B2C2 remote tech challenge (data analysis) v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aerial-youth",
   "metadata": {},
   "source": [
    "# B2C2 remote tech challenge (data analysis)\n",
    "\n",
    "Author: Alex de Vries.\n",
    "Date: 30 - May 2021.\n",
    "\n",
    "My reflection on this test is shown at the end in chapter 6. I hope this file gives a glimpse of the Python skills I currently have. \n",
    "- 1) Instructions\n",
    "- 2) Library importing & data loading\n",
    "- 3) Data exploration\n",
    "- 4) Train model\n",
    "- 5) Summarized results (pickle the model)\n",
    "- 6) Review on the process\n",
    "\n",
    "\n",
    "## 1) Instructions\n",
    "Use the features to predict:\n",
    "a) The sign of the target (is it positive or negative)\n",
    "b) The magnitude of the target\n",
    "\n",
    "Write two scripts:\n",
    "The first to load in the data set, train the models and save the model to pickle\n",
    "The second to load in the data set and apply it the model to it (we will run this on an unseen data set to test out of sample)\n",
    "\n",
    "Feel free to submit any other images / commentary on anything of interest during your data exploration, also further steps you might have taken given more time. The evaluation is based on the approach taken to modelling this data, rather than the absolute results of mean squared error etc. Please use this as a chance to showcase your deeper understanding of the modelling and data exploration. Please use Jupyter Notebooks if possible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "substantial-short",
   "metadata": {},
   "source": [
    "## 2) library importing & data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "norwegian-midwest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: sklearn in /home/alex/.local/lib/python3.6/site-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in /home/alex/.local/lib/python3.6/site-packages (from sklearn) (0.24.2)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/alex/.local/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.19.5)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/alex/.local/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/alex/.local/lib/python3.6/site-packages (from scikit-learn->sklearn) (2.1.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /home/alex/.local/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.5.4)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: seaborn in /home/alex/.local/lib/python3.6/site-packages (0.11.1)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /home/alex/.local/lib/python3.6/site-packages (from seaborn) (3.3.4)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/alex/.local/lib/python3.6/site-packages (from seaborn) (1.1.5)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/alex/.local/lib/python3.6/site-packages (from seaborn) (1.5.4)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/alex/.local/lib/python3.6/site-packages (from seaborn) (1.19.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/alex/.local/lib/python3.6/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/alex/.local/lib/python3.6/site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/alex/.local/lib/python3.6/site-packages (from matplotlib>=2.2->seaborn) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/alex/.local/lib/python3.6/site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/alex/.local/lib/python3.6/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: six in /home/alex/.local/lib/python3.6/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/lib/python3/dist-packages (from pandas>=0.23->seaborn) (2018.3)\n"
     ]
    }
   ],
   "source": [
    "# load all relevant libraries\n",
    "# note: I am running Jupyter Notebooks on Linux Mint. Some libraries require an additional step before importing.\n",
    "import sys\n",
    "!{sys.executable} -m pip install sklearn\n",
    "!{sys.executable} -m pip install seaborn\n",
    "\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.metrics import r2_score # R2\n",
    "from sklearn.metrics import mean_squared_error # RMSE\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "southwest-binary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the test data\n",
    "with open(\"candidate-test-data.pkl\", 'rb') as f:\n",
    "    df = pickle.load(f, encoding=\"latin1\") \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "friendly-despite",
   "metadata": {},
   "source": [
    "## 3) data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "collaborative-affair",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---displaying test data---\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_10</th>\n",
       "      <th>feature_11</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_12</th>\n",
       "      <th>target_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.34</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2.585</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>1.85</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.87</td>\n",
       "      <td>0</td>\n",
       "      <td>8.960</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.99</td>\n",
       "      <td>11</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>12.44</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>7</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.31</td>\n",
