WeeklyAssignment5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) We want to make categories for the apartments: ​ Really cheap (cheaper than one\n",
    "standard deviation of the price distribution ─we assume it Normal─ to the lower side), ​ Cheap\n",
    "(between the really cheap ones and the average price), ​ Expensive (between the average\n",
    "and one standard deviation to higher side), and ​ Really expensive ​ (more than one standard\n",
    "deviation to the higher side). How many apartments have each category?**\n",
    "\n",
    "The first step is to read the data and store it in a dataframe. We then select only those fields which will be used as features for the classification method in another dataframe, called \"data\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "myairbnbs = pd.read_csv(\"listings.csv\")\n",
    "data = myairbnbs[[\"price\",\"neighbourhood_group\", \"neighbourhood\", \"latitude\", \"longitude\", \"room_type\", \"minimum_nights\",\n",
    "\"number_of_reviews\", \"reviews_per_month\", \"availability_365\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that some values in the column \"reviews_per_month\" are not available. This would pose a problem later on the analysis, so we replace those NaN values with the value 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.02\n",
       "1        0.25\n",
       "2        0.48\n",
       "3        2.38\n",
       "4        1.71\n",
       "         ... \n",
       "20423     NaN\n",
       "20424     NaN\n",
       "20425     NaN\n",
       "20426     NaN\n",
       "20427     NaN\n",
       "Name: reviews_per_month, Length: 20428, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"reviews_per_month\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        0.02\n",
       "1        0.25\n",
       "2        0.48\n",
       "3        2.38\n",
       "4        1.71\n",
       "         ... \n",
       "20423    0.00\n",
       "20424    0.00\n",
       "20425    0.00\n",
       "20426    0.00\n",
       "20427    0.00\n",
       "Name: reviews_per_month, Length: 20428, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"reviews_per_month\"] = data[\"reviews_per_month\"].fillna(0)\n",
    "data[\"reviews_per_month\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since some of the variables are categorical, we can't directly use them on decision trees. We use one-hot encoding (dummy variables) to be able to use this variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    }\n",
       "\n",
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       "        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>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Ciutat Vella</th>\n",
       "      <th>neighbourhood_group_Eixample</th>\n",
       "      <th>neighbourhood_group_Gràcia</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbourhood_la Verneda i la Pau</th>\n",
       "      <th>neighbourhood_la Vila Olímpica del Poblenou</th>\n",
       "      <th>neighbourhood_la Vila de Gràcia</th>\n",
       "      <th>neighbourhood_les Corts</th>\n",
       "      <th>neighbourhood_les Roquetes</th>\n",
       "      <th>neighbourhood_les Tres Torres</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>room_type_Hotel room</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2.17306</td>\n",
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       "      <td>41.41203</td>\n",
       "      <td>2.22114</td>\n",
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       "      <td>41.40145</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>20428 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  latitude  longitude  minimum_nights  number_of_reviews  \\\n",
       "0        130  41.40889    2.18555               3                  1   \n",
       "1         60  41.40420    2.17306               1                 20   \n",
       "2        210  41.41203    2.22114               3                 51   \n",
       "3         32  41.40145    2.15645               1                268   \n",
       "4         60  41.40950    2.15938               1                182   \n",
       "...      ...       ...        ...             ...                ...   \n",
       "20423     50  41.41177    2.16373              32                  0   \n",
       "20424    508  41.38576    2.17438               3                  0   \n",
       "20425   1031  41.40758    2.17242               3                  0   \n",
       "20426     89  41.39138    2.18240               3                  0   \n",
       "20427     50  41.40357    2.17408               1                  0   \n",
       "\n",
       "       reviews_per_month  availability_365  neighbourhood_group_Ciutat Vella  \\\n",
       "0                   0.02               304                                 0   \n",
       "1                   0.25               312                                 0   \n",
       "2                   0.48               359                                 0   \n",
       "3                   2.38                46                                 0   \n",
       "4                   1.71               324                                 0   \n",
       "...                  ...               ...                               ...   \n",
       "20423               0.00               349                                 0   \n",
       "20424               0.00               362                                 1   \n",
       "20425               0.00               362                                 0   \n",
       "20426               0.00                22                                 1   \n",
       "20427               0.00               362                                 0   \n",
       "\n",
       "       neighbourhood_group_Eixample  neighbourhood_group_Gràcia  ...  \\\n",
       "0                                 0                           0  ...   \n",
       "1                                 1                           0  ...   \n",
