ADD Regression.ipynb

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   "source": [
    "# SISU Digital Humanities: Textual and Language Analysis on Social Media\n",
    "Created by Tom van Nuenen (tom.van_nuenen@kcl.ac.uk) <br />\n"
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    "# Linear regression\n",
    "\n",
    "This optional notebook introduces multiple linear regression in Python.\n",
    "Linear regressions explain the relationship between a dependent/explanatory variable and an independent/target variable. It is a type of **predictive analysis**. \n",
    "\n",
    "The overall idea of regression is to examine \n",
    "1. Whether a set of predictor variables does a good job in predicting an outcome (dependent) variable, and \n",
    "2. Which variables in particular are significant predictors of the outcome variable\n",
    "\n",
    "Linear regression tries to fit a linear equation to the observed data in order to explain this relation. It works with *continuous* variables (floats).\n",
    "- *Simple linear regression* uses a single explanatory variable\n",
    "- *Multiple linear regression* uses several explanatory variables \n",
    "\n",
    "After working through this notebook, you'll be able to:\n",
    "\n",
    "1. Know how to implement multiple linear regression analysis in Python;\n",
    "2. Understand what kinds of questions you can ask when using regression."
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     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import datasets ## imports datasets from scikit-learn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline"
   ]
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   "source": [
    "## Test using Boston house prices\n",
    "\n",
    "The Boston house prices dataset includes information on housing prices and possibly correlated features. Let's use it to test a sklearn's `LinearRegression` model."
   ]
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    "data = datasets.load_boston() ## loads Boston dataset from datasets library \n",
    "\n",
    "# load up the data\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "# the target/dependent variable is missing. Let's add it to our dataset.\n",
    "df['MEDV'] = data.target"
   ]
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   "source": [
    "These are the data we can work with:"
   ]
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     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
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   "source": [
    "print(data.DESCR)"
   ]
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   "source": [
    "...and here they are in a df:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  MEDV\n",
       "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98  24.0\n",
       "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14  21.6\n",
       "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03  34.7\n",
       "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94  33.4\n",
       "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33  36.2\n",
       "\n",
       "[5 rows x 14 columns]"
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   "source": [
    "df.head()"
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   "source": [
    "### Exploring the data\n",
    "\n",
    "As we've noted before, exploratory data analysis is a very important step before training the model. You have to know something about the data you analyze, in order to do so properly.\n",
    "We can use some visualizations to understand the relationship of the target variable with other features.\n",
    "\n",
    "Let's start by looking at our target variable, MEDV."
   ]
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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.distplot(df['MEDV'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ER-jpBonZn9L"
   },
   "source": [
    "We see that the values of MEDV are distributed normally with few outliers.\n",
    "\n",
    "Next, we can create a correlation matrix that measures the linear relationships between the variables. This correlation matrix can be formed by using the `corr` function from the pandas dataframe library. We will use the `heatmap` function from the seaborn library to plot the correlation matrix. This way, we can get an overview of features that seem correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3417,
     "status": "ok",
     "timestamp": 1596644528797,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "qsY1EhTYZn9M",
    "outputId": "b6f4aeff-3119-45b1-c04e-198e83271751"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb591f18518>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.corr().round(2)\n",
    "# annot = True to print the values inside the square\n",
    "sns.heatmap(data=correlation_matrix, annot=True, cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "irnEUW4TZn9P"
   },
   "source": [
    "The correlation coefficient of these variables ranges from -1 to 1. If the value is close to 1 (dark red), it means that there is a strong positive correlation between the two variables. When it is close to -1 (dark blue), the variables have a strong negative correlation. Can you see the variables that seem to be strongly correlated with MEDV?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YY2hbI0zZn9P"
   },
   "source": [
    "### Fitting a model: beware of multicolinearity\n",
    "\n",
    "To fit a linear regression model, we select those features which have a high correlation with our target variable MEDV. By looking at the correlation matrix we can see that RM has a strong positive correlation with MEDV (0.7) where as LSTAT has a high negative correlation with MEDV(-0.74).\n",
    "\n",
    "An important point in selecting features for a linear regression model is to check for *multicolinearity*. The features RAD, TAX have a correlation of 0.91: they are strongly correlated to each other. \n",
