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brusangues/preprocessing.ipynb

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preprocessing.ipynb
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preprocessing.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"JZzYcrpUGO7Q",
"aUBhesMPGZ3i",
"vQW_RTjIGVjs"
],
"authorship_tag": "ABX9TyN2n8isSKs3gC56N1xhYMve",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/brusangues/934e3976053186cf2e6cf3b40341a413/preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Pré Processamento + Outras Técnicas de Modelagem"
],
"metadata": {
"id": "AcjmjP0sF_fz"
}
},
{
"cell_type": "markdown",
"source": [
"## 1. Dados"
],
"metadata": {
"id": "JZzYcrpUGO7Q"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, KFold\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.feature_selection import RFECV\n",
"from imblearn.over_sampling import SMOTE\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.metrics import accuracy_score"
],
"metadata": {
"id": "Rj7FPFrO4aNU"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Generate a synthetic dataset\n",
"from sklearn.datasets import make_classification\n",
"X, y = make_classification(n_samples=1000, n_features=10, n_informative=6, n_redundant=2, n_repeated=2, weights=[0.8,0.2], random_state=42)\n",
"df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(10)])"
],
"metadata": {
"id": "9ut6H6-f4bbp"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Adding a categorical feature\n",
"np.random.seed(42)\n",
"df['categorical_feature'] = np.random.choice(['A', 'B', 'C'], size=len(df))\n",
"\n",
"# Adding a random feature\n",
"df['random_feature'] = np.random.rand(len(df))\n",
"\n",
"# Target\n",
"df['target'] = y"
],
"metadata": {
"id": "Eg1evARP57kg"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cols_num = [f'feature_{i}' for i in range(10)] + ['random_feature']\n",
"cols_cat = ['categorical_feature']\n",
"cols_features = cols_num + cols_cat"
],
"metadata": {
"id": "x-I1UI2J79OB"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot the distribution\n",
"def plot_distribution(df, feature):\n",
" mean_ = df[feature].mean()\n",
" median_ = df[feature].median()\n",
" variance_ = df[feature].var()\n",
" std_ = df[feature].std()\n",
" plt.figure(figsize=(8, 6))\n",
" sns.histplot(df[feature], kde=True)\n",
" plt.title('Distribution of '+feature)\n",
" plt.xlabel(feature)\n",
" plt.ylabel('Frequency')\n",
" plt.axvline(mean_, color='red', linestyle='dashed', linewidth=1, label=f'Mean: {mean_:.2f}')\n",
" plt.axvline(median_, color='green', linestyle='dashed', linewidth=1, label=f'Median: {median_:.2f}')\n",
" plt.axvline(mean_+variance_, color='blue', linestyle='dashed', linewidth=1, label=f'Variance: {variance_:.2f}')\n",
" plt.axvline(mean_-variance_, color='blue', linestyle='dashed', linewidth=1)\n",
" plt.axvline(mean_+std_, color='orange', linestyle='dashed', linewidth=1, label=f'Standard Deviation: {std_:.2f}')\n",
" plt.axvline(mean_-std_, color='orange', linestyle='dashed', linewidth=1)\n",
" plt.legend()\n",
" plt.show()\n",
"plot_distribution(df, \"feature_1\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "lk_KYGjF4riP",
"outputId": "9878270d-3a89-44e6-9a62-3f56c2708b0e"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"variancia = ((df.feature_1 - df.feature_1.mean())**2).sum()/len(df)\n",
"std = variancia**0.5\n",
"print(variancia)\n",
"print(std)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0S02lRPk9VCf",
"outputId": "4ea44164-fc34-42ea-a7b4-4b37417e4e84"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"5.177426786571343\n",
"2.2753959625901032\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Print initial dataset information\n",
"print(\"Initial dataset shape:\", df.shape)\n",
"print(\"Initial class distribution:\")\n",
"print(df['target'].value_counts())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FBjxh1g95l-6",
"outputId": "2c03cef2-8e4e-4cf9-9a0c-6c76a1fa0b19"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Initial dataset shape: (1000, 13)\n",
"Initial class distribution:\n",
"target\n",
"0 792\n",
"1 208\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## 2. Imputer, Scaler, Encoder Básicos"
],
"metadata": {
"id": "aUBhesMPGZ3i"
}
},
{
"cell_type": "code",
"source": [
"# Splitting into train and test\n",
"X_train, X_test, y_train, y_test = train_test_split(df.drop(columns=['target']), df['target'], test_size=0.2, random_state=42)"
],
"metadata": {
"id": "jtuJ6cL26f9p"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X_train.isna().any()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "drzHgwpxF58c",
