lewiuberg/tutorial 6.ipynb

## tutorial 6.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Activity sheet 6\n# Classification - Logistic Regression\n**Objective**\\\nThe aim of this activity sheet is to load a customer dataset belonging to a telecommunication company and develop a classifier using the dataset. The overarching objective of the model is to predict the churn rate. This kind of insight would enable the company to take some pro-active steps and retain its existing customers. In a broader sense this activity sheet is an application of machine learning in business analytics. The classification technique to be employed in this sheet is logistic regression.\n\n**Dataset description**\\\nAs explained in the previous section, you will be using a telecom company data for predicting customer churn. In this dataset, each row represents one customer. The general hypothesis in this business is that, it’s cheaper to retain customers rather than acquiring new ones. Therefore, the focus would be to predict the customers who are more likely to stay with the company.\n\nThe dataset includes information about: /\n* Customers who left within the last month – the column is called Churn.\n* Services that each customer has signed up for.\n* Customer account information.\n* Demographic info about customers."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Data pre-processing\nLoad telecommunication churn data from the csv file. Select the feature set for the modelling. You do not necessarily need all the given variables. However, the output of interest (dependent variable) must be churn rate. Use scikit-learn for train-test splitting of the dataset."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy import stats\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import jaccard_score, confusion_matrix, plot_confusion_matrix, accuracy_score, classification_report, log_loss\n\nfrom eval_tools import cm_plot\n\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.read_csv(\"dataset6.csv\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n\n   longmon  ...  pager  internet  callwait  confer  ebill  loglong  logtoll  \\\n0     4.40  ...    1.0       0.0       1.0     1.0    0.0    1.482    3.033   \n1     9.45  ...    0.0       0.0       0.0     0.0    0.0    2.246    3.240   \n2     6.30  ...    0.0       0.0       0.0     1.0    0.0    1.841    3.240   \n3     6.05  ...    1.0       1.0       1.0     1.0    1.0    1.800    3.807   \n4     7.10  ...    0.0       0.0       1.0     1.0    0.0    1.960    3.091   \n\n   lninc  custcat  churn  \n0  4.913      4.0    1.0  \n1  3.497      1.0    1.0  \n2  3.401      3.0    0.0  \n3  4.331      4.0    0.0  \n4  4.382      3.0    0.0  \n\n[5 rows x 28 columns]",
            "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>tenure</th>\n      <th>age</th>\n      <th>address</th>\n      <th>income</th>\n      <th>ed</th>\n      <th>employ</th>\n      <th>equip</th>\n      <th>callcard</th>\n      <th>wireless</th>\n      <th>longmon</th>\n      <th>...</th>\n      <th>pager</th>\n      <th>internet</th>\n      <th>callwait</th>\n      <th>confer</th>\n      <th>ebill</th>\n      <th>loglong</th>\n      <th>logtoll</th>\n      <th>lninc</th>\n      <th>custcat</th>\n      <th>churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>11.0</td>\n      <td>33.0</td>\n      <td>7.0</td>\n      <td>136.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4.40</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.482</td>\n      <td>3.033</td>\n      <td>4.913</td>\n      <td>4.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>33.0</td>\n      <td>33.0</td>\n      <td>12.0</td>\n      <td>33.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.45</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.246</td>\n      <td>3.240</td>\n      <td>3.497</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>23.0</td>\n      <td>30.0</td>\n      <td>9.0</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.30</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.841</td>\n      <td>3.240</td>\n      <td>3.401</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>38.0</td>\n      <td>35.0</td>\n      <td>5.0</td>\n      <td>76.0</td>\n      <td>2.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>6.05</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.800</td>\n      <td>3.807</td>\n      <td>4.331</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.0</td>\n      <td>35.0</td>\n      <td>14.0</td>\n      <td>80.0</td>\n      <td>2.0</td>\n      <td>15.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>7.10</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.960</td>\n      <td>3.091</td>\n      <td>4.382</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 28 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Logistic Regression Assumptions**\n* Binary logistic regression requires the dependent variable to be binary.