       "      <td>11</td>\n",
       "      <td>1.88</td>\n",
       "      <td>-7.23</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>4</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>0.924577</td>\n",
       "      <td>12.34</td>\n",
       "      <td>12</td>\n",
       "      <td>1.57</td>\n",
       "      <td>-6.76</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>11</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-9.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_10  feature_11  feature_8  feature_9  feature_2  feature_3  \\\n",
       "0       10.10           0      0.085       0.28   1.000000       1.34   \n",
       "1       12.20           0      2.585      -0.11   1.000000       2.36   \n",
       "2       -1.87           0      8.960      -0.20   1.000000       2.99   \n",
       "3       -0.04           0      0.057      -1.57   1.000000      14.31   \n",
       "4        0.67           1      0.045      -1.58   0.924577      12.34   \n",
       "\n",
       "   feature_0  feature_1  feature_6  feature_7  feature_4  feature_5  \\\n",
       "0         12      -0.29       1.75      -1.13          4      -0.76   \n",
       "1          0      -2.56       1.85      -2.80          7      -2.95   \n",
       "2         11      -2.92      12.44      -3.90          7      -3.51   \n",
       "3         11       1.88      -7.23      -3.18          4       2.41   \n",
       "4         12       1.57      -6.76      -2.87         11       2.41   \n",
       "\n",
       "   feature_12  target_0  \n",
       "0        0.66     -6.50  \n",
       "1       -1.05     -4.30  \n",
       "2        0.39      0.72  \n",
       "3        0.59    -10.24  \n",
       "4        0.68     -9.53  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"---displaying test data---\")\n",
    "print(type(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dental-dancing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_10</th>\n",
       "      <th>feature_11</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_12</th>\n",
       "      <th>target_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "      <td>271995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.116311</td>\n",
       "      <td>0.618478</td>\n",
       "      <td>6.957020</td>\n",
       "      <td>-0.886414</td>\n",
       "      <td>0.402306</td>\n",
       "      <td>0.132220</td>\n",
       "      <td>7.740466</td>\n",
       "      <td>-0.590151</td>\n",
       "      <td>0.173645</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>6.841946</td>\n",
       "      <td>0.194936</td>\n",
       "      <td>0.148393</td>\n",
       "      <td>-1.638634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.029769</td>\n",
       "      <td>0.485761</td>\n",
       "      <td>19.790288</td>\n",
       "      <td>3.012869</td>\n",
       "      <td>0.411511</td>\n",
       "      <td>21.754544</td>\n",
       "      <td>5.015524</td>\n",
       "      <td>34.527937</td>\n",
       "      <td>16.175515</td>\n",
       "      <td>6.160517</td>\n",
       "      <td>4.074344</td>\n",
       "      <td>9.458349</td>\n",
       "      <td>6.693244</td>\n",
       "      <td>9.150847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-144.640000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-72.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-436.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-513.570000</td>\n",
       "      <td>-279.370000</td>\n",
       "      <td>-107.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-193.280000</td>\n",
       "      <td>-118.140000</td>\n",
       "      <td>-196.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066000</td>\n",
       "      <td>-1.950000</td>\n",
       "      <td>0.029606</td>\n",
       "      <td>-6.520000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-11.530000</td>\n",
       "      <td>-4.790000</td>\n",
       "      <td>-1.830000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-2.570000</td>\n",
       "      <td>-1.780000</td>\n",
       "      <td>-4.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.126000</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>0.199156</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>-0.080000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.710000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.876000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.989446</td>\n",
       "      <td>7.010000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.730000</td>\n",
       "      <td>4.980000</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>210.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1668.343000</td>\n",
       "      <td>67.780000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.770000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>515.620000</td>\n",
       "      <td>278.350000</td>\n",
       "      <td>128.390000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>177.690000</td>\n",
       "      <td>96.480000</td>\n",
       "      <td>192.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          feature_10     feature_11      feature_8      feature_9  \\\n",
       "count  271995.000000  271995.000000  271995.000000  271995.000000   \n",
       "mean        0.116311       0.618478       6.957020      -0.886414   \n",
