05-Classification.ipynb

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   "source": [
    "# <div class=\"alert alert-info\" style=\"text-align: center;\" markdown=\"1\">Cllasification</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## `Naive Bayes`\n",
    "\n",
    "- Naive Bayes uses the probability of observing feature values given an outcome to make predictions.\n",
    "\n",
    "- It is based on the Bayes theorem, which is used to calculate the probability of a hypothesis given our prior knowledge.\n",
    "\n",
    "#### Conditional Probability\n",
    "\n",
    "- The probability of an event given that another event has occurred is called conditional probability.\n",
    "\n",
    "- The conditional probability of event A given event B is denoted by P(A|B).\n",
    "\n",
    "\n",
    "P(A|B) = P(A and B) / P(B)\n"
   ]
  },
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   "cell_type": "markdown",
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    "### Exact Naive Bayes Algorithm\n",
    "\n",
    "1. Find all records whrere the predictor values are the same as the record to be classified.\n",
    "2. Calculate the probability of each class.\n",
    "3. Assign the class with the highest probability to the record.\n",
    "\n",
    "- When number of predictor variables is large, the number of records required to make accurate predictions increases exponentially.\n",
    "\n",
    "> Not Practical for large datasets."
   ]
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    "### Naive Solution\n",
    "\n",
    "1. Estimate individual probabilities for each predictor variable given the class.\n",
    "2. Multiply the probabilities together and by the proportion of records in class to get the probability of the record belonging to a class.\n",
    "3. Estimate the probability of the record belonging to each class and assign the class with the highest probability to the record.\n",
    "\n",
    "\n",
    "$P(Y=i|X1,X2,X3,...,Xp)$\n",
    "\n",
    "$P(Y=i|X1,X2,X3,...,Xp) = \\frac{P(Y=i) * P(X_1,...,X_p|Y=i)}{P(Y=0)P(X_1,...,X_p|Y=0) + P(Y=1)P(X_1,...,X_p|Y=1)}$\n",
    "\n",
    "$P(Y=i|X1,X2,X3,...,Xp) = \\frac{P(Y=i) * P(X_1|Y=i) * ... * P(X_p|Y=i)}{P(Y=0)P(X_1|Y=0)...P(X_p|Y=0) + P(Y=1)P(X_1|Y=1)...P(X_p|Y=1)}$"
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>status</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>purpose</th>\n",
       "      <th>...</th>\n",
       "      <th>delinq_2yrs_zero</th>\n",
       "      <th>pub_rec_zero</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>grade</th>\n",
       "      <th>outcome</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>purpose_</th>\n",
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       "      <th>borrower_score</th>\n",
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       "      <td>Charged Off</td>\n",
       "      <td>2500</td>\n",
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       "      <td>2.39320</td>\n",
       "      <td>1687</td>\n",
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       "      <td>car</td>\n",
       "      <td>...</td>\n",
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       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.65</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>5600</td>\n",
       "      <td>60 months</td>\n",
       "      <td>40000</td>\n",
       "      <td>5.55</td>\n",
       "      <td>4.57170</td>\n",
       "      <td>5210</td>\n",
       "      <td>32.6</td>\n",
       "      <td>small_business</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1.4</td>\n",
       "      <td>default</td>\n",
       "      <td>5</td>\n",
       "      <td>small_business</td>\n",
       "      <td>OWN</td>\n",
       "      <td>&gt; 1 Year</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>5375</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15000</td>\n",
       "      <td>18.08</td>\n",
       "      <td>9.71600</td>\n",
       "      <td>9279</td>\n",
       "      <td>36.5</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>9000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>30000</td>\n",
       "      <td>10.08</td>\n",
       "      <td>12.21520</td>\n",
       "      <td>10452</td>\n",
       "      <td>91.7</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.50</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>10000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>100000</td>\n",
       "      <td>7.06</td>\n",
       "      <td>3.90888</td>\n",
       "      <td>11997</td>\n",
       "      <td>55.5</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5.4</td>\n",
       "      <td>default</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.55</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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       "   Unnamed: 0       status  loan_amnt       term  annual_inc    dti  \\\n",
       "0           1  Charged Off       2500  60 months       30000   1.00   \n",
       "1           2  Charged Off       5600  60 months       40000   5.55   \n",
       "2           3  Charged Off       5375  60 months       15000  18.08   \n",
       "3           4  Charged Off       9000  36 months       30000  10.08   \n",
       "4           5  Charged Off      10000  36 months      100000   7.06   \n",
       "\n",
       "   payment_inc_ratio  revol_bal  revol_util             purpose  ...  \\\n",
       "0            2.39320       1687         9.4                 car  ...   \n",
       "1            4.57170       5210        32.6      small_business  ...   \n",
       "2            9.71600       9279        36.5               other  ...   \n",