"outputId": "5b8c60ac-7c7f-4d28-dd16-aff0e8a59473"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"feature_0 False\n",
"feature_1 False\n",
"feature_2 False\n",
"feature_3 False\n",
"feature_4 False\n",
"feature_5 False\n",
"feature_6 False\n",
"feature_7 False\n",
"feature_8 False\n",
"feature_9 False\n",
"categorical_feature False\n",
"random_feature False\n",
"dtype: bool"
],
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"</div><br><label><b>dtype:</b> bool</label>"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"X_train.iloc[0] = None\n",
"X_train.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "ZHSYuJo6IPyB",
"outputId": "cfbddbc6-478e-4784-8174-0eaea817a114"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n",
"29 NaN NaN NaN NaN NaN NaN \n",
"535 -1.552422 -1.552422 0.448236 1.523645 1.890484 0.448236 \n",
"695 3.507829 3.507829 -3.908697 0.714744 2.316889 -3.908697 \n",
"557 -1.835071 -1.835071 0.615162 2.742249 0.897326 0.615162 \n",
"836 -0.690349 -0.690349 0.222104 1.644994 -0.281019 0.222104 \n",
"\n",
" feature_6 feature_7 feature_8 feature_9 categorical_feature \\\n",
"29 NaN NaN NaN NaN None \n",
"535 0.099853 -0.170691 2.598180 0.048981 B \n",
"695 -0.024302 0.263981 4.302303 -2.949223 B \n",
"557 -2.063467 0.513576 -0.734699 4.259375 A \n",
"836 -1.497624 1.024875 -0.169188 3.196580 C \n",
"\n",
" random_feature \n",
"29 NaN \n",
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"695 0.909033 \n",
"557 0.409334 \n",
"836 0.607905 "
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}
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"imputer = SimpleImputer(\n",
" strategy='constant', # mean, median, most_frequent, constant\n",
" fill_value=-1,\n",
")\n",
"\n",
"X_train_imp = imputer.fit_transform(X_train[cols_num])\n",
"X_train_imp = pd.DataFrame(X_train_imp, columns=imputer.feature_names_in_)\n",
"X_train_imp.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "ngdvpshJF04m",
"outputId": "68930ee8-9503-4d09-a142-8684d90bbe3e"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n",
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"4 -0.690349 -0.690349 0.222104 1.644994 -0.281019 0.222104 \n",
"\n",
" feature_6 feature_7 feature_8 feature_9 random_feature \n",
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"3 -2.063467 0.513576 -0.734699 4.259375 0.409334 \n",
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}
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# SIMPLE Scaling, Imputing\n",
"scaler = StandardScaler(\n",
" with_mean=True,\n",
" with_std=True,\n",
")\n",
"\n",
"X_train_scl = scaler.fit_transform(X_train_imp[cols_num])\n",
"X_train_scl = pd.DataFrame(X_train_scl, columns=scaler.feature_names_in_)\n",
"X_train_scl.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "VQqEo2kk7kCW",
"outputId": "a46423de-1ac1-42cd-eaee-8ca3d122aee5"
},
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n",
"0 -1.136786 -1.136786 -0.244821 -0.538756 -0.187074 -0.244821 \n",
"1 -1.380778 -1.380778 0.604554 0.923549 1.438838 0.604554 \n",
"2 0.854219 0.854219 -1.950741 0.454838 1.678692 -1.950741 \n",
"3 -1.505617 -1.505617 0.702454 1.629660 0.880182 0.702454 \n",
"4 -1.000020 -1.000020 0.471929 0.993865 0.217357 0.471929 \n",
"\n",
" feature_6 feature_7 feature_8 feature_9 random_feature \n",
"0 -0.016562 -0.770236 -0.584953 -0.937890 -5.056780 \n",
"1 0.990009 -0.246145 1.408590 -0.433178 -0.163514 \n",
"2 0.876384 0.028551 2.352745 -1.875748 1.403823 \n",
"3 -0.989833 0.186285 -0.437966 1.592631 -0.287271 \n",
"4 -0.471982 0.509406 -0.124649 1.081272 0.384739 "
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" <td>0.702454</td>\n",
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" <td>-0.989833</td>\n",
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"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-7466fe19-3936-4cc6-a74e-97ffc074bf13 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "X_train_scl",
"summary": "{\n \"name\": \"X_train_scl\",\n \"rows\": 800,\n \"fields\": [\n {\n \"column\": \"feature_0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -2.949488498145816,\n \"max\": 2.77684823170384,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5864276041318114,\n 0.986438191788683,\n 1.140876264989094\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -2.949488498145816,\n \"max\": 2.77684823170384,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5864276041318114,\n 0.986438191788683,\n 1.140876264989094\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -2.6858461899151145,\n \"max\": 3.4968665291480803,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.540398803641626,\n 