Task 4 - Model Answer - Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering and Modelling\n",
    "\n",
    "---\n",
    "\n",
    "1. Import packages\n",
    "2. Load data\n",
    "3. Modelling\n",
    "\n",
    "---\n",
    "\n",
    "## 1. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Shows plots in jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Set plot style\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "                                 id  cons_12m  cons_gas_12m  cons_last_month  \\\n",
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       "\n",
       "   forecast_cons_12m  forecast_discount_energy  forecast_meter_rent_12m  \\\n",
       "0           0.000000                       0.0                 0.444045   \n",
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       "4           2.650065                       0.0                 2.122969   \n",
       "\n",
       "   forecast_price_energy_off_peak  forecast_price_energy_peak  \\\n",
       "0                        0.114481                    0.098142   \n",
       "1                        0.145711                    0.000000   \n",
       "2                        0.165794                    0.087899   \n",
       "3                        0.146694                    0.000000   \n",
       "4                        0.116900                    0.100015   \n",
       "\n",
       "   forecast_price_pow_off_peak  ...  months_modif_prod  months_renewal  \\\n",
       "0                    40.606701  ...                  2               6   \n",
       "1                    44.311378  ...                 76               4   \n",
       "2                    44.311378  ...                 68               8   \n",
       "3                    44.311378  ...                 69               9   \n",
       "4                    40.606701  ...                 71               9   \n",
       "\n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data_for_predictions.csv')\n",
    "df.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 3. Modelling\n",
    "\n",
    "We now have a dataset containing features that we have engineered and we are ready to start training a predictive model. Remember, we only need to focus on training a `Random Forest` classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sampling\n",
    "\n",
    "The first thing we want to do is split our dataset into training and test samples. The reason why we do this, is so that we can simulate a real life situation by generating predictions for our test sample, without showing the predictive model these data points. This gives us the ability to see how well our model is able to generalise to new data, which is critical.\n",
    "\n",
    "A typical % to dedicate to testing is between 20-30, for this example we will use a 75-25% split between train and test respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14606 entries, 0 to 14605\n",
      "Data columns (total 63 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   id                                          14606 non-null  object \n",
      " 1   cons_12m                                    14606 non-null  float64\n",
      " 2   cons_gas_12m                                14606 non-null  float64\n",
      " 3   cons_last_month                             14606 non-null  float64\n",
      " 4   forecast_cons_12m                           14606 non-null  float64\n",
      " 5   forecast_discount_energy                    14606 non-null  float64\n",
      " 6   forecast_meter_rent_12m                     14606 non-null  float64\n",
      " 7   forecast_price_energy_off_peak              14606 non-null  float64\n",
      " 8   forecast_price_energy_peak                  14606 non-null  float64\n",
      " 9   forecast_price_pow_off_peak                 14606 non-null  float64\n",
      " 10  has_gas                                     14606 non-null  int64  \n",
      " 11  imp_cons                                    14606 non-null  float64\n",
      " 12  margin_gross_pow_ele                        14606 non-null  float64\n",
      " 13  margin_net_pow_ele                          14606 non-null  float64\n",
      " 14  nb_prod_act                                 14606 non-null  int64  \n",
      " 15  net_margin                                  14606 non-null  float64\n",
      " 16  pow_max                                     14606 non-null  float64\n",
      " 17  var_year_price_off_peak_var                 14606 non-null  float64\n",
      " 18  var_year_price_peak_var                     14606 non-null  float64\n",
      " 19  var_year_price_mid_peak_var                 14606 non-null  float64\n",
      " 20  var_year_price_off_peak_fix                 14606 non-null  float64\n",
      " 21  var_year_price_peak_fix                     14606 non-null  float64\n",
      " 22  var_year_price_mid_peak_fix                 14606 non-null  float64\n",
