficient (also called Pearson’s r) between every pair of attributes using the corr() method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-qEUR_Rqe10"
      },
      "source": [
        "corr_matrix = housing.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQnt3FY4qjIb"
      },
      "source": [
        "Now let’s look at how much each attribute correlates with the median house value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfRzAEoqqixw",
        "outputId": "31ce5995-b1ad-4233-ed4b-38ddf7c9b442"
      },
      "source": [
        "corr_matrix['median_house_value'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "median_house_value    1.000000\n",
              "median_income         0.687160\n",
              "total_rooms           0.135097\n",
              "housing_median_age    0.114110\n",
              "households            0.064506\n",
              "total_bedrooms        0.047689\n",
              "population           -0.026920\n",
              "longitude            -0.047432\n",
              "latitude             -0.142724\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7QJJ7cwq0av"
      },
      "source": [
        "The correlation coefficient ranges from –1 to 1. When it is close to 1, it means that there is a strong positive correlation; for example, the median house value tends to go up when the median income goes up. When the coefficient is close to –1, it means that there is a strong negative correlation; you can see a small negative correlation between the latitude and the median house value (i.e., prices have a slight tendency to go down when you go north). Finally, coefficients close to zero mean that there is no\n",
        "linear correlation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltsWttzYrDj1"
      },
      "source": [
        "Another way to check for correlation between attributes is to use Pandas’ function, which plots every numerical attribute against every other scatter_matrix numerical attribute"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yZZ1oBArNH8"
      },
      "source": [
        "Since there are now 11 numerical attributes, you would get 11^2 = 121 plots, which would not fit on a page, so let’s just focus on a few promising attributes that seem most correlated with the median housing value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 827
        },
        "id": "uxGAn5E5rDSq",
        "outputId": "48cd5826-cdeb-4947-eeca-23c1178e9e5c"
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "attributes = ['median_house_value', 'median_income', 'total_rooms',\n",
        "              'housing_median_age']\n",
        "scatter_matrix(housing[attributes], figsize=(12,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f6f4410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f609190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f5bc810>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f6b3e90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f676550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f6aabd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb6057f110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f7e3c90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f7e3a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4f6866d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dcb6350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dced9d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dc98b90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dc64710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dc1ad90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb4dbdd450>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzDJoRBxto9L"
      },
      "source": [
        "The main diagonal (top left to bottom right) would be full of straight lines if Pandas plotted each variable against itself, which would not be very useful. So instead Pandas displays a histogram of each attribute"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVuqTNKJtsaO"
      },
      "source": [
        "The most promising attribute to predict the median house value is the median income, so let’s zoom in on their correlation scatterplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ukDzC9XNtt9F",
        "outputId": "e25acdac-eb47-4709-8fa2-5d996f731057"
      },
      "source": [
        "housing.plot(kind='scatter', x='median_income', y='median_house_value',\n",
        "             alpha=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb4db1fe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6C5mQRFua0C"
      },
      "source": [
        "This plot reveals a few things. First, the correlation is indeed very strong; you can clearly see the upward trend and the points are not too dispersed. Second, the price cap that we noticed earlier is clearly visible as a horizontal line at 500,000. But this plot reveals other less obvious straight lines: a horizontal line around 450,000, another around 350,000, perhaps one around 280,000, and a few more below that. You may want to try removing the corresponding districts to prevent your algorithms from learning to reproduce these data quirks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGS_-6R-u5Oo"
      },
      "source": [
        "## Experimenting with Attribute Combinations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0hSl6WUu6tP"
      },
      "source": [
        "You identified a few data quirks that you may want to clean up before feeding the data to a Machine Learning algorithm, and you found interesting correlations between attributes, in particular with the target attribute. You also noticed that some attributes have a tail-heavy distribution, so you may want to trans form them"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeRoTe6kvQon"
      },
      "source": [
        "One last thing you may want to do before actually preparing the data for Machine Learning algorithms is to try out various attribute combinations. For example, the total number of rooms in a district is not very useful if you don’t know how many households there are. What you really want is the number of rooms per household. Similarly, the total number of bedrooms by itself is not very useful: you probably want to compare it to the number of rooms. And the population per household also seems like an interesting attribute combination to look at. Let’s create these new attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeqM411evYf3"
      },
      "source": [
        "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
        "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
        "housing[\"population_per_household\"] = housing[\"population\"]/housing[\"households\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R--9f-SEv4E5"
      },
      "source": [
        "And now let’s look at the correlation matrix again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iz7mzHPXv5kA",
        "outputId": "270858e9-ddf3-49e4-f181-189e546c91e8"
      },
      "source": [
        "corr_matrix = housing.corr()\n",
        "corr_matrix['median_house_value'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "median_house_value          1.000000\n",
              "median_income               0.687160\n",
              "rooms_per_household         0.146285\n",
              "total_rooms                 0.135097\n",