       "2                                 0                           0  ...   \n",
       "3                                 0                           1  ...   \n",
       "4                                 0                           1  ...   \n",
       "...                             ...                         ...  ...   \n",
       "20423                             0                           0  ...   \n",
       "20424                             0                           0  ...   \n",
       "20425                             1                           0  ...   \n",
       "20426                             0                           0  ...   \n",
       "20427                             1                           0  ...   \n",
       "\n",
       "       neighbourhood_la Verneda i la Pau  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "...                                  ...   \n",
       "20423                                  0   \n",
       "20424                                  0   \n",
       "20425                                  0   \n",
       "20426                                  0   \n",
       "20427                                  0   \n",
       "\n",
       "       neighbourhood_la Vila Olímpica del Poblenou  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "...                                            ...   \n",
       "20423                                            0   \n",
       "20424                                            0   \n",
       "20425                                            0   \n",
       "20426                                            0   \n",
       "20427                                            0   \n",
       "\n",
       "       neighbourhood_la Vila de Gràcia  neighbourhood_les Corts  \\\n",
       "0                                    0                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    1                        0   \n",
       "4                                    0                        0   \n",
       "...                                ...                      ...   \n",
       "20423                                0                        0   \n",
       "20424                                0                        0   \n",
       "20425                                0                        0   \n",
       "20426                                0                        0   \n",
       "20427                                0                        0   \n",
       "\n",
       "       neighbourhood_les Roquetes  neighbourhood_les Tres Torres  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              0   \n",
       "...                           ...                            ...   \n",
       "20423                           0                              0   \n",
       "20424                           0                              0   \n",
       "20425                           0                              0   \n",
       "20426                           0                              0   \n",
       "20427                           0                              0   \n",
       "\n",
       "       room_type_Entire home/apt  room_type_Hotel room  \\\n",
       "0                              1                     0   \n",
       "1                              1                     0   \n",
       "2                              1                     0   \n",
       "3                              0                     0   \n",
       "4                              1                     0   \n",
       "...                          ...                   ...   \n",
       "20423                          1                     0   \n",
       "20424                          1                     0   \n",
       "20425                          1                     0   \n",
       "20426                          1                     0   \n",
       "20427                          0                     0   \n",
       "\n",
       "       room_type_Private room  room_type_Shared room  \n",
       "0                           0                      0  \n",
       "1                           0                      0  \n",
       "2                           0                      0  \n",
       "3                           1                      0  \n",
       "4                           0                      0  \n",
       "...                       ...                    ...  \n",
       "20423                       0                      0  \n",
       "20424                       0                      0  \n",
       "20425                       0                      0  \n",
       "20426                       0                      0  \n",
       "20427                       1                      0  \n",
       "\n",
       "[20428 rows x 92 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_data = pd.get_dummies(data)\n",
    "processed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to get the labels. We divide the price range as stated in the exercise and make a list with the label corresponding to each apartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cheap: 16747\n",
      "Really Expensive: 374\n",
      "Expensive: 3307\n"
     ]
    }
   ],
   "source": [
    "prices = processed_data[\"price\"]\n",
    "sdev_price = prices.std()\n",
    "mean_price = prices.mean()\n",
    "price_categories = []\n",
    "for price in prices:\n",
    "    if price == mean_price:\n",
    "        price_categories.append(\"Average\")\n",
    "    elif price < (mean_price - sdev_price):\n",
    "        price_categories.append(\"Really Cheap\")\n",
    "    elif price < mean_price and price >= (mean_price - sdev_price):\n",
    "        price_categories.append(\"Cheap\")\n",
    "    elif price > mean_price and price <= (mean_price + sdev_price):\n",
    "        price_categories.append(\"Expensive\")\n",
    "    else:\n",
    "        price_categories.append(\"Really Expensive\")\n",
    "for tipo in set(price_categories):\n",