    "\n",
    "Consider the simplest case where 𝑌 is regressed against 𝑋 and 𝑍 and where 𝑋 and 𝑍 are highly positively correlated. Then the effect of 𝑋 on 𝑌 is hard to distinguish from the effect of 𝑍 on 𝑌 because any increase in 𝑋 tends to be associated with an increase in 𝑍.\n",
    "\n",
    "In short, we should remove these features. The same goes for the features DIS and AGE which have a correlation of -0.75.\n",
    "\n",
    "Based on the above observations we will RM and LSTAT as our features. Using a scatter plot, let’s see how these features vary with MEDV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1833,
     "status": "ok",
     "timestamp": 1596644533194,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "0vqcuTpQZn9Q",
    "outputId": "c577fa22-8a05-4d39-b7d1-09f9bc2c877e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "features = ['LSTAT', 'RM']\n",
    "target = df['MEDV']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = df[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(col)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('MEDV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a2Al7iBKZn9T"
   },
   "source": [
    "The prices increase as the value of RM increases linearly. There are few outliers and the data seems to be capped at 50.\n",
    "The prices tend to decrease with an increase in LSTAT. Though it doesn’t look to be following exactly a linear line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fuGg2_JwZn9U"
   },
   "source": [
    "### Splitting the data\n",
    "\n",
    "Next, we split the data into training and testing sets. We train the model with 80% of the samples and test with the remaining 20%. We do this to assess the model’s performance on unseen data. To split the data we use `train_test_split` function provided by scikit-learn . We finally print the sizes of our training and test set to verify if the splitting has occurred properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1003,
     "status": "ok",
     "timestamp": 1596644537076,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "crQ-PmCVZn9U"
   },
   "outputs": [],
   "source": [
    "# Defining independent (X) and dependent (Y) variables\n",
    "X = df[['LSTAT','RM']]\n",
    "Y = df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 753,
     "status": "ok",
     "timestamp": 1596644537080,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "UP460BpUZn9X",
    "outputId": "b8bc4bf7-90b5-4c53-cedd-3f4e8a69c54a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 2)\n",
      "(102, 2)\n",
      "(404,)\n",
      "(102,)\n"
     ]
    }
   ],
   "source": [
    "# Defining split \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8J4oeFS7Zn9a"
   },
   "source": [
    "Let's fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1190,
     "status": "ok",
     "timestamp": 1596644540455,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "_X8xDPCxZn9b"
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lap7lG-mZn9d"
   },
   "source": [
    "And let's evaluate it using RMSE and R2-score. \n",
    "\n",
    "The **RMSE** is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data: how close the observed data points are to the model's predicted values. \n",
    "\n",
    "The **R2** socre is the proportion of the variance in the dependent variable that is predictable from the independent variable(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 817,
     "status": "ok",
     "timestamp": 1596644541088,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "cHw7UZHZZn9e",
    "outputId": "693cba54-7586-4ce1-c505-da53bbb89a90",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "RMSE is 5.6371293350711955\n",
      "R2 score is 0.6300745149331701\n",
      "\n",
      "\n",
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "RMSE is 5.137400784702911\n",
      "R2 score is 0.6628996975186952\n"
     ]
    }
   ],
   "source": [
    "# model evaluation for training set\n",
    "y_train_predict = model.predict(X_train)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
    "r2 = r2_score(Y_train, y_train_predict)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# model evaluation for testing set\n",
    "y_test_predict = model.predict(X_test)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
    "r2 = r2_score(Y_test, y_test_predict)\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L6I9TFwmZn9g"
   },
   "source": [
    "In our case of multivariable linear regression, the regression model has to find the most optimal coefficients for all the attributes. Let's see what coefficients our regression model has chosen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 904,
     "status": "ok",
     "timestamp": 1596644547629,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "wsFpMXpYZn9h",
    "outputId": "f7c256a9-6961-46bb-8a72-5631f684b76c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-0.717230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>4.589388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coefficient\n",
       "LSTAT    -0.717230\n",
       "RM        4.589388"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_, X.columns, columns=['Coefficient'])  \n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bKwWQJujZn9k"
   },
   "source": [
    "And let's make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1004,
     "status": "ok",
     "timestamp": 1596644553043,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "24EnV_XIZn9l",
    "outputId": "38efb66d-fbbf-4e9c-fcad-6dcc4930785b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29.33966548 25.64922478 32.82056043 32.74412501 31.71376526]\n"
     ]
    }
   ],
   "source": [
    "y_predict = lm.predict(X)\n",
    "print(y_predict[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gN_gMZtfZn9n"
   },
   "source": [
    "Finding the intercept and regression coeficient works as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 907,