0.29997149744440366,\n -1.1143266013216306\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_3\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485205,\n \"min\": -3.180207046197294,\n \"max\": 3.242804270904773,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.6153250963627569,\n -1.2509317349665197,\n -0.03618059192712211\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_4\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -4.036982201683837,\n \"max\": 3.3714356332927977,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.14409320643942253,\n -0.13878702618836603,\n -0.32701725495642636\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_5\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -2.6858461899151145,\n \"max\": 3.4968665291480803,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.540398803641626,\n 0.29997149744440366,\n -1.1143266013216306\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_6\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -3.5538881984054607,\n \"max\": 3.365266402093275,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.42571745185376064,\n -1.5049873370766111,\n -1.9152322578603043\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_7\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -3.5802295179264108,\n \"max\": 2.7718795254025226,\n \"num_unique_values\": 800,\n \"samples\": [\n -1.346356440747795,\n -0.6208672595034782,\n 1.467184669283804\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_8\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -3.507018430238859,\n \"max\": 2.7758908106284848,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.19044171150261044,\n -1.1234797696653887,\n 0.16240168532838903\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_9\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485196,\n \"min\": -3.1008310334666,\n \"max\": 3.7900497763084617,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.22694435556142464,\n 0.09208855925805284,\n 0.7282284803265933\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"random_feature\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -5.056780315607009,\n \"max\": 1.7096941149903555,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.41600906329314363,\n 1.6603773433953388,\n -1.2740543971713765\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"plot_distribution(X_train, \"feature_0\")\n",
"plot_distribution(X_train_scl, \"feature_0\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"collapsed": true,
"id": "4eMt4PTk8jry",
"outputId": "f0c02ed8-ee60-4ba8-f3ec-8ad45bcb063a"
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"# SIMPLE Scaling, Imputing\n",
"scaler = MinMaxScaler(\n",
" feature_range=(-1,1),\n",
" clip=False,\n",
")\n",
"\n",
"X_train_scl = scaler.fit_transform(X_train_imp[cols_num])\n",
"X_train_scl = pd.DataFrame(X_train_scl, columns=scaler.feature_names_in_)\n",
"X_train_scl.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "KGM1fzoQ-xdQ",
"outputId": "c5f9a9b3-db45-4790-838c-d847193f647b"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" feature_0 feature_1 feature_2 feature_3 feature_4 feature_5 \\\n",
"0 -0.366889 -0.366889 -0.210371 -0.177504 0.039334 -0.210371 \n",
"1 -0.452107 -0.452107 0.064387 0.277829 0.478270 0.064387 \n",
"2 0.328496 0.328496 -0.762206 0.131882 0.543022 -0.762206 \n",
"3 -0.495709 -0.495709 0.096056 0.497698 0.327453 0.096056 \n",
"4 -0.319122 -0.319122 0.021485 0.299724 0.148515 0.021485 \n",
"\n",
" feature_6 feature_7 feature_8 feature_9 random_feature \n",
"0 0.022473 -0.115256 -0.069837 -0.372231 -1.000000 \n",
"1 0.313426 0.049757 0.564755 -0.225744 0.446327 \n",
"2 0.280582 0.136246 0.865303 -0.644434 0.909592 \n",
"3 -0.258853 0.185910 -0.023047 0.362224 0.409747 \n",
"4 -0.109167 0.287647 0.076689 0.213808 0.608377 "
],
"text/html": [
"\n",
" <div id=\"df-4e8d8f62-c7df-4c27-949e-6296bace7d20\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature_0</th>\n",
" <th>feature_1</th>\n",
" <th>feature_2</th>\n",
" <th>feature_3</th>\n",
" <th>feature_4</th>\n",
" <th>feature_5</th>\n",
" <th>feature_6</th>\n",
" <th>feature_7</th>\n",
" <th>feature_8</th>\n",
" <th>feature_9</th>\n",
" <th>random_feature</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.366889</td>\n",
" <td>-0.366889</td>\n",
" <td>-0.210371</td>\n",
" <td>-0.177504</td>\n",
" <td>0.039334</td>\n",
" <td>-0.210371</td>\n",
" <td>0.022473</td>\n",
" <td>-0.115256</td>\n",
" <td>-0.069837</td>\n",
" <td>-0.372231</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.452107</td>\n",
" <td>-0.452107</td>\n",
" <td>0.064387</td>\n",
" <td>0.277829</td>\n",