              "housing_median_age          0.114110\n",
              "households                  0.064506\n",
              "total_bedrooms              0.047689\n",
              "population_per_household   -0.021985\n",
              "population                 -0.026920\n",
              "longitude                  -0.047432\n",
              "latitude                   -0.142724\n",
              "bedrooms_per_room          -0.259984\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcbhlIuZwnrM"
      },
      "source": [
        "The new attribute is much more correlated with bedrooms_per_room the median house value than the total number of rooms or bedrooms. Apparently houses with a lower bedroom/room ratio tend to be more expensive. The number of rooms per household is also more informative than the total number of rooms in a district—obviously the larger the houses, the more expensive they are"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a7qwJ9ewscW"
      },
      "source": [
        "# Prepare the Data for Machine Learning Algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znL85Kw7xBHP"
      },
      "source": [
        "It’s time to prepare the data for your Machine Learning algorithms. Instead of just doing this manually, you should write functions to do that, for several good reasons\n",
        "\n",
        "‧ This will allow you to reproduce these transformations easily on any dataset (e.g.,\n",
        "the next time you get a fresh dataset).\n",
        "\n",
        "‧ You will gradually build a library of transformation functions that you can reuse\n",
        "in future projects.\n",
        "\n",
        "‧ You can use these functions in your live system to transform the new data before\n",
        "feeding it to your algorithms.\n",
        "\n",
        "‧ This will make it possible for you to easily try various transformations and see\n",
        "which combination of transformations works best."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcT_-clKz30A"
      },
      "source": [
        "But first let’s revert to a clean training set (by copying once again), strat_train_set and let’s separate the predictors and the labels since we don’t necessarily want to apply the same transformations to the predictors and the target values (note that drop() creates a copy of the data and does not affect _set) strat_train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tPPAUbFzWiq"
      },
      "source": [
        "housing = strat_train_set.drop('median_house_value', axis=1)\n",
        "housing_labels = strat_train_set['median_house_value'].copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocvG4CcU0GPw"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAqiDhml0JwA"
      },
      "source": [
        "Most Machine Learning algorithms cannot work with missing features, so let’s create a few functions to take care of them. You noticed earlier that the total_bedrooms attribute has some missing values, so let’s fix this. You have three options\n",
        "\n",
        "• Get rid of the corresponding districts.\n",
        "\n",
        "• Get rid of the whole attribute.\n",
        "\n",
        "• Set the values to some value (zero, the mean, the median, etc.)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYWo5C700h3w"
      },
      "source": [
        "You can accomplish these easily using DataFrame’s dropna(), drop(), and fillna() methods\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "housing.dropna(subset=[\"total_bedrooms\"]) # option 1\n",
        "housing.drop(\"total_bedrooms\", axis=1) # option 2\n",
        "median = housing[\"total_bedrooms\"].median()\n",
        "housing[\"total_bedrooms\"].fillna(median) # option 3\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es4r31cc04Jn"
      },
      "source": [
        "Scikit-Learn provides a handy class to take care of missing values: Imputer. Here is how to use it. First, you need to create an instance, specifying that you want Imputer to replace each attribute’s missing values with the median of that attribute"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qf8JyGW_08kv"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(strategy='median')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRdgbRXs1GLX"
      },
      "source": [
        "Since the median can only be computed on numerical attributes, we need to create a copy of the data without the text attribute proximity ocean_proximity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVyBEa571Iav"
      },
      "source": [
        "housing_num = housing.drop('ocean_proximity', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gSxx1LS27-g"
      },
      "source": [
        "Now you can fit the instance to the training data using the method imputer fit()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jM_eNbTk2-M_",
        "outputId": "4d4ab063-27ba-4dea-d840-f06237c8b532"
      },
      "source": [
        "imputer.fit(housing_num)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
              "              missing_values=nan, strategy='median', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-2_iQSGZq77"
      },
      "source": [
        "The has simply computed the median of each attribute and stored the result imputer in its instance variable. Only the attribute had missing statistics_ total_bedrooms values, but we cannot be sure that there won’t be any missing values in new data after the system goes live, so it is safer to apply the to all the numerical attributes imputer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec7xNLrPZ3jU",
        "outputId": "96ae8cb6-8ef5-4872-93d4-f6410797c5c7"
      },
      "source": [
        "imputer.statistics_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
              "        408.    ,    3.5409])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEtlzq0TZ7Ib",
        "outputId": "00c7e79d-c9fa-4c08-beb0-365f2aad4f49"
      },
      "source": [
        "housing_num.median().values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
              "        408.    ,    3.5409])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beccQn7DaELU"
      },
      "source": [
        "Now you can use this “trained”\n",
        "to transform the training set by replacing\n",
        "imputer\n",
        "missing values by the learned medians"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iif11FFuaE1l"
      },
      "source": [
        "X = imputer.transform(housing_num)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ISGaYZaaRRj"
      },
      "source": [
        "The result is a plain Numpy array containing the transformed features. If you want to put it back into a Pandas DataFrame, it’s simple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIDGO4g6aSbc"
      },
      "source": [
        "housing_tr = pd.DataFrame(X, columns=housing_num.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ApE2M60ehx3"
      },
      "source": [
        "## Handling Text and Categorical Attributes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vtk6ryZnekA6"
      },
      "source": [
        "Earlier we left out the categorical attribute because it is a text ocean_proximity attribute so we cannot compute its median. Most Machine Learning algorithms prefer to work with numbers anyway, so let’s convert these text labels to numbers. \n",
        "\n",
        "Scikit-Learn provides a transformer for this task called LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_40uZfHesP8",
        "outputId": "1460616a-9550-4d0f-d1b6-8f5e5fcc9f9a"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "housing_cat = housing['ocean_proximity']\n",
        "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
        "housing_cat_encoded"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 4, ..., 1, 0, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "To8XilRGfOtb"
      },
      "source": [
        "This is better: now we can use this numerical data in any ML algorithm. You can look at the mapping that this encoder has learned using the attribute (“<1H classes_ OCEAN” is mapped to 0, “INLAND” is mapped to 1, etc.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzVghKoLfUD7",
        "outputId": "8ac164b8-681c-418b-d831-b89be2587a94"
      },
      "source": [
        "print(encoder.classes_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIpW33j-f-TL"
      },
      "source": [
        "One issue with this representation is that ML algorithms will assume that two nearby values are more similar than two distant values. Obviously this is not the case (for example, categories 0 and 4 are more similar than categories 0 and 1)\n",
        "\n",
        "To fix this issue, a common solution is to create one binary attribute per category: one attribute equal to 1 when the category is “<1H OCEAN” (and 0 otherwise), another attribute equal to 1 when the category is “INLAND” (and 0 otherwise), and so on. This is called one-hot encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzcmgXcCguvi"
      },
      "source": [
        "Scikit-Learn provides a encoder to convert integer categorical values OneHotEncoder into one-hot vectors. Let’s encode the categories as one-hot vectors. Note that expects a 2D array, but is a 1D array, so we fit_transform() housing_cat_encoded need to reshape it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTchIvfPg3A4",
        "outputId": "dd5a08ac-d357-4de6-874e-a7c552ed6945"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "encoder = OneHotEncoder()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
        "housing_cat_1hot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 16512 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u1g4xxuhRiF"
      },
      "source": [
        "Notice that the output is a SciPy sparse matrix, instead of a NumPy array. This is very useful when you have categorical attributes with thousands of categories. After one hot encoding we get a matrix with thousands of columns, and the matrix is full of zeros except for one 1 per row. Using up tons of memory mostly to store zeros would be very wasteful, so instead a sparse matrix only stores the location of the nonzero elements. You can use it mostly like a normal 2D array, but if you really want to con ,19 vert it to a (dense) NumPy array, just call the method toarray()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3M6NIAUphcxr",
        "outputId": "446eef60-af8f-4dc3-c17c-2c64abb8ebcd"
      },
      "source": [
        "housing_cat_1hot.toarray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       ...,\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAtDnPHmhoPz"
      },
      "source": [
        "We can apply both transformations (from text categories to integer categories, then from integer categories to one-hot vectors) in one shot using the LabelBinarizer class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQKvyJtrh1H6",
        "outputId": "45698381-717c-4e8e-fed0-226ddd5ce5bf"
      },
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "encoder = LabelBinarizer()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
        "housing_cat_1hot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 1],\n",
              "       ...,\n",
              "       [0, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gf7muQ6XiSC8"
      },
      "source": [
        "Note that this returns a dense NumPy array by default. You can get a sparse matrix instead by passing to the constructor sparse_output=True LabelBinarizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmo8-CV0iTHy"
      },
      "source": [
        "## Custom Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVMsUrG-iX8i"
      },
      "source": [
        "Although Scikit-Learn provides many useful transformers, you will need to write your own for tasks such as custom cleanup operations or combining specific attributes. You will want your transformer to work seamlessly with Scikit-Learn functionalities (such as pipelines), and since Scikit-Learn relies on duck typing (not inher itance), all you need is to create a class and implement three methods: fit() (returning self), transform(), and fit_transform()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpgx7sb37Qxo"
      },
      "source": [
        "You can get the last one for free by simply adding as a base class. Also, if you add TransformerMixin BaseEstima as a base class (and avoid and in your constructor) you will get tor *args **kargs two extra methods and _params()) that will be useful for auto (get_params() set matic hyperparameter tuning. For example, here is a small transformer class that adds the combined attributes we discussed earlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "La4GvSpZiVQK"
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
        "\n",
        "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
        "  def __init__(self, add_bedrooms_per_room = True):\n",
        "    self.add_bedrooms_per_room = add_bedrooms_per_room\n",
        "  def fit(self, X, y=None):\n",
        "    return self\n",
        "  def transform(self, X, y=None):\n",
        "    rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
        "    population_per_household = X[:, population_ix] / X[:, household_ix]\n",
        "    if self.add_bedrooms_per_room:\n",
        "      bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
        "      return np.c_[X, rooms_per_household, population_per_household,\n",
        "                   bedrooms_per_room]\n",
        "    else:\n",
        "      return np.c_[X, rooms_per_household, population_per_household]\n",
        "\n",
        "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
        "housing_extra_attribs = attr_adder.transform(housing.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBq6XGHnxMBB"
      },
      "source": [
        "In this example the transformer has one hyperparameter, add_bedrooms_per_room, set to True by default (it is often helpful to provide sensible defaults). This hyperparameter will allow you to easily find out whether adding this attribute helps the Machine Learning algorithms or not. More generally, you can add a hyperparameter to gate any data preparation step that you are not 100% sure about. The more you automate these data preparation steps, the more combinations you can automatically try out, making it much more likely that you will find a great combination (and saving you a lot of time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25Es1iMEx0Kh"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4As8eHaBkfP"
      },
      "source": [