      " 23  var_year_price_off_peak                     14606 non-null  float64\n",
      " 24  var_year_price_peak                         14606 non-null  float64\n",
      " 25  var_year_price_mid_peak                     14606 non-null  float64\n",
      " 26  var_6m_price_off_peak_var                   14606 non-null  float64\n",
      " 27  var_6m_price_peak_var                       14606 non-null  float64\n",
      " 28  var_6m_price_mid_peak_var                   14606 non-null  float64\n",
      " 29  var_6m_price_off_peak_fix                   14606 non-null  float64\n",
      " 30  var_6m_price_peak_fix                       14606 non-null  float64\n",
      " 31  var_6m_price_mid_peak_fix                   14606 non-null  float64\n",
      " 32  var_6m_price_off_peak                       14606 non-null  float64\n",
      " 33  var_6m_price_peak                           14606 non-null  float64\n",
      " 34  var_6m_price_mid_peak                       14606 non-null  float64\n",
      " 35  churn                                       14606 non-null  int64  \n",
      " 36  offpeak_diff_dec_january_energy             14606 non-null  float64\n",
      " 37  offpeak_diff_dec_january_power              14606 non-null  float64\n",
      " 38  off_peak_peak_var_mean_diff                 14606 non-null  float64\n",
      " 39  peak_mid_peak_var_mean_diff                 14606 non-null  float64\n",
      " 40  off_peak_mid_peak_var_mean_diff             14606 non-null  float64\n",
      " 41  off_peak_peak_fix_mean_diff                 14606 non-null  float64\n",
      " 42  peak_mid_peak_fix_mean_diff                 14606 non-null  float64\n",
      " 43  off_peak_mid_peak_fix_mean_diff             14606 non-null  float64\n",
      " 44  off_peak_peak_var_max_monthly_diff          14606 non-null  float64\n",
      " 45  peak_mid_peak_var_max_monthly_diff          14606 non-null  float64\n",
      " 46  off_peak_mid_peak_var_max_monthly_diff      14606 non-null  float64\n",
      " 47  off_peak_peak_fix_max_monthly_diff          14606 non-null  float64\n",
      " 48  peak_mid_peak_fix_max_monthly_diff          14606 non-null  float64\n",
      " 49  off_peak_mid_peak_fix_max_monthly_diff      14606 non-null  float64\n",
      " 50  tenure                                      14606 non-null  int64  \n",
      " 51  months_activ                                14606 non-null  int64  \n",
      " 52  months_to_end                               14606 non-null  int64  \n",
      " 53  months_modif_prod                           14606 non-null  int64  \n",
      " 54  months_renewal                              14606 non-null  int64  \n",
      " 55  channel_MISSING                             14606 non-null  int64  \n",
      " 56  channel_ewpakwlliwisiwduibdlfmalxowmwpci    14606 non-null  int64  \n",
      " 57  channel_foosdfpfkusacimwkcsosbicdxkicaua    14606 non-null  int64  \n",
      " 58  channel_lmkebamcaaclubfxadlmueccxoimlema    14606 non-null  int64  \n",
      " 59  channel_usilxuppasemubllopkaafesmlibmsdf    14606 non-null  int64  \n",
      " 60  origin_up_kamkkxfxxuwbdslkwifmmcsiusiuosws  14606 non-null  int64  \n",
      " 61  origin_up_ldkssxwpmemidmecebumciepifcamkci  14606 non-null  int64  \n",
      " 62  origin_up_lxidpiddsbxsbosboudacockeimpuepw  14606 non-null  int64  \n",
      "dtypes: float64(46), int64(16), object(1)\n",
      "memory usage: 7.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14606, 61)\n",
      "(14606,)\n"
     ]
    }
   ],
   "source": [
    "# Make a copy of our data\n",
    "train_df = df.copy()\n",
    "\n",
    "# Separate target variable from independent variables\n",
    "y = df['churn']\n",
    "X = df.drop(columns=['id', 'churn'])\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10954, 61)\n",
      "(10954,)\n",
      "(3652, 61)\n",
      "(3652,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training\n",
    "\n",
    "Once again, we are using a `Random Forest` classifier in this example. A Random Forest sits within the category of `ensemble` algorithms because internally the `Forest` refers to a collection of `Decision Trees` which are tree-based learning algorithms. As the data scientist, you can control how large the forest is (that is, how many decision trees you want to include).\n",
    "\n",
    "The reason why an `ensemble` algorithm is powerful is because of the laws of averaging, weak learners and the central limit theorem. If we take a single decision tree and give it a sample of data and some parameters, it will learn patterns from the data. It may be overfit or it may be underfit, but that is now our only hope, that single algorithm. \n",
    "\n",