" <td>0.478270</td>\n",
" <td>0.064387</td>\n",
" <td>0.313426</td>\n",
" <td>0.049757</td>\n",
" <td>0.564755</td>\n",
" <td>-0.225744</td>\n",
" <td>0.446327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.328496</td>\n",
" <td>0.328496</td>\n",
" <td>-0.762206</td>\n",
" <td>0.131882</td>\n",
" <td>0.543022</td>\n",
" <td>-0.762206</td>\n",
" <td>0.280582</td>\n",
" <td>0.136246</td>\n",
" <td>0.865303</td>\n",
" <td>-0.644434</td>\n",
" <td>0.909592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.495709</td>\n",
" <td>-0.495709</td>\n",
" <td>0.096056</td>\n",
" <td>0.497698</td>\n",
" <td>0.327453</td>\n",
" <td>0.096056</td>\n",
" <td>-0.258853</td>\n",
" <td>0.185910</td>\n",
" <td>-0.023047</td>\n",
" <td>0.362224</td>\n",
" <td>0.409747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.319122</td>\n",
" <td>-0.319122</td>\n",
" <td>0.021485</td>\n",
" <td>0.299724</td>\n",
" <td>0.148515</td>\n",
" <td>0.021485</td>\n",
" <td>-0.109167</td>\n",
" <td>0.287647</td>\n",
" <td>0.076689</td>\n",
" <td>0.213808</td>\n",
" <td>0.608377</td>\n",
" </tr>\n",
" </tbody>\n",
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"type": "dataframe",
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"summary": "{\n \"name\": \"X_train_scl\",\n \"rows\": 800,\n \"fields\": [\n {\n \"column\": \"feature_0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3494819231752694,\n \"min\": -1.0,\n \"max\": 1.0000000000000002,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.23496618137943925,\n 0.3746752507297406,\n 0.42861482169327547\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3494819231752694,\n \"min\": -1.0,\n \"max\": 1.0000000000000002,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.23496618137943925,\n 0.3746752507297406,\n 0.42861482169327547\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.32368496872361086,\n \"min\": -1.0,\n \"max\": 1.0000000000000002,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.04363412636308997,\n -0.03413992432372633,\n -0.4916407538238003\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_3\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3115752213869622,\n \"min\": -1.0,\n \"max\": 0.9999999999999999,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.2013459658695541,\n -0.39926143175434275,\n -0.021011703373833346\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_4\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.27013206026916164,\n \"min\": -1.0,\n \"max\": 0.9999999999999998,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.12873639183350544,\n 0.05236914610601587,\n 0.0015539159284231607\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_5\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.32368496872361086,\n \"min\": -1.0,\n \"max\": 1.0000000000000002,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.04363412636308997,\n -0.03413992432372633,\n -0.4916407538238003\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_6\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.28923348134941074,\n \"min\": -1.0,\n \"max\": 0.9999999999999998,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.09579394386527518,\n -0.4077597684603944,\n -0.5263421515608161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_7\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3150530256086802,\n \"min\": -1.0,\n \"max\": 1.0,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.29665153354863827,\n -0.06822687134727543,\n 0.5892089234554544\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_8\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.31852301161376817,\n \"min\": -1.0,\n \"max\": 1.0,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.1769898306635549,\n -0.24126274335790737,\n 0.16806402094730616\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_9\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2904202275938607,\n \"min\": -1.0,\n \"max\": 1.0,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.16588698680481528,\n -0.07329130168806994,\n 0.11134109542614046\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"random_feature\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2957598072117984,\n \"min\": -1.0,\n \"max\": 1.0,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.6176191708203939,\n 0.9854232001906308,\n 0.11807883329330267\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"plot_distribution(X_train, \"feature_0\")\n",
"plot_distribution(X_train_scl, \"feature_0\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "A3tdwzdCLOKa",
"outputId": "5b7f4160-12bf-45ed-8aa1-c6b82620c68b"
},
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Encoder\n",