       "std         9.029769       0.485761      19.790288       3.012869   \n",
       "min      -144.640000       0.000000       0.000000     -72.200000   \n",
       "25%        -2.520000       0.000000       0.066000      -1.950000   \n",
       "50%         0.050000       1.000000       1.126000      -0.350000   \n",
       "75%         2.710000       1.000000       5.876000       0.310000   \n",
       "max       210.300000       1.000000    1668.343000      67.780000   \n",
       "\n",
       "           feature_2      feature_3      feature_0      feature_1  \\\n",
       "count  271995.000000  271995.000000  271995.000000  271995.000000   \n",
       "mean        0.402306       0.132220       7.740466      -0.590151   \n",
       "std         0.411511      21.754544       5.015524      34.527937   \n",
       "min         0.000000    -436.000000       0.000000    -513.570000   \n",
       "25%         0.029606      -6.520000       1.000000     -11.530000   \n",
       "50%         0.199156       0.140000      10.000000       0.300000   \n",
       "75%         0.989446       7.010000      11.000000      11.730000   \n",
       "max         1.000000     408.770000      15.000000     515.620000   \n",
       "\n",
       "           feature_6      feature_7      feature_4      feature_5  \\\n",
       "count  271995.000000  271995.000000  271995.000000  271995.000000   \n",
       "mean        0.173645       0.003039       6.841946       0.194936   \n",
       "std        16.175515       6.160517       4.074344       9.458349   \n",
       "min      -279.370000    -107.250000       0.000000    -193.280000   \n",
       "25%        -4.790000      -1.830000       4.000000      -2.570000   \n",
       "50%         0.050000      -0.080000       8.000000       0.040000   \n",
       "75%         4.980000       1.550000      11.000000       2.850000   \n",
       "max       278.350000     128.390000      13.000000     177.690000   \n",
       "\n",
       "          feature_12       target_0  \n",
       "count  271995.000000  271995.000000  \n",
       "mean        0.148393      -1.638634  \n",
       "std         6.693244       9.150847  \n",
       "min      -118.140000    -196.430000  \n",
       "25%        -1.780000      -4.470000  \n",
       "50%         0.000000      -0.830000  \n",
       "75%         1.900000       1.660000  \n",
       "max        96.480000     192.430000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "palestinian-riding",
   "metadata": {},
   "source": [
    "Useful to note from the describe() table above is that the count is 271995 for all, and some features have a negative minimum value. For some analysis this is relevant, but not in this case.\n",
    "\n",
    "additional data exploration steps done which are useful but showing the output will cluther the notebook file:\n",
    "<br/> df.info()      (check if non-null and see the type for each column, useful: all non-zero columns and types are numerical)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-dinner",
   "metadata": {},
   "source": [
    "## 3.1 Evaluate Outliers\n",
    "This visual inspection is not possible with many more features. In this case it still gives a clear overview of the details of the 13 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fundamental-diana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(14,1, figsize = (25,25))\n",
    "plot_10 = sns.boxplot(df[\"feature_10\"], ax = axs[0])\n",
    "plot_11 = sns.boxplot(df[\"feature_11\"], ax = axs[1])\n",
    "plot_8 = sns.boxplot(df[\"feature_8\"], ax = axs[2])\n",
    "plot_9 = sns.boxplot(df[\"feature_9\"], ax = axs[3])\n",
    "plot_2 = sns.boxplot(df[\"feature_2\"], ax = axs[4])\n",
    "plot_3 = sns.boxplot(df[\"feature_3\"], ax = axs[5])\n",
    "plot_0 = sns.boxplot(df[\"feature_0\"], ax = axs[6])\n",
    "plot_1 = sns.boxplot(df[\"feature_1\"], ax = axs[7])\n",
    "plot_6 = sns.boxplot(df[\"feature_6\"], ax = axs[8])\n",
    "plot_7 = sns.boxplot(df[\"feature_7\"], ax = axs[9])\n",
    "plot_4 = sns.boxplot(df[\"feature_4\"], ax = axs[10])\n",
    "plot_5 = sns.boxplot(df[\"feature_5\"], ax = axs[11])\n",
    "plot_12 = sns.boxplot(df[\"feature_12\"], ax = axs[12])\n",
    "plot_target = sns.boxplot(df[\"target_0\"], ax = axs[13])\n",
    "fig.suptitle('boxplot for each feature to visualy inspect outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "urban-marijuana",
   "metadata": {},
   "source": [
    "## 3.2) Select features \n",
    "There are many ways to deal with outliers and select features. What follows is an overview of three methods and discuss why I pick one of the methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "gorgeous-compilation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method 1: reduce outliers based on a specified quantile.\n",
    "# This code shows how to exclude the 10th percentile from the data for the target field. \n",
    "# This can be helpful to not overfit the model.\n",
    "\n",
    "# I performed a quick test removing outliers and found a decrease in predictive ability.\n",
    "# Therefore I remove no outliers.\n",
    "\n",
    "df_test = df\n",
    "#Q1 = df_test.target_0.quantile(0.10)\n",
    "#Q3 = df_test.target_0.quantile(0.90)\n",
    "#IQR = Q3 - Q1\n",
