Week1_MachineLearningPart1.ipynb

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        "<a href=\"https://colab.research.google.com/github/carolinehaoud/MachineLearningPortfolio/blob/main/Week1_MachineLearningPart1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "id": "8edf816c",
      "metadata": {
        "id": "8edf816c"
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      "source": [
        "# **Week 1: Machine Learning Part 1 - Regression**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6e8558d1",
      "metadata": {
        "id": "6e8558d1"
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      "source": [
        "Machine learning (ML) is the study of computer programs which learn from experience rather than being explicitly programmed. ML algorithms can help us make predictions from data, understand complex datasets, and drive decision-making. Today, ML applications can be found everywhere, from self-driving cars like Teslas, to personal voice assistants like Alexa and Siri, to content recommendation systems as in Netflix and Spotify.\n",
        "\n",
        "In this lesson, we will practice solving a common machine learning problem: regression. We will create and train regression models to learn from data and make predictions using a popular ML library called [Scikit-learn](https://scikit-learn.org/stable/).\n",
        "\n",
        "<img src=\"https://www.besanttechnologies.com/wp-content/uploads/2019/12/applications-of-machine-learning.jpg\" alt=\"drawing\" width=\"600\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before starting this notebook, please run the following cell to authenticate with your Google account and have access to the files (in Google Drive) for this lesson."
      ],
      "metadata": {
        "id": "Szw0MFlRV4rM"
      },
      "id": "Szw0MFlRV4rM"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7m-PcRZ0V15F",
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      "id": "7m-PcRZ0V15F",
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        {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "s9Vimfw6kgwf"
      },
      "id": "s9Vimfw6kgwf",
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "id": "d164263f",
      "metadata": {
        "id": "d164263f"
      },
      "source": [
        "## 1. What is regression?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d61e7ac",
      "metadata": {
        "id": "8d61e7ac"
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      "source": [
        "Regression is the task of using input variables to predict a continuous-valued output (such as temperature, price, exam score, etc.). In machine learning, the inputs are often called **features** and the output is called the **label**. Some common examples of regression problems are:\n",
        "- Google Maps: using distance and traffic conditions to estimate travel time\n",
        "- NYC Subway: using a train's current location and speed to predict time of arrival\n",
        "- Weather report: using atmospheric conditions to forecast temperature\n",
        "\n",
        "While there are many kinds of models to solve regression problems, the most popular regression model by far is **linear regression**. Linear regression fits a line through your data, using the feature(s) to predict the label. The slope and intercept of this fitted line can then be used to predict the label for new data points which you do not have the label for.\n",
        "\n",
        "In the figure below, we have a single feature x and a label y. The data points shown in blue are (x, y) pairs. The linear regression model, shown in red, fits a line to these data points. With this line, given a new value of x, we can predict the value of y.\n",
        "\n",
        "<img src=\"https://miro.medium.com/max/640/1*LEmBCYAttxS6uI6rEyPLMQ.png\" alt=\"drawing\" width=\"600\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8bed8ac5",
      "metadata": {
        "id": "8bed8ac5"
      },
      "source": [
        "## 2. Regression example: predicting climate change in NYC"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this example, we will use linear regression to understand and forecast the effects of climate change on our city. We are given a dataset with two variables: year and AverageTemperature. Using this dataset, we will train a linear regression model to predict the AverageTemperature (the label) from the year (the feature). Then, we will see how close our predictions come to the actual values!\n",
        "\n",
        "First, let's load a **training dataset** containing the average yearly temperatures from 1800 to 1949. These will be the datapoints we use to train our model."
      ],
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              "     year  AverageTemperature\n",
              "0    1800            9.577167\n",
              "1    1801            9.999917\n",
              "2    1802           10.285667\n",
              "3    1803           10.071250\n",
              "4    1804            9.685833\n",
              "..    ...                 ...\n",
              "138  1945            9.942833\n",
              "139  1946           10.631083\n",
              "140  1947            9.907000\n",
              "141  1948            9.826000\n",
              "142  1949           11.386167\n",
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              "      <th>141</th>\n",
              "      <td>1948</td>\n",
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              "      <th>142</th>\n",
              "      <td>1949</td>\n",
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      ],
      "source": [
        "nyc_temp_train_df = pd.read_csv('/content/drive/MyDrive/data sci/NYC_yearly_temp_train.csv')\n",
        "nyc_temp_train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise: Celsius to Fahrenheit\n",
        "\n",
        "The AverageTemperature column lists temperatures in degrees Celsius. Convert this column to Fahrenheit using the following formula:\n",
        "\n",
        "$F = C \\cdot \\frac{9}{5} + 32$"
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      "source": [
        "### Your code here\n",
        "nyc_temp_train_df['AverageTemperature'] = nyc_temp_train_df['AverageTemperature']*(9/5)+32\n",
        "nyc_temp_train_df\n"
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              "     year  AverageTemperature\n",
              "0    1800            49.23890\n",
              "1    1801            49.99985\n",
              "2    1802            50.51420\n",
              "3    1803            50.12825\n",
              "4    1804            49.43450\n",
              "..    ...                 ...\n",
              "138  1945            49.89710\n",
              "139  1946            51.13595\n",
              "140  1947            49.83260\n",
              "141  1948            49.68680\n",
              "142  1949            52.49510\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise: Plot average temperatures over the years\n",
        "\n",
        "Before training an ML model, it's always a good idea to explore and visualize the data you are working with. Use a scatter plot (`plt.scatter()` in matplotlib) to visualize the average temperatures over the years in NYC."
      ],
      "metadata": {
        "id": "GCFihzRJd0MM"
      },
      "id": "GCFihzRJd0MM"
    },
    {
      "cell_type": "code",
      "source": [
        "### Your code here\n",
        "plt.scatter(nyc_temp_train_df['year'], nyc_temp_train_df['AverageTemperature'])\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Temperature (F)', fontsize=12)\n"
      ],
      "metadata": {
        "id": "b3uOEb2Xkbjc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "b12e6030-db59-425d-ca32-37ee07fe2371"
      },
      "id": "b3uOEb2Xkbjc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-bfd02720413e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m### Your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnyc_temp_train_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnyc_temp_train_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AverageTemperature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Temperature (F)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see from the plot that there is a clear upward trend in the yearly average temperatures. So, let's train a linear regression model to fit this data using the *LinearRegression* class in scikit-learn."
      ],
      "metadata": {
        "id": "BG3el7tQefID"
      },
      "id": "BG3el7tQefID"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0e5fed0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a0e5fed0",
        "outputId": "4fb1217e-6b6a-4222-e2fb-187b15d46869"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "model = LinearRegression()                            # Initialize LinearRegression model\n",
        "model.fit(X=nyc_temp_train_df[['year']],              # Train model on the data\n",
        "          y=nyc_temp_train_df['AverageTemperature'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can use this trained model to make predictions of the average temperature for each year in our training set (1800 - 1949)."
      ],
      "metadata": {
        "id": "BHA4F-BdfVkX"
      },
      "id": "BHA4F-BdfVkX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7813e668",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7813e668",
        "outputId": "659892dc-c299-442a-8113-c2dc09dc9823"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([48.3271371 , 48.33576592, 48.34439475, 48.35302357, 48.36165239,\n",
              "       48.37028122, 48.37891004, 48.38753886, 48.39616769, 48.40479651,\n",
              "       48.41342533, 48.42205416, 48.4393118 , 48.44794062, 48.45656945,\n",
              "       48.48245592, 48.49108474, 48.49971356, 48.50834239, 48.51697121,\n",
              "       48.52560003, 48.53422886, 48.54285768, 48.5514865 , 48.56011533,\n",
              "       48.56874415, 48.57737297, 48.5860018 , 48.59463062, 48.60325944,\n",
              "       48.61188826, 48.62051709, 48.62914591, 48.64640356, 48.65503238,\n",
              "       48.6636612 , 48.67229003, 48.68091885, 48.68954767, 48.6981765 ,\n",
              "       48.70680532, 48.71543414, 48.72406297, 48.73269179, 48.74132061,\n",
              "       48.74994943, 48.75857826, 48.76720708, 48.7758359 , 48.78446473,\n",
              "       48.79309355, 48.80172237, 48.8103512 , 48.81898002, 48.82760884,\n",
              "       48.83623767, 48.84486649, 48.85349531, 48.86212414, 48.87075296,\n",
              "       48.87938178, 48.8880106 , 48.89663943, 48.90526825, 48.9225259 ,\n",
              "       48.93115472, 48.93978354, 48.94841237, 48.95704119, 48.96567001,\n",
              "       48.98292766, 48.99155648, 49.00018531, 49.00881413, 49.01744295,\n",
              "       49.02607178, 49.0347006 , 49.04332942, 49.05195824, 49.06058707,\n",
              "       49.06921589, 49.07784471, 49.08647354, 49.09510236, 49.10373118,\n",
              "       49.11236001, 49.12098883, 49.12961765, 49.13824648, 49.1468753 ,\n",
              "       49.15550412, 49.16413295, 49.17276177, 49.18139059, 49.19001941,\n",
              "       49.19864824, 49.20727706, 49.21590588, 49.23316353, 49.24179235,\n",
              "       49.25042118, 49.25905   , 49.26767882, 49.27630765, 49.28493647,\n",
              "       49.29356529, 49.30219412, 49.31082294, 49.31945176, 49.32808058,\n",
              "       49.33670941, 49.34533823, 49.35396705, 49.36259588, 49.3712247 ,\n",
              "       49.37985352, 49.38848235, 49.39711117, 49.40573999, 49.41436882,\n",
              "       49.42299764, 49.43162646, 49.44025529, 49.44888411, 49.45751293,\n",
              "       49.46614176, 49.47477058, 49.4833994 , 49.49202822, 49.50065705,\n",
              "       49.50928587, 49.51791469, 49.52654352, 49.53517234, 49.54380116,\n",
              "       49.55242999, 49.56105881, 49.56968763, 49.57831646, 49.58694528,\n",
              "       49.5955741 , 49.60420293, 49.61283175])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "temp_predictions_train = model.predict(X=nyc_temp_train_df[['year']])\n",
        "temp_predictions_train"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can plot these predictions (in red) along with the actual temperature values (in blue) to see how close our model's predictions are to the actual values. Note that the predictions are in a straight line since this is a linear regression model.\n",
        "\n",
        "How well do you think our model's predictions compare to the actual temperatures? Did it do a good job?"