        "One of the most important transformations you need to apply to your data is feature scaling. With few exceptions, Machine Learning algorithms don’t perform well when the input numerical attributes have very different scales. This is the case for the hous ing data: the total number of rooms ranges from about 6 to 39,320, while the median incomes only range from 0 to 15. Note that scaling the target values is generally not required"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDf1gWHKBlWP"
      },
      "source": [
        "There are two common ways to get all attributes to have the same scale: min-max scaling and standardization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMlflOgwBo0X"
      },
      "source": [
        "Min-max scaling (many people call this normalization) is quite simple: values are shifted and rescaled so that they end up ranging from 0 to 1. We do this by subtract ing the min value and dividing by the max minus the min. Scikit-Learn provides a transformer called for this. It has a hyperparameter MinMaxScaler feature_range that lets you change the range if you don’t want 0–1 for some reason"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANas_J-SB4cg"
      },
      "source": [
        "Standardization is quite different: first it subtracts the mean value (so standardized values always have a zero mean), and then it divides by the variance so that the result ing distribution has unit variance\n",
        "\n",
        "Unlike min-max scaling, standardization does not bound values to a specific range, which may be a problem for some algorithms (e.g., neural networks often expect an input value ranging from 0 to 1). However, standardization is much less affected by outliers. For example, suppose a district had a median income equal to 100 (by mistake). Min-max scaling would then crush all the other values from 0–15 down to 0–0.15, whereas standardization would not be much affec ted. Scikit-Learn provides a transformer called StandardScaler for standardization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWvhJycBR3Wd"
      },
      "source": [
        "As with all the transformations, it is important to fit the scalers to the training data only, not to the full dataset (including the test set). Only then can you use them to transform the training set and the test set (and new data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvwBINJmCj8J"
      },
      "source": [
        "## Transformation Pipelines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQ76ZMlmCmJB"
      },
      "source": [
        "As you can see, there are many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn provides the class to help with Pipeline such sequences of transformations. Here is a small pipeline for the numerical attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fwXHXo5BBKY"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', SimpleImputer(strategy='median')),\n",
        "        ('attribs_adder', CombinedAttributesAdder()),\n",
        "        ('std_scaler', StandardScaler()),\n",
        "])\n",
        "\n",
        "housing_num_tr = num_pipeline.fit_transform(housing_num)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eTaH_WYDwCJ"
      },
      "source": [
        "The Pipeline constructor takes a list of name/estimator pairs defining a sequence of steps. All but the last estimator must be transformers (i.e., they must have a fit_transform() method)\n",
        "\n",
        "When you call the pipeline’s fit() method, it calls fit_transform() sequentially on all transformers, passing the output of each call as the parameter to the next call, until it reaches the final estimator, for which it just calls the fit() method \n",
        "\n",
        "The pipeline exposes the same methods as the final estimator. In this example, the last estimator is a StandardScaler, which is a transformer, so the pipeline has a transform() method that applies all the transforms to the data in sequence (it also has a fit_transform method that we could have used instead of calling fit() and then transform())\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMe-8qDQxFAf"
      },
      "source": [
        "Each subpipeline starts with a selector transformer: it simply transforms the data by selecting the desired attributes (numerical or categorical), dropping the rest, and con verting the resulting DataFrame to a NumPy array. There is nothing in Scikit-Learn to handle Pandas DataFrames, so we need to write a simple custom transformer for 20 this task"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDh4uLI1xFf1"
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, attribute_names):\n",
        "        self.attribute_names = attribute_names\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return X[self.attribute_names].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0ZTb1cC2q6p"
      },
      "source": [
        "Earlier versions of the book applied different transformations to different columns using a solution based on a DataFrameSelector transformer and a FeatureUnion (see below). It is now preferable to use the ColumnTransformer class that was introduced in Scikit-Learn 0.20\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "try:\n",
        "    from sklearn.compose import ColumnTransformer\n",
        "except ImportError:\n",
        "    from future_encoders import ColumnTransformer # Scikit-Learn < 0.20\n",
        "```\n",
        "\n",
        "```\n",
        "num_attribs = list(housing_num)\n",
        "cat_attribs = [\"ocean_proximity\"]\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_attribs),\n",
        "        (\"cat\", OneHotEncoder(), cat_attribs),\n",
        "    ])\n",
        "housing_prepared = full_pipeline.fit_transform(housing)\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "au2aocQgpc_0"
      },
      "source": [
        "You now have a pipeline for numerical values, and you also need to apply the LabelBinarizer on the categorical values: how can you join these transformations into a single pipeline?\n",
        "\n",
        "Scikit-Learn provides a FeatureUnion class for this. You give it a list of  transformers (which can be entire transformer pipelines), and when its transform() method is called it runs each transformer’s transform() method in parallel, waits for their output, and then concatenates them and returns the result (and of course calling its fit() method calls all each transformer’s fit() method). A full pipeline handling both numerical and categorical attributes may look like this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-v233A6oqirv"
      },
      "source": [
        "from sklearn.pipeline import FeatureUnion\n",
        "\n",
        "num_attribs = list(housing_num)\n",
        "cat_attribs = ['ocean_proximity']\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('selector', DataFrameSelector(num_attribs)),\n",
        "        ('imputer', SimpleImputer(strategy='median')),\n",
        "        ('attribs_adder', CombinedAttributesAdder()),\n",
        "        ('std_scaler', StandardScaler())              \n",
        "])\n",
        "\n",
        "cat_pipeline = Pipeline([\n",
        "        ('selector', DataFrameSelector(cat_attribs)),\n",
        "        ('cat_encoder', OneHotEncoder(sparse=False)),                \n",
        "])\n",
        "\n",
        "full_pipeline = FeatureUnion(transformer_list=[\n",
        "        ('num_pipeline', num_pipeline),\n",