    "#df_test = df_test[(df_test.target_0 >= Q1 - 1.5*IQR) & (df_test.target_0 <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "forty-bubble",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Before selecting features we need to define the training and testing data using all features.\n",
    "\n",
    "x = df_test.drop(columns=['target_0'])\n",
    "y = df_test.target_0\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "isolated-meaning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Method 2: Correlation metric\n",
    "\n",
    "f_selector = SelectKBest(score_func=f_regression, k='all')\n",
    "f_selector.fit(x_train, y_train)\n",
    "# transform train input data\n",
    "X_train_fs = f_selector.transform(x_train)\n",
    "# transform test input data\n",
    "X_test_fs = f_selector.transform(x_test)\n",
    "# Plot the scores for the features\n",
    "plt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n",
    "plt.xlabel(\"feature index\")\n",
    "plt.ylabel(\"F-value (transformed from the correlation values)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "biblical-story",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Method 3: mutual correlation statistic\n",
    "# (takes some time to run)\n",
    "f_selector = SelectKBest(score_func=mutual_info_regression, k='all')\n",
    "f_selector.fit(x_train, y_train)\n",
    "# transform train input data\n",
    "X_train_fs = f_selector.transform(x_train)\n",
    "# transform test input data\n",
    "X_test_fs = f_selector.transform(x_test)\n",
    "# Plot the scores for the features\n",
    "plt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n",
    "plt.xlabel(\"feature index\")\n",
    "plt.ylabel(\"Estimated MI value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "better-board",
   "metadata": {},
   "source": [
    "When viewing both plots we find that the correlation plot shows fewer features being relevant than the mutual correlation plot. When viewing the boxplots earlier in this document we find that there are many outliers. This is known to attribute to a higher mutual correlation between the feature and the target. Because of so many outliers we will focus on the results from the correlation plot. We pick any feature with an f-value above 300: 10,11, 2, 1,6,7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "developing-century",
   "metadata": {},
   "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>feature_11</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>target_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.924577</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-9.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_11  feature_2  feature_9  target_0\n",
       "0           0   1.000000       0.28     -6.50\n",
       "1           0   1.000000      -0.11     -4.30\n",
       "2           0   1.000000      -0.20      0.72\n",
       "3           0   1.000000      -1.57    -10.24\n",
       "4           1   0.924577      -1.58     -9.53"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe with only the selected features.\n",
    "# If there are many features this process can not be done manually. \n",
    "# With the given time and the small number of features I keep it simple.\n",
    "\n",
    "# df2 = df_test.drop(['feature_11', 'feature_8', 'feature_4', 'feature_2', 'feature_0'], axis = 1)\n",
    "# df2 = df_test\n",
    "\n",
    "columns = ['feature_11', 'feature_2', 'feature_9', 'target_0']\n",
    "df2 = pd.DataFrame(df_test, columns=columns)\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "conventional-murray",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271995, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corporate-vitamin",
   "metadata": {},
   "source": [
    "## Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "compact-concord",
   "metadata": {},
   "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>feature_11</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.924577</td>\n",
       "      <td>-1.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_11  feature_2  feature_9\n",
       "0           0   1.000000       0.28\n",
       "1           0   1.000000      -0.11\n",
       "2           0   1.000000      -0.20\n",
       "3           0   1.000000      -1.57\n",
       "4           1   0.924577      -1.58"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df2.drop(columns=['target_0'])\n",
    "y = df2.target_0\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "lasting-debut",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flush-february",
   "metadata": {},
   "source": [
    "# 4) Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "developing-lambda",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression(normalize=True)\n",
    "lr = lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "composed-clerk",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suited-surprise",
   "metadata": {},
   "source": [
    "### 4.1) Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "valued-proof",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00559261010818024"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "alive-contribution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.169427497458804"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_mse = mean_squared_error(y_test, y_pred)\n",
    "lr_rmse = np.sqrt(lr_mse)\n",
    "lr_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "unique-calculation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 84.0784\n",