      ],
      "metadata": {
        "id": "pya58Tkmfroz"
      },
      "id": "pya58Tkmfroz"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bcf20742",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "bcf20742",
        "outputId": "67e92e08-6fb4-40ba-cfce-ef3010861486"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f77a236a110>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(nyc_temp_train_df['year'], nyc_temp_train_df['AverageTemperature'], label='Actual')\n",
        "plt.plot(nyc_temp_train_df['year'], temp_predictions_train, label='Predicted',\n",
        "         color='tab:red', linewidth=2)\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Temperature (F)', fontsize=12)\n",
        "plt.title('NYC yearly average temperature', fontsize=14)\n",
        "plt.legend(fontsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While it's nice to make predictions on our training dataset, the real value of machine learning comes from being able to make predictions on new, unseen examples. So, let's use this trained model to make predictions on a **test dataset**: the years from 1950 to 2030. These are years which our model has not seen during training, so we would like to see how well it can generalize to these new examples.\n",
        "\n",
        "First, create a 81x1 dimensional numpy array for the 81 years between 1950 and 2030. This will be the feature for our test dataset."
      ],
      "metadata": {
        "id": "Y3B92HxlgT5Y"
      },
      "id": "Y3B92HxlgT5Y"
    },
    {
      "cell_type": "code",
      "source": [
        "years_test = np.array(list(range(1950, 2031))).reshape(81, 1)\n",
        "years_test"
      ],
      "metadata": {
        "id": "PMt9YMcShS2w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47623a81-b6db-42d2-dd2a-d83a0265b6a2"
      },
      "id": "PMt9YMcShS2w",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1950],\n",
              "       [1951],\n",
              "       [1952],\n",
              "       [1953],\n",
              "       [1954],\n",
              "       [1955],\n",
              "       [1956],\n",
              "       [1957],\n",
              "       [1958],\n",
              "       [1959],\n",
              "       [1960],\n",
              "       [1961],\n",
              "       [1962],\n",
              "       [1963],\n",
              "       [1964],\n",
              "       [1965],\n",
              "       [1966],\n",
              "       [1967],\n",
              "       [1968],\n",
              "       [1969],\n",
              "       [1970],\n",
              "       [1971],\n",
              "       [1972],\n",
              "       [1973],\n",
              "       [1974],\n",
              "       [1975],\n",
              "       [1976],\n",
              "       [1977],\n",
              "       [1978],\n",
              "       [1979],\n",
              "       [1980],\n",
              "       [1981],\n",
              "       [1982],\n",
              "       [1983],\n",
              "       [1984],\n",
              "       [1985],\n",
              "       [1986],\n",
              "       [1987],\n",
              "       [1988],\n",
              "       [1989],\n",
              "       [1990],\n",
              "       [1991],\n",
              "       [1992],\n",
              "       [1993],\n",
              "       [1994],\n",
              "       [1995],\n",
              "       [1996],\n",
              "       [1997],\n",
              "       [1998],\n",
              "       [1999],\n",
              "       [2000],\n",
              "       [2001],\n",
              "       [2002],\n",
              "       [2003],\n",
              "       [2004],\n",
              "       [2005],\n",
              "       [2006],\n",
              "       [2007],\n",
              "       [2008],\n",
              "       [2009],\n",
              "       [2010],\n",
              "       [2011],\n",
              "       [2012],\n",
              "       [2013],\n",
              "       [2014],\n",
              "       [2015],\n",
              "       [2016],\n",
              "       [2017],\n",
              "       [2018],\n",
              "       [2019],\n",
              "       [2020],\n",
              "       [2021],\n",
              "       [2022],\n",
              "       [2023],\n",
              "       [2024],\n",
              "       [2025],\n",
              "       [2026],\n",
              "       [2027],\n",
              "       [2028],\n",
              "       [2029],\n",
              "       [2030]])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, use the trained model to make temperature predictions on these new years in our test dataset."
      ],
      "metadata": {
        "id": "vmyJ4BAkiemS"
      },
      "id": "vmyJ4BAkiemS"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d5f7615f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5f7615f",
        "outputId": "9b6f2fc2-4ac7-4374-d29a-9c2257dbd0f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([49.62146057, 49.63008939, 49.63871822, 49.64734704, 49.65597586,\n",
              "       49.66460469, 49.67323351, 49.68186233, 49.69049116, 49.69911998,\n",
              "       49.7077488 , 49.71637763, 49.72500645, 49.73363527, 49.7422641 ,\n",
              "       49.75089292, 49.75952174, 49.76815056, 49.77677939, 49.78540821,\n",
              "       49.79403703, 49.80266586, 49.81129468, 49.8199235 , 49.82855233,\n",
              "       49.83718115, 49.84580997, 49.8544388 , 49.86306762, 49.87169644,\n",
              "       49.88032527, 49.88895409, 49.89758291, 49.90621174, 49.91484056,\n",
              "       49.92346938, 49.9320982 , 49.94072703, 49.94935585, 49.95798467,\n",
              "       49.9666135 , 49.97524232, 49.98387114, 49.99249997, 50.00112879,\n",
              "       50.00975761, 50.01838644, 50.02701526, 50.03564408, 50.04427291,\n",
              "       50.05290173, 50.06153055, 50.07015937, 50.0787882 , 50.08741702,\n",
              "       50.09604584, 50.10467467, 50.11330349, 50.12193231, 50.13056114,\n",
              "       50.13918996, 50.14781878, 50.15644761, 50.16507643, 50.17370525,\n",
              "       50.18233408, 50.1909629 , 50.19959172, 50.20822054, 50.21684937,\n",
              "       50.22547819, 50.23410701, 50.24273584, 50.25136466, 50.25999348,\n",
              "       50.26862231, 50.27725113, 50.28587995, 50.29450878, 50.3031376 ,\n",
              "       50.31176642])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "temp_predictions_test = model.predict(years_test)\n",
        "temp_predictions_test"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot the predictions for these new test dataset years, along with the training dataset predictions and actual temperatures from before. This lets us forecast yearly temperatures in NYC into the future, as far as 2030!"
      ],
      "metadata": {
        "id": "vCZKiJ4oixvf"
      },
      "id": "vCZKiJ4oixvf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00abeb08",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "00abeb08",
        "outputId": "595d8c1f-4529-412b-e222-a01a55f0abe5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f77a23e6210>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(nyc_temp_train_df['year'], nyc_temp_train_df['AverageTemperature'], label='Actual')\n",
        "plt.plot(nyc_temp_train_df['year'], temp_predictions_train, label='Predicted',\n",
        "         color='tab:red', linewidth=2)\n",
        "plt.plot(years_test, temp_predictions_test, label='Predicted (test)',\n",
        "        linestyle='--', color='tab:red', linewidth=2)\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Temperature (C)', fontsize=12)\n",
        "plt.title('NYC yearly average temperature', fontsize=14)\n",
        "plt.legend(fontsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "How do we know if our test set predictions are any good? Luckily, we also have a dataset with the actual AverageTemperature labels for the years 1950 - 2013. Let's load in this dataset."
      ],
      "metadata": {
        "id": "vLLVSxUqjeTY"
      },
      "id": "vLLVSxUqjeTY"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73401145",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "73401145",
        "outputId": "0454d67f-9bed-4149-8b38-75810d5f0326"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  AverageTemperature\n",
              "0   1950            49.50365\n",
              "1   1951            50.49380\n",
              "2   1952            50.94065\n",
              "3   1953            52.11410\n",
              "4   1954            50.55560\n",
              "..   ...                 ...\n",
              "59  2009            50.25530\n",
              "60  2010            52.44365\n",
              "61  2011            52.29005\n",
              "62  2012            53.54870\n",
              "63  2013            53.89500\n",
              "\n",
              "[64 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee97efd5-b456-4475-8130-b6cf481268e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>AverageTemperature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1950</td>\n",
              "      <td>49.50365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1951</td>\n",
              "      <td>50.49380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1952</td>\n",
              "      <td>50.94065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1953</td>\n",
              "      <td>52.11410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1954</td>\n",
              "      <td>50.55560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2009</td>\n",
              "      <td>50.25530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2010</td>\n",
              "      <td>52.44365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>2011</td>\n",
              "      <td>52.29005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2012</td>\n",
              "      <td>53.54870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>2013</td>\n",
              "      <td>53.89500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee97efd5-b456-4475-8130-b6cf481268e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee97efd5-b456-4475-8130-b6cf481268e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee97efd5-b456-4475-8130-b6cf481268e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "nyc_temp_test_df = pd.read_csv('/content/drive/MyDrive/data sci/Copy of NYC_yearly_temp_test.csv')\n",
        "# Remembering to convert the test set temperatures into Fahrenheit!\n",
        "nyc_temp_test_df['AverageTemperature'] = nyc_temp_test_df['AverageTemperature'] * (9/5) + 32\n",
        "nyc_temp_test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's add in the test dataset actual labels to our plot. How well do you think our test set predictions (red dotted line) compare against the actual test set labels (light blue points)?"