    "    print(tipo+\": \"+str(price_categories.count(tipo)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "478.97, 134.59\n"
     ]
    }
   ],
   "source": [
    "print(\"%.2f, %.2f\"%(sdev_price, mean_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the categories is not present. That is because the standard deviation is much higher than the mean, so the category \"really cheap\", which should be composed by those apartments which are further down than one standard deviation from the mean, doesn't even show.\n",
    "\n",
    "This is explained because there are some really expensive apartments which have a high impact on the standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) We want to know what feature defines better the price between the following features:\n",
    "neighbourhood_group, neighbourhood, latitude, longitude, room_type, minimum_nights,\n",
    "number_of_reviews, reviews_per_month, availability_365.\n",
    "Make a Decision Tree Classifier, plot the resulting decision tree and draw your conclusions.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just fit the decision tree to the processed data without the price column, then plot the tree with a depth of 1, to see the most important variables, which should be those in the upper nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = processed_data.drop('price', axis=1)\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "dt_clf = dt_clf.fit(training_data, price_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1116.0, 1902.6000000000001, 'X[89] <= 0.5\\ngini = 0.301\\nsamples = 20428\\nvalue = [16747, 3307, 374]'),\n",
       " Text(558.0, 1359.0, 'X[2] <= 14.5\\ngini = 0.467\\nsamples = 10041\\nvalue = [6672, 3025, 344]'),\n",
       " Text(279.0, 815.4000000000001, 'X[65] <= 0.5\\ngini = 0.524\\nsamples = 7151\\nvalue = [4053, 2794, 304]'),\n",
       " Text(139.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(418.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(837.0, 815.4000000000001, 'X[15] <= 0.5\\ngini = 0.172\\nsamples = 2890\\nvalue = [2619, 231, 40]'),\n",
       " Text(697.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(976.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(1674.0, 1359.0, 'X[4] <= 0.045\\ngini = 0.058\\nsamples = 10387\\nvalue = [10075, 282, 30]'),\n",
       " Text(1395.0, 815.4000000000001, 'X[5] <= 318.5\\ngini = 0.15\\nsamples = 2253\\nvalue = [2071, 153, 29]'),\n",
       " Text(1255.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(1534.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(1953.0, 815.4000000000001, 'X[1] <= 2.226\\ngini = 0.031\\nsamples = 8134\\nvalue = [8004, 129, 1]'),\n",
       " Text(1813.5, 271.79999999999995, '\\n  (...)  \\n'),\n",
       " Text(2092.5, 271.79999999999995, '\\n  (...)  \\n')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(40,40))\n",
    "plot_tree(dt_clf, max_depth = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To know the most important features, we use the \"feature\\_importances\\_\" attribute of the decision tree. As stated in the package documentation, the importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.\n",
    "We make a list that tuples the importance with the position of the feature in the original list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.16556683644833578, 0), (0.15521111944213525, 1), (0.14051980999155092, 89)]\n"
     ]
    }
   ],
   "source": [
    "array = []\n",
    "for c, feature in enumerate(dt_clf.feature_importances_):\n",
    "    array.append((feature,c))\n",
    "print(sorted(array, reverse = True)[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this we know that the features 0, 1 and 89 are the most important ones. To retrieve the names we just need to ask the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['latitude', 'longitude', 'room_type_Private room'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(training_data.columns[[0,1,89]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important feature is the location of the apartment, defined by its latitude and longitude. This matches with what we saw in the previous assignment. There are neighbourhoods in Barcelona in which the average price of the apartments is higher than in other neighbourhoods. Those close to the center will usually be more expensive.\n",
    "Another feature that determines the price category strongly is the \"room_type_Private room\" or, in other words, if the rented room is private or not. It is common sense that private rooms tend to be pricier than those shared.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) Try two Random Forests Classification: one classifying the apartments with the previous\n",
    "categories as classes of apartments, and a second one where the numerical price is the\n",
    "classifier feature. Measure the out-of-bag accuracy (you will have to set the\n",
    "oob_score=True to be able to get within the variable ​ oob\\_score\\_ ​ the score otherwise is\n",
    "not computed). Which one works better?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will train a classifier and a regressor, each with 500 trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.874\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators = 500, oob_score=True)\n",
    "rfc_fit = rfc.fit(training_data, price_categories)\n",
    "print(\"%.3f\"%(rfc_fit.oob_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.436\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators = 500, oob_score=True)\n",
    "rfr_fit = rfr.fit(training_data, prices)\n",
    "print(\"%.3f\"%(rfr_fit.oob_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The out of bag score is higher in the classifier than in the regressor. That could be because it is easier to correctly predict by chance that an apartment is from a price category than assigning it a specific price value, since the price categories are ranges of values."
   ]
  }
 ],
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