       "3           12.21520      10452        91.7  debt_consolidation  ...   \n",
       "4            3.90888      11997        55.5               other  ...   \n",
       "\n",
       "  delinq_2yrs_zero  pub_rec_zero  open_acc  grade  outcome emp_length  \\\n",
       "0                1             1         3    4.8  default          1   \n",
       "1                1             1        11    1.4  default          5   \n",
       "2                1             1         2    6.0  default          1   \n",
       "3                1             1         4    4.2  default          1   \n",
       "4                1             1        14    5.4  default          4   \n",
       "\n",
       "             purpose_ home_   emp_len_ borrower_score  \n",
       "0      major_purchase  RENT   > 1 Year           0.65  \n",
       "1      small_business   OWN   > 1 Year           0.80  \n",
       "2               other  RENT   > 1 Year           0.60  \n",
       "3  debt_consolidation  RENT   > 1 Year           0.50  \n",
       "4               other  RENT   > 1 Year           0.55  \n",
       "\n",
       "[5 rows x 21 columns]"
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   "source": [
    "df_loan = pd.read_csv('data/loan_data.csv.gz')\n",
    "df_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'status', 'loan_amnt', 'term', 'annual_inc', 'dti',\n",
       "       'payment_inc_ratio', 'revol_bal', 'revol_util', 'purpose',\n",
       "       'home_ownership', 'delinq_2yrs_zero', 'pub_rec_zero', 'open_acc',\n",
       "       'grade', 'outcome', 'emp_length', 'purpose_', 'home_', 'emp_len_',\n",
       "       'borrower_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['purpose_', 'home_', 'emp_len_',]\n",
    "target = 'outcome'"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df_loan[predictors], drop_first=True, prefix='', prefix_sep='')\n",
    "y = df_loan[target]"
   ]
  },
  {
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   "execution_count": 17,
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    {
     "data": {
      "text/plain": [
       "(45342, 9)"
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    "X.shape"
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       "   debt_consolidation  home_improvement  major_purchase  medical  other  \\\n",
       "0                   0                 0               1        0      0   \n",
       "1                   0                 0               0        0      0   \n",
       "\n",
       "   small_business  OWN  RENT   > 1 Year  \n",
       "0               0    0     1          1  \n",
       "1               1    1     0          1  "
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       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "model_naive = MultinomialNB(alpha=1.0, fit_prior=True)\n",
    "model_naive.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>debt_consolidation</th>\n",
       "      <th>home_improvement</th>\n",
       "      <th>major_purchase</th>\n",
       "      <th>medical</th>\n",
       "      <th>other</th>\n",
       "      <th>small_business</th>\n",
       "      <th>OWN</th>\n",
       "      <th>RENT</th>\n",
       "      <th>&gt; 1 Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     debt_consolidation  home_improvement  major_purchase  medical  other  \\\n",
       "146                   0                 0               0        0      0   \n",
       "\n",
       "     small_business  OWN  RENT   > 1 Year  \n",
       "146               1    0     0          1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_loan = X.loc[146:146, :]\n",
    "new_loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predcted Class: ['default']\n"
     ]
    }
   ],
   "source": [
    "print('Predcted Class:', model_naive.predict(new_loan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Probabilities:\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>default</th>\n",
       "      <th>paid off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666796</td>\n",
       "      <td>0.333204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    default  paid off\n",
       "0  0.666796  0.333204"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilitites = pd.DataFrame(model_naive.predict_proba(new_loan), columns=df_loan[target].astype(\"category\").cat.categories)\n",
    "print('Predicted Probabilities:')\n",
    "probabilitites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Discriminant Analysis`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Covanriance Matrix\n",
    "\n",
    "- The covariance matrix is a square matrix that contains the variances and covariances associated with different variables.\n",
    "- The diagonal elements of the matrix contain the variances of the variables.\n",
    "- The off-diagonal elements contain the covariances between the variables.\n",
    "\n",
    "\n",
    "$Cov(X,Y) = \\frac{\\sum_{i=1}^{n}(X_i - \\bar{X})(Y_i - \\bar{Y})}{n-1}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Linear Discriminant\n",
    "\n",
    "- If we have two groups corresponds to the record $(x_0, y_0)$ for which y = 0 and $(x_1, y_1)$ for which y = 1, the Fisher's linear discriminant method finds the linear combination $a_1x_1 + a_2x_2$ that maximizes the between-group variance relative to the within-group variance.\n",
    "\n",
    "$\\frac{SS_{between}}{SS_{within}}$\n",
    "\n",
    "$SS_{between} = \\sum_{i=1}^{n}(\\bar{y}_i - \\bar{y})^2$\n",
    "$SS_{within} = \\sum_{i=1}^{n}\\sum_{j=1}^{n}(y_{ij} - \\bar{y}_i)^2$\n",
    "\n",
    "- The between-group variance is the variance of the means of the two groups.\n",