\n* For a binary regression, the factor level 1 of the dependent variable should represent the desired outcome.\n* Only the meaningful variables should be included.\n* The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.\n* The independent variables are linearly related to the log odds.\n* Logistic regression requires quite large sample sizes.\n\n[Source](https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Looking for missing values."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.info()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 200 entries, 0 to 199\nData columns (total 28 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   tenure    200 non-null    float64\n 1   age       200 non-null    float64\n 2   address   200 non-null    float64\n 3   income    200 non-null    float64\n 4   ed        200 non-null    float64\n 5   employ    200 non-null    float64\n 6   equip     200 non-null    float64\n 7   callcard  200 non-null    float64\n 8   wireless  200 non-null    float64\n 9   longmon   200 non-null    float64\n 10  tollmon   200 non-null    float64\n 11  equipmon  200 non-null    float64\n 12  cardmon   200 non-null    float64\n 13  wiremon   200 non-null    float64\n 14  longten   200 non-null    float64\n 15  tollten   200 non-null    float64\n 16  cardten   200 non-null    float64\n 17  voice     200 non-null    float64\n 18  pager     200 non-null    float64\n 19  internet  200 non-null    float64\n 20  callwait  200 non-null    float64\n 21  confer    200 non-null    float64\n 22  ebill     200 non-null    float64\n 23  loglong   200 non-null    float64\n 24  logtoll   200 non-null    float64\n 25  lninc     200 non-null    float64\n 26  custcat   200 non-null    float64\n 27  churn     200 non-null    float64\ndtypes: float64(28)\nmemory usage: 43.9 KB\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Looking for variables that had a strong correlation relationship with \"churn\" to filter out unnecessary variables. The values to be used should be above 0.5 for a strong positive relationship, and below -0.5 for a strong negative relationship. However, the values calculated were not within these limits, so other values were selected to have something to work with."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Setting the upper and lower limits.\nge = 0.3 #0.25\nle = -0.3 #-0.3\n# Calculating the correlation between all features (Correlation Matrix).\ncorr = df.corr()\n# Selecting all correlated values of X greater than n and lower than m.\nchurn_corr_mat = corr[corr[\"churn\"].ge(ge) | corr[\"churn\"].le(le)]\n# Getting the sorted list of features correlated to X.\nchurn_corr_lst = churn_corr_mat[\"churn\"].sort_values(\n    ascending=False).index.tolist()\n# Defining a sliced dataframe from the sortet list of features\nchurn_corr_df = df[churn_corr_lst]\n# Extracting the X feature column from the dataframe\n# based on the sorted list greater than n.\nchurn_corr_lst_df = churn_corr_df.corr()[[\"churn\"]].sort_values(by=[\n    \"churn\"], ascending=False)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(3, 5))\n#sns.set(font_scale=1.2)\n\nax = sns.heatmap(\n    churn_corr_lst_df,\n    vmin=-1,\n    cmap=\"coolwarm_r\",\n    annot=True,\n    annot_kws={\"size\": 12})\nax.set_yticklabels(\n    ax.get_yticklabels(),\n    fontsize=12,\n    horizontalalignment=\"right\");",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 216x360 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Looking for multicollinearity."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "sns.pairplot(churn_corr_df, hue=\"churn\", diag_kind=\"hist\", diag_kws = {\"alpha\":1, \"bins\":15}, markers=[\"s\", \"D\"], corner=True);",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 771.875x720 with 14 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(8, 8))\n\nax = sns.heatmap(\n    churn_corr_df.corr(),\n    vmin=-1, vmax=1, center=0,\n    cmap=sns.diverging_palette(20, 220, n=200),\n    square=True,\n    annot=True,\n    linewidths=0.5,\n    cbar_kws={\"shrink\": .82},\n    fmt=\".2f\",\n    annot_kws={\"size\": 10}\n)\n\nax.set_xticklabels(\n    ax.get_xticklabels(),\n    rotation=45,\n    horizontalalignment='right'\n);",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Multicollinearity diagnostic is considered, pairwise correlation coefficients between predictors. Correlation cutoff = 0.8 \\\n[Source](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/)"
    },
    {
      "metadata": {
        "trusted": true,
        "editable": false,
        "deletable": false,
        "run_control": {
          "frozen": true
        }
      },
      "cell_type": "code",