        "        ('cat_pipeline', cat_pipeline),\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HqVViU0xb0D"
      },
      "source": [
        "And you can run the whole pipeline simply"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ng99_FBoxcds",
        "outputId": "f2aa5595-df89-4a34-f90b-2fe448b6ffee"
      },
      "source": [
        "housing_prepared = full_pipeline.fit_transform(housing)\n",
        "housing_prepared\n",
        "housing_prepared.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16512, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueznJL3T3jEr"
      },
      "source": [
        "# Select and Train a Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLeh6F9F3nHG"
      },
      "source": [
        "At last! You framed the problem, you got the data and explored it, you sampled a training set and a test set, and you wrote transformation pipelines to clean up and prepare your data for Machine Learning algorithms automatically. You are now ready to select and train a Machine Learning model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rbLHzcr3r5I"
      },
      "source": [
        "## Training and Evaluating on the Training Set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVidDsmC3upD"
      },
      "source": [
        "Let’s first train a Linear Regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDupLStm30_d",
        "outputId": "d17948d1-be4b-49cd-c8ad-76e1579f648d"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNZDnFBk4DhW"
      },
      "source": [
        "Done! You now have a working Linear Regression model. Let’s try it out on a few\n",
        "instances from the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYtE6K2W4GYA",
        "outputId": "9f4bb1f4-35a4-4fd4-a3af-1fbe7911fff7"
      },
      "source": [
        "some_data = housing.iloc[:5]\n",
        "some_labels = housing_labels.iloc[:5]\n",
        "some_data_prepared = full_pipeline.transform(some_data)\n",
        "print('Predictions:\\t', lin_reg.predict(some_data_prepared))\n",
        "print('labels:\\t\\t', list(some_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions:\t [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
            " 189747.55849879]\n",
            "labels:\t\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwvuYMKS41Fc"
      },
      "source": [
        "Although the predictions are not exactly accurate (e.g., the second prediction is off by more than 50%!). Let’s measure this regression model’s RMSE on the whole training set using Scikit-Learn’s mean_squared_error function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ou7mitBe46bN",
        "outputId": "302c46d7-ad18-44db-b9b2-5ca508b0ca5a"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "housing_predictions = lin_reg.predict(housing_prepared)\n",
        "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "lin_rmse = np.sqrt(lin_mse)\n",
        "lin_rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68628.19819848923"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRSvDsw45ONv"
      },
      "source": [
        "this is better than nothing but clearly not a great score: most districts’ median_housing_values range between 120,000 and 265,000, so a typical prediction error of 68,628 is not very satisfying\n",
        "\n",
        "This is an example of a model underfitting the training data. When this happens it can mean that the features do not provide enough information to make good predictions, or that the model is not powerful enough\n",
        "\n",
        "As we saw in the previous chapter, the main ways to fix underfitting are to select a more powerful model, to feed the training algorithm with better features, or to reduce the constraints on the model. This model is not regularized, so this rules out the last option. You could try to add more features (e.g., the log of the popula tion), but first let’s try a more complex model to see how it does"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zb0-wWlj8gyW"
      },
      "source": [
        "Let’s train a DecisionTreeRegressor. This is a powerful model, capable of finding complex nonlinear relationships in the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulz79Ewm8oRL",
        "outputId": "882bac01-e314-4e0a-9192-03a8e21590ad"
      },
      "source": [
        " from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        " tree_reg = DecisionTreeRegressor()\n",
        " tree_reg.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9eMY6P488pk"
      },
      "source": [
        "Now that the model is trained, let’s evaluate it on the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uf8o7dt8_LK",
        "outputId": "03da8a86-39c0-4c1c-83c1-cc58a0b81505"
      },
      "source": [
        "housing_predictions = tree_reg.predict(housing_prepared)\n",
        "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "tree_rmse = np.sqrt(tree_mse)\n",
        "tree_rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkujrTUZ9PtR"
      },
      "source": [
        "Wait, what!? No error at all? Could this model really be absolutely perfect? Of course, it is much more likely that the model has badly overfit the data. How can you be sure?\n",
        "\n",
        "As we saw earlier, you don’t want to touch the test set until you are ready to launch a model you are confident about, so you need to use part of the training set for train ing, and part for model validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msSMWSXg9Zqd"
      },
      "source": [
        "## Better Evaluation Using Cross-Validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI5XRj5myANy"
      },
      "source": [
        "One way to evaluate the Decision Tree model would be to use the train_test_split function to split the training set into a smaller training set and a validation set, then train your models against the smaller training set and evaluate them against the vali dation set. It’s a bit of work, but nothing too difficult and it would work fairly well\n",
        "\n",
        "A great alternative is to use Scikit-Learn’s cross-validation feature. The following code performs K-fold cross-validation: it randomly splits the training set into 10 distinct subsets called folds, then it trains and evaluates the Decision Tree model 10 times, picking a different fold for evaluation every time and training on the other 9 folds. The result is an array containing the 10 evaluation scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5L43i9cP-IuF"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
        "                         scoring='neg_mean_squared_error', cv=10)\n",
        "tree_rmse_scores = np.sqrt(-scores)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd43vKLV3Hcj"
      },
      "source": [
        "Let’s look at the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhTKH0Ru3F_L",
        "outputId": "97280185-6359-49ff-e5b0-daaa04c6079e"
      },
      "source": [
        "def display_scores(scores):\n",
        "  print('Scores:', scores)\n",
        "  print('Mean:', scores.mean())\n",
        "  print('Standard Deviation:', scores.std())\n",
        "\n",
        "display_scores(tree_rmse_scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [68383.50873732 66639.091556   70589.67499588 70132.40449107\n",