    "- The within-group variance is the sum of the variances of the two groups.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['borrower_score', 'payment_inc_ratio']\n",
    "target = 'outcome'\n",
    "\n",
    "X = df_loan[predictors]\n",
    "y = df_loan[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearDiscriminantAnalysis()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearDiscriminantAnalysis<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\">?<span>Documentation for LinearDiscriminantAnalysis</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearDiscriminantAnalysis()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "model_lda = LinearDiscriminantAnalysis()\n",
    "model_lda.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>borrower_score</th>\n",
       "      <td>6.862178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <td>-0.114816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "borrower_score     6.862178\n",
       "payment_inc_ratio -0.114816"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model_lda.scalings_, index=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>default</th>\n",
       "      <th>paid off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247468</td>\n",
       "      <td>0.752532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.164256</td>\n",
       "      <td>0.835744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.418215</td>\n",
       "      <td>0.581785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.577558</td>\n",
       "      <td>0.422442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.367282</td>\n",
       "      <td>0.632718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    default  paid off\n",
       "0  0.247468  0.752532\n",
       "1  0.164256  0.835744\n",
       "2  0.418215  0.581785\n",
       "3  0.577558  0.422442\n",
       "4  0.367282  0.632718"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pd.DataFrame(model_lda.predict_proba(X), columns=df_loan[target].astype(\"category\").cat.categories)\n",
    "pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "center = np.mean(model_lda.means_, axis=0)\n",
    "slope = - model_lda.scalings_[0]/model_lda.scalings_[1]\n",
    "intercept = center[1] - center[0] * slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Unnamed: 0</th>\n",
       "      <th>status</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>purpose</th>\n",
       "      <th>...</th>\n",
       "      <th>pub_rec_zero</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>grade</th>\n",
       "      <th>outcome</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>purpose_</th>\n",
       "      <th>home_</th>\n",
       "      <th>emp_len_</th>\n",
       "      <th>borrower_score</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>2500</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.39320</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>car</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.247468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>5600</td>\n",
       "      <td>60 months</td>\n",
       "      <td>40000</td>\n",
       "      <td>5.55</td>\n",
       "      <td>4.57170</td>\n",
       "      <td>5210</td>\n",
       "      <td>32.6</td>\n",
       "      <td>small_business</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1.4</td>\n",
       "      <td>default</td>\n",
       "      <td>5</td>\n",
       "      <td>small_business</td>\n",
       "      <td>OWN</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.164256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>5375</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15000</td>\n",
       "      <td>18.08</td>\n",
       "      <td>9.71600</td>\n",
       "      <td>9279</td>\n",
       "      <td>36.5</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.418215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>9000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>30000</td>\n",
       "      <td>10.08</td>\n",
       "      <td>12.21520</td>\n",
       "      <td>10452</td>\n",
       "      <td>91.7</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.577558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>10000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>100000</td>\n",
       "      <td>7.06</td>\n",
       "      <td>3.90888</td>\n",
       "      <td>11997</td>\n",
       "      <td>55.5</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5.4</td>\n",
       "      <td>default</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>RENT</td>\n",
       "      <td>&gt; 1 Year</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.367282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       status  loan_amnt       term  annual_inc    dti  \\\n",
       "0           1  Charged Off       2500  60 months       30000   1.00   \n",
       "1           2  Charged Off       5600  60 months       40000   5.55   \n",
       "2           3  Charged Off       5375  60 months       15000  18.08   \n",
       "3           4  Charged Off       9000  36 months       30000  10.08   \n",
       "4           5  Charged Off      10000  36 months      100000   7.06   \n",
       "\n",
       "   payment_inc_ratio  revol_bal  revol_util             purpose  ...  \\\n",
       "0            2.39320       1687         9.4                 car  ...   \n",
       "1            4.57170       5210        32.6      small_business  ...   \n",
       "2            9.71600       9279        36.5               other  ...   \n",
       "3           12.21520      10452        91.7  debt_consolidation  ...   \n",