      ],
      "metadata": {
        "id": "GkGM7T3okPyV"
      },
      "id": "GkGM7T3okPyV"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a4a49840",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "a4a49840",
        "outputId": "2c74fcf8-b326-4bde-cfae-835db61bbb8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f77a249c310>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(nyc_temp_train_df['year'], nyc_temp_train_df['AverageTemperature'], label='Actual')\n",
        "plt.plot(nyc_temp_train_df['year'], temp_predictions_train, label='Predicted',\n",
        "         color='tab:red', linewidth=2)\n",
        "plt.scatter(nyc_temp_test_df['year'], nyc_temp_test_df['AverageTemperature'], label='Actual (test)',\n",
        "            color='lightblue')\n",
        "plt.plot(years_test, temp_predictions_test, label='Predicted (test)',\n",
        "        linestyle='--', color='tab:red', linewidth=2)\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Temperature (C)', fontsize=12)\n",
        "plt.title('NYC yearly average temperature', fontsize=14)\n",
        "plt.legend(fontsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1e88d6d",
      "metadata": {
        "id": "b1e88d6d"
      },
      "source": [
        "## 3. Your turn: predicting bicycle traffic on Brooklyn Bridge!\n",
        "\n",
        "<img src=\"https://media.tacdn.com/media/attractions-splice-spp-674x446/09/de/33/d1.jpg\" alt=\"drawing\" width=\"600\"/>\n",
        "\n",
        "Now, you will train a linear regression model to predict bicycle traffic from temperature. You are given a dataset containing the daily temperature and number of bicycles crossing Brooklyn Bridge. Let's load in this training data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bike_train_df = pd.read_csv('/content/drive/MyDrive/data sci/Copy of brooklyn_bridge_bicycles_train.csv')\n",
        "bike_train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "HitJ7SNCq-pu",
        "outputId": "b42eebb1-48f7-4441-9068-afe0e9d07a5b"
      },
      "id": "HitJ7SNCq-pu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Date  Temperature  Number of bicycles\n",
              "0   2016-04-01 00:00:00         78.1              1704.0\n",
              "1   2016-04-02 00:00:00         55.0               827.0\n",
              "2   2016-04-04 00:00:00         44.1               521.0\n",
              "3   2016-04-05 00:00:00         42.1              1416.0\n",
              "4   2016-04-06 00:00:00         45.0              1885.0\n",
              "5   2016-04-07 00:00:00         57.0              1276.0\n",
              "6   2016-04-08 00:00:00         46.9              1982.0\n",
              "7   2016-04-09 00:00:00         43.0               504.0\n",
              "8   2016-04-10 00:00:00         48.9              1447.0\n",
              "9   2016-04-13 00:00:00         57.0              2840.0\n",
              "10  2016-04-15 00:00:00         64.0              2770.0\n",
              "11  2016-04-16 00:00:00         66.0              2384.0\n",
              "12  2016-04-17 00:00:00         73.9              3147.0\n",
              "13  2016-04-19 00:00:00         71.1              3501.0\n",
              "14  2016-04-20 00:00:00         68.0              3450.0\n",
              "15  2016-04-21 00:00:00         71.1              3436.0\n",
              "16  2016-04-22 00:00:00         78.1              2975.0\n",
              "17  2016-04-23 00:00:00         70.0              2055.0\n",
              "18  2016-04-24 00:00:00         68.0              2798.0\n",
              "19  2016-04-26 00:00:00         60.1              1997.0\n",
              "20  2016-04-30 00:00:00         64.0              3199.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13f0d594-3788-4639-acc5-bebe4641ce95\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Number of bicycles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-04-01 00:00:00</td>\n",
              "      <td>78.1</td>\n",
              "      <td>1704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-02 00:00:00</td>\n",
              "      <td>55.0</td>\n",
              "      <td>827.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-04 00:00:00</td>\n",
              "      <td>44.1</td>\n",
              "      <td>521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-05 00:00:00</td>\n",
              "      <td>42.1</td>\n",
              "      <td>1416.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-06 00:00:00</td>\n",
              "      <td>45.0</td>\n",
              "      <td>1885.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-04-07 00:00:00</td>\n",
              "      <td>57.0</td>\n",
              "      <td>1276.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-04-08 00:00:00</td>\n",
              "      <td>46.9</td>\n",
              "      <td>1982.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-04-09 00:00:00</td>\n",
              "      <td>43.0</td>\n",
              "      <td>504.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-04-10 00:00:00</td>\n",
              "      <td>48.9</td>\n",
              "      <td>1447.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-04-13 00:00:00</td>\n",
              "      <td>57.0</td>\n",
              "      <td>2840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2016-04-15 00:00:00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2016-04-16 00:00:00</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2016-04-17 00:00:00</td>\n",
              "      <td>73.9</td>\n",
              "      <td>3147.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2016-04-19 00:00:00</td>\n",
              "      <td>71.1</td>\n",
              "      <td>3501.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2016-04-20 00:00:00</td>\n",
              "      <td>68.0</td>\n",
              "      <td>3450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2016-04-21 00:00:00</td>\n",
              "      <td>71.1</td>\n",
              "      <td>3436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2016-04-22 00:00:00</td>\n",
              "      <td>78.1</td>\n",
              "      <td>2975.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2016-04-23 00:00:00</td>\n",
              "      <td>70.0</td>\n",
              "      <td>2055.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2016-04-24 00:00:00</td>\n",
              "      <td>68.0</td>\n",
              "      <td>2798.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2016-04-26 00:00:00</td>\n",
              "      <td>60.1</td>\n",
              "      <td>1997.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2016-04-30 00:00:00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>3199.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13f0d594-3788-4639-acc5-bebe4641ce95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13f0d594-3788-4639-acc5-bebe4641ce95 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13f0d594-3788-4639-acc5-bebe4641ce95');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, it's always a good idea to visualize your data before training a model. Use a scatter plot to plot the number of bicycles vs temperature."
      ],
      "metadata": {
        "id": "Gkv9kGRHuQCi"
      },
      "id": "Gkv9kGRHuQCi"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "\n",
        "plt.scatter (bike_train_df[['Temperature']], bike_train_df['Number of bicycles'])\n",
        "plt.xlabel('Temperature (F)', fontsize=12)\n",
        "plt.ylabel('Number of bicycles', fontsize=12)\n",
        "plt.title('Number of bicycles over Brooklyn Bridge', fontsize=14)"
      ],
      "metadata": {
        "id": "OHeclaEgr3Ud",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "5448365f-c2a4-405f-87f6-b91088a07b5a"
      },
      "id": "OHeclaEgr3Ud",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of bicycles over Brooklyn Bridge')"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train a linear regression model in sklearn to predict number of bicycles from temperature."
      ],
      "metadata": {
        "id": "Fy5kFkNGuxGq"
      },
      "id": "Fy5kFkNGuxGq"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model = LinearRegression()\n",
        "\n",
        "model.fit(bike_train_df[['Temperature']], bike_train_df['Number of bicycles'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1uY8t_RsZ-k",
        "outputId": "ff1a119c-b3af-4fe5-efec-0cc08d04d50a"
      },
      "id": "g1uY8t_RsZ-k",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use your trained model to make predictions on the training data."
      ],
      "metadata": {
        "id": "EBJ9FCIGvG6v"
      },
      "id": "EBJ9FCIGvG6v"
    },
    {
      "cell_type": "code",
      "source": [
        "### Your code here\n",
        "\n",
        "### TODO: fill in the line below\n",
        "bike_predictions_train = model.predict(bike_train_df[['Temperature']])\n",
        "bike_predictions_train"
      ],
      "metadata": {
        "id": "Xl1Eo-N8s3ET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a66ecca-aa50-411a-f230-c8fb8fbe223c"
      },
      "id": "Xl1Eo-N8s3ET",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3197.77751825, 1879.60970132, 1257.61709506, 1143.49001135,\n",
              "       1308.97428273, 1993.73678504, 1417.39501227, 1194.84719902,\n",
              "       1531.52209598, 1993.73678504, 2393.18157804, 2507.30866176,\n",
              "       2958.11064244, 2798.33272524, 2621.43574548, 2798.33272524,\n",
              "       3197.77751825, 2735.56282919, 2621.43574548, 2170.6337648 ,\n",
              "       2393.18157804])"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot our model's predictions (red line) along with the actual data points (blue)."