      "source": "# THE THERE ARE TOO FEW OBSERVATIONS IN THE DATA SET \nchurn_corr_df = churn_corr_df.drop([\"equipmon\", \"loglong\"], axis=1)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "churn_corr_df.info()",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 200 entries, 0 to 199\nData columns (total 5 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   churn     200 non-null    float64\n 1   callcard  200 non-null    float64\n 2   loglong   200 non-null    float64\n 3   employ    200 non-null    float64\n 4   tenure    200 non-null    float64\ndtypes: float64(5)\nmemory usage: 7.9 KB\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "churn_corr_df.head()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "   churn  callcard  loglong  employ  tenure\n0    1.0       1.0    1.482     5.0    11.0\n1    1.0       0.0    2.246     0.0    33.0\n2    0.0       0.0    1.841     2.0    23.0\n3    0.0       1.0    1.800    10.0    38.0\n4    0.0       1.0    1.960    15.0     7.0",
            "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>churn</th>\n      <th>callcard</th>\n      <th>loglong</th>\n      <th>employ</th>\n      <th>tenure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.482</td>\n      <td>5.0</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.246</td>\n      <td>0.0</td>\n      <td>33.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.841</td>\n      <td>2.0</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.800</td>\n      <td>10.0</td>\n      <td>38.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.960</td>\n      <td>15.0</td>\n      <td>7.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Looking for outliers."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "churn_corr_df[[\"employ\", \"tenure\"]].hist(bins=15, figsize=(8,6));",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(14, 6))\nax = sns.boxplot(\n    data=churn_corr_df[[\"employ\", \"tenure\"]], orient=\"h\", whis=1.5, palette=\"Set2\")",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1008x432 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "editable": false,
        "deletable": false,
        "run_control": {
          "frozen": true
        }
      },
      "cell_type": "code",
      "source": "# THE THERE ARE TOO FEW OBSERVATIONS IN THE DATA SET\n# Dropping one row had a massive effect.\n\nthreshold = 3\nz_scores = stats.zscore(churn_corr_df)\nabs_z_scores = np.abs(z_scores)\nfiltered_entries = (abs_z_scores < threshold).all(axis=1)\nchurn_corr_df = churn_corr_df[filtered_entries]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "churn_corr_df[[\"employ\", \"tenure\"]].hist(bins=15, figsize=(8,6));",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 2 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(14, 6))\nax = sns.boxplot(\n    data=churn_corr_df[[\"employ\", \"tenure\"]], orient=\"h\", whis=1.5, palette=\"Set2\")",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1008x432 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Selecting features (dependent) and target (independent).**"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = churn_corr_df.drop(\"churn\", axis=1)\ny = np.asarray(churn_corr_df.churn)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X.head()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "   callcard  loglong  employ  tenure\n0       1.0    1.482     5.0    11.0\n1       0.0    2.246     0.0    33.0\n2       0.0    1.841     2.0    23.0\n3       1.0    1.800    10.0    38.0\n4       1.0    1.960    15.0     7.0",
            "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>callcard</th>\n      <th>loglong</th>\n      <th>employ</th>\n      <th>tenure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1.482</td>\n      <td>5.0</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>2.246</td>\n      <td>0.0</td>\n      <td>33.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>1.841</td>\n      <td>2.0</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>1.800</td>\n      <td>10.0</td>\n      <td>38.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>1.960</td>\n      <td>15.0</td>\n      <td>7.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "scaler = StandardScaler()",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = scaler.fit_transform(X)",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X[0:5]",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "array([[ 0.64686916, -0.97509593, -0.58477841, -1.13518441],\n       [-1.54590766,  0.07226665, -1.14437497, -0.11604313],\n       [-1.54590766, -0.48294519, -0.92053635, -0.57928917],\n       [ 0.64686916, -0.53915182, -0.02518185,  0.11557989],\n       [ 0.64686916, -0.31980887,  0.53441472, -1.32048283]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "y[0:5]",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "array([1., 1., 0., 0., 0.])