       "4            3.90888      11997        55.5               other  ...   \n",
       "\n",
       "  pub_rec_zero  open_acc  grade  outcome  emp_length            purpose_  \\\n",
       "0            1         3    4.8  default           1      major_purchase   \n",
       "1            1        11    1.4  default           5      small_business   \n",
       "2            1         2    6.0  default           1               other   \n",
       "3            1         4    4.2  default           1  debt_consolidation   \n",
       "4            1        14    5.4  default           4               other   \n",
       "\n",
       "   home_   emp_len_ borrower_score   default  \n",
       "0   RENT   > 1 Year           0.65  0.247468  \n",
       "1    OWN   > 1 Year           0.80  0.164256  \n",
       "2   RENT   > 1 Year           0.60  0.418215  \n",
       "3   RENT   > 1 Year           0.50  0.577558  \n",
       "4   RENT   > 1 Year           0.55  0.367282  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_0 = (0-intercept)/slope\n",
    "x_20 = (20-intercept)/slope\n",
    "\n",
    "lda_df = pd.concat([df_loan, pred[\"default\"]], axis=1)\n",
    "lda_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots()\n",
    "g = sns.scatterplot(x='borrower_score', y='payment_inc_ratio', hue='default', data=lda_df, ax=ax, legend=False, palette=sns.diverging_palette(240, 10, n=9, as_cmap=True))\n",
    "ax.set_ylim(0, 20)\n",
    "ax.set_xlim(0.15, 0.8)\n",
    "ax.plot([x_0, x_20], [0, 20], linewidth=3)\n",
    "ax.plot(*model_lda.means_.T, 'x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Logistic Regression`\n",
    "\n",
    "- Logistic regression is a classification algorithm that is used to predict the probability of a categorical dependent variable.\n",
    "\n",
    "**$P(Y=1|X) = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1X_1 + \\beta_2X_2 + ... + \\beta_pX_p)}}$**\n",
    "\n",
    "#### Odds\n",
    "\n",
    "- The odds of an event is the ratio of the probability of the event occurring to the probability of the event not occurring.\n",
    "\n",
    "$Odds = \\frac{P(Y=1|X)}{1 - P(Y=1|X)}$\n",
    "\n",
    "$P = \\frac{Odds}{1 + Odds}$\n",
    "\n",
    "$Odds = e^{\\beta_0 + \\beta_1X_1 + \\beta_2X_2 + ... + \\beta_pX_p}$\n",
    "\n",
    "#### Log Odds\n",
    "\n",
    "- The log odds is the natural logarithm of the odds.\n",
    "\n",
    "$log(Odds) = \\beta_0 + \\beta_1X_1 + \\beta_2X_2 + ... + \\beta_pX_p$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['payment_inc_ratio', 'purpose_', 'home_', 'emp_len_', 'borrower_score']\n",
    "target = 'outcome'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df_loan[predictors], drop_first=True, prefix='', prefix_sep='')\n",
    "y = df_loan[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishabh/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model_logit = LogisticRegression()\n",
    "model_logit.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6364739093996736"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model_logit.predict(X)\n",
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression and The Generalized Linear Model (GLM)\n",
    "\n",
    "- Logistic regression is a type of generalized linear model (GLM) that uses the logit link function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1e+42, solver=&#x27;liblinear&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1e+42, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=1e+42, solver='liblinear')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logit = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "model_logit.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicted Values from Logistic Regression\n",
    "\n",
    "**$\\hat{P} = \\frac{1}{1 + e^{-(\\gamma)}}$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>default</th>\n",
       "      <th>paid off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45342.000000</td>\n",
       "      <td>45342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.757850</td>\n",
       "      <td>-0.760423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.378032</td>\n",
       "      <td>0.390419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.768873</td>\n",
       "      <td>-3.538865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.985728</td>\n",
       "      <td>-0.977164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.697366</td>\n",
       "      <td>-0.688946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.472209</td>\n",
       "      <td>-0.467076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.029476</td>\n",
       "      <td>-0.064787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            default      paid off\n",
       "count  45342.000000  45342.000000\n",
       "mean      -0.757850     -0.760423\n",
       "std        0.378032      0.390419\n",
       "min       -2.768873     -3.538865\n",
       "25%       -0.985728     -0.977164\n",
       "50%       -0.697366     -0.688946\n",
       "75%       -0.472209     -0.467076\n",
       "max       -0.029476     -0.064787"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_prob = pd.DataFrame(model_logit.predict_log_proba(X), columns=df_loan[target].astype(\"category\").cat.categories)\n",
    "log_prob.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>default</th>\n",
       "      <th>paid off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.242502</td>\n",
       "      <td>0.757498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.314407</td>\n",
       "      <td>0.685593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.516627</td>\n",