      ],
      "metadata": {
        "id": "_QwVnIIHvISh"
      },
      "id": "_QwVnIIHvISh"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(bike_train_df['Temperature'], bike_train_df['Number of bicycles'])\n",
        "plt.xlabel('Temperature (F)', fontsize=12)\n",
        "plt.ylabel('Number of bicycles', fontsize=12)\n",
        "plt.title('Number of bicycles over Brooklyn Bridge', fontsize=14)\n",
        "\n",
        "plt.plot(bike_train_df['Temperature'], bike_predictions_train, label='Predicted', color='tab:red')\n",
        "plt.legend(fontsize=12)"
      ],
      "metadata": {
        "id": "PE-Rp_xms-yD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "82b49ae1-e30d-430f-c980-e6cdb142e05c"
      },
      "id": "PE-Rp_xms-yD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f77a24f8250>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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r5YEV4cv88FHbbQGPAe4Pp3sPKDSzTsAg4CV3XxIW+5eAQ9OZe7Y588wz+cMf/gDAW2+9RZ8+fTKyXDNj9uzZGVmWiGTO6AkzNhb9KhXrKhk9YUZEGUldMtbGamZ5ZvYRsIigeE8MB/0lbM6/0cxahrEiYF7M5PPDWE3x+GUNNbNJZjZp8eLFDb4umdCjRw8KCgpo1aoVHTp04Mwzz2TFihV1T1gP++67LzNm1P3POWbMGPbZZ58GXbaIZIcF5RX1ikv0Mlb43b3S3XcDugADzGxnYATQF9gDaAv8voGWdae7l7h7Sfv2Se9R0CQ8++yzrFixgg8//JBJkyZx7bXXVhu+fv36iDITEQl0LiyoV1yil/FeVe5eDrwGHOruX4fN+WuA+4AB4WhlQNeYybqEsZriWa2oqIjDDjuMTz75BDPjlltuoXfv3vTu3RuA5557jt12243CwkL23ntvpk6dunHaKVOm0L9/f7beemtOPPFEVq9evXHY66+/TpcuXTa+njdvHkOGDKF9+/Zsu+22DBs2jNLSUs4//3zeffddWrVqRWFhIQBr1qzhkksuoVu3bnTo0IHzzz+fiooffuGPHj2aTp060blzZ+699950v0UiOaexdKgbPqgPBfl51WIF+XkMH5SZw4hSf5nq1d/ezArD5wXAwcD08Lg9FpxPNxj4JJzkGeD0sHf/QGCZu38NTAAOMbM2ZtYGOCSMZbV58+bxwgsv0K9fPwDGjx/PxIkT+eyzz5gyZQpnn302d9xxB9999x3nnXceRx99NGvWrGHt2rUMHjyY0047jSVLlnD88cfz5JNPJl1GZWUlRx55JN27d2fu3LmUlZVx0kknUVxczO23385ee+3FihUrKC8vB+Cyyy5j5syZfPTRR8yePZuysjKuvvpqAF588UVuuOEGXnrpJWbNmsXLL7+cmTdKJEdUdagrK6/A+aFDXRTFf3C/In6+exF54WnReWb8fPcidexrxDLVq78TMNbM8gh+bDzu7s+Z2atm1h4w4CPg/HD8F4DDgdnAKuAsAHdfYmbXAB+E413t7ksaIsFvRo5kTen0hphVjVoW96Xj5ZenPP7gwYNp3rw522yzDUcccQSXX3451157LSNGjKBt27YA3HnnnZx33nnsueeeAJxxxhmMHDmS9957DzNj3bp1XHTRRZgZxx13HH//+9+TLuv9999nwYIFjB49mubNg49FTcf13Z0777yTqVOnbszj8ssv55RTTmHUqFE8/vjjnHXWWey8884AXHnllTzyyCMpr7eI1K62DnWZLrjjp5Tx5OQyKj3or13pzpOTyyjp3lbFv5HKSOF396lAvyTxA2sY34ELahh2L5ATbcfjx4/noIMOSoh37frD0Y4vv/ySsWPHcvPNN2+MrV27lgULFmBmFBUVVbtAUffu3ZMua968eXTv3n1j0a/N4sWLWbVqFbvvvvvGmLtvvPTxggULqg2raZkismkaU4e6xvQjRFKTs5fsjVefPfGoxRbyrl27csUVV3DFFVckjPfGG29QVlaGu2+c5quvvqJnz54J43bt2pWvvvqK9evXJxT/+CsbtmvXjoKCAj799FOKihL/sTt16sS8eT+cfPHVV1/VbwVFpFadCwsoS1Lko+hQ15h+hEhqdMm0Ju7cc8/l9ttvZ+LEibg7K1eu5Pnnn+f7779nr732onnz5tx0002sW7eOcePG8f777yedz4ABA+jUqROXXXYZK1euZPXq1bzzzjsAdOjQgfnz57N27VoguNLeueeey29/+1sWLVoEQFlZGRMmBN0tTjjhBMaMGcNnn33GqlWruOqqqzLwTojkjsbUoU69+pseFf4mrqSkhLvuuothw4bRpk0bevXqxZgxYwBo0aIF48aNY8yYMbRt25bHHnuMIUOGJJ1PXl4ezz77LLNnz6Zbt2506dKFxx57DIADDzyQnXbaiY4dO9KuXTsA/vrXv9KrVy8GDhxI69atOeiggzZeE+Cwww7joosu4sADD6RXr14ceGDSIzoisokG9yti1JBdKCoswICiwgJGDdklkqb1xvQjRFJj7rVdQK/pKykp8UmTJiXES0tLKS4ujiAjSUbbQ6TpGj+ljNETZrCgvILOhQUMH9RHx/cjZmaT3b0k2TAd4xcRkc0yuJ9O32tK1NQvIiKSQ1T4RUREcogKv4iISA7J6cKf7R0bmwptBxGRzMnZwp+fn1/tpjISnYqKCvLz86NOQ0QkJ+Rs4d9uu+0oKytj1apV2uOMiLuzatUqysrK2G677aJOR0QkJ+Ts6XytW7cGguvKr1u3LuJscld+fj4dOnTYuD1ERCS9crbwQ1D8VXBERCSX5GxTv4iISC5S4RcREckhKvwiIiI5JKeP8YuIgG4yI7lFhV9Ectr4KWWMGDeNinWVAJSVVzBi3DQAFX/JSmrqF5GcNnrCjI1Fv0rFukpGT5gRUUaSS1aXljLnqKNZv3Rpxpapwi8iOW1BefIreNYUF2kIC0ddR2nfYr44dghrZs1i6QMPZmzZauoXkZzWubCAsiRFvnNhQQTZSDZbv3Qps/baOyHe5tRTaX/hrzOWhwq/iOS04YP6VDvGD1CQn8fwQX0izEpS1RQ6Zi5/4QXKfndxQrzHE09QsPNOGc9HhV9EclpVkWjsxUMSNeaOmV5ZydyTTmb1tGnV4i1792L7ceOwCG9MpsIvIjlvcL+iyAuF1F9tHTOj2p6rZ8zki2OOSYh3GjWKwmMHR5BRIhV+ERFpkhpTx8xFN9zAd3ffkxDv/fZbNG/XLuP51EaFX0REmqSoO2ZWLlvGzD0HJsQLTziBTldflZEcNoUKv4iINElRdcxc/t//UnbhbxLiPR57lIJdd03rshuCCr+IiDRJmeyY6Rs28OWpp1Hx4YfV4i26d2eHZ5/BWrRo8GWmiwq/iIg0WenumLlm9mzmHHlUQrzjNVfT5vjj07bcdFLhFxERibPon//ku9tuT4j3evMN8rfbLoKMGo4Kv4iICFD5/ffM3GNAQnybY4+l86iREWSUHir8IiKS075/9VXm/+qChHj3hx9my/79IsgovTJS+M1sC+BNoGW4zCfc/c9mtj3wKLAtMBk4zd3XmllL4H5gd+A74ER3nxvOawRwDlAJXOjuEzKxDiIikj18wwa+OutsVk2cWC2e37kzO7z4H5o1oc569ZWpPf41wIHuvsLM8oG3zew/wO+AG939UTO7naCg3xb+XeruvczsJOCvwIlm9iPgJGAnoDPwspnt6O6VyRYqIiISa82cL5hz+OEJ8Y5//hNtTj45gowyLyOF390dWBG+zA8fDhwInBLGxwJXEhT+Y8LnAE8A/zIzC+OPuvsa4Aszmw0MAN5N/1qIiEhTtfjWW/n2ppsT4r3eeJ38Dh0iyCg6GTvGb2Z5BM35vYBbgM+BcndfH44yH6g6J6MImAfg7uvNbBnB4YAi4L2Y2cZOIyIislHlipXMHDgQ1q+vFm991FF0vv6vBPuT0Yri7oIZK/xhc/xuZlYIPAX0TdeyzGwoMBSgW7du6VqMiIg0QiveeIN5552fEO/+wP1succeEWSUXFR3F8x4r353Lzez14C9gEIzax7u9XcBysLRyoCuwHwzaw5sQ9DJrypeJXaa2GXcCdwJUFJS4ulaFxERaRzcnXnnDmXl229Xizdv356eL79Es5YtI8qsZlHdXbBZ2uYcw8zah3v6mFkBcDBQCrwGHBeOdgbwdPj8mfA14fBXw34CzwAnmVnL8IyA3sD7mVgHERFpfFZ9+CGlfYuZXvyjakW/w+WXUzy9lN5vvdkoiz5Ed3fBTO3xdwLGhsf5mwGPu/tzZvYZ8KiZXQtMAaruaXgP8EDYeW8JQU9+3P1TM3sc+AxYD1ygHv0iIrln7oknUfHxxwnxXq++Qn7nzhFkVH9R3V3Qgh3p7FVSUuKTJk2KOg0REdlMleXlzBy4V9JhfUs/axSd9eoj/hg/BHcXHDVkl81u6jezye5ekmyYrtwnIiKN2nf33sei669PiHe44grannZqBBk1jEzeXTCWCr+IiDQ67s704h8lHbbj+xPJa906wxmlR7rvLpiMCr+IiDQaFVOnMveEExPiWw4YQPf7x0aQUfZR4RcRkch9ecaZCdfNB+jx+GMU/PjHEWSUvVT4RUQkEpXLlzNzwJ5JhzXFznpNhQq/SJaI4tKfIptiyYMPsfDaaxPi2w0fzrbnnB1BRrlFhV8kC0R16U+RVNXaWe+9d8krLMxwRrkrI1fuE5H0qu3SnyJRWv3ZZxuvrBdri11/TPH0Uoqnl6roZ5j2+EWyQFSX/pToNPZDO1+ddx4r33gzId794YfYsn//CDKSKir8Ilkgqkt/SjQa66GdyhUrmVmS9GJx9P3sU6yZGpkbA20FkSwwfFAfCvLzqsUK8vMYPqhPRBlJOjW2QztLH3uc0r7FCUW//UW/2dicr6LfeGiPXyQLRHXpz2zR2JvN4zWGQzu1ddbr/c7bNN9224zlIvWjwi+SJaK49Gc2aKzN5rWJ8tDO6hkz+eKYYxLiLXfckR2eeTrJFNLYqO1FRHJaY2s2T0UUh3bm//pCSvsWJxT9bmPHUjy9VEW/CdEev4jktMbQbF5fmTq0s2HVKmb03z3psL6ffoLl5SUdJo2bCr+I5LSmekZEOg/tlD81nq9HjEiIt/vV/9H+wgvTskzJHBV+Eclpwwf1qXaMH3L3jIjSvsVJ473fepPm7dtnOBtJFxV+EclpuX5GxJrPP2fOEUcmxFt0707PCS9GkJGkmwq/iOS8XDwjomz4pSx/9tmEeNd77qbVT34SQUaSKSr8IiI5YsPq1czYrV/SYX0/mYY1V0nIBZt0Op+ZFZhZy4ZORkREGt6y556ntG9xQtHf9pfn/HBlPRX9nJHSljazG4DH3f19MzsCeAJwMzvR3RPbikREJHKlO+0MlZUJ8V6vv0Z+x44RZCSNQao/8X4B/Cl8/ifgVGAZcCOgwi8i0kisnTuXzw89LCHevGNHer/+WgQZSWOTauHf0t1Xmdm2wA7u/iSAmXVPX2oiIpKqBX/4A8ueeDIh3vWO22m1//4RZCSNVaqFf6aZ/QLoBbwEYGbtgMZ7aSsRkSy3Ye1aZvx416TD+k6biuXnZzgjaQpSLfy/Av4JrAPODmODgP+mIykREalZxaefMvfnxyXE25x+Gh0vvzyCjKQpSanwu/sHwN5xsYeAh9KRlIiIJPrmLyNZ+sADCfFer7xMflFuXYdANl3K52+Y2cHAScB27n6UmZUArd391bRlJyKS49YvXcqsvfZOiLc943Q6JLmevkhdUj2d79fAb4C7gar2pQrgJuJaAkREZPMte+55FlxySUJ8+6fGsUVx8mvqi6Qi1T3+i4CfuftcM/t9GJsO5N5dLERE0sTXr+eLE05gzWel1eIt+/Zl+yf+rYvsSINI9VO0NTAvfO7h33xgbYNnJCKSY1bPmMEXxwxOiHe+/q9sc/TREWQk2SzVwv8mcBnwl5jYhYCuBiEisokWXj+aJffemxDv/b93aN62bQQZSS5I9Vr9vwaONbO5wNZmNgM4AfhdKhObWVcze83MPjOzT83sN2H8SjMrM7OPwsfhMdOMMLPZZjbDzAbFxA8NY7PN7LJUV1REpDGoXLaM0r7FlPYtrlb025xyysbr5qvoSzqlejrf12a2BzAA6EbQ7P++u29IcTnrgYvd/UMz2xqYbGYvhcNudPcbYkc2sx8RnEGwE9AZeNnMdgwH3wIcDMwHPjCzZ9z9sxTzEBGJxPIXJ1B20UUJ8R7//jcFu+wcQUaSq1LuKeLuDkwMH/Xi7l8DX4fPvzezUqC2k06PAR519zXAF2Y2m+BHB8Bsd58DYGaPhuOq8ItIo+OVlXx5yi+o+PjjavEWO+zADk+P15X1JBI1Fn4zm8cPHflq5O7d6rNAM+sB9CP4AfETYJiZnQ5MImgVWErwo+C9mMnm88MPhXlx8T3rs3wRkXRbM2sWc45K7JTXaeRICoccG0FGIj+obY//1IZemJm1Ap4ELnL35WZ2G3ANwQ+Ma4C/8cMlgTdnOUOBoQDdutXrd4mIyCZbdOM/+O6OOxLivd9+i+bt2kWQkUiiGgu/u7/RkAsys3yCov+Qu48Ll3fMvh4AACAASURBVLEwZvhdwHPhyzKga8zkXcIYtcQ3cvc7gTsBSkpK6my1EBHZVJXLlzNzQGLDY+Hxx9HpmmsiyEikdin16jezcWa2b1xsXzN7IsXpDbgHKHX3v8fEO8WMdizwSfj8GeAkM2tpZtsDvYH3gQ+A3ma2vZm1IOgA+EwqOYiINKTvX36Z0r7FCUW/x2OPUjy9VEVfGq1UO/ftDxwfF3sXGJ/i9D8BTgOmmdlHYexy4GQz242gqX8ucB6Au39qZo8TdNpbD1zg7pUAZjYMmADkAfe6+6cp5iAisll8wwa+PP10KiZNrhbP79qVns8/h7VoEVFmIqlLtfCvBrYClsfEWhHcprdO7v42YEkGvVDLNH+h+gWDquIv1DadiEhDWzNnDnMOPyIh3vGqq2hz4gkRZCSy6VIt/BOAO8zsvLBTXmvgX8CL6UtNRCRai2/+F9/ecktCvNcbb5DfYbsIMhLZfKkW/ouBB4ElZrYEaAv8h6D5XkQka1SuWMHMPQaAV+8XvM3gwXS+blS95jV+ShmjJ8xgQXkFnQsLGD6oD4P71XYJE5H0S/XKfUuBI8LOeF2Aee7+TVozExHJoKWPP843f/pzQrz7ww+xZf/+9Z7f+ClljBg3jYp1lQCUlVcwYtw0ABV/iVRKhd/MbgTGuvtHhFfgExFp6nzDBqbvuhusq95dqXnHjvSc8CLNWrbc5HmPnjBjY9GvUrGuktETZqjwS6RSberPAyaY2WLgAYJz8eenLy0RkfSp+PRT5v78uIR427PPpsOlwxtkGQvKK+oVF8mUVJv6LzSz3wKHAb8A/mBmE4H7gXHuviKNOYqINIiy4Zey/NlnE+I7/OcFWm6/fYMuq3NhAWVJinznwoIGXY5IfaV6W17cvdLdn3P3k4GBQHtgDPCNmd1tZmq7EpFGp3L58o23wY0t+vldutC39DOKp5c2eNEHGD6oDwX5edViBfl5DB/Up8GXJVIfKRd+M2ttZueY2WvAmwQ32dkXKAZWEPTyFxFpFMqfGp/0ynpF//gHxdNL6fXySwQXFU2Pwf2KGDVkF4oKCzCgqLCAUUN20fF9iVyqnfueAAYRFPzbgfHhLXOrhv8OWJaWDEVEUuTuzNxjABtWJB597DN5Es222iqj+QzuV6RCL41Oqp373gOG1XQKn7tvMLMODZeWiEjqVs+YyRfHHJMQb3PKyXT8058iyEik8Uq18L8M5McGzKwb0MbdPwZw91UNnJuISK0W/OEPLHviyYT4Ds8/R8uePSPISKTxS7XwPwgcHRfLJzi178cNmpGISC0qV6xgZskeCfG89u3o/eabaT1uL5INUi383dx9TmzA3T83sx4NnpGISBLLnn2OBcMTz7HvfMMNbHNk4g10RCS5VAv/fDPr7+4fVgXMrD+wID1piYgEnfVm7bMvld99lzBsx0kfkNeqVQRZiTRtqRb+G4Gnzex64HOgJ3AJSW6bKyKyudbMns2cI49KiG9z3M/pfO21EWQkkj1SvXLfXWZWDpwDdAXmARe7+xPpTE5Ecss3V1/N0ocfSYhv//TTbNFnxwgyEsk+qe7x4+7/Bv6dxlxEJAdtWLmSGbuXJMSbtW7NjhPfU2c9kQZWY+E3s9Pc/YHw+dk1jefu96YjMRHJbstfnEDZRRclxDtdN4rCwYMjyEgkN9S2x38ywel6AKfVMI4DKvwikhJ3Z/bPfsb6BYl3997x/YnktW4dQVYiuaXGwu/uh8c8/2lm0hGRbLTmiy+Yc9jhCfHWRx9F0fXXR5CRSO5K+Ri/mRUCRwCdCU7je97dy9OVmIg0fQtHXceSsWMT4ts/NY4tiosjyEhEUr1Jz4HAOGAG8CXQDbjFzH7u7q+kMT8RaWJ83Tq+vuqqhEvpWsuW9JnyIdYs5ZuCikgapLrH/y9gqLs/XhUws+OBW4C+6UhMRJqWNXPmMPfkU9iwrPqNOjtefRVtTjghoqxEJF6qhb8zEH8njKeAuxo2HRFpStydpQ89zMK4i+q0PvwwOo0cSbMttogoMxGpSaqF/wHgAuCmmNj/Afc3eEYi0uhVlpczf9ivWTVpUrV40Y1/p/Vhh0WUlYikorbz+N8iOF0PoBlwvpldCpQBRUAH4L20ZygijcbK//2Pr84+p1qsRa+edLvnHvI7dIgoKxGpj9r2+O+Oe61mfZEc5OvXs3DkyIRL6ba74ALaXfArddYTaWJqO48/8RwcEckZa+fOZe4vTk24M16Pfz9OwS67RJSViGyulM/jF5HcsPTRR/nmyquqxbY+5BA6XzeKZltuGVFWItJQVPhFhMrly5l/4W9Y9V71bjudb7iBbY48IqKsJArjp5QxesIMFpRX0LmwgOGD+jC4X1HUaUkDUuEXyWEr35vIV2eeWS3WokcPut13L/mdOkWTlERm/JQyRoybRsW6SgDKyisYMW4agIp/FqmxV46ZvRfz/M+ZSUdE0s3Xr+ebkSMp7Vtcrehve/559P3sU3q++B8V/Rw1esKMjUW/SsW6SkZPmBFRRpIOtXXH3dHMqq6+cfHmLMTMuprZa2b2mZl9ama/CeNtzewlM5sV/m0Txs3MbjKz2WY21cz6x8zrjHD8WWZ2xubkJZJL1n71FbP225/pO+/C0vsf2Bjv8egjFE8vZbuLLlIP/Ry3oLyiXnFpmmpr6n8amGlmc4ECM3sz2Ujuvl8Ky1kPXOzuH5rZ1sBkM3sJOBN4xd2vM7PLgMuA3wOHAb3Dx57AbcCeZtYW+DNQQnCNgclm9oy7L00hB5GctPTf/+abP/6pWqzVgQdSNPp6mm21VVqWqePETVPnwgLKkhT5zoUFEWQj6VLb6Xxnmdk+QA9gD+CeTV2Iu38NfB0+/97MSgkuAnQMcEA42ljgdYLCfwxwv7s78J6ZFZpZp3Dcl9x9CUD44+FQoPoJxiI5rnLFCsp+cxEr33mnWrzTdaMoHDw4rcvWceKma/igPtW2HUBBfh7DB/WJMCtpaLV27nP3t4G3zaxFQ53Xb2Y9gH7ARKBD+KMA4BuCqwFC8KNgXsxk88NYTXHZDNo7yx4r3n6Heb/8ZbVYfteudB9zH/lFmdmmtR0n1ueqcavaPvo+yG4p9ep393vN7ADgdIJCWwY84O6v1WdhZtaK4GY/F7n7cjOLXYabmdc4cf2WMxQYCtCtW7eGmGXW0t5Z0+dr1zL9x7smxLf95Tm0/+1vsby8jOaj48RN2+B+Rfrfz3Ip9eQxs18CjxPslY8jaLZ/xMzOTXVBZpZPUPQfcvdxYXhh2IRP+HdRGC8DusZM3iWM1RSvxt3vdPcSdy9p3759qinmJPXibbpWvjeR0r7FCUW/6z13B531Lrkk40Ufaj4erOPEIo1DqufxXwoc7O4fVwXM7DGCQl7nNfwt2LW/Byh197/HDHoGOAO4Lvz7dEx8mJk9StC5b5m7f21mE4CRVb3/gUOAESmugyShvbOm58vTz2DV++8nxHu98Qb5HbaLIKPqdJxYpHFLtfBvC3wWF5sBtE1x+p8ApwHTzOyjMHY5QcF/3MzOAb4ETgiHvQAcDswGVgFnAbj7EjO7BvggHO/qqo5+smnUi7dpWLdoEbP32z8hXrD77vR46MEIMqqZjhOLNG4WdJyvYySzp4GvgN+7+yoz2woYBWzv7kelOcfNUlJS4pPi7hkuP4g/xg/B3tmoIbvoi7oR+O6ee1k0enRCvNuY+9hq4MAIMhKRpsDMJrt7SbJhqe7xnw88BiwzsyUEe/r/A05umBQlKto7a3x8/Xqm75z87nd9p36MtWiR4YxEJJuk2qv/a2A/M+sCdAYWuPv8tGYmGaNevI3DqsmT+fIXpybE2134a9r/6lcRZCQi2aheN+kJi70KfkR0vn12+urcoax8662EeK/XXtU180WkwenufE2EzrfPLuu//ZZZ++ybEN9i553Z/ol/R5CRiOQKFf4mQldDyw5L7n+AhSNHJsS73nUXrfbdJ4KMRCTX1Fn4zawZwTXy33b3tWnPSJLS+fZNl1dWMn2nnZMO6zP1Y5qps56IZFCdV+5z9w3A0yr60dLV0Jqeio8+Cq6sF1f0t/2/8ymeXkrx9FIVfRHJuFSb+t80s4Hu/l5as5Ea6WpoTce8C4ax4pVXEuI9X36ZFl10WEZEopVq4f8S+E94IZ95wMar/rj7n2qcShqMzrdv3NYvXcqsvfZOiLfs3Zsdnn0mgoxERJJLtfAXAOPD513SlIvUQefbNz5LH32Ub668KiHe5bZb2fqnP40gIxGR2qV6AZ+z0p2ISFPhGzYw/Uc7JR3W56MpNNtiiwxnJCKSupRP5zOzvsDxQAd3H2ZmfYCW7j41bdmJNCIV0z5h7vHHJ8Tbnn02HS4dHkFGIiL1l1LhN7PjgVsJbsN7CjAM2Jrg7noHpS07kUag7He/Y/kL/0mI95zwIi26d48gIxGRTZfqHv/VwEHu/rGZnRjGPgZ2TU9aItGqLC9n5sC9EuItundnhxf/g5lFkJWIyOZLtfBvB1Q16XvM37rv6SvShJQ/8QRf/+GPCfGim2+i9cEHR5CRiEjDSrXwTwZOA+6PiZ0EvN/gGYlkmG/YwIz+u+OrVycM6/PhZJptuWUEWYmIpEeqhf9C4L9mdg6wlZlNAHYEDklbZiJptrq0lC+OHZIQb3PaaXS84vIIMhIRSb9UT+ebHvbqPxJ4juAiPs+5+4p0JieSDgt+fxnLnn46Ib7DCy/QcoftI8hIRHJVFLdbT/l0PndfZWbvAF8AC1T0pSmp/P57Zu4xICHevFMner36ijrriUjGRXW79Tpv0gNgZt3M7C1gLvA8MNfM3jIzncskjdqyp5+mtG9xQtEvuvHvFE8vpfdrr6roi0gkarvdejqlusc/lqCD36HuvtLMWgHXhPED0pSbyCZxd2YO3IsNy5YlDOszeRLNttoqgqxERKqL6nbrqRb+3YFD3H0dgLuvMLPfA9+lLTORelozaxZzjjo6IV544ol0uurKzCckIlKLzoUFlCUp8um+3Xqqhf89YADwTkysBHi3wTMSqaev//Rnyh9/PCG+w7PP0LJ37wgyEhGpW1S3W6+x8JvZ1TEvPwdeMLPnCXr0dwUOBx5Oa3YiNahcsZKZJSUJ8by2ben9zts6bi8ijV5Ut1uvbY+/a9zrceHf7YA1wFOAbkMmGbX8hRco+93FCfHO1/+VbY5ObOYXEWnMorjdeo2FX7filcbC3Zm9/wGsX7QoYdiOkz4gr1WrCLISEWma6nNb3i2BXkC1b1l3/19DJyUCsGbOHOYcfkRCfJtjj6XzqJERZCQi0vSlelve04F/AWuB2C6IDnRLQ16Sw775y0iWPvBAQnz7p8ezRZ/0dnoREcl2qe7xXw/83N1fSmcykrs2rFrFjP67J8SbbbUVO076QJ31REQaSKqFfy3wehrzkBy1/L//pezC3yTEO40cSeGQYyPISEQku6Va+P8I/N3MrnL3b9OZkGQ/d+fzQYey7quvEobtOPE98rbZJoKsRERyQ6qFfyZwNfCrmCZXA9zd89KRmGSftV9+yeeDDk2Itz7iCIr+dkMEGYmI5J5UC/8DwP3AY1Tv3CdSp4XXj2bJvfcmxHs8+QQFO+0UQUYiIrkr1cK/LfAnd/dNWYiZ3QscCSxy953D2JXAucDicLTL3f2FcNgI4BygErjQ3SeE8UOBfwJ5wN3uft2m5CPpt2H1ambs1i9xQPPm9J36MdYspRtDiohIA0v12/c+4LTNWM4YILGNF250993CR1XR/xFwErBTOM2tZpZnZnnALcBhwI+Ak8NxpRH5/rXXKO1bnFD0O151FcXTSyn+ZJqKvohIhFLd4x8ADDOzK4CFsQPcfb+6Jnb3N82sR4rLOgZ41N3XAF+Y2exw+QCz3X0OgJk9Go77WYrzlTSac9TRrJk1KyHe+93/0bxNmwgyEhGRZFIt/HeFj4Y2LLw40CTgYndfChQR3A2wyvwwBsENgmLjeyabqZkNBYYCdOum6wuly9r58/n8oIMT4lsffBBdbr45goxERKQuKRV+dx+bhmXfBlxDcPW/a4C/AWc3xIzd/U7gToCSkpJN6pcgNVv0j3/w3e13JMR7PP4YBT/+cQQZiYhIqlK9ZG+NBdndE7trp8DdNx4yMLO7gOfCl2VUvzNglzBGLXFJsw1r1jBj192SDuv76SdYXuJZneOnlGX8dpMiIlK7VJv64zv2dQR6Au8Am1T4zayTu38dvjwW+CR8/gzwsJn9HegM9AbeJ7huQG8z256g4J8EnLIpy5bUrXjrbeade25CvMMVV9D2tFNrnG78lDJGjJtGxbpKAMrKKxgxbhqAir+ISIRSber/aXwsbAUoTmV6M3sEOABoZ2bzgT8DB5jZbgRN/XOB88JlfWpmjxN02lsPXODuleF8hgETCE7nu9fdP01l+Q0ll/ZgvzjueFZ/8klCvPc7b9N8223rnH70hBkbi36VinWVjJ4wI2vfMxGRpsA28dR8zKwZ8K27t23YlBpWSUmJT5o0abPnE78HC1CQn8eoIbtkTSFbt2ABsw/8WUJ8q/33o9sdicf0a7P9Zc+T7JNlwBfXJd5qV0REGo6ZTXb3kmTDUj3GH3/i9ZbAqUD5ZubWZGTzHuziW2/l25sSe+F3f/hhtuyf5CI8KehcWEBZeeJFHjsXFmzS/EREpGGkeox/PSTswJURXHkvJyxIUsRqizd2vnYt03+8a9JhfT+ZhjVP9aOR3PBBfZK2kAwf1Gez5isiIpsn1W/37eNer8y1u/Rlyx7synff5auzEk/S2O7SS9n27LMabDlVrSC50idCRKSpSLVz35fpTqSxa+p7sHNPPZWKSZMT4r3fepPm7dunZZmD+xWp0IuINDK1Fn4ze43EJv5Y7u6JvcGyUFPcg123cCGz9z8gIb7lwIF0H3Nf5hMSEZHI1bXH/2AN8SLgQoJOfjmjqezBfnvXXSz+298T4t3uH8tWAwYkmUJERHJFrYXf3e+JfW1m2wIjCDr1PQZcnb7UpD583Tqm75L8crl9p03F8vMznJGIiDRGqZ7O1xoYDgwjuLRuf3f/PJ2JSWpWffABX552ekK8/W9/S7vzhkaQkYiINGZ1HeMvAC4CLgZeB/bJ9NXyJLmvzj6blf97NyHe643Xye/QIYKMRESkKahrj38u0Ay4nuDWuR3MrFpVcfdX05OaxFu/eDGz9t0vIV7Qvz89Hn4ogoxERKSpqavwVxD06v+/GoY7sEODZiQJvhszhkXX/TUh3u3ee9hq770jyEgao1y6l4SIbLq6Ovf1yFAeEsfXr2f6zrskHdZ36sdYixYZzkgaM90NUURSFX8NfonYqg+nUNq3OKHotxs2jOLppRRPL1XRlwS13UtCRCTW5l2QXRrMvPP/jxWvv54Q7/XKy+QXaY9Napdt95IQkfRR4Y/Q+iVLmLX3TxLiW+y0E9s/+UQEGUlTlS33khCR9FNTfwSWPPQQpX2LE4p+1zvvoHh6qYq+1NvwQX0oyM+rFmtK95IQkczRHn+GeGUl03faOemwPh9/RLOWLTOckWSTpngvCRGJhgp/mlVMm8bc409IiG973nls99uLIshIslVTuZeEiERLhT9Nvrn6GpY+/HBCvOdL/6VF164RZCQiIqLC36A2rF7NghEj+P4/L1aLt+zdix2efTairERERH6gwt8AKj75lLnHHw/uPwTN6HbP3bqynoiINCoq/JvI3fn2ttv49qabq8ULTzqRjldcodvgiohIo6TCX0/rFi1i3rlDWTOj+hXRut59N632STwnX0REpDFR4a8Hd2f2fvtvfF2w++50+dfNNG/TJsKsREREUqfCXw9mRpdbb2Hd/Pm0Oe00zCzqlEREROpFhb+etj7wwKhTEBER2WS6ZK+IiEgOUeEXERHJISr8IiIiOUSFX0REJIeo8IuIiOQQFX4REZEckpHCb2b3mtkiM/skJtbWzF4ys1nh3zZh3MzsJjObbWZTzax/zDRnhOPPMrMzMpG7iIhINsnUHv8Y4NC42GXAK+7eG3glfA1wGNA7fAwFboPghwLwZ2BPYADw56ofCyIiIpKajBR+d38TWBIXPgYYGz4fCwyOid/vgfeAQjPrBAwCXnL3Je6+FHiJxB8TIiIiUosoj/F3cPevw+ffAB3C50XAvJjx5oexmuIiIiKSokbRuc/dHfA6R0yRmQ01s0lmNmnx4sUNNVsREZEmL8rCvzBswif8uyiMlwFdY8brEsZqiidw9zvdvcTdS9q3b9/giYuIiDRVURb