"encoder = OneHotEncoder(\n",
" max_categories=2,\n",
" min_frequency=2,\n",
" sparse_output=False,\n",
" handle_unknown='infrequent_if_exist',\n",
")\n",
"\n",
"X_train_enc = encoder.fit_transform(X_train[cols_cat])\n",
"print(encoder.categories_)\n",
"print(encoder.n_features_in_)\n",
"X_train_enc = pd.DataFrame(X_train_enc, columns=encoder.get_feature_names_out())\n",
"print(X_train_enc.shape)\n",
"pd.concat([X_train[cols_cat].reset_index(), X_train_enc],axis=1).head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "O1MqR_QwLc-8",
"outputId": "ed6ff9fd-8498-4156-8c30-3c324cd952b3"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[array(['A', 'B', 'C', None], dtype=object)]\n",
"1\n",
"(800, 2)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" index categorical_feature categorical_feature_A \\\n",
"0 29 None 0.0 \n",
"1 535 B 0.0 \n",
"2 695 B 0.0 \n",
"3 557 A 1.0 \n",
"4 836 C 0.0 \n",
"\n",
" categorical_feature_infrequent_sklearn \n",
"0 1.0 \n",
"1 1.0 \n",
"2 1.0 \n",
"3 0.0 \n",
"4 1.0 "
],
"text/html": [
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"</div>\n",
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" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-1bfbcc11-39e7-47fb-83d1-1f9e41f47e02\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1bfbcc11-39e7-47fb-83d1-1f9e41f47e02')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-1bfbcc11-39e7-47fb-83d1-1f9e41f47e02 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"pd\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 305,\n \"min\": 29,\n \"max\": 836,\n \"num_unique_values\": 5,\n \"samples\": [\n 535,\n 836,\n 695\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"categorical_feature\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"B\",\n \"A\",\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"categorical_feature_A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.447213595499958,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"categorical_feature_infrequent_sklearn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4472135954999579,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"# Preprocessing for numerical and categorical features\n",
"\n",
"numeric_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())])\n",
"categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
"\n",
"preprocessor = ColumnTransformer(transformers=[('num', numeric_transformer, cols_num), ('cat', categorical_transformer, cols_cat)])\n",
"\n",
"preprocessor"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "bM8jLviX7dz0",
"outputId": "6e05cdd1-b09c-4d87-83dd-f8072944f55b"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ColumnTransformer(transformers=[('num',\n",
" Pipeline(steps=[('imputer', SimpleImputer()),\n",
" ('scaler', StandardScaler())]),\n",
" ['feature_0', 'feature_1', 'feature_2',\n",
" 'feature_3', 'feature_4', 'feature_5',\n",
" 'feature_6', 'feature_7', 'feature_8',\n",
" 'feature_9', 'random_feature']),\n",
" ('cat', OneHotEncoder(handle_unknown='ignore'),\n",
" ['categorical_feature'])])"
],
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\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-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 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-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: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
" text-align: center;\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>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
" (&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;,\n",
" &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;,\n",
" &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;,\n",
" &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]),\n",
" (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
" [&#x27;categorical_feature&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
" (&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;,\n",
" &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;,\n",
" &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;,\n",
" &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]),\n",
" (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
" [&#x27;categorical_feature&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;, &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;, &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;, &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;categorical_feature&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"X_train = preprocessor.fit_transform(X_train)\n",