    "With `ensemble` methods, instead of banking on 1 single trained model, we can train 1000's of decision trees, all using different splits of the data and learning different patterns. It would be like asking 1000 people to all learn how to code. You would end up with 1000 people with different answers, methods and styles! The weak learner notion applies here too, it has been found that if you train your learners not to overfit, but to learn weak patterns within the data and you have a lot of these weak learners, together they come together to form a highly predictive pool of knowledge! This is a real life application of many brains are better than 1.\n",
    "\n",
    "Now instead of relying on 1 single decision tree for prediction, the random forest puts it to the overall views of the entire collection of decision trees. Some ensemble algorithms using a voting approach to decide which prediction is best, others using averaging. \n",
    "\n",
    "As we increase the number of learners, the idea is that the random forest's performance should converge to its best possible solution.\n",
    "\n",
    "Some additional advantages of the random forest classifier include:\n",
    "\n",
    "- The random forest uses a rule-based approach instead of a distance calculation and so features do not need to be scaled\n",
    "- It is able to handle non-linear parameters better than linear based models\n",
    "\n",
    "On the flip side, some disadvantages of the random forest classifier include:\n",
    "\n",
    "- The computational power needed to train a random forest on a large dataset is high, since we need to build a whole ensemble of estimators.\n",
    "- Training time can be longer due to the increased complexity and size of thee ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=1000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=1000\n",
    ")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `scikit-learn` documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>, has a lot of information about the algorithm and the parameters that you can use when training a model.\n",
    "\n",
    "For this example, I am using `n_estimators` = 1000. This means that my random forest will consist of 1000 decision trees. There are many more parameters that you can fine-tune within the random forest and finding the optimal combinations of parameters can be a manual task of exploration, trial and error, which will not be covered during this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "\n",
    "Now let's evaluate how well this trained model is able to predict the values of the test dataset.\n",
    "\n",
    "We are going to use 3 metrics to evaluate performance:\n",
    "\n",
    "- Accuracy = the ratio of correctly predicted observations to the total observations\n",
    "- Precision = the ability of the classifier to not label a negative sample as positive\n",
    "- Recall = the ability of the classifier to find all the positive samples\n",
    "\n",
    "The reason why we are using these three metrics is because a simple accuracy is not always a good measure to use. To give an example, let's say you're predicting heart failures with patients in a hospital and there were 100 patients out of 1000 that did have a heart failure. \n",
    "\n",
    "If you predicted 80 out of 100 (80%) of the patients that did have a heart failure correctly, you might think that you've done well! However, this also means that you predicted 20 wrong and what may the implications of predicting these remaining 20 patients wrong? Maybe they miss out on getting vital treatment to save their lives. \n",
    "\n",
    "As well as this, what about the impact of predicting negative cases as positive (people not having heart failure being predicted that they did), maybe a high number of false positives means that resources get used up on thee wrong people and a lot of time is wasted when they could have been helping the real heart failure sufferers. \n",
    "\n",
    "This is just an example, but it illustrates why other performance metrics are necessary such `Precision` and `Recall`, which are good measures to use in a classification scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_test, predictions).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3286\n",
       "1     366\n",
       "Name: churn, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positives: 17\n",
      "False positives: 4\n",
      "True negatives: 3282\n",
      "False negatives: 349\n",
      "\n",
      "Accuracy: 0.9033406352683461\n",
      "Precision: 0.8095238095238095\n",
      "Recall: 0.04644808743169399\n"
     ]
    }
   ],
   "source": [
    "print(f\"True positives: {tp}\")\n",
    "print(f\"False positives: {fp}\")\n",
    "print(f\"True negatives: {tn}\")\n",
    "print(f\"False negatives: {fn}\\n\")\n",
    "\n",
    "print(f\"Accuracy: {metrics.accuracy_score(y_test, predictions)}\")\n",
    "print(f\"Precision: {metrics.precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall: {metrics.recall_score(y_test, predictions)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these results there are a few things to point out:\n",
    "\n",
    "<b>Note:</b> If you are running this notebook yourself, you may get slightly different answers!\n",