     "status": "ok",
     "timestamp": 1596644562638,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "evbhtM4lZn9o",
    "outputId": "8135f81c-9c90-45c9-a602-e084abce4eb2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 2.7362403426066066\n",
      "Coefficients: \n",
      " [-0.71722954  4.58938833]\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', model.intercept_)\n",
    "print('Coefficients: \\n', model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X8pydxKMZn9q"
   },
   "source": [
    "Let's print the R² score of our model. This score provides a measure of how well observed outcomes are replicated by our model, based on the proportion of total variation of outcomes explained by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 653,
     "status": "ok",
     "timestamp": 1596644563707,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "C45gibLaZn9r",
    "outputId": "b98ce1ec-ed18-411a-f4f6-3b670cd288c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6364367726715208"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rwOXIRzrZn9u"
   },
   "source": [
    "### Using `statsmodels`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SgN6BoqvZn9u"
   },
   "source": [
    "Let's make another model with `statsmodels`, just to see the difference. The nice thing about `statsmodels` is that it outputs a nice summary of the model, including metrics for model evaluation such as the R-squared value (a value ranging from 0 to 1 that indicates to what extent X perfectly accounts for all the variation in Y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rURYhsFkZn9v"
   },
   "outputs": [],
   "source": [
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XpBcckrcZn9w",
    "outputId": "08f18dfe-1b5f-4e41-fcd0-2ee407492a14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   MEDV   R-squared:                       0.948\n",
      "Model:                            OLS   Adj. R-squared:                  0.948\n",
      "Method:                 Least Squares   F-statistic:                     4637.\n",
      "Date:                Mon, 05 Aug 2019   Prob (F-statistic):               0.00\n",
      "Time:                        13:16:52   Log-Likelihood:                -1582.9\n",
      "No. Observations:                 506   AIC:                             3170.\n",
      "Df Residuals:                     504   BIC:                             3178.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "LSTAT         -0.6557      0.031    -21.458      0.000      -0.716      -0.596\n",
      "RM             4.9069      0.070     69.906      0.000       4.769       5.045\n",
      "==============================================================================\n",
      "Omnibus:                      145.153   Durbin-Watson:                   0.834\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              442.157\n",
      "Skew:                           1.351   Prob(JB):                     9.70e-97\n",
      "Kurtosis:                       6.698   Cond. No.                         4.72\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uBve5qloZn9z"
   },
   "source": [
    "## Regression on Reddit: Preprocessing\n",
    "\n",
    "We'll start by cleaning up our data a bit. We'll use r/amitheasshole; see the Classification notebook from week 5-3 for more info. Let's load it up. First, make sure the .csv in the same folder as this notebook (you can find it in the Class Dropbox)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 16503,
     "status": "ok",
     "timestamp": 1596644585108,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "rdPlCnJbGIdT"
   },
   "outputs": [],
   "source": [
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18883,
     "status": "ok",
     "timestamp": 1596644607137,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "LaEjctJwtPnI"
   },
   "outputs": [],
   "source": [
    "downloaded = drive.CreateFile({'id':\"1OGjtSWvPKmDseWQuzWA2EfoX_41ohhGo\"})   \n",
    "downloaded.GetContentFile('amita-submissions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 23578,
     "status": "ok",
     "timestamp": 1596644611856,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "1cY6-yUcZOoq",
    "outputId": "7c37ca90-e5d6-4e17-b208-ff5edb4a0d3f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (7,11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# load into df\n",
    "df = pd.read_csv(\"amita-submissions.csv\", lineterminator='\\n', encoding=\"utf8\")[:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1031,
     "status": "ok",
     "timestamp": 1596644612899,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "BobIKSa8Zh2t",
    "outputId": "235dd90f-505d-4fcd-bec3-2f3911e09c2f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16267"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean up empty entries\n",
    "df = df.drop(['augmented_at', 'augmented_count', 'distinguish'], axis=1)\n",
    "df = df[~df['selftext'].isin(['[removed]', '[deleted]' ])].dropna(subset=['selftext'])\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1014,
     "status": "ok",
     "timestamp": 1596644612902,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "54WPCo_FZn92",
    "outputId": "3e0e2ffd-7746-450c-9622-5ab9c192d98a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>idint</th>\n",
       "      <th>idstr</th>\n",
       "      <th>created</th>\n",
       "      <th>self</th>\n",
       "      <th>nsfw</th>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>selftext</th>\n",
       "      <th>score</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>textlen</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>flair_text</th>\n",
       "      <th>flair_css_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87247149</td>\n",
       "      <td>t3_1fy0bx</td>\n",
       "      <td>1370724175</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>flignir</td>\n",