       "      <td>0.483373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.588000</td>\n",
       "      <td>0.412000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.458618</td>\n",
       "      <td>0.541382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    default  paid off\n",
       "0  0.242502  0.757498\n",
       "1  0.314407  0.685593\n",
       "2  0.516627  0.483373\n",
       "3  0.588000  0.412000\n",
       "4  0.458618  0.541382"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob = pd.DataFrame(model_logit.predict_proba(X), columns=df_loan[target].astype(\"category\").cat.categories)\n",
    "prob.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ODDS Ratio for a binary variable\n",
    "\n",
    "$odds ratio = \\frac{odds(Y = 1|X = 1)}{odds(Y = 1|X = 0)}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum Likelihood Estimation\n",
    "\n",
    "- The maximum likelihood estimation is a method used to estimate the parameters of a model by maximizing the likelihood function.\n",
    "\n",
    "- The likelihood function is the probability of observing the data given the parameters of the model.\n",
    "\n",
    "$P(Y|X) = \\prod_{i=1}^{n}P(Y_i|X_i)$\n",
    "\n",
    "$ deviance = -2\\sum_{i=1}^{n}y_ilog(\\hat{p}_i) + (1-y_i)log(1-\\hat{p}_i)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Assessing The Model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td> 45342</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td> 45330</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>Logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -28757.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 28 Jul 2024</td> <th>  Deviance:          </th> <td>  57515.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>14:08:14</td>     <th>  Pearson chi2:      </th> <td>4.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>4</td>        <th>  Pseudo R-squ. (CS):</th>  <td>0.1112</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>payment_inc_ratio</th>  <td>    0.0797</td> <td>    0.002</td> <td>   32.058</td> <td> 0.000</td> <td>    0.075</td> <td>    0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>borrower_score</th>     <td>   -4.6126</td> <td>    0.084</td> <td>  -55.203</td> <td> 0.000</td> <td>   -4.776</td> <td>   -4.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>debt_consolidation</th> <td>    0.2494</td> <td>    0.028</td> <td>    9.030</td> <td> 0.000</td> <td>    0.195</td> <td>    0.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_improvement</th>   <td>    0.4077</td> <td>    0.047</td> <td>    8.747</td> <td> 0.000</td> <td>    0.316</td> <td>    0.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>major_purchase</th>     <td>    0.2296</td> <td>    0.054</td> <td>    4.277</td> <td> 0.000</td> <td>    0.124</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>medical</th>            <td>    0.5105</td> <td>    0.087</td> <td>    5.882</td> <td> 0.000</td> <td>    0.340</td> <td>    0.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>other</th>              <td>    0.6207</td> <td>    0.039</td> <td>   15.738</td> <td> 0.000</td> <td>    0.543</td> <td>    0.698</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>small_business</th>     <td>    1.2153</td> <td>    0.063</td> <td>   19.192</td> <td> 0.000</td> <td>    1.091</td> <td>    1.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OWN</th>                <td>    0.0483</td> <td>    0.038</td> <td>    1.271</td> <td> 0.204</td> <td>   -0.026</td> <td>    0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RENT</th>               <td>    0.1573</td> <td>    0.021</td> <td>    7.420</td> <td> 0.000</td> <td>    0.116</td> <td>    0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> > 1 Year</th>          <td>   -0.3567</td> <td>    0.053</td> <td>   -6.779</td> <td> 0.000</td> <td>   -0.460</td> <td>   -0.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>              <td>    1.6381</td> <td>    0.074</td> <td>   22.224</td> <td> 0.000</td> <td>    1.494</td> <td>    1.783</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}      &        y         & \\textbf{  No. Observations:  } &    45342    \\\\\n",
       "\\textbf{Model:}              &       GLM        & \\textbf{  Df Residuals:      } &    45330    \\\\\n",
       "\\textbf{Model Family:}       &     Binomial     & \\textbf{  Df Model:          } &       11    \\\\\n",
       "\\textbf{Link Function:}      &      Logit       & \\textbf{  Scale:             } &    1.0000   \\\\\n",
       "\\textbf{Method:}             &       IRLS       & \\textbf{  Log-Likelihood:    } &   -28757.   \\\\\n",
       "\\textbf{Date:}               & Sun, 28 Jul 2024 & \\textbf{  Deviance:          } &    57515.   \\\\\n",
       "\\textbf{Time:}               &     14:08:14     & \\textbf{  Pearson chi2:      } &  4.54e+04   \\\\\n",
       "\\textbf{No. Iterations:}     &        4         & \\textbf{  Pseudo R-squ. (CS):} &   0.1112    \\\\\n",
       "\\textbf{Covariance Type:}    &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                             & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{payment\\_inc\\_ratio} &       0.0797  &        0.002     &    32.058  &         0.000        &        0.075    &        0.085     \\\\\n",
       "\\textbf{borrower\\_score}     &      -4.6126  &        0.084     &   -55.203  &         0.000        &       -4.776    &       -4.449     \\\\\n",
       "\\textbf{debt\\_consolidation} &       0.2494  &        0.028     &     9.030  &         0.000        &        0.195    &        0.303     \\\\\n",