            " 70794.98006628 74531.63888708 70808.5008784  70629.67928088\n",
            " 77648.18481077 69732.68316098]\n",
            "Mean: 70989.0346864678\n",
            "Standard Deviation: 2920.208968189508\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BCxsYXs0PWC"
      },
      "source": [
        "Now the Decision Tree doesn’t look as good as it did earlier. In fact, it seems to per form worse than the Linear Regression model! Notice that cross-validation allows you to get not only an estimate of the performance of your model, but also a measure of how precise this estimate is (i.e., its standard deviation)\n",
        "\n",
        "The Decision Tree has a score of approximately 71,200, generally 3,200. You would not have this information if you just used one validation set. But cross-validation comes at the cost of training the model several times, so it is not always possible"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODLNjoJk0Y7G"
      },
      "source": [
        "Let’s compute the same scores for the Linear Regression model just to be sure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OzlphQm0bka",
        "outputId": "4a01b9af-cc7a-4a5d-ec71-c5ffa0cd504e"
      },
      "source": [
        "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
        "                             scoring='neg_mean_squared_error', cv=10)\n",
        "\n",
        "lin_rmse_scores = np.sqrt(-lin_scores)\n",
        "display_scores(lin_rmse_scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
            " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
            " 71552.91566558 67665.10082067]\n",
            "Mean: 69052.46136345083\n",
            "Standard Deviation: 2731.674001798344\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QDMPUd01F4Q"
      },
      "source": [
        "That’s right: the Decision Tree model is overfitting so badly that it performs worse than the Linear Regression model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKNS8DGu1IWi"
      },
      "source": [
        "Let’s try one last model now: the RandomForestRegressor. Random Forests work by training many Decision Trees on random subsets of the features, then averaging out their predictions\n",
        "\n",
        "Building a model on top of many other models is called Ensemble Learning, and it is often a great way to push ML algo rithms even further"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvcRhccp1fPe",
        "outputId": "752d02b2-9166-42f7-da8c-7325ecdcd624"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "forest_reg = RandomForestRegressor()\n",
        "forest_reg.fit(housing_prepared, housing_labels)\n",
        "\n",
        "housing_predictions = forest_reg.predict(housing_prepared)\n",
        "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "forest_rmse = np.sqrt(forest_mse)\n",
        "forest_rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18654.855254375416"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPgwFVBF3Epf",
        "outputId": "edef5b70-eaef-4802-ffcd-6defb95b98b7"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
        "                                scoring='neg_mean_squared_error', cv=10)\n",
        "forest_rmse_scores = np.sqrt(-forest_scores)\n",
        "display_scores(forest_rmse_scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [49836.48904251 47808.0953156  50068.91143815 52411.82744357\n",
            " 49760.72857795 53282.13621263 49010.69879409 47864.16146386\n",
            " 53006.28414804 50358.7686052 ]\n",
            "Mean: 50340.8101041609\n",
            "Standard Deviation: 1871.5762998765497\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QB8lwjvr3jbX"
      },
      "source": [
        "Wow, this is much better: Random Forests look very promising. However, note that the score on the training set is still much lower than on the validation sets, meaning that the model is still overfitting the training set. Possible solutions for overfitting are to simplify the model, constrain it (i.e., regularize it), or get a lot more training data\n",
        "\n",
        "However, before you dive much deeper in Random Forests, you should try out many other models from various categories of Machine Learning algorithms (several Sup port Vector Machines with different kernels, possibly a neural network, etc.), without spending too much time tweaking the hyperparameters. The goal is to shortlist a few (two to five) promising models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw3uNRC2SGh9"
      },
      "source": [
        "You should save every model you experiment with, so you can come back easily to any model you want. Make sure you save both the hyperparameters and the trained parameters, as well as the cross-validation scores and perhaps the actual predictions as well\n",
        "\n",
        "This will allow you to easily compare scores across model types, and compare the types of errors they make. You can easily save Scikit-Learn models by using Python’ pcikle module, or using sklearn.externals.joblib, which is more efficient at serializing  large NumPy arrays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxv3mFw6SUkL"
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "\n",
        "joblib.dump(my_model. 'my_model.pkl')\n",
        "my_model_loaded = joblib.load('my_model.pkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCjncyWL38TB"
      },
      "source": [
        "# Fine-Tune Your Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lp9m22Wh4HxU"
      },
      "source": [
        "Let’s assume that you now have a shortlist of promising models. You now need to fine-tune them. Let’s look at a few ways you can do that"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bv8ZoVs74E7G"
      },
      "source": [
        "## Grid Search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbaXiDPc4G2F"
      },
      "source": [
        "One way to do that would be to fiddle with the hyperparameters manually, until you find a great combination of hyperparameter values. This would be very tedious work, and you may not have time to explore many combinations\n",
        "\n",
        "Instead you should get Scikit-Learn’ GridSearchCV to search for you. All you need to do is tell it which hyperparameters you want it to experiment with, and what values to try out, and it will evaluate all the possible combinations of hyperparameter values, using cross-validation. For example, the following code searches for the best combi nation of hyperparameter values for the RandomForestRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-srYAUxSw7h"
      },
      "source": [
        "When you have no idea what value a hyperparameter should have, a simple approach is to try out consecutive powers of 10 (or a smaller number if you want a more fine-grained search, as shown in this example with the n_estimators hyperparameter)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tde2dUoQ6flQ"
      },
      "source": [
        "This param_grid tells Scikit-Learn to first evaluate all 3 x 4 = 12 combinations of n_estimators and max_features hyperparameter values specified in the first dict, then try all 2 3 = 6 combinations of hyperparameter values in the second dict, but this time with the bootstrap hyperparameter set to False instead of True (which is the default value for this hyperparameter) \n",