       "      <td>AItA: I like air conditioning and my coworkers...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I work in an office that requires me to wear a...</td>\n",
       "      <td>2</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>1067</td>\n",
       "      <td>1</td>\n",
       "      <td>not the asshole</td>\n",
       "      <td>not</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118961390</td>\n",
       "      <td>t3_1ytr72</td>\n",
       "      <td>1393275159</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Irishman_reddit</td>\n",
       "      <td>[AITA] Construction worker here</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I have been on a parking structure project for...</td>\n",
       "      <td>63</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>565</td>\n",
       "      <td>9</td>\n",
       "      <td>too close to call</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118969807</td>\n",
       "      <td>t3_1ytxov</td>\n",
       "      <td>1393278651</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bobsmit</td>\n",
       "      <td>[AITA] I wrote an explanation in TIL and came ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Here is the post in question](http://www.redd...</td>\n",
       "      <td>51</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>510</td>\n",
       "      <td>13</td>\n",
       "      <td>asshole</td>\n",
       "      <td>ass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118975728</td>\n",
       "      <td>t3_1yu29c</td>\n",
       "      <td>1393281184</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>WoopAhhh</td>\n",
       "      <td>[AITA] Threw my parent's donuts away</td>\n",
       "      <td>NaN</td>\n",
       "      <td>My parents are diabetic, morbidly obese, and a...</td>\n",
       "      <td>142</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>735</td>\n",
       "      <td>27</td>\n",
       "      <td>asshole</td>\n",
       "      <td>ass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118978034</td>\n",
       "      <td>t3_1yu41e</td>\n",
       "      <td>1393282238</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>ThisIsMeYoRightHere</td>\n",
       "      <td>[AITA] I Put My Empty Beer on a Bar Table</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Relevant Facts:\\n\\n1) It was a crowded bar, th...</td>\n",
       "      <td>46</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>660</td>\n",
       "      <td>7</td>\n",
       "      <td>nothing happened</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idint      idstr  ...         flair_text  flair_css_class\n",
       "0   87247149  t3_1fy0bx  ...    not the asshole              not\n",
       "1  118961390  t3_1ytr72  ...  too close to call              NaN\n",
       "2  118969807  t3_1ytxov  ...            asshole              ass\n",
       "3  118975728  t3_1yu29c  ...            asshole              ass\n",
       "4  118978034  t3_1yu41e  ...   nothing happened              NaN\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8lfHuxGyZn94"
   },
   "source": [
    "Let's look at our Y – which will be the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1009,
     "status": "ok",
     "timestamp": 1596644616262,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "4Vk52qFhZn94",
    "outputId": "83afec92-3eb2-48a6-fdf8-672aed8482aa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.distplot(df['score'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 554
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2481,
     "status": "ok",
     "timestamp": 1596644659565,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "pbhlbhHTZn96",
    "outputId": "f7558e6d-1a24-4fe8-9fb2-e245b3843c67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb58c3fff98>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.corr().round(2)\n",
    "# annot = True to print the values inside the square\n",
    "sns.heatmap(data=correlation_matrix, annot=True, cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NbRRmyH8Zn99"
   },
   "source": [
    "Looks like `score` and `num_comments` are related. This makes intuitive sense. Let's grab  both `num_comments` and `textlen` as our X in order to predict `score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1964,
     "status": "ok",
     "timestamp": 1596644672638,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "5DMS3dJZZn99",
    "outputId": "02b643d6-b6c5-4138-b1a3-d8fd23423b36"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XX3xxh78Nw/CfxfP99/bt2xMwTNjd0nU1HZI6SWpt82rpuhoSOwAAokQOljrkMAAAmCtscWrIkCFqbm7WFVdcoQkTJvinfAOdNTS2RPU4AAAIjhwsdchhAAAwV9i79S1btkyPPfaYDh06pKuuukrf/va3tXLlSh0/flxZWVnKyspKxThhA+78nKgeBwAAwZGDpQ45DAAA5gpbnJKkwYMH684779TatWt13XXX6bXXXtOoUaNUXV2d7PHBRiZXlMiV3fGflCvbqckVJSaNCAAAeyMHSw1yGAAAzBX2sr72Pv74Y7311lvatGmTSktLlZ+fn6xxwYZ8PRm40w0AAIlFDpZc5DAAAJgrbHHq0KFDWrlypZ5//nkdPXpUEydO1B/+8Ie0uzsMEqN8WD8SOQAAEoAcLLXIYQAAME/Y4tT555+vgQMHauLEifrSl74kSdq1a5d27drlf055eXnyRggAAJCByMEAAECmCFucKiwsVEtLi5599lk9++yzXZY7HA698sorSRkcAABApiIHAwAAmSJscWrt2rWpGAcAAADaIQcDAACZIqqG6IBdVFXX09QUAABkJPIgAIDdUJxC2qmqrteTq3aotc0rSWpobNGTq3ZIEokZAABIa+RBAAA7cpo9ACDRlq6r8SdkPq1tXi1dV2PSiAAAAFKDPAgAYEfMnELaaWhsiepxoDMuhwiO7wYAohNsv5ms/Sl5EADAjihOIe2483MCJmDu/BwTRgO74XKI4PhuACA6wfab//r0kNZvqU/K/pQ8CABgRxSnkHYmV5R0SAQlyZXt1OSKEhNHBbsIdTlEphdg+G6QKszQQ7oItt9ct6lWXkNdHk/E/jSWPIhtDgBgNopTSDu+ZIokC7Hgcojg+G6QCszQQzoJtn/sXJgK9/xoRJsHsc0BAKyA4hTSUvmwfiRUiAmXQwTHd4NUYIYe0kmw/abTEbhAlaj9aTR5ENscAMAKKE7BdEwlh5VwWWhwfDdIBWbowQoSlZsE22+OPKtfh55TvsfN2J+yzQEArIDiFEzFVHJYDZeFBsd3g1Rghh7MlsjcJNR+8wsDCyyxP2WbAwBYAcUpmIqp5LAiLgsNju8GycYMPZgt0blJsP2mVVhYVvAAACAASURBVPanbHMAACugOAVTMZUcANAeM/RgtkzLTdjmAABWQHEKprLiVPJAfSYmXNjTtPEAQKaxyowSZCYr5Sap6svJNgcAMJvT7AEgs02uKJEru+M/QzOnkvv6TPiSUl+fidfe2W3KeAAAQGpZJTcJlpNUVdendBwAAKQCxSmYqnxYP00bN9R/NtKdn6Np44aadvYuWJ+Jp1ZtN2U8AAAgtaySm4TqfQUAQLrhsj6YzkpTyYP1k9h/sCnFIwEAAGaxQm6Sab