+Z4CqnvlnAE/HxE8Pe/cPBJaFhwQmAIeYWZuwU98hYUxERERSlJG785nZI8ABQDszm0/QO/864HEzOwf4EjghHP0F4HBgNrAKOAvA3ZeY2TXAB+F4V7t7fIdBERERqYUFh9ezV0lJiU+aNCnqNERERDLGzCa7e0myYY2ic5+IiIhkhgq/iIhIDlHhFxERySEq/CIiIjlEhV9ERCSHqPCLiIjkkIycxy8igfFTyhg9YQYLyivoXFjA8EF9GNxPt5wQkcxR4RfJkPFTyhgxbhoV6yoBKCuvYMS4aQAq/iKSMWrqF8mQ0RNmbCz6VSrWVTJ6woyIMhKRXKTCL5IhC8or6hUXEUkHFX6RDOlcWFCvuIhIOqjwi2TI8EF9KMjPqxYryM9j+KA+EWUkIrlInftEMqSqA5969YtIlFT4RTJocL8iFXoRiZSa+kVERHKICr+IiEgOUeEXERHJISr8IiIiOUSFX0REJIeo8IuIiOQQFX4REZEcosIvIiKSQ1T4RUREcogKv4iISA5R4RcREckhKvwiIiI5RIVfREQkh6jwi4iI5BAVfhERkRyiwi8iIpJDVPhFRERySPOoExAYP6WM0RNmsKC8gs6FBQwf1IfB/YqiTktERLJQ5IXfzOYC3wOVwHp3LzGztsBjQA9gLnCCuy81MwP+CRwOrALOdPcPo8i7NvUp5OOnlDFi3DQq1lUCUFZewYhx0wBU/EVEpME1lqb+n7r7bu5eEr6+DHjF3XsDr4SvAQ4DeoePocBtGc+0DlWFvKy8AueHQj5+SlnS8UdPmLGx6FepWFfJ6AkzMpCtiIjkmsZS+OMdA4wNn48FBsfE7/fAe0ChmXWKIsGa1LeQLyivqFdcRERkczSGwu/Af81sspkNDWMd3P3r8Pk3QIfweREwL2ba+WGs0ahvIe9cWFCvuIiIyOZoDIV/H3fvT9CMf4GZ7Rc70N2d4MdBysxsqJlNMrNJixcvbsBU61bfQj58UB8K8vOqxQry8xg+qE+D5yYiIhJ54Xf3svDvIuApYACwsKoJP/y7KBy9DOgaM3mXMBY/zzvdvcTdS9q3b5/O9BPUt5AP7lfEqCG7UFRYgAFFhQWMGrKLOvaJiEhaRNqr38y2Apq5+/fh80OAq4FngDOA68K/T4eTPAMMM7NHgT2BZTGHBBqFqoJdn9PzBvcrUqEXEZGMiPp0vg7AU8FZejQHHnb3F83sA+BxMzsH+BI4IRz/BYJT+WYTnM53VuZTrpsKuYiINFaRFn53nwPsmiT+HfCzJHEHLshAaiIiIlkp8mP8IiIikjkq/CIiIjlEhV9ERCSHqPCLiIjkEBV+ERGRHKLCLyIikkNU+EVERHKIBafGZy8zW0xwEaBs0g74Nuok0izb1zHb1w+yfx21fk1fNq9jd3dPes36rC/82cjMJrl7SdR5pFO2r2O2rx9k/zpq/Zq+XFjHZNTULyIikkNU+EVERHKICn/TdGfUCWRAtq9jtq8fZP86av2avlxYxwQ6xi8iIpJDtMcvIiKSQ1T4mwgzyzOzKWb2XPh6jJl9YWYfhY/dos5xU5nZXDObFq7HpDDW1sxeMrNZ4d82Uee5OWpYxyvNrCxmGx4edZ6byswKzewJM5tuZqVmtlc2bcMa1i+btl+fmPX4yMyWm9lF2bINa1m/rNmG9aGm/ibCzH4HlACt3f1IMxsDPOfuT0Sb2eYzs7lAibt/GxO7Hlji7teZ2WVAG3f/fVQ5bq4a1vFKYIW73xBVXg3FzMYCb7n73WbWAtgSuJws2YY1rN9FZMn2i2VmeUAZsCdwAVmyDavErd9ZZOE2rIv2+JsAM+sCHAHcHXUuGXQMMDZ8PhYYHGEuUgsz2wbYD7gHwN3Xuns5WbINa1m/bPUz4HN3/5Is2YZxYtcvJ6nwNw3/AC4FNsTF/2JmU83sRjNrGUFeDcWB/5rZZDMbGsY6uPvX4fNvgA7RpNZgkq0jwLBwG97bVJtRge2BxcB94eGou81sK7JnG9a0fpAd2y/eScAj4fNs2YaxYtcPsnMb1kqFv5EzsyOBRe4+OW7QCKAvsAfQFmjKzW/7uHt/4DDgAjPbL3agB8ejmvoxqWTreBvQE9gN+Br4W4T5bY7mQH/gNnfvB6wELosdoYlvw5rWL1u230bhYYyjgX/HD2vi2xBIun5Ztw1TocLf+P0EODo8RvwocKCZPejuX3tgDXAfMCDKJDeHu5eFfxcBTxGsy0Iz6wQQ/l0UXYabL9k6uvtCd6909w3AXTTdbTgfmO/uE8PXTxAUymzZhknXL4u2X6zDgA/dfWH4Olu2YZVq65el27BOKvyNnLuPcPcu7t6DoInqVXc/Neaf0QiOu30SYZqbzMy2MrOtq54DhxCsyzPAGeFoZwBPR5Ph5qtpHau2YehYmug2dPdvgHlm1icM/Qz4jCzZhjWtX7ZsvzgnU70ZPCu2YYxq65el27BO6tXfhJjZAcAlYa/+V4H2gAEfAee7+4oo89sUZrYDwR4wBE2qD7v7X8xsW+BxoBvB3RVPcPclEaW5WWpZxwcImhgdmAucF3M8tUmx4HTSu4EWwByC3tLNyJ5tmGz9biJLth9s/FH6FbCDuy8LY9n0f5hs/bLmf7A+VPhFRERyiJr6RUREcogKv4iISA5R4RcREckhKvwiIiI5RIVfREQkh6jwi0hWM7Mfmdmk8JoXtY3XIbzzziHqLQAABFJJREFUXlO+/LVInVT4RRopM1sR89hgZhUxr38RdX6bwoLbEx+U4cVeA9wQXnK2KoeKuPe3c3g1t9eAobXOTaSJU+EXaaTcvVXVg+DCI0fFxB6KOr94Zta8sS0jvDLbT4HxcYNi38tW7r4gjD8EnNcAqYo0Wir8Ik2MmTUzs8vM7HMz+87MHjeztuGwHmbmZnaWmc0zs6Vmdr6Z7RHegazczP4VM68zzewdM/uXmS0zs+lm9rOY4duY2T1m9rWZlZnZteH9zGOnvdHMvgOuNLOeZvZqmNe3ZvaQmRWG4z9AcAW4Z8O97EvN7AAzmx+3fhtbBczsSjN7wsweNLPlwJm15ZTEwQTXZl+d4ts7EdjBzLqnOL5Ik6PCL//f3v2EaFWFcRz/PsHAEM0wixkjpQSbBBURBXVTuHFTs9CF2aIMFy4kKFroSiEjTBD6IyK4rJUIoRLYYpRAXFTLWgwUKol/YqZilFFsaPLn4pwXjjff6TY2xJ37+8DAe997znufdzPnnnOe+z7WPG+T6jNsAhYDk8CxSpuNwAvAa6SyzvuAzcAqYHtEbKq0vQwMAu8Bpzo3EsBnwAwwDKwl1RnYVel7hVSu9SDpJ6QP5bhWAM8CBwAk7eDhlYvDNb/vFlJhnAHSjPyfYiqtBn6seR0kzQCXgDV1+5g1jQd+s+bZDeyTdD1XZzwAbKssg38g6Q9Jo6QysickTeQqgRdJA2bHBPCppD8lnSQNlCMR8TTwCvCupLu5suAnpGJRHTclHZU0I+mepEuSzkmalvQr8DHpBuVxfCPpTK6g1l8jptIAMPWI98/k1Y9bEVHdBpjK/cwWpHnfkzOz/9xS4HRE3C/e+4s06+4YL17fe8TxU8XxjU7iW3aVNGNfCvQAvxQJ8U8A14q25WvyzcIR4CWgL7efrPWtuiuvUSem0mSOo2qrpPNd+vQBt+YQp1kjeMZv1jzXgJclDRR/vXk2PxdLKo+6PQfczNeZBgaL6/RLWlW0rVb5+jC/t1pSP/AGafm/W/u7wJOdg7xXP1RpU/apE1PpB2B5l3N/k1dNhoHv6/YxaxoP/GbNcxw42ElAi4ihiNjyGJ+3CHgnInoi4lXS3vxXuTzpKPBRRPTnpMLnK/kBVX3AHeB2RCwB9lbOjwPLiuOfgN6IGImIHmA/0PU5+jnEdA5YFxG9s8Rc2gD8LOlqzfZmjeOB36x5jgBfAqMRMQV8S0qym6vvSImAv5ES9LZJ+j2fe5NUg36MtGz+BfDMLJ/1PrAOuA2cBU5Vzh8C9ue99T25LvpbpFr3N0grANeZXe2Y8rP5X5MSBOt4nXRjZbZgxcNbe2bWJhGxE9gl6cX/O5b5EhErgc+BDZrlH15ELAIuAGv/xeN/Zo3j5D4zW9AkjQHra7SbIG1zmC1oXuo3MzNrES/1m5mZtYhn/GZmZi3igd/MzKxFPPCbmZm1iAd+MzOzFvHAb2Zm1iIe+M3MzFrkAR7xvmzaD7oeAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let's load in a test dataset to make predictions on data points that our model has not seen before."
      ],
      "metadata": {
        "id": "_Z0X7zBEvOY4"
      },
      "id": "_Z0X7zBEvOY4"
    },
    {
      "cell_type": "code",
      "source": [
        "bike_test_df = pd.read_csv('/content/drive/MyDrive/data sci/Copy of brooklyn_bridge_bicycles_test.csv')\n",
        "bike_test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "JSHiut0zri2S",
        "outputId": "5c3df8c7-adea-410c-ae47-e3db92f51138"
      },
      "id": "JSHiut0zri2S",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date  Temperature  Number of bicycles\n",
              "0  2016-04-03 00:00:00         39.9               526.0\n",
              "1  2016-04-11 00:00:00         62.1              2005.0\n",
              "2  2016-04-12 00:00:00         57.0              1045.0\n",