"X_test = preprocessor.transform(X_test)"
],
"metadata": {
"id": "57jARawROgaq"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import joblib\n",
"\n",
"joblib.dump(preprocessor, 'preprocessor.joblib')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b8dkRYorOTvX",
"outputId": "9f0ea255-956c-4781-e3a9-67000b864698"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['preprocessor.joblib']"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"preprocessor = joblib.load('preprocessor.joblib')\n",
"preprocessor"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "g8TuTgGnOYvw",
"outputId": "b8708aff-a115-46c7-ceb2-f94c423cb127"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ColumnTransformer(transformers=[('num',\n",
" Pipeline(steps=[('imputer', SimpleImputer()),\n",
" ('scaler', StandardScaler())]),\n",
" ['feature_0', 'feature_1', 'feature_2',\n",
" 'feature_3', 'feature_4', 'feature_5',\n",
" 'feature_6', 'feature_7', 'feature_8',\n",
" 'feature_9', 'random_feature']),\n",
" ('cat', OneHotEncoder(handle_unknown='ignore'),\n",
" ['categorical_feature'])])"
],
"text/html": [
"<style>#sk-container-id-2 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\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: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-2 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
" text-align: center;\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>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
" (&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;,\n",
" &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;,\n",
" &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;,\n",
" &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]),\n",
" (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
" [&#x27;categorical_feature&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
" (&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;,\n",
" &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;,\n",
" &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;,\n",
" &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]),\n",
" (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
" [&#x27;categorical_feature&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;feature_0&#x27;, &#x27;feature_1&#x27;, &#x27;feature_2&#x27;, &#x27;feature_3&#x27;, &#x27;feature_4&#x27;, &#x27;feature_5&#x27;, &#x27;feature_6&#x27;, &#x27;feature_7&#x27;, &#x27;feature_8&#x27;, &#x27;feature_9&#x27;, &#x27;random_feature&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;categorical_feature&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"df_prep = pd.DataFrame(X_train, columns=preprocessor.get_feature_names_out())\n",
"df_prep[\"target\"] = y_train\n",
"df_prep.head()"
],
"metadata": {
"id": "1CaQwKtWe2WZ",
"outputId": "8bea7c15-563e-4f71-af9e-a2743c0ec269",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" num__feature_0 num__feature_1 num__feature_2 num__feature_3 \\\n",
"0 0.000000 0.000000 0.000000 8.042830e-18 \n",
"1 -1.383320 -1.383320 0.604270 9.230428e-01 \n",
"2 0.853487 0.853487 -1.951120 4.542467e-01 \n",
"3 -1.508260 -1.508260 0.702174 1.629281e+00 \n",
"4 -1.002253 -1.002253 0.471641 9.933707e-01 \n",
"\n",
" num__feature_4 num__feature_5 num__feature_6 num__feature_7 \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 1.438635 0.604270 0.989988 -0.247201 \n",
"2 1.678495 -1.951120 0.876363 0.027597 \n",
"3 0.879967 0.702174 -0.989854 0.185390 \n",
"4 0.217127 0.471641 -0.472002 0.508631 \n",
"\n",
" num__feature_8 num__feature_9 num__random_feature \\\n",
"0 0.000000 -5.344722e-17 1.909428e-16 \n",
"1 1.408159 -4.345915e-01 -1.726278e-01 \n",
"2 2.352517 -1.877956e+00 1.420409e+00 \n",
"3 -0.438792 1.592334e+00 -2.984138e-01 \n",
"4 -0.125407 1.080694e+00 3.846149e-01 \n",
"\n",
" cat__categorical_feature_A cat__categorical_feature_B \\\n",
"0 0.0 0.0 \n",
"1 0.0 1.0 \n",
"2 0.0 1.0 \n",
"3 1.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
" cat__categorical_feature_C cat__categorical_feature_None target \n",
"0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 1.0 0.0 0.0 "
],
"text/html": [
"\n",
" <div id=\"df-aae298aa-2bcf-4c57-b2b0-60c824c9c578\" class=\"colab-df-container\">\n",
" <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>num__feature_0</th>\n",
" <th>num__feature_1</th>\n",
" <th>num__feature_2</th>\n",
" <th>num__feature_3</th>\n",
" <th>num__feature_4</th>\n",
" <th>num__feature_5</th>\n",
" <th>num__feature_6</th>\n",
" <th>num__feature_7</th>\n",
" <th>num__feature_8</th>\n",
" <th>num__feature_9</th>\n",
" <th>num__random_feature</th>\n",
" <th>cat__categorical_feature_A</th>\n",