        "\n",
        "All in all, the grid search will explore 12 + 6 = 18 combinations of RandomForestRegressor hyperparameter values, and it will train each model five times (since we are using five-fold cross validation). In other words, all in all, there will be 18 x 5 = 90 rounds of training\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y52vku0H5IKi",
        "outputId": "f597ccaa-d677-495b-e99b-886d4f19cc0b"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [\n",
        "              {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
        "              {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}\n",
        "]\n",
        "\n",
        "forest_reg = RandomForestRegressor()\n",
        "\n",
        "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
        "                           scoring='neg_mean_squared_error')\n",
        "\n",
        "grid_search.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
              "                                             criterion='mse', max_depth=None,\n",
              "                                             max_features='auto',\n",
              "                                             max_leaf_nodes=None,\n",
              "                                             max_samples=None,\n",
              "                                             min_impurity_decrease=0.0,\n",
              "                                             min_impurity_split=None,\n",
              "                                             min_samples_leaf=1,\n",
              "                                             min_samples_split=2,\n",
              "                                             min_weight_fraction_leaf=0.0,\n",
              "                                             n_estimators=100, n_jobs=None,\n",
              "                                             oob_score=False, random_state=None,\n",
              "                                             verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
              "                          'n_estimators': [3, 10, 30]},\n",
              "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
              "                          'n_estimators': [3, 10]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eo3Qoxm07sKD"
      },
      "source": [
        "It may take quite a long time, but when it is done you can get the\n",
        "best combination of parameters like this:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6FC1Jhq92K3",
        "outputId": "99976f9c-9f8a-44c4-9f48-cd5c2a06da3a"
      },
      "source": [
        "grid_search.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 6, 'n_estimators': 30}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UOakrlF-pr3"
      },
      "source": [
        "You can also get the best estimator directly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0o4ZQ_1-s8v",
        "outputId": "7df1e133-0ef5-498f-d204-d2fc233b1b27"
      },
      "source": [
        "grid_search.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features=6, max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDntChwa_vhP"
      },
      "source": [
        "And of course the evaluation scores are also available"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWs5QbSC_wEu",
        "outputId": "290d18cf-1db3-4ed2-f567-4f464f4532b9"
      },
      "source": [
        "cvres = grid_search.cv_results_\n",
        "for mean_score, params in zip(cvres['mean_test_score'], cvres['params']):\n",
        "  print(np.sqrt(-mean_score), params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "64319.388088758715 {'max_features': 2, 'n_estimators': 3}\n",
            "56343.51822680347 {'max_features': 2, 'n_estimators': 10}\n",
            "53151.21748612346 {'max_features': 2, 'n_estimators': 30}\n",
            "60079.56543726766 {'max_features': 4, 'n_estimators': 3}\n",
            "52947.38416165102 {'max_features': 4, 'n_estimators': 10}\n",
            "50483.937326474224 {'max_features': 4, 'n_estimators': 30}\n",
            "58725.271802289855 {'max_features': 6, 'n_estimators': 3}\n",
            "52586.47148749159 {'max_features': 6, 'n_estimators': 10}\n",
            "50019.92896043082 {'max_features': 6, 'n_estimators': 30}\n",
            "59629.728335152286 {'max_features': 8, 'n_estimators': 3}\n",
            "52028.69883972505 {'max_features': 8, 'n_estimators': 10}\n",
            "50112.34956980309 {'max_features': 8, 'n_estimators': 30}\n",
            "61777.01298550664 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
            "54341.0693411001 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
            "60012.61755198601 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
            "52351.27819046922 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
            "58939.3851299728 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
            "51817.311271352984 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fN34mqYcBVXp"
      },
      "source": [
        "In this example, we obtain the best solution by setting the hyperpara max_features meter to 6, and the hyperparameter to 30. The RMSE score for this n_estimators combination is 49,959, which is slightly better than the score you got earlier using the default hyperparameter values (which was 52,634). Congratulations, you have suc cessfully fine-tuned your best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVpFk1qsD2f_"
      },
      "source": [
        "## Randomized Search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vpunu-6D40j"
      },
      "source": [
        "The grid search approach is fine when you are exploring relatively few combinations, like in the previous example, but when the hyperparameter search space is large, it is often preferable to use RandomizedSearchCV instead. This class can be used in much the same way as the GridSearchCV class, but instead of trying out all possible combinations, it evaluates a given number of random combinations by selecting a random value for each hyperparameter at every iteration. This approach has two main bene fits\n",
        "\n",
        "*   If you let the randomized search run for, say, 1,000 iterations, this approach will explore 1,000 different values for each hyperparameter (instead of just a few values per hyperparameter with the grid search approach)\n",
        "*   You have more control over the computing budget you want to allocate to hyper parameter search, simply by setting the number of iterations\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_sG2fXgHVSg"
      },
      "source": [
        "## Ensemble Methods"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaJuWJVQHYHd"
      },
      "source": [
        "Another way to fine-tune your system is to try to combine the models that perform best. The group (or “ensemble”) will often perform better than the best individual model (just like Random Forests perform better than the individual Decision Trees they rely on), especially if the individual models make very different types of errors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPZdQXqnHwyR"
      },
      "source": [
        "## Analyze the Best Models and Their Errors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zwtrmI_HzNp"
      },
      "source": [