              "3  2016-04-14 00:00:00         62.1              2861.0\n",
              "4  2016-04-18 00:00:00         81.0              3871.0\n",
              "5  2016-04-25 00:00:00         66.9              3463.0\n",
              "6  2016-04-27 00:00:00         62.1              3343.0\n",
              "7  2016-04-28 00:00:00         57.9              2486.0\n",
              "8  2016-04-29 00:00:00         57.0              2375.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b0db9119-03c4-493f-adfc-f2ca20a2b393\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Number of bicycles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-04-03 00:00:00</td>\n",
              "      <td>39.9</td>\n",
              "      <td>526.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-11 00:00:00</td>\n",
              "      <td>62.1</td>\n",
              "      <td>2005.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-12 00:00:00</td>\n",
              "      <td>57.0</td>\n",
              "      <td>1045.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-14 00:00:00</td>\n",
              "      <td>62.1</td>\n",
              "      <td>2861.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-18 00:00:00</td>\n",
              "      <td>81.0</td>\n",
              "      <td>3871.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-04-25 00:00:00</td>\n",
              "      <td>66.9</td>\n",
              "      <td>3463.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-04-27 00:00:00</td>\n",
              "      <td>62.1</td>\n",
              "      <td>3343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-04-28 00:00:00</td>\n",
              "      <td>57.9</td>\n",
              "      <td>2486.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-04-29 00:00:00</td>\n",
              "      <td>57.0</td>\n",
              "      <td>2375.0</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use your trained model to make predictions on the test set temperatures."
      ],
      "metadata": {
        "id": "sz45It8AvZqO"
      },
      "id": "sz45It8AvZqO"
    },
    {
      "cell_type": "code",
      "source": [
        "### Your code here\n",
        "\n",
        "# TODO: fill in the line below\n",
        "bike_predictions_test = model.predict(X=bike_test_df[['Temperature']])\n",
        "bike_predictions_test"
      ],
      "metadata": {
        "id": "hAxz_iXStRwF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "id": "hAxz_iXStRwF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1017.95021926, 2284.76084851, 1993.73678504, 2284.76084851,\n",
              "       3363.26178964, 2558.66584943, 2284.76084851, 2045.09397271,\n",
              "       1993.73678504])"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can compare our predictions against the true values to see how well the model did! What do you think?"
      ],
      "metadata": {
        "id": "CxydSphmvjS4"
      },
      "id": "CxydSphmvjS4"
    },
    {
      "cell_type": "code",
      "source": [
        "bike_test_df['Number of bicycles (predicted)'] = bike_predictions_test\n",
        "bike_test_df"
      ],
      "metadata": {
        "id": "rQsIGOa7r5lu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "6bf58b5a-c386-42ba-a46e-5d6abf187da4"
      },
      "id": "rQsIGOa7r5lu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date  Temperature  Number of bicycles  \\\n",
              "0  2016-04-03 00:00:00         39.9               526.0   \n",
              "1  2016-04-11 00:00:00         62.1              2005.0   \n",
              "2  2016-04-12 00:00:00         57.0              1045.0   \n",
              "3  2016-04-14 00:00:00         62.1              2861.0   \n",
              "4  2016-04-18 00:00:00         81.0              3871.0   \n",
              "5  2016-04-25 00:00:00         66.9              3463.0   \n",
              "6  2016-04-27 00:00:00         62.1              3343.0   \n",
              "7  2016-04-28 00:00:00         57.9              2486.0   \n",
              "8  2016-04-29 00:00:00         57.0              2375.0   \n",
              "\n",
              "   Number of bicycles (predicted)  \n",
              "0                     1017.950219  \n",
              "1                     2284.760849  \n",
              "2                     1993.736785  \n",
              "3                     2284.760849  \n",
              "4                     3363.261790  \n",
              "5                     2558.665849  \n",
              "6                     2284.760849  \n",
              "7                     2045.093973  \n",
              "8                     1993.736785  "
            ],
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              "      <th>Date</th>\n",
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              "      <th>6</th>\n",
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              "      <td>62.1</td>\n",
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