      "Root Mean Squared Error 9.1694\n",
      "Coefficient of Determination (r2) 0.0056\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error: %.4f' % mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error %0.4f' %lr_rmse)\n",
    "print('Coefficient of Determination (r2) %.4f' % r2_score(y_test, y_pred))\n",
    "\n",
    "# The model shows a relatively small amount of mean squared error and a significant R-squared value (r < 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "certified-olive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: -1.1680\n",
      "Coefficients:\n"
     ]
    },
    {
     "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>feature_11</th>\n",
       "      <td>0.248899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_2</th>\n",
       "      <td>-1.209225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_9</th>\n",
       "      <td>0.145702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Coefficient\n",
       "feature_11     0.248899\n",
       "feature_2     -1.209225\n",
       "feature_9      0.145702"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff = pd.DataFrame(lr.coef_, x.columns, columns=['Coefficient'])\n",
    "print('Intercept: %.4f' %lr.intercept_)\n",
    "print('Coefficients:')\n",
    "coeff\n",
    "\n",
    "# Only these three features give a coefficient not close to 0.\n",
    "# This indicates that the other features do not improve the model.\n",
    "# Still, the model does not give a linear relation when comparing the test and prediction data. (shown in the following plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "relevant-webmaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting y_test and y_pred\n",
    "plt.style.use('default')\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.scatter(y_test, y_pred, alpha=0.3)\n",
    "plt.suptitle('Test vs Predictions', fontsize=15)               \n",
    "plt.xlabel('Test', fontsize=10)                               \n",
    "plt.ylabel('Predictions', fontsize=10)                        \n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lonely-competition",
   "metadata": {},
   "source": [
    "# 5) summarize results\n",
    "There are multiple things I would change in my analysis. At the end I found out they proper way to pickle models and load them. All the steps done in the previous chapters are combined in the 'train_model' function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "sustained-nothing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nCoeff of the predictor:  [ 0.24889853 -1.20922534  0.14570178]\n",
      "Intercept:  -1.1680020075459632\n"
     ]
    }
   ],
   "source": [
    "# change these paths to a local directory for saving models.\n",
    "Project_path = \"/home/alex/Desktop/creative/self_study/\"\n",
    "model_path = Project_path + \"linear_regression_model.pickle\"\n",
    "\n",
    "def train_model(data):\n",
    "    columns = ['feature_11', 'feature_2', 'feature_9', 'target_0']\n",
    "    df = pd.DataFrame(data, columns=columns)\n",
    "    lr = LinearRegression(normalize=True)\n",
    "    \n",
    "    x = df.drop(columns=['target_0'])\n",
    "    y = df.target_0\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42) \n",
    "    \n",
    "    model = lr.fit(x_train, y_train) \n",
    "    y_pred = lr.predict(x_test)\n",
    "    lr_mse = mean_squared_error(y_test, y_pred)\n",
    "    lr_rmse = np.sqrt(lr_mse)\n",
    "    \n",
    "    print('n'+\"Coeff of the predictor: \",lr.coef_)\n",
    "    print(\"Intercept: \",lr.intercept_)\n",
    "\n",
    "    return model\n",
    "    \n",
    "lrmodel = train_model(df)\n",
    "pickle.dump(lrmodel, open(model_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "treated-ordinary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scoring value: 0.005977425224869215\n"
     ]
    }
   ],
   "source": [
    "LR_model = pickle.load(open(model_path,'rb'))\n",
    "pred = LR_model.score(x,y)\n",
    "print (\"scoring value:\", pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "separate-bedroom",
   "metadata": {},
   "source": [
    "# 6) Review on the process\n",
    "I would change multiple things if I would do it again. There is not that much time, and a lot was spent on investigating the qualities of the data. This is what I normally do, but in this case the focus should lie on choosing the right ML technique and model fitting. \n",
    "\n",
    "Secondly, instead of focussing on linear regression I could compare results for different techniques such as logistic regression, random forrest, decision tree, gaussian naive bayes or k-nearest neighbors (most common techniques). The results in my current model are not great, although the significant features were found. It would then be easier to get the direction and magnitude of the target.\n",
    "\n",
    "Lastly, I would spend more time on manipulating the features such as normalize the data to potentially find more relations and improve prediction. In the end I hope this project does give a general overview of my data analysis skills in Python. With more time, and now a better understanding of pickle a better model would be the result."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 },
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