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(\"Train set:\", X_train.shape,  y_train.shape)\nprint(\"Test set:\", X_test.shape,  y_test.shape)",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Train set: (160, 4) (160,)\nTest set: (40, 4) (40,)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model development\nDevelop your model using Logistic Regression from Scikit-learn package in Python. This function implements logistic regression and can use different numerical optimisers to find parameters, including ‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’ solvers. Feel free to Google the exhaustive information about the pros and cons of these optimisers. The Logistic Regression feature in Scikit-learn, supports regularisation. The regularisation is a technique used to solve the overfitting problem in machine learning models. The ‘C’ parameter indicates inverse of regularization strength which must be a positive float. Smaller values of ‘C’ specify stronger regularization. For this activity sheet accept ‘C’ as just a parameter, the mathematics behind regularisation is not so important under the realm of this course. Fit your model with train set. Predict outputs from the model using your testing dataset. Use predict_proba function on your testing dataset to return estimates for all classes, ordered by the label of classes. In this dataset you have two classes (0 & 1), so you should expect the outcome of predict_proba ( ) to display the probability for both these classes."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf = LogisticRegression(C=0.01, solver='liblinear')\nclf.fit(X_train,y_train)\nclf",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "LogisticRegression(C=0.01, solver='liblinear')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred = clf.predict(X_test)",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred_prob = clf.predict_proba(X_test)",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model evaluation\nUse Jaccard Index to evaluate the accuracy of your model. Scikit-learn can be used very easily to perform this task. Now use another evaluation matrix, log loss for evaluating the accuracy of the obtained model."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The higher the Jaccard score higher the accuracy of the classifier.\n\nHere weighted since the test set has a low amount of observations."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "jaccard_score(y_test, y_pred, average=\"weighted\")",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "0.6921212121212121"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Log loss measures how far the prediction is from the actual label when the output is a probability value, and should be low."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "log_loss(y_test, y_pred_prob)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "0.5672894611120263"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf.score(X_test, y_test)\n# accuracy_score(y_test, y_pred)",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "0.8"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(classification_report(y_test, y_pred))",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n         0.0       0.93      0.81      0.86        31\n         1.0       0.54      0.78      0.64         9\n\n    accuracy                           0.80        40\n   macro avg       0.73      0.79      0.75        40\nweighted avg       0.84      0.80      0.81        40\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cm = confusion_matrix(y_test, y_pred)",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cm_plot(cm, legend=True)",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\n            # True Prositive\n            TP = np.diag(cf_matrix) : 7.00\n\n            # False Negative, Type II Error\n            FN = cf_matrix.sum(axis=1) - np.diag(cf_matrix) : 2.00\n\n            # False Positive, Type I Error\n            FP = cf_matrix.sum(axis=0) - np.diag(cf_matrix) : 6.00\n\n            # True Negative\n            TN = cf_matrix.sum() - (FP + FN + TP) : 25.00\n\n            # Sensitivity, hit rate, recall, or true positive rate\n            TPR = TP/(TP+FN) : 0.78\n\n            # Specificity, true negative rate or negative recall\n            TNR = TN/(TN+FP) : 0.81\n\n            # Precision or positive predictive value\n            PPV = TP/(TP+FP) : 0.54\n\n            # Negative predictive value\n            NPV = TN/(TN+FN) : 0.93\n\n            # Fall out or false positive rate\n            FPR = FP/(FP+TN) : 0.19\n\n            # False negative rate\n            FNR = FN/(TP+FN) : 0.22\n\n            # False discovery rate\n            FDR = FP/(TP+FP) : 0.46\n\n            # F1 score is the harmonic mean of positive predictive value\n            #  and sensitivity\n            F1 = 2 * (PPV * TPR)/(PPV + TPR) : 0.64\n\n            # Overall accuracy\n            ACC = (TP+TN)/(TP+FP+FN+TN) : 0.80\n            \n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "# False positive