2vAACZjZlTQDvB+kn06ZWb4pEAAIBMFiwnMbMvJwAAyUJxCmgnWJ+Ja8eVmjQiAACQiazS+woAgFTgsj6gnWC3U75wxCDt23fY5NEBAIBMESwnMftyQwAAkoHilA2k6jbCOMEKfSYAAEBqWDnPIicBAGQKilMW57uNsO9uLb7bCEsiWQEAAIgDeRYAANaQkuJUZWWlVq9erX//+99avny5Bg8eLEnauXOnZs2apUOHDqmgoECVlZU67bTTkrbMjkLdRpikqSurnP20yjgAAJmNHCy0ePOscMd78gEAACKTkoboF198sZ5++mkNGDCgw+Nz5szR1KlTtXr1ak2dOlWzZ89O6jI74jbCkfOd/fR9N76zn1XV9R2eM3Phek1/aK1mLlzfYVkqxwEAQCqQg4UWT54V7nifqHwgFbkLAABmS0lxqqysTMXFxR0ea2ho0LZt2zR+/HhJ0vjx47Vt2zYdOHAgKcvsitsIRy7U2U8pdUWjcOMAACBVyMFCiyfPCne8T0Q+wAkvAECmSElxKpC6ujr17dtXWVlZkqSsrCwVFRWprq4uKcvsitsIRy7c2c9UFY2Y7QYAsDJysM/Fk2eFO94nIh/ghBcAIFPQED0ItzsvaesuLOwZ8XMnXNhT+T2766lV27X/YJP69MrVteNKdeGIQUkbn10V9srVvoNNAR8vLOypA0GSwQONLRHFJNK4hRsHUovv3L6InT0RN8Qj1flXPHlWuON9IvKBeHMXu0inz5JOiIs1ERfrIjbxMa04VVxcrD179sjj8SgrK0sej0d79+5VcXGxDMNI+LJoNTQckddrJPxzFxb21L59h6N6zbBTClR5U3mHx6JdRyaYNOr0DnfckU6c/Zw06nTt23dYvfNzAp6t7J2fE/b7jCZu4caB1Ille4M1EDt7skvcnE5HUosgVmflHMyM/CvWPCvc8T4R+UA8uYtd2GW/kWmIizURF+siNpEJlYOZdlmf2+1WaWmpVqxYIUlasWKFSktL1bt376QsQ/orH9ZP08YN9feJcOfnaNq4of674sR7iWSkDUnDjQMAADORg8XHlw88vnybumU7lJd74lxv5+N9IvIB2jsAADKFwzCMxJ+e6uS+++7TmjVrtH//fvXq1UsFBQVauXKlampqNGvWLDU2Nio/P1+VlZU644wzJCkpy6JhpZlTSJxYb+lc/ckh/fLZTV3OflJ0sja2N/sidvZkl7hl0swpu+VgVs+/fA3KU5kPxJq72IVd9huZhrhYE3GxLmITmVA5WEqKU3Zk9eQIqXXnr6sC9o1w5+do3s0jTRgRIsH2Zl/Ezp7sErdMKk7ZjdXzr5kL1we8zI58IHZ22W9kGuJiTcTFuohNZCx5WR9gJ/sDFKYk7sAHAEAm4Y68AAAkB8UpIAJ9euUGfNzXRwIAAKS/YMd98gEAAOJj2t36ADu5dlxpwJ5TZjYkTfceFAAAWM3kipKAPafMyAfIAwAA6YTiFBCBC0cMUuPhZsskgZ0bsjY0tujJVTskicQUAIAk8R1jzc4HyAMAAOmG4hQkcfYtEuXD+lnmO1m6rqbDWVtJam3zaum6GsuMEQCAdBAoRzK7+Tl5AAAg3VCcAmffbIiGrAAAJJ9VcyTyAABAuqEhOkKefYM10ZAVAIDks2qORB4AAEg3zJwCZ99soPMlBWeXuLV+S70lGrICAJCu4s2RktU2wUqN2QEASASKU5A7PydgkmX22Tf6YJ0Q6JKC9VvqNfKsftpc05Dx3w8AAMkST46UzEsCo23MTk4FALA6ilOw5Nk3q/Z4MEOwSwo21zSY3pAVAIB0Fk+OlOym5ZHeqIWcCgBgB/ScgsqH9dO0cUP9ZwHd+TmaNm6oqQmLVXs8mIHLLgEAMEc8OZJVjt/kVAAAO2DmFCRFfvYtVayS0FmBVS+7BAAgE8SaI1nl+E1OBQCwA4pTsKRgCV1ebrZmLlyfUT0TrHTZJT0rAACZLNLjYFV1vZpb27o8bsbx2ypFMgAAQsm655577jF7EFbU1NQqw0j8ek86KUfHjrUmfsVppmcPl7Z+1CCP9/MgZGc5dPy4V0dbPJKkphaPNn6wT6+8s1sFPXM0qCivy3qqquv12F/e0zOv/Et/31yrnj1cAZ8XjplxG1SUJ/fJ3bWrvlFNLR6583N01SWDU14U8vWsONJ0ItluavFo60cNcp/cPabvNBXY3uyL2NmTXeLmcDjUo4fL7GEggFTmX9HmCIGOg4HyEN/zmls7XkqXl5utq8cMSfnxO1BO5cp26qpLBlvi+G2X/UamIS7WRFysi9hEJlQOxswppFwkZx0D3YWmubVNR5s9XdZ3pKktYGPPdGoAaoXLLpPd2BUAgFSJJUcIdByUuuYhwZ7nW8fjy7eldPZxtHf2AwDADBSnkFLRJIOdCzLTH1obdL2BiiQUUxKLnhUAgHQRS44Q6njX/rXBnnekqc0/6yrVJ8yscJILAIBQKE4hpeIpGAXrmeDTeRnFlMSiZwWQueg3h3QTS44QaR4S7nk+8ZwwY5sEAKQbp9kDQGaJp2B0dok75PLORZJgRROKKbGZXFEiV3bHXYZZjdkBpI5vxqtvP+2b8VFVXW/yyIDYxZIjBDoOBnptuOe159uuqqrrNXPhek1/aK1mLlwfcvtimwQApCOKU0ipWAtGVdX1Wr8leNIVqEhCMSWxyof107RxQ/2xcufnaNq4oZypBdJcqBmvgF3FkiP4joMndc/qsqz9awMdLwO9xrcs2mIT2yQAIB1xWR9SanJFSYeeU1JkBaNQzUWjaarOtPf40LMCyDxcIo10FGuO4DsOhrusrvPxsnPPTenz/CfalgdskwCAdERxCikVTTLYPvELZd7NI0O+H8UU66Jnhjn43hEN+s0hkyVqfxkq/3l8+baArwmW/7BNAgDSEcUppFwkBaNAZxgDIRGzr1hu44348b0jWrHOeAWsLJJ9YbDn/OvTQ1q/pT7q/Wiw/CfaYhPbJAAgHdFzCpYU6jI+HxIxe6Nnhjn43hEt+s0hHUWyLwz2nHWbagM+/vjybWGbmQcSbf8rtkkAQDpi5hQsKdylfFyKZH/0zDAH3ztiwSXSSDeR7AuDPcdrhF5vtLNRY+l/xTYJAEg3FKeQdLH0a3A6Aid/Tof0xJ2jkzRSpBI9M8zB9w4Ake0Lgz0nWI7i45tFtXRdTcQn0ig2AQAyHZf1IamivT2yT7CkL1QyCHuJ5TbeiB/fOwBEti8M9pyKc/p3eTyQSHMeAABAcQpJFmt/m2CzOJjdkT7omWEOvncAiGxfGOw514wZ2uHxUOjpBwBAZLisD0kVa38b7kSTGbiMwRx87wAQ2b4w2HN8j0dyd2F6+gEAEB7FKSRVrP1tYmkOalex9OQCAADm65yvBMKsbwAAwqM4haSKZwZUJszu6HzGNZa7/AAAAPP48pXfr96hV9+t7bL87BK3CaMCAMBeLNFzavTo0Ro7dqwmTpyoiRMn6o033pAkbdq0SRMmTNCYMWM0ffp0NTQ0+F8T6zKkFv1tQou1JxcAAIlADpY4m2sCf9ZgjwMAgM9ZZubUY489psGDB/v/9nq9mjlzph588EGVlZVp4cKFmj9/vh588MGYlyG5gl2elgkzoGIVa08uAAAShRwscqEuxeeYDgBA7CxTnOps69atysnJUVlZmSRpypQpuvjii/Xggw/GvAyRi7QPUvvntZfKy9Oi7dlkpR5PsfbkAgAgWcjBAgt0Kf5vVmzTkr99oCNNbXI6JK/R9XUOSdMfWitJysvN1lWXDOakHQAAnVimOHXHHXfIMAyNGDFCt99+u+rq6tS/f3//8t69e8vr9erQoUMxLysoKEjpZ7KrSPsghbtDje/ytGQmYNH2bLJajyfuSggAMBs5WGQCXYrvMaQjTW2SAhemJKn9w0ea2vTbF7dLorckAADtWaI49fTTT6u4uFitra26//77NXfuXF166aWmjsntzkvaugsLeyZt3Ymw7O9VAfsgLfv7Tk248D9CPq+zA40tcX3e197ZradWbdf+g03q0ytX144r1YUjBkU91nDPf+aVDwM+v71kxG3ChT2V37N7yM+I+Fh9e0NwxM6eiJu9WC0HMyv/CpVv+JYl6vK8No8RNE/JVOw3rIm4WBNxsS5iEx9LFKeKi4slSS6XS1OnTtV3v/tdXXvttaqt/fyOJwcOHJDT6VRBQYGKi4tjWhaNhoYj8gY7BRaHwsKe2rfvcMLXm0j7DjYFfbz92IM9r73e+Tkxf97Os5z2HWzSL5/dpMbDzf6zjZGONdyYDx87rr++9mHQs5ih4hbvZYLDTilQ5U3lHcdp8X8jdmGH7Q2BETt7skvcnE5HUosgdmK1HMyM/CtUviEp5CzxWAXLU6zUeiBV7LLfyDTExZqIi3URm8iEysFMv1vfsWPHdPjwiSAahqEXX3xRpaWlOvPMM9Xc3Ky3335bkvTMM89o7NixkhTzMkQmWL+jzo+H64sU7+VpkdzJLtKxhnvc937R8iW0vrOpvssEq6rro14XAACpRA52Qqh8I9CyRAiUj5BTAAAymekzpxoaGnTbbbfJ4/HI6/WqpKREc+bMkdPp1MMPP6w5c+aopaVFAwYM0Lx58yQp5mWIzNklbr36bm2HxwIVmgI9zycRZ/siuetNtD2bJleU6PHl26J6v1BCJbTpfqYT6ScTz9gDmSyTcrDX3tmt362oTuhd9k7qnqWW4161eaKb6ZWd5dDZJW7NXLi+w3iC5RRL/vYB+2IAQNpzGIaR+LnTaSBTL+sL1uT8ouH9dc2YoTE9L9Yfu76krTN3fo7m3Twy5ve47dF1OtrsCbve9oLFzXf3nUB+M2t00GVIDatvb1YSaJt2ZTs1bdxQU34UETt7skvcuKzPupKRf1VV1+upl95Xy/HPj/3t92+h8g0pcJHqpO5Z6u7KVkNji/8ufe78HJ1d4tbmmgZ/TnJ2iVv/3L7Hn3fk5Wbr3KFFWr+lvsv+NtQMrRsv/2LaFqjsst/INMTFmoiLdRGbyITKwUyfOQVrCTZ9fXNNQ9TPi/fOeJHOiiof1i+qhG3qpUMSdoc8d35OyITWruw2g8Zu47UiZgECSFdL19V0KExJJ/Zvjy/fpqXranR2iTtgsciXFwQ6GdfU4tXR5hPHf6/x+fMD7S/bn7STTpx8C7S/9RW5gn0G9sUAgHRGccrCzPjBHenU9kieF+2P3UCfd9q4oUG/g1i/H99zEvHdRntZoR3EW1RMtVDjnXAhd8yIVKyXtQCA1YXajzU0tmj9lnqNPKtfhxlP7fOCf316qEsbA2+nCw8iLeZXVdcHHU+oCWPsiwEA6Y7ilMW0L7i0l6oCQaQzgSJ5XjQ/doMVGKaNGxrwUrt4CyjRzrYKtR4pMYUuqzBrBk2sxcZQ4+U23ZFL11mAABBs/+bT2ubV5pqGoJf2v7Vjb0TvE66A5MtdQo2zubUtaOsBAADSGcUpCwnWx8knEQWCcAWAQDOBpBPNz9uLZMZQND92oy2IWOkSpEQVuqzCjBk08RQbmfGTGMFucNB52wcAu5lcUdKl51RnoY4ZR5raInqfcAWkUHf+c2U7/f2pAi2z84xsAAAi4TR7APhcJLcrjucHdyS3KC4f1k8jz+paDFi/pb7L86aNG+pPxNz5OV0aJ0+uKJEru+M/sWAJVrQFBgoSyRMsuU7mWdtQxcZwzBhvOurcVy7c4wBgF+XD+unWb3wp5HPiPWZEUkAKlaOMPKuf1m+p7zJrKi8327QbUwAAkErMnLKQSAor8SRPkc42CvRjtH3jUN9sq3AzhqK55C3aS4rivQSJBtrBmdFHK55iYzr2/TIDBV8A6ezCEYP0uxXVQfdpza1tmv7Q2phzgm7Zji55UmehcpfNNQ0BT1DmdMsiPwEAZASKUxYSridCvD+442127luWjN5O0RYYAl2CFOn3Y7eG36lmRh+teIqN6dj3ywz0nAKQ7oK1LnBI/hlLseYEkbw+VK7z+PJtAdeb6hMEnLwDAJiF4pSFBEuaJAVMEKJNIOJtdu7T2ubVH19+P6HJSjQFhqrqeq3fUt/lcYcjxG1u2rFSv6pESEYimeo+WvHOfkq3vl9mYAYagHQXKNcI1IC8tc2rJ5ZvC1owCidYThEs15EkpyPw3fpSeYKAk3cAADNRnLKA9sWFvNxsdct26GizJ2yBJlgCIQUu8kT643NyRYl+++J2tXmCF3uONntUVV2f8AJVrHdnk6SW40ZESVQ6Xb6ULokks5/MRwwAZAJfruHLvQLdGU+SIjvdFVxDY4tmLlzfZT/aOdfxHccDFaZSfYIg3U7eAQDsheKUyToXF440tcmV7dSNl38xZCIQLIH448vv63ibEbJYEcmPTyNQlhRgDGYkK+FmdYUbVzpdvpROiSSzn8xHDABkgnB3R06USE4YBTvh5nQo5Y3Q0+nkHQDAfihOmSzW4kKwRCHQGcD264vkx+fSdTUKMWkq7BiSLdxlh+HGlU6XL5FIAgAQnUjujpwo4XK6YMdrr5H6GdDpdPIOAGA/TrMHkOliLS5EmyhEU6yI9LlmJSuTK0rkyg7+TzfcuMqH9dO0cUP9z3Pn59j2Ns2h7mYIAAC6ivUEjtOR+Pez0nE8UH5l15N3AAD7YeaUyaI9S9W+P1W07xPvmNozM1nxFZH++PL7XWaKRTqudLl8KZ1mgQEAkAqR5DmdZTmkC87pr/Vb6qOedRUqp2tubevyuFnHcXoPAgDMRHHKZNEUF2LtkRBtkhNoTFkOKbd7to40tVkiWenc0DTSJCrdbpFMIgkAQHRC3R05EFe2Q9PGlap8WD99YWBBVCcJo83p8nKzddUlg6M+jicqv0mXk3cAAPuhOGWyaIoL0fRI8N2SONwd/wK9r50KHtEkUelyZ7vOSCQBAIhcqBnYgSy646IOry0f1k8zF64PWKDKy81WTresmHO6nG5ZMRWm0jG/AQBkFopTFhBpcSGaKejXjw99t79wiUw6FjzS6c52AAAgduXD+mnpupqwxalgl+QFm/ke6aynRN7QhPwGAJAOaIhuI9H0jXp8+TbNXLheVdX1AZeHSmTSFXe2AwAAPpEc/88ucQd8PN6bqwRrrh5L03XyGwBAOmDmlI1E2yOh/WwoqeNleolOZOzQy4lbJAMAAJ9IGqOv31KvLwwsCJjTxDPL3GsEf7yquj6q9YbLb+yQowEAQHHKIiJJHDr3gjqpe5ZajnvV5gmS4ejEbKg/vvy+jrcZHS7hC8adnxNTk3E79DrgznYAAMAnkpN+wS6Pi7fgE6owFm0OFSq/sUuOZmUU9wAgNShOWUCgxOHx5dv0+PJtcmU7NPKsYm2uaehyUPz96h167d3asOuPpNmndCKRObvEHXUSE+wSwceXb9PSdTWWOYjbqdE7AABIrs55QTCdl/1+9Q692i7/CpYrhSpqnF3i7rCO9sL1iwq03pFn9dO6TbXyGicuDRx51ueN2+lHFTuKewCQOhSnLCDUXfha24wuCdBvX9yulf/YqdqGprjf23fmzpfcxNJUM1xCZ6WDeDo2ejcLZxIBAHbW/jgWzg2Va1VxTn99YWBBwKJSa5tXS/72gX99ebnZampuk29ye+dWC+u3BO4J6hNsTFXV9frti9v9s+YbGlu0eMV2OWT4LxX0Gp9fjmiXflRWzSloNg8AqUNxygKiTRDaPEbEhSlXtlOubk4daWrrssydn6N5N4/s8Njjy7dFPcZwPRs4iKcfziQCAOys83EsHK8hvfpurdZvqQv6nCNNbf58K1De5Wu10NTiCdpzyicvN3CKvuRvH3Rp5+A1uq7Ml3vZod/ma+/stmxOYZfiHgCkA+7WZwHJTBA8Xq+OtwW+rK+hsUXTH1qr2x5d57+rX7CxhBrj5IoSubJD/1PiIJ5eMvFujwCA9BFq1noorW1hqkphHG0OX5iSpKbmtg53XK6qrtfMhesDFr2CaWhsCZijWa3f5lOrtls2p4glLwYAxIbilMmqquvV3Bp5ohEtj1dqOR46Czra7NFvVmxTVXV90FsmN7d2TJLa63w75UASdRD3JWfTH1qrmQvXBx0TkosziQAAO7P68cpjyF+c8c3yinbMTseJ/2+fo7nzczRt3FDTZyS1t/9g4KsBrBAjOxT3ACBdcFmfiaKdUp5MHiP4JX3SiQJWqCnWvl5OgT5Tog7iXEpmHXa4TAAAgGDCtSSwAt/4Yp3l5TVO3Plv2rihXdo4JFO0/aP69MrVvgAFKivkFNxMBwBSh+KUiZb87QNLFKYiFUnvqGQexGlKaR2hblsNAIDVhbpbnpVMf2htXK9PdZ4Uy4nEa8eV6pfPbrJsTsHNdAAgNShOmaCqul5PvbQ97OV2VtTQ2KKZC9eHLDgl8iAeyZ10rH7mMx1xJhEAYGebaxrMHkLKxJonxXIHvUhOJHZe73Xjh2nauKHkFACQ4ShOpdhr7+zWb1Zsk8d+dSm/VF1OF+llj1aY9p2JOJMIALCbqup6/fHl93W0OfDNYtLV71fv0OaaBjU0tsjpOHHJX6giUKytFML1pAy03l/9+T1dO3ZISi89BABYD8WpFPvfZVtsXZjyScU08Uh6LFhp2jcAALCueUs2avuuQ2YPwxTtL2H03S2wfcHpX58e0rpNtfIaJxqpZ2c5utyZsLXNqyV/+yDoDKjJFSVhe1IGyu1ajnu0eMWJvqec9AKAzEVxKoV+v3qHDh87bvYwEqahsSWmKd+RqKquDzsNnWnfAAAgEv/zl00ZW5gKpbXN2+WGOF5DXQpTPkea2vT71Tv0hYEFXWZAPb58m0pPLdDhY8c7FKCysxxqbm0L2T/L17xd6ligSlaemex1J4LVxwcAieYwDCMN5vEkXkPDEXm9iftqfr96hy0ab0bjpO5ZOt5mdGlgGe8tisNdzufOz0n51O/Cwp7at+9wQtZFspE6iYwbUovY2ZNd4uZ0OuR255k9DASQ6PzrRJ/P99VyPLMu40tnrmyHXN2ydKSpTe78HJ1d4tY/t+/pcKlmXm62rrpkcND8KtjdpdvnsFXV9Vrytw90pKnN/5yTumdp6qVDJCW372Yk40s2u+zPU8kKOTxxsS5iE5lQOVjaFqd27typWbNm6dChQyooKFBlZaVOO+20iF+fyOSoqrq+y1mpdJCXm93hgO0Tb/Fo5sL1QWdNpfrA7JOonY0Vko1MwkHCvoidPdklbhSnkiueHIz8C4hdTjeHJHW56VKWU/IE6ZSR5ZDkcMgTZLvLcjqUm5MVMOdvr/TUAo06u3+Xgl57rmyHpo0rlRS6uNe5N1xON4faPEaXz5CXm61zhxb5+6n5ipVv7djrH0P7Yqav35or26HjHkPtfwX7luXlZqupua1DGxZXtkMjzyrW+i31XU6e53RzyGg32zBcYTQagYphEy78j5DH2VgKaJG8JlGFOSvNhkzEWNqvo7BXriaNOt32v+mSXYQNlYNl3XPPPfck7J0sZMaMGbryyit13333yeVyaeHChbriiisifn1TU6sSVbab85t/JmZFFhNsZlNTi0cTR50e83qfeeVfQZdNv6zUlA3+pJNydOxYa9zreewv73U5YHu8hnbVN+or554S9/rRUaLihtQjdvZkl7g5HA716OEyexhpK54cjPwLiJ3HG7gIFWqbMsItN4Ln/O3t/6xZGz/YF/K5Hq+08YN9eu/DfTracqLw1NTi0daPGuQ+ubsGFeWpqrpev1mxTS3tLi31eAOPsbXNq4/rD6up3bo+rj/cYQwe7+fjN9o91uVztltn57fyeKWP6w8HLOB1/s5b27zaXLNffQpyNago9pMgvhPavt8Nvu+pb+8eKjq5e1Sv8X23sb4mlvUmanzJWncixtJ5Hcea2xL2ecySzBj5hMrBnAl5B4tpaGjQtm3bNH78eEnS+PHjtW3bNh04cMDkkdlPTresgI+783OC3iUv3rvnhVqv3SvR4e5iAwCAnZGDAQin882hfDdakk7MqLL7zaPaPIb/88Qq0M0DWtu8emrV9qhfE2oskbwmlvUmanzJWncixpLMz2MWsz9TWjZEr6urU9++fZWVdaKwkpWVpaKiItXV1al3794RrYPp/lLPHt307Uln6Vd/fq9Dr4acblm6bvwwSQq6rLCwZ8zve934YUlZb7wS8d6FvXK172BTwMfN/GzpjO/VvoidPRG3zBZvDkb+BWSmA40tKizsqQNpcsLW93nieX0g+w82BV1vsNeEGkskr4llvYkaX7LWnYixJPPzmMXsz5SWxalESHRDTrtxZTs15eL/0LBTCnTt2CFdrjsddkqBJAVdFk/PkVDvaVYvk0T1UZk06vSAPacmjTrdFn1a7MYu/W/QFbGzJ7vEjZ5T1pXp+ReQqXrn52jfvsPqnZ+TFlcU+D5PPK8P9D306ZUbdL3BXhNqLJG8Jpb1Jmp8yVp3IsaSzM9jllR8plA5WFoWp4qLi7Vnzx55PB5lZWXJ4/Fo7969Ki4uNntopshyfD591tek71+fHgp698DOjc/Kh/ULejldqGXxSNZ6zeb7TGbf6QMAgGSwUg7WLUviJn2A9bT/bSKdOFE7uaJEkjS5okS/WbHNcpf2OSRdOLx/h2brwWRnOfyfJ1aTK0oCntC+9v8aykfzmlBjieQ1saw3UeNL1roTMZZkfh6zmP2Z0rIheo8ePfTGG28oOztbQ4cO1fLly7Vv3z5961vfingd0uidYwAAENVJREFUiWzIOXHU6Xrh7zsTs7L/43B8/t8ndc/SdeNKdevks1TUK1e76hvV1OKROz9HV39liG6+4ixNHHW6Jo46XeO+fKoGFeXpS1/oE/C5t04+S1859xTbNnFLlkQ2+R1UlKevnHuKJo46ne86yezSnBldETt7skvcaIiePPHmYInMvy4febperNopJmIhU+R0cyg7y9Gl4XeWM3jT8yzHiZkMQZc7HTqpe3bYpuilpxboigvO0Ae7DwZ9rivboemXfVHDBxd2+A3S/u52g4ryVNgrV+9/ckDH/68puu8uhJ3HmJebrZFn9dPhY63+dX35i321/7Mm/xhc2Q7l5pwYv9NxovG5K9vRZb/gW5aXmy2Pp2NTdN+4Lys/TeO+fGqH31B5udmSPr+TYF5utq4ZE/9duAcV5cl9cvcu39OY/z496HE22GtCjSWS18Sy3kSNL1nrTsRYOq+jsFeuplz8H7aedJDMGPmEysEchpGoFMBaampqNGvWLDU2Nio/P1+VlZU644wzIn59sqaV2+WSB3RE3OyJuNkXsbMnu8SNy/qSK54cjPwr8xAbayIu1kRcrIvYRCbjLuuTpJKSEv35z382exgAAAAZhRwMAABEy2n2AAAAAAAAAJC5KE4BAAAAAADANBSnAAAAAAAAYBqKUwAAAAAAADANxSkAAAAAAACYJm3v1hcvp9Nhy3UjeYibPRE3+yJ29mSHuNlhjJmK/CszERtrIi7WRFysi9iEF+o7chiGYaRwLAAAAAAAAIAfl/UBAAAAAADANBSnAAAAAAAAYBqKUwAAAAAAADANxSkAAAAAAACYhuIUAAAAAAAATENxCgAAAAAAAKahOAUAAAAAAADTUJwCAAAAAACAaShOAQAAAAAAwDQUp1Jk586duvLKKzVmzBhdeeWV+vjjj80eUsaqrKzU6NGjNWTIEH3wwQf+x0PFKNZlSJyDBw/qxhtv1JgxY3T55Zfr1ltv1YEDByRJmzZt0oQJEzRmzBhNnz5dDQ0N/tfFugyJdfPNN2vChAmaNGmSpk6dqu3bt0tiu7OLX/3qVx32mWxzsBP2F6k1evRojR07VhMnTtTEiRP1xhtvSGK/kWqpzHfZxiIXLC7BthuJbScVUv07g9gEYSAlrrnmGmPZsmWGYRjGsmXLjGuuucbkEWWut956y6itrTUuuugi4/333/c/HipGsS5D4hw8eNB48803/X8/9NBDxo9+9CPD4/EYl1xyifHWW28ZhmEYCxYsMGbNmmUYhhHzMiReY2Oj/79ffvllY9KkSYZhsN3ZwdatW43rr7/ev89km4