        "You will often gain good insights on the problem by inspecting the best models. For example, the RandomForestRegressor can indicate the relative importance of each attribute for making accurate predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4cLpqoJH8ap",
        "outputId": "f3ef0922-b770-4797-f551-f19de0c0a825"
      },
      "source": [
        "feature_importances = grid_search.best_estimator_.feature_importances_\n",
        "feature_importances"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.48760512e-02, 7.46154309e-02, 4.05356145e-02, 1.78883646e-02,\n",
              "       1.57040987e-02, 1.79654291e-02, 1.57316385e-02, 3.27341657e-01,\n",
              "       5.62844414e-02, 1.03091900e-01, 6.80046508e-02, 1.31851903e-02,\n",
              "       1.56715363e-01, 6.06263806e-05, 3.43978075e-03, 4.55976390e-03])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1ZiIY_uIHxn"
      },
      "source": [
        "Let’s display these importance scores next to their corresponding attribute names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfqNKEa3IIE6",
        "outputId": "e0a6f541-6c48-400d-cdd7-2d51cec4d949"
      },
      "source": [
        "extra_attribs = ['rooms_per_hhold', 'pop_per_hhold', 'bedrooms_per_room']\n",
        "cat_one_hot_attribs = list(encoder.classes_)\n",
        "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
        "sorted(zip(feature_importances, attributes), reverse=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.3273416565640251, 'median_income'),\n",
              " (0.1567153625463171, 'INLAND'),\n",
              " (0.10309189991290504, 'pop_per_hhold'),\n",
              " (0.08487605121418065, 'longitude'),\n",
              " (0.07461543088697799, 'latitude'),\n",
              " (0.06800465078452964, 'bedrooms_per_room'),\n",
              " (0.05628444135705771, 'rooms_per_hhold'),\n",
              " (0.04053561449335395, 'housing_median_age'),\n",
              " (0.017965429056707876, 'population'),\n",
              " (0.01788836464299348, 'total_rooms'),\n",
              " (0.015731638478160434, 'households'),\n",
              " (0.01570409873394003, 'total_bedrooms'),\n",
              " (0.013185190296230878, '<1H OCEAN'),\n",
              " (0.00455976390228622, 'NEAR OCEAN'),\n",
              " (0.0034397807497033527, 'NEAR BAY'),\n",
              " (6.062638063051791e-05, 'ISLAND')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zy9zNYkgI_UW"
      },
      "source": [
        "With this information, you may want to try dropping some of the less useful features (e.g., apparently only one ocean_proximity category is really useful, so you could try dropping the others)\n",
        "\n",
        "You should also look at the specific errors that your system makes, then try to under stand why it makes them and what could fix the problem (adding extra features or, on the contrary, getting rid of uninformative ones, cleaning up outliers, etc.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG2fvNgHJP1R"
      },
      "source": [
        "## Evaluate Your System on the Test Set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8xd7NC3JRlb"
      },
      "source": [
        "After tweaking your models for a while, you eventually have a system that performs sufficiently well. Now is the time to evaluate the final model on the test set. There is nothing special about this process; just get the predictors and the labels from your test set, run your full_pipeline to transform the data (call transform(), not fit_transform()!), and evaluate the final model on the test set "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IXPEyKlJ1Hd",
        "outputId": "38ed787d-0782-437b-ec50-03d6fcf571ab"
      },
      "source": [
        "final_model = grid_search.best_estimator_\n",
        "\n",
        "X_test = strat_test_set.drop('median_house_value', axis=1)\n",
        "y_test = strat_test_set['median_house_value'].copy()\n",
        "\n",
        "X_test_prepared = full_pipeline.transform(X_test)\n",
        "final_predictions = final_model.predict(X_test_prepared)\n",
        "\n",
        "final_mse = mean_squared_error(y_test, final_predictions)\n",
        "final_rmse = np.sqrt(final_mse)\n",
        "\n",
        "final_rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47101.46017964903"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYe2mo7lKiYr"
      },
      "source": [
        "The performance will usually be slightly worse than what you measured using cross validation if you did a lot of hyperparameter tuning (because your system ends up fine-tuned to perform well on the validation data, and will likely not perform as well on unknown datasets)\n",
        "\n",
        "It is not the case in this example, but when this happens you must resist the temptation to tweak the hyperparameters to make the numbers look good on the test set; the improvements would be unlikely to generalize to new data\n",
        "\n",
        "Now comes the project prelaunch phase: you need to present your solution (high lighting what you have learned, what worked and what did not, what assumptions were made, and what your system’s limitations are), document everything, and create nice presentations with clear visualizations and easy-to-remember statements (e.g., “the median income is the number one predictor of housing prices”"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZU-Kd_ZMKxA-"
      },
      "source": [
        "# Launch, Monitor, and Maintain Your System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8o-EhwsKzoM"
      },
      "source": [
        "You also need to write monitoring code to check your system’s live performance at regular intervals and trigger alerts when it drops. This is important to catch not only sudden breakage, but also performance degradation. This is quite common because models tend to “rot” as data evolves over time, unless the models are regularly trained on fresh data\n",
        "\n",
        "Evaluating your system’s performance will require sampling the system’s predictions and evaluating them. This will generally require a human analysis. These analysts may be field experts, or workers on a crowdsourcing platform (such as Amazon Mechanical Turk or CrowdFlower). Either way, you need to plug the human evalua tion pipeline into your system\n",
        "\n",
        "You should also make sure you evaluate the system’s input data quality. Sometimes performance will degrade slightly because of a poor quality signal (e.g., a malfunc tioning sensor sending random values, or another team’s output becoming stale), but it may take a while before your system’s performance degrades enough to trigger an alert. If you monitor your system’s inputs, you may catch this earlier. Monitoring the inputs is particularly important for online learning systems\n",
        "\n",
        "Finally, you will generally want to train your models on a regular basis using fresh data. You should automate this process as much as possible. If you don’t, you are very likely to refresh your model only every six months (at best), and your system’s perfor mance may fluctuate severely over time. If your system is an online learning system, you should make sure you save snapshots of its state at regular intervals so you can easily roll back to a previously working state\n"
      ]
    }
  ]
}