rate\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve\nlogit_roc_auc = roc_auc_score(y_test, clf.predict(X_test))\nfpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\nplt.figure(figsize=(10, 6))\n# Area Under the Curve (AUC)\nplt.plot(fpr, tpr, label=\"Logistic Regression (AUC = %0.2f)\" % logit_roc_auc)\nplt.plot([0, 1], [0, 1],\"r--\")\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Receiver operating characteristic\")\nplt.legend(loc=\"lower right\")\nplt.savefig(\"Log_ROC\")\nplt.show()",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Tuning\nDevelop logistic regression model again, using the same dataset and play with different values for ‘solver’ and ‘regularisation’. Compare the model performance using Jaccard Index/logLoss every time. In this experiment you will be able to get a sense about the best tuning parameters corresponding to this dataset."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Explained by Isah, and saved for referencing purposes:\n\n> Model parameter = learnable variables of the model that are estimated from the data only. Eg. the weights of link in neural networks, the coefficient of simple linear regression\n\n> Model hyperparameter or tuning parameter =  variables of the model that the user has to provide during the modeling process manually. e.g., the k in KNN, etc. Usually, the user has to tune the model (model selection) to select the appropriate values of the parameter"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def log_reg(X_train, y_train, solver, C=0.01):\n    clf = LogisticRegression(C=C, solver=solver)\n    clf.fit(X_train,y_train)\n    y_pred = clf.predict(X_test)\n    y_pred_prob = clf.predict_proba(X_test)\n    \n    print(\"Accuracy score:\", accuracy_score(y_test, y_pred))\n    print(\"Jaccard score:\", jaccard_score(y_test, y_pred, average=\"weighted\"))\n    print(\"Log loss:\", log_loss(y_test, y_pred_prob))\n    \n    return y_test, y_pred, y_pred_prob",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "solvers = [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"]",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "for solver in solvers:\n    print()\n    print(solver)\n    log_reg(X_train, y_train, solver)\n    #cm = confusion_matrix(y_test, y_pred)\n    #cm_plot(cm)",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nnewton-cg\nAccuracy score: 0.775\nJaccard score: 0.6006250000000001\nLog loss: 0.47094414419392033\n\nlbfgs\nAccuracy score: 0.775\nJaccard score: 0.6006250000000001\nLog loss: 0.47094454470363123\n\nliblinear\nAccuracy score: 0.8\nJaccard score: 0.6921212121212121\nLog loss: 0.5672894611120263\n\nsag\nAccuracy score: 0.775\nJaccard score: 0.6006250000000001\nLog loss: 0.4709469474796763\n\nsaga\nAccuracy score: 0.775\nJaccard score: 0.6006250000000001\nLog loss: 0.4709455161533668\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Example of weighted average**"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Predicted = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]\nActual = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "jaccard_score(Actual, Predicted)",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "0.5"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "jaccard_score(Actual, Predicted, average=\"weighted\")",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "0.8636363636363636"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# How many 0 was correctly predicted.\njaccard_score(Actual, Predicted, pos_label=0, average=\"binary\")",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "0.9"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# How many 1 was correctly predicted.\njaccard_score(Actual, Predicted, pos_label=1, average=\"binary\")",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "0.5"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Testing tuple unpacking**"
    },
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      "metadata": {
        "trusted": true
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      "cell_type": "code",
      "source": "display(url := (\"https://www.amazon.com/War-Art-Trough-Creative-battles/dp/561651651651/?keyword=war+of+art\"))\ndisplay(url.split(\"/\")[2:-1])\ndomain, *rest, asin = url.split(\"/\")[2:-1]\ndisplay(domain)\ndisplay(asin)\ndisplay(rest)",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "'https://www.amazon.com/War-Art-Trough-Creative-battles/dp/561651651651/?keyword=war+of+art'"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "'www.amazon.com'"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "'561651651651'"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "['War-Art-Trough-Creative-battles', 'dp']"
          },
          "metadata": {}
        }
      ]
    },
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      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
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