PdsL9Irc75lWHEvm9gvxG7VOa7bGORCxaXQNuNYbDtpEoqf2cQm+AoTqXA/v37jREjRhhtbW2GYRhGW1ubMWLECKOhocHkkWW29geBUDGKdRmS66WXXjKmTZtmvPfee8Zll13mf7yhocE455xzDMMwYl6G5Hr++eeNK664gu3OBlpaWoxvfvObxu7du/37TLY52An7i9QL9COb/YZ5kp3vso3FJtLiFNuOOZL5O4PYBJdt9sytTFBXV6e+ffsqKytLkpSVlaWioiLV1dWpd+/eJo8OUugYGYYR0zJimzxer1dLlizR6NGjVVdXp/79+/uX9e7dW16vV4cOHYp5WUFBQUo/T6a4++67tX79ehmGoSeeeILtzgZ+8YtfaMKECRo4cKD/MbY52Ak5mDnuuOMOGYahESNG6Pbbb2e/YRHJOO5yTE6czttNfn4+244Jkv07g9gER88pALZz7733qkePHrr66qvNHgqicP/99+u1117TD37wAz388MNmDwdhvPvuu9q6daumTp1q9lAA2MjTTz+tv/71r3ruuedkGIbmzp1r9pAAy2O7sQ5+Z5iH4lQKFBcXa8+ePfJ4PJIkj8ejvXv3qri42OSRwSdUjGJdhuSorKzUrl279Oijj8rpdKq4uFi1tbX+5QcOHJDT6VRBQUHMy5BckyZN0oYNG9SvXz+2Owt76623VFNTo4svvlijR49WfX29rr/+eu3atYttDrbB/iL1fN+ty+XS1KlTtXHjRo7VFpGMfJdtLDECbTe+x9l2UicVvzOITXAUp1LA7XartLRUK1askCStWLFCpaWlTHW1kFAxinUZEu/nP/+5tm7dqgULFsjlckmSzjzzTDU3N+vtt9+WJD3zzDMaO3ZsXMuQWEePHlVdXZ3/77Vr1+rkk09mu7O4b3/72/r73/+utWvXau3aterXr58WL16sG264gW0OtsH+IrWOHTumw4cPS5IMw9CLL76o0tJSjtUWkYzjLttY/IJtNxJ5biql6ncGsQnOYRiGYfYgMkFNTY1mzZqlxsZG5efnq7KyUmeccYbZw8pI9913n9asWaP9+/erV69eKigo0MqVK0PGKNZlSJwPP/xQ48eP12mnnabu3btLkgYOHKgFCxZo48aNmjNnjlpaWjRgwADNmzdPffr0kaSYlyFx9u/fr5tvvllNTU1yOp06+eSTdeedd2rYsGFsdzYyevRoLVq0SIMHD2abg62wv0id3bt367bbbpPH45HX61VJSYl+/OMfq6ioiP1GiqUy32Ubi1yguCxatCjodiPFvn2w7UQu1b8ziE1gFKcAAAAAAABgGi7rAwAAAAAA/7+9+4+pqv7jOP7k171AtdFQbl6t5vqDtpzbtXs18uIN0lkEpPgLp01WGjYcODaU0XLJ9Kapo800+nGbupyzAnUObVQ6derFgRRtmRW61LjxQ1pDyIvA7Y/2Pd+uYPL97uIt93psdzufe87n/fmcz93uPnufzzlHJGyUnBIRERERERERkbBRckpERERERERERMJGySkREREREREREQkbJadERERERERERCRslJwSERERERGRsLly5QrJycn09fWFuysiEiZKTomIiIiIiMhtpaenc+rUqX9MHBG5eyg5JSLyD1RaWkpFRUW4uyEiIiIiIjLilJwSERERERGRv1VSUkJLSwvLly/HZrPx/vvv89VXX5Gbm4vdbic7O5u6ujoAzp49y5QpU/D5fAB89913OBwOmpubh4xzs66uLsrKynA6naSmplJRUUF/fz8A1dXVLFy4kI0bN+JwOEhPT+fYsWN3biBEZEQoOSUiIZGeno7H4yErK4vHH3+clStX4vf7jQnEXyUnJ/PTTz8Bf64Qev3111m6dCk2m43c3Fza29tZv349DoeDZ555hm+//fa27ft8PlasWMETTzzBlClTKC8vB2BgYIDt27eTlpZGSkoKq1atoqurC/jv8w2qqqpwuVw4HA727NlDU1MTWVlZ2O12Iw78ORnKzc3F7XZjt9t5+umnOXv2LNXV1bhcLlJSUti3b59xfG9vLxs3buSpp57iySefZM2aNVy/fh2Auro6pk2bxocffkhKSgpOp5OqqioA9u7dy8GDB/F4PNhsNpYvXw7Ae++9R2pqKjabjZkzZ3L69On/9+cSERER+Z9s2rQJq9VKZWUljY2NZGdnk5+fzyuvvMKZM2dYvXo1hYWFdHZ2MmnSJHJzc1m9ejXXr1+npKSEoqIiHnnkkUFxli1bNqit0tJSoqOjqa2tZf/+/Zw8eZJPPvnE2N/U1MT48ePxer0sXbqUV199lUAgcCeHQ0RCTMkpEQmZw4cP88EHH/Dll19y/vx5qqurh11v5cqVeL1eTCYTCxYs4LHHHsPr9TJz5kzeeOONv63f399Pfn4+VquVI0eOcPz4cTIyMoA/E0r79u1j165dfPHFF/T09AQlnAC+/vpramtrqaiowO12U1lZyY4dO6ipqeHw4cOcOXPGOLapqYnk5GTq6urIzMykuLiYb775hs8//5xNmzZRXl5Od3c3AJs3b+bixYvs37+f2tpa2tra2LZtmxGro6ODrq4ujh8/zvr16ykvL+e3335jwYIFZGVl8dJLL9HY2EhlZSUXLlxg9+7dfPrppzQ2NuLxeBg7duywxldEREQk1A4cOMC0adNwuVxERkYydepUJkyYYKxiWrFiBdeuXWPevHkkJSWxaNGiYcXt6Ojg2LFjlJWVER8fT2JiInl5edTU1BjHWK1W5s+fT1RUFLNnz6a9vZ2Ojo4ROU8RuTOUnBKRkHnhhRewWCwkJCSQlpbGuXPnhlVvxowZTJgwAbPZzIwZMzCbzcyaNYuoqCgyMjJuG6epqYm2tjZWrVpFfHw8ZrMZu90OwMGDB8nLy+PBBx/knnvuobi4mEOHDgW9DaagoACz2YzT6SQ+Pp7MzEwSExOxWCzY7faglVvjxo1jzpw5Rt98Ph8FBQWYTCacTicmk4lLly4RCAT4+OOPKSsrIyEhgXvvvZf8/PygiVV0dDQFBQXExMTgcrmIj4/n4sWLQ55jVFQUvb29NDc3c+PGDcaNG8dDDz00rPEVERERCbWWlhY+++wz7Ha78WloaKC9vR2AmJgYZs+ezffff8+LL75IRETEsOP29fXhdDqNuGvWrKGzs9M4ZtSoUcZ2XFwcAD09PSE8OxG506LD3QERuXuMHj3a2I6Li6OtrW1Y9RITE43t2NjYoAlHbGzsbScbPp8Pq9VKdPTgv7S2tragFUZjx46lr6+Pq1evDtm+2WweVP5r+zf3FYInSGazme7ubjo7O/n999/Jyckx9gUCAQYGBoxyQkJCUJ/j4uJuea4PP/wwZWVlbN26lR9//BGn00lpaSkWi+UWoyIiIiIycsaMGcPzzz/PunXrhtzf2trK22+/TU5ODhs2bKCqqgqTyXTbuA888AAmkwmv1zvk3E5E7k5aOSUiIyouLs54zhJgXE0LpTFjxuDz+YJWQ/1HUlISP//8s1FuaWkhOjo6KMk0Eu6//35iY2Opqamhvr6e+vp6GhoaaGxsHFb9oa4uZmVlsWfPHo4ePUpERASbN28OdbdFREREbmnUqFFcvnwZgOzsbI4ePcqJEyfo7+/H7/dTV1fHL7/8QiAQoLS0lLlz5+J2u0lKSuKtt94aMs7NkpKSmDp1Khs2bODatWsMDAxw6dKloMcsiMjdR8kpERlRjz76KD/88APnzp3D7/ezdevWkLcxceJERo8ezZYtW+jp6cHv99PQ0ABAZmYmO3fu5PLly3R3d1NRUcGzzz474lfiIiMjmTdvHm6321il1drayokTJ4ZVPzExkStXrhjlCxcucPr0aXp7ezGZTJjNZiIj9RcuIiIid87LL7/MO++8g91u59ChQ2zfvp13332XlJQUXC4XHo+HgYEBdu3axdWrVykqKiIiIgK32011dTX19fWD4ng8nkHtvPnmm9y4cYOMjAwcDgeFhYUjcoFTRP45tE5SREbU+PHjKSgoIC8vj9jYWIqLi9m7d29I24iKiqKyspJ169aRlpYGYLw1cM6cObS2trJ48WL8fj9Op5PXXnstpO3fSklJCdu2bWP+/Pn8+uuvWCwWFi5cSGpq6m3rzp07l6KiIux2O5MnT6awsJAtW7bQ3NxMTEwMNptt0IPdRUREREbS9OnTmT59etB3H3300aDjlixZwpIlS4yyxWLB6/X+bZzz588b2/fddx9r165l7dq1g2Ln5OQEPTbh5roi8u8UEdA7N0VEREREREREJEx0T4iIiIiIiIiIiISNbusTkX+FlpYWnnvuuSH31dTUYLVa73CPREREREREJBR0W5+IiIiIiIiIiISNbusTEREREREREZGwUXJKRERERERERETCRskpEREREREREREJGyWnREREREREREQkbJScEhERERERERGRsPkDH2GiOa7NgakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "features = ['num_comments', 'textlen']\n",
    "target = df['score']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = df[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(col)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('MEDV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1043,
     "status": "ok",
     "timestamp": 1596644676869,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "JsSLk1tgZn9_"
   },
   "outputs": [],
   "source": [
    "# define the data/predictors a X\n",
    "X = df[['num_comments']]\n",
    "\n",
    "# Put the target (housing value -- MEDV) in another DataFrame\n",
    "Y = df[['score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1332,
     "status": "ok",
     "timestamp": 1596644677386,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "HRyhqXP6Zn-C",
    "outputId": "c6caeb19-b095-440c-edf1-c958feca6d1d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13013, 1)\n",
      "(3254, 1)\n",
      "(13013, 1)\n",
      "(3254, 1)\n"
     ]
    }
   ],
   "source": [
    "# Defining split \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1226,
     "status": "ok",
     "timestamp": 1596644678860,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "n0-5GqwgZn-E"
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8ELFg3ogZn-G"
   },
   "source": [
    "And let's evaluate it using RMSE and R2-score. \n",
    "\n",
    "The **RMSE** is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data: how close the observed data points are to the model's predicted values. \n",