    "\n",
    "- Within the test set about 10% of the rows are churners (churn = 1).\n",
    "- Looking at the true negatives, we have 3282 out of 3286. This means that out of all the negative cases (churn = 0), we predicted 3282 as negative (hence the name True negative). This is great!\n",
    "- Looking at the false negatives, this is where we have predicted a client to not churn (churn = 0) when in fact they did churn (churn = 1). This number is quite high at 348, we want to get the false negatives to as close to 0 as we can, so this would need to be addressed when improving the model.\n",
    "- Looking at false positives, this is where we have predicted a client to churn when they actually didnt churn. For this value we can see there are 4 cases, which is great!\n",
    "- With the true positives, we can see that in total we have 366 clients that churned in the test dataset. However, we are only able to correctly identify 18 of those 366, which is very poor.\n",
    "- Looking at the accuracy score, this is very misleading! Hence the use of precision and recall is important. The accuracy score is high, but it does not tell us the whole story.\n",
    "- Looking at the precision score, this shows us a score of 0.82 which is not bad, but could be improved.\n",
    "- However, the recall shows us that the classifier has a very poor ability to identify positive samples. This would be the main concern for improving this model!\n",
    "\n",
    "So overall, we're able to very accurately identify clients that do not churn, but we are not able to predict cases where clients do churn! What we are seeing is that a high % of clients are being identified as not churning when they should be identified as churning. This in turn tells me that the current set of features are not discriminative enough to clearly distinguish between churners and non-churners. \n",
    "\n",
    "A data scientist at this point would go back to feature engineering to try and create more predictive features. They may also experiment with optimising the parameters within the model to improve performance. For now, lets dive into understanding the model a little more.\n",
    "\n",
    "### Model understanding\n",
    "\n",
    "A simple way of understanding the results of a model is to look at feature importances. Feature importances indicate the importance of a feature within the predictive model, there are several ways to calculate feature importance, but with the Random Forest classifier, we're able to extract feature importances using the built-in method on the trained model. In the Random Forest case, the feature importance represents the number of times each feature is used for splitting across all trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = pd.DataFrame({\n",
    "    'features': X_train.columns,\n",
    "    'importance': model.feature_importances_\n",
    "}).sort_values(by='importance', ascending=True).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 25))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(feature_importances)), feature_importances['importance'], color='b', align='center')\n",
    "plt.yticks(range(len(feature_importances)), feature_importances['features'])\n",
    "plt.xlabel('Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this chart, we can observe the following points:\n",
    "\n",
    "- Net margin and consumption over 12 months is a top driver for churn in this model\n",
    "- Margin on power subscription also is an influential driver\n",
    "- Time seems to be an influential factor, especially the number of months they have been active, their tenure and the number of months since they updated their contract\n",
    "- The feature that our colleague recommended is in the top half in terms of how influential it is and some of the features built off the back of this actually outperform it\n",
    "- Our price sensitivity features are scattered around but are not the main driver for a customer churning\n",
    "\n",
    "The last observation is important because this relates back to our original hypothesis:\n",
    "\n",
    "    > Is churn driven by the customers' price sensitivity?\n",
    "\n",
    "Based on the output of the feature importances, it is not a main driver but it is a weak contributor. However, to arrive at a conclusive result, more experimentation is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba_predictions = model.predict_proba(X_test)\n",
    "probabilities = proba_predictions[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.reset_index()\n",
    "X_test.drop(columns='index', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['churn'] = predictions.tolist()\n",
    "X_test['churn_probability'] = probabilities.tolist()\n",
    "X_test.to_csv('out_of_sample_data_with_predictions.csv')"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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