       "\\textbf{home\\_improvement}   &       0.4077  &        0.047     &     8.747  &         0.000        &        0.316    &        0.499     \\\\\n",
       "\\textbf{major\\_purchase}     &       0.2296  &        0.054     &     4.277  &         0.000        &        0.124    &        0.335     \\\\\n",
       "\\textbf{medical}             &       0.5105  &        0.087     &     5.882  &         0.000        &        0.340    &        0.681     \\\\\n",
       "\\textbf{other}               &       0.6207  &        0.039     &    15.738  &         0.000        &        0.543    &        0.698     \\\\\n",
       "\\textbf{small\\_business}     &       1.2153  &        0.063     &    19.192  &         0.000        &        1.091    &        1.339     \\\\\n",
       "\\textbf{OWN}                 &       0.0483  &        0.038     &     1.271  &         0.204        &       -0.026    &        0.123     \\\\\n",
       "\\textbf{RENT}                &       0.1573  &        0.021     &     7.420  &         0.000        &        0.116    &        0.199     \\\\\n",
       "\\textbf{ $>$ 1 Year}         &      -0.3567  &        0.053     &    -6.779  &         0.000        &       -0.460    &       -0.254     \\\\\n",
       "\\textbf{const}               &       1.6381  &        0.074     &    22.224  &         0.000        &        1.494    &        1.783     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Generalized Linear Model Regression Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                45342\n",
       "Model:                            GLM   Df Residuals:                    45330\n",
       "Model Family:                Binomial   Df Model:                           11\n",
       "Link Function:                  Logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -28757.\n",
       "Date:                Sun, 28 Jul 2024   Deviance:                       57515.\n",
       "Time:                        14:08:14   Pearson chi2:                 4.54e+04\n",
       "No. Iterations:                     4   Pseudo R-squ. (CS):             0.1112\n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "payment_inc_ratio      0.0797      0.002     32.058      0.000       0.075       0.085\n",
       "borrower_score        -4.6126      0.084    -55.203      0.000      -4.776      -4.449\n",
       "debt_consolidation     0.2494      0.028      9.030      0.000       0.195       0.303\n",
       "home_improvement       0.4077      0.047      8.747      0.000       0.316       0.499\n",
       "major_purchase         0.2296      0.054      4.277      0.000       0.124       0.335\n",
       "medical                0.5105      0.087      5.882      0.000       0.340       0.681\n",
       "other                  0.6207      0.039     15.738      0.000       0.543       0.698\n",
       "small_business         1.2153      0.063     19.192      0.000       1.091       1.339\n",
       "OWN                    0.0483      0.038      1.271      0.204      -0.026       0.123\n",
       "RENT                   0.1573      0.021      7.420      0.000       0.116       0.199\n",
       " > 1 Year             -0.3567      0.053     -6.779      0.000      -0.460      -0.254\n",
       "const                  1.6381      0.074     22.224      0.000       1.494       1.783\n",
       "======================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "y_nums = [1 if yi == 'default' else 0 for yi in y]\n",
    "model_logit_sm = sm.GLM(y_nums, X.assign(const=1), family=sm.families.Binomial())\n",
    "result = model_logit_sm.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Generalized Linear Model Regression Results                             \n",
      "=====================================================================================================\n",
      "Dep. Variable:     ['outcome[default]', 'outcome[paid off]']   No. Observations:                45342\n",
      "Model:                                                   GLM   Df Residuals:                    45321\n",
      "Model Family:                                       Binomial   Df Model:                           20\n",
      "Link Function:                                         Logit   Scale:                          1.0000\n",
      "Method:                                                 IRLS   Log-Likelihood:                -28731.\n",
      "Date:                                       Sun, 28 Jul 2024   Deviance:                       57462.\n",
      "Time:                                               14:08:41   Pearson chi2:                 4.54e+04\n",
      "No. Iterations:                                            6   Pseudo R-squ. (CS):             0.1122\n",
      "Covariance Type:                                   nonrobust                                         \n",
      "==================================================================================================\n",
      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------\n",
      "Intercept                          1.5756      0.331      4.765      0.000       0.928       2.224\n",
      "purpose_[T.debt_consolidation]     0.2486      0.028      8.998      0.000       0.194       0.303\n",
      "purpose_[T.home_improvement]       0.4097      0.047      8.757      0.000       0.318       0.501\n",
      "purpose_[T.major_purchase]         0.2382      0.054      4.416      0.000       0.132       0.344\n",
      "purpose_[T.medical]                0.5206      0.087      5.980      0.000       0.350       0.691\n",
      "purpose_[T.other]                  0.6284      0.040     15.781      0.000       0.550       0.706\n",
      "purpose_[T.small_business]         1.2250      0.063     19.305      0.000       1.101       1.349\n",
      "home_[T.OWN]                       0.0498      0.038      1.309      0.191      -0.025       0.124\n",
      "home_[T.RENT]                      0.1577      0.021      7.431      0.000       0.116       0.199\n",
      "emp_len_[T. > 1 Year]             -0.3526      0.053     -6.699      0.000      -0.456      -0.249\n",
      "bs(payment_inc_ratio, df=8)[0]     0.7042      0.342      2.060      0.039       0.034       1.374\n",
      "bs(payment_inc_ratio, df=8)[1]     0.6621      0.198      3.351      0.001       0.275       1.049\n",
      "bs(payment_inc_ratio, df=8)[2]     0.8118      0.245      3.309      0.001       0.331       1.293\n",
      "bs(payment_inc_ratio, df=8)[3]     1.0377      0.223      4.644      0.000       0.600       1.476\n",
      "bs(payment_inc_ratio, df=8)[4]     1.1901      0.233      5.112      0.000       0.734       1.646\n",
      "bs(payment_inc_ratio, df=8)[5]     2.8404      0.316      8.980      0.000       2.220       3.460\n",
      "bs(payment_inc_ratio, df=8)[6]    -1.3427      1.229     -1.092      0.275      -3.752       1.067\n",
      "bs(payment_inc_ratio, df=8)[7]     7.1094      6.393      1.112      0.266      -5.420      19.639\n",
      "bs(borrower_score, df=3)[0]       -2.9011      0.533     -5.448      0.000      -3.945      -1.857\n",
      "bs(borrower_score, df=3)[1]       -2.6056      0.196    -13.284      0.000      -2.990      -2.221\n",
      "bs(borrower_score, df=3)[2]       -5.7421      0.508    -11.313      0.000      -6.737      -4.747\n",
      "==================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "formula = ('outcome ~ bs(payment_inc_ratio, df=8) + purpose_ + ' +\n",
    "           'home_ + emp_len_ + bs(borrower_score, df=3)')\n",
    "model = smf.glm(formula=formula, data=df_loan, family=sm.families.Binomial())\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.genmod.generalized_linear_model import GLMResults\n",
    "\n",
    "def partialResidualPlot(model, df, outcome, feature, fig, ax):\n",
    "    y_actual = [0 if s == 'default' else 1 for s in df[outcome]]\n",
    "    y_pred = model.predict(df)\n",
    "    org_params = model.params.copy()\n",
    "    zero_params = model.params.copy()\n",
    "    # set model parametes of other features to 0\n",
    "    for i, name in enumerate(zero_params.index):\n",
    "        if feature in name:\n",
    "            continue\n",
    "        zero_params[i] = 0.0\n",
    "    model.initialize(model.model, zero_params)\n",
    "    feature_prediction = model.predict(df)\n",
    "    ypartial = -np.log(1/feature_prediction - 1)\n",
    "    ypartial = ypartial - np.mean(ypartial)\n",
    "    model.initialize(model.model, org_params)\n",
    "    results = pd.DataFrame({\n",
    "        'feature': df[feature],\n",
    "        'residual': -2 * (y_actual - y_pred),\n",
    "        'ypartial': ypartial/ 2,\n",
    "    })\n",
    "    results = results.sort_values(by=['feature'])\n",
    "\n",
    "    ax.scatter(results.feature, results.residual, marker=\".\", s=72./fig.dpi)\n",
    "    ax.plot(results.feature, results.ypartial, color='black')\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel(f'Residual + {feature} contribution')\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "partialResidualPlot(results, df_loan, 'outcome', 'payment_inc_ratio', fig, ax)\n",
    "ax.set_xlim(0, 25)\n",
    "ax.set_ylim(-2.5, 2.5)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Evaluating The Classification Models`\n",
    "\n",
    "### Confusion Matrix\n",
    "\n",
    "- A confusion matrix is a table that is used to evaluate the performance of a classification model.\n",
    "\n",
    "- The confusion matrix contains the actual and predicted classes.\n",
    "\n",
    "- The confusion matrix is used to calculate the accuracy, precision, recall, and F1 score of a classification model.\n",
    "\n",
    "| Actual\\Predicted |    1   |    0   |\n",
    "|------------------|--------|--------|\n",
    "| 1                | 5 (TP) | 1 (FN) |\n",
    "| 0                | 2 (FP) | 4 (TN) |\n",
    "\n",
    "\n",
    "### Accuracy\n",
    "\n",
    "- The accuracy of a classification model is the proportion of correctly classified records.\n",
    "\n",
    "$Accuracy = \\frac{TP + TN}{TP + TN + FP + FN}$\n",
    "\n",
    "### Precision\n",
    "\n",
    "- The precision of a classification model is the proportion of correctly classified positive records.\n",
    "\n",
    "$Precision = \\frac{TP}{TP + FP}$\n",
    "\n",
    "### Recall\n",
    "\n",
    "- The recall of a classification model is the proportion of positive records that are correctly classified.\n",
    "\n",
    "$Recall = \\frac{TP}{TP + FN}$\n",
    "\n",
    "### Specificity\n",
    "\n",
    "- The specificity of a classification model is the proportion of negative records that are correctly classified.\n",
    "\n",
    "$Specificity = \\frac{TN}{TN + FP}$\n",
    "\n",
    "### F1 Score\n",
    "\n",
    "- The F1 score is the harmonic mean of the precision and recall.\n",
    "\n",
    "$F1 = 2 * \\frac{Precision * Recall}{Precision + Recall}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Y_pred = default</th>\n",
       "      <th>Y_pred = paid off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Y = default</th>\n",
       "      <td>14336</td>\n",
       "      <td>8335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y = paid off</th>\n",
       "      <td>8148</td>\n",
       "      <td>14523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Y_pred = default  Y_pred = paid off\n",
       "Y = default              14336               8335\n",
       "Y = paid off              8148              14523"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model_logit.predict(X)\n",
    "pred_y = model_logit.predict(X) == 'default'\n",
    "true_y = y == 'default'\n",
    "true_pos = true_y & pred_y\n",
    "false_pos = ~true_y & pred_y\n",
    "true_neg = ~true_y & ~pred_y\n",
    "false_neg = true_y & ~pred_y\n",
    "\n",
    "confusion_matrix = pd.DataFrame([[np.sum(true_pos), np.sum(false_neg)],\n",
    "                                 [np.sum(false_pos), np.sum(true_neg)]],\n",
    "                                columns=['Y_pred = default', 'Y_pred = paid off'],\n",
    "                                index=['Y = default', 'Y = paid off'])\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.6376089663760897\n",
      "Recall: 0.6323496978518812\n",
      "Specificity: 0.6405981209474659\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision:\", confusion_matrix.iloc[0, 0] / sum(confusion_matrix.iloc[:, 0]))\n",
    "print(\"Recall:\", confusion_matrix.iloc[0, 0] / sum(confusion_matrix.iloc[0, :]))\n",
    "print(\"Specificity:\", confusion_matrix.iloc[1, 1] / sum(confusion_matrix.iloc[1, :]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve\n",
    "\n",
    "- The ROC curve is a graphical representation of the (recall/sensitivity) versus the specificity.\n",
    "\n",
    "- The ROC curve shows the trade-off between the recall/sensitivity and the specificity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>recall</th>\n",
       "      <th>specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000044</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000132</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.999956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.999956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     recall  specificity\n",
       "0  0.000000     1.000000\n",
       "1  0.000044     1.000000\n",
       "2  0.000132     1.000000\n",
       "3  0.000132     0.999956\n",
       "4  0.000265     0.999956"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y, model_logit.predict_proba(X)[:, 0], pos_label='default')\n",
    "roc_df = pd.DataFrame({'recall': tpr, 'specificity': 1 - fpr})\n",
    "roc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "roc_df.plot(x='specificity', y='recall', ax=ax)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_xlim(1, 0)\n",
    "ax.plot([1, 0], [0, 1], linestyle='--')\n",
    "ax.set_xlabel('Specificity')\n",
    "ax.set_ylabel('Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AUC (Area Under the Curve)\n",
    "\n",
    "- The AUC is the area under the ROC curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.691710795288669\n",
      "0.6917108731135808\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "\n",
    "print(np.sum(roc_df.recall[:-1] * np.diff(1-roc_df.specificity)))\n",
    "print(roc_auc_score([1 if yi == \"default\" else 0 for yi in y], model_logit.predict_proba(X)[:,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lift\n",
    "\n",
    "- The Lift curve is the ratio of the cumulative gains to the diagonal line corresponding to random selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Strategies for Imbalanced Data`\n",
    "\n",
    "### Under Sampling\n",
    "\n",
    "- Under sampling is a technique used to balance the classes in a dataset by reducing the number of records in the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default     50.0\n",
       "paid off    50.0\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.outcome.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over Sampling\n",
    "\n",
    "- Over sampling is a technique used to balance the classes in a dataset by increasing the number of records in the minority class.\n",
    "- We can get same effect by applying weights to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1e+42, solver=&#x27;liblinear&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1e+42, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=1e+42, solver='liblinear')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_weight = 1 / np.mean(df_loan.outcome == 'default')\n",
    "wt = [default_weight if yi == 'default' else 1 for yi in df_loan.outcome]\n",
    "\n",
    "model_logit_weighted = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "model_logit_weighted.fit(X, y, sample_weight=wt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of loans predicted to default: 81.9218384720568\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of loans predicted to default:\", np.mean(model_logit_weighted.predict(X) == 'default') * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Generation\n",
    "\n",
    "- Data generation is a technique used to balance the classes in a dataset by generating synthetic records for the minority class.\n",
    "\n",
    "\n",
    "### SMOTE (Synthetic Minority Over-sampling Technique)\n",
    "\n",
    "- The SMOTE algorithm finds a record similar to the record being upsampled and creates a new record that is randomly weighted average of the original record and the neighbor record.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost-Based Classification\n",
    "\n",
    "- Cost-based classification is a technique used to balance the classes in a dataset by assigning different costs to the classes.\n",
    "\n",
    "- If the expected cost of a default of a new loan is C and the expected return from paid off is R, then the expected return from the loan is:\n",
    "\n",
    "$E = R * P(Y=0) + C * P(Y=1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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