    "\n",
    "The **R2** socre is the proportion of the variance in the dependent variable that is predictable from the independent variable(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 598,
     "status": "ok",
     "timestamp": 1596644679224,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "ACtUJ0aoZn-H",
    "outputId": "b52ba115-c22f-40c3-a379-9d9a7b52f695",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "RMSE is 392.839227658504\n",
      "R2 score is 0.5512593232866039\n",
      "\n",
      "\n",
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "RMSE is 274.79711943663364\n",
      "R2 score is 0.6698997721577419\n"
     ]
    }
   ],
   "source": [
    "# model evaluation for training set\n",
    "y_train_predict = model.predict(X_train)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
    "r2 = r2_score(Y_train, y_train_predict)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# model evaluation for testing set\n",
    "y_test_predict = model.predict(X_test)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
    "r2 = r2_score(Y_test, y_test_predict)\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YtbLcxWuZn-J"
   },
   "source": [
    "In our case of multivariable linear regression, the regression model has to find the most optimal coefficients for all the attributes. Let's see what coefficients our regression model has chosen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 945,
     "status": "ok",
     "timestamp": 1596644683173,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "P7ocHQkSZn-K",
    "outputId": "dce227b3-a8ef-4786-90dd-685ab2c47b6c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_comments</th>\n",
       "      <td>4.523345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Coefficient\n",
       "num_comments     4.523345"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_, X.columns, columns=['Coefficient'])  \n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I3c6Aw0xZn-M"
   },
   "source": [
    "And let's make some predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1169,
     "status": "ok",
     "timestamp": 1596644690007,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "287V_6U3Zn-M",
    "outputId": "b7deef5d-0ecb-4b83-f66d-e5293fd37779"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-48.23687993]\n",
      " [-12.0501189 ]\n",
      " [  6.04326162]\n",
      " [ 69.37009342]\n",
      " [-21.09680915]]\n"
     ]
    }
   ],
   "source": [
    "y_predict = lm.predict(X)\n",
    "print(y_predict[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CT4n49AMZn-O"
   },
   "source": [
    "Finding the intercept and regression coeficient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 760,
     "status": "ok",
     "timestamp": 1596644690008,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "9pvbfGcYZn-P",
    "outputId": "8f5e2ae4-fb65-4c7f-87bc-e0a7aef968d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " [-52.76022505]\n",
      "Coefficients: \n",
      " [[4.52334513]]\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', model.intercept_)\n",
    "print('Coefficients: \\n', model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8dFo1zftZn-Q"
   },
   "source": [
    "Let's print the R² score of our model. This score provides a measure of how well observed outcomes are replicated by our model, based on the proportion of total variation of outcomes explained by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 901,
     "status": "ok",
     "timestamp": 1596644691686,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "eCr0uxg-Zn-R",
    "outputId": "eaa3008d-a4ce-4456-8243-e50f2a4c80d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5681811613875367"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cNkllcPcZn-S"
   },
   "source": [
    "Let's see the output of `statsmodels` as well. For more info on how to interpret R-squared values, see https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 929,
     "status": "ok",
     "timestamp": 1596644692224,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "7tuO42ElZn-T"
   },
   "outputs": [],
   "source": [
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 610,
     "status": "ok",
     "timestamp": 1596644692228,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "whu1fyrHZn-V",
    "outputId": "141cbe51-2a98-4499-eb63-0ad4d8d19be8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                  score   R-squared (uncentered):                   0.567\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.567\n",
      "Method:                 Least Squares   F-statistic:                          2.129e+04\n",
      "Date:                Wed, 05 Aug 2020   Prob (F-statistic):                        0.00\n",
      "Time:                        16:24:53   Log-Likelihood:                     -1.1948e+05\n",
      "No. Observations:               16267   AIC:                                  2.390e+05\n",
      "Df Residuals:                   16266   BIC:                                  2.390e+05\n",
      "Df Model:                           1                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "num_comments     4.1891      0.029    145.909      0.000       4.133       4.245\n",
      "==============================================================================\n",
      "Omnibus:                    38521.155   Durbin-Watson:                   1.967\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        993566025.812\n",
      "Skew:                          23.510   Prob(JB):                         0.00\n",
      "Kurtosis:                    1212.825   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  }
 ],
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   "name": "Week 5-X Regression.ipynb",
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  },
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