" <th>cat__categorical_feature_B</th>\n",
" <th>cat__categorical_feature_C</th>\n",
" <th>cat__categorical_feature_None</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.042830e-18</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.344722e-17</td>\n",
" <td>1.909428e-16</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.383320</td>\n",
" <td>-1.383320</td>\n",
" <td>0.604270</td>\n",
" <td>9.230428e-01</td>\n",
" <td>1.438635</td>\n",
" <td>0.604270</td>\n",
" <td>0.989988</td>\n",
" <td>-0.247201</td>\n",
" <td>1.408159</td>\n",
" <td>-4.345915e-01</td>\n",
" <td>-1.726278e-01</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.853487</td>\n",
" <td>0.853487</td>\n",
" <td>-1.951120</td>\n",
" <td>4.542467e-01</td>\n",
" <td>1.678495</td>\n",
" <td>-1.951120</td>\n",
" <td>0.876363</td>\n",
" <td>0.027597</td>\n",
" <td>2.352517</td>\n",
" <td>-1.877956e+00</td>\n",
" <td>1.420409e+00</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>-1.508260</td>\n",
" <td>-1.508260</td>\n",
" <td>0.702174</td>\n",
" <td>1.629281e+00</td>\n",
" <td>0.879967</td>\n",
" <td>0.702174</td>\n",
" <td>-0.989854</td>\n",
" <td>0.185390</td>\n",
" <td>-0.438792</td>\n",
" <td>1.592334e+00</td>\n",
" <td>-2.984138e-01</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.002253</td>\n",
" <td>-1.002253</td>\n",
" <td>0.471641</td>\n",
" <td>9.933707e-01</td>\n",
" <td>0.217127</td>\n",
" <td>0.471641</td>\n",
" <td>-0.472002</td>\n",
" <td>0.508631</td>\n",
" <td>-0.125407</td>\n",
" <td>1.080694e+00</td>\n",
" <td>3.846149e-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aae298aa-2bcf-4c57-b2b0-60c824c9c578')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
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"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
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" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-aae298aa-2bcf-4c57-b2b0-60c824c9c578 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-aae298aa-2bcf-4c57-b2b0-60c824c9c578');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
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"\n",
"\n",
"<div id=\"df-4a546a0d-c789-4f20-9f1f-05d7bb1b6f26\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a546a0d-c789-4f20-9f1f-05d7bb1b6f26')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
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"</svg>\n",
" </button>\n",
"\n",
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"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
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" --hover-bg-color: #434B5C;\n",
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" }\n",
"\n",
" .colab-df-quickchart {\n",
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" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-4a546a0d-c789-4f20-9f1f-05d7bb1b6f26 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_prep",
"summary": "{\n \"name\": \"df_prep\",\n \"rows\": 800,\n \"fields\": [\n {\n \"column\": \"num__feature_0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485192,\n \"min\": -2.9533005204765415,\n \"max\": 2.7776726475803657,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5854785039950913,\n 0.9858129678854807,\n 1.1403760848297404\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485192,\n \"min\": -2.9533005204765415,\n \"max\": 2.7776726475803657,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5854785039950913,\n 0.9858129678854807,\n 1.1403760848297404\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -2.686253356734007,\n \"max\": 3.496691275192368,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5401126529119122,\n 0.29967632831330193,\n -1.114674820616371\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_3\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -3.1814592598388938,\n \"max\": 3.2427190381266513,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.6161113030171713,\n -1.251833423401571,\n -0.03686157526485563\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_4\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485205,\n \"min\": -4.037304755084932,\n \"max\": 3.3712753310728036,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.14386222244469027,\n -0.13902420552100667,\n -0.32725855670462417\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_5\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.00062558654852,\n \"min\": -2.686253356734007,\n \"max\": 3.496691275192368,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.5401126529119122,\n 0.29967632831330193,\n -1.114674820616371\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_6\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485196,\n \"min\": -3.5539095371969434,\n \"max\": 3.3652462510259746,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.425738253671544,\n -1.5050083241590715,\n -1.9152533153643467\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_7\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485196,\n \"min\": -3.5825237890571957,\n \"max\": 2.7719448110357128,\n \"num_unique_values\": 800,\n \"samples\": [\n -1.3478209165628563,\n -0.6220622448401615,\n 1.4667653124853741\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_8\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -3.5085018726123307,\n \"max\": 2.7757531232959645,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.1897502391280206,\n -1.1244526747949186,\n 0.16170420697708912\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__feature_9\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -3.1037138080736497,\n \"max\": 3.790963300314536,\n \"num_unique_values\": 800,\n \"samples\": [\n -0.22824385913088358,\n 0.09096481612408885,\n 0.7274551970491806\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num__random_feature\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0006255865485199,\n \"min\": -1.6904748825047096,\n \"max\": 1.7312944978187548,\n \"num_unique_values\": 800,\n \"samples\": [\n 0.4163975077573524,\n 1.6811691025807411,\n -1.3013771192758103\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cat__categorical_feature_A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4760716782203087,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cat__categorical_feature_B\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4695949310299938,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cat__categorical_feature_C\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4686678588856313,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cat__categorical_feature_None\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03535533905932811,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.41594744555642255,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"source": [
"## 3. Correlação entre Variáveis e Matriz de Correlação"
],
"metadata": {
"id": "vQW_RTjIGVjs"
}
},
{
"cell_type": "code",
"source": [
"# covariance\n",
"print(df.feature_0.cov(df.feature_2))\n",
"covariancia = ((df.feature_0 - df.feature_0.mean())*(df.feature_2 - df.feature_2.mean())).sum()/(len(df)-1)\n",
"covariancia"
],
"metadata": {
"id": "BRI7QDfhRl72",
"outputId": "df30e436-b0d7-47fb-a28b-ea684a369a29",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-2.911031228783343\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"np.float64(-2.9110312287833424)"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"# pearson correlation measure\n",
"print(df.feature_0.corr(df.feature_2, method='pearson'))\n",
"\n",
"numerador = ((df.feature_0 - df.feature_0.mean())*(df.feature_2 - df.feature_2.mean())).sum()\n",
"denominador_0 = ((df.feature_0 - df.feature_0.mean())**2).sum()\n",
"denominador_2 = ((df.feature_2 - df.feature_2.mean())**2).sum()\n",
"pearson_corr = numerador/((denominador_0*denominador_2)**0.5)\n",
"pearson_corr"
],
"metadata": {
"id": "g8Tldj_oSScy",
"outputId": "71497abd-17ab-461d-f307-add60880cd78",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-0.7356143163408604\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"np.float64(-0.7356143163408603)"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"# Outra fórmula\n",
"df.feature_0.cov(df.feature_2)/(df.feature_0.std()*df.feature_2.std())"
],
"metadata": {
"id": "DB2n9pIJWdvJ",
"outputId": "483d9f3b-81b3-4440-af7f-5ee17deae43f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"np.float64(-0.7356143163408606)"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"# Correlation Matrix (exclude categorical feature)\n",
"numeric_df = df_prep.select_dtypes(include=[np.number])\n",
"plt.figure(figsize=(8,6))\n",
"sns.heatmap(numeric_df.corr(method=\"pearson\"), annot=True, cmap='coolwarm', fmt=\".1g\")\n",
"plt.title(\"Correlation Matrix (Pearson Correlation)\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "ujBF51Yh5nwC",
"outputId": "326aef1e-ae8a-4a0d-acc5-0a5700cef96c"
},
"execution_count": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 4. Outras Técnicas (work in progress)"
],
"metadata": {
"id": "nDA5OHodO1jb"
}
},
{
"cell_type": "code",
"source": [
"# Balancing the dataset\n",
"smote = SMOTE(random_state=42)\n",
"X_train, y_train = smote.fit_resample(X_train, y_train)\n",
"print(\"Balanced class distribution:\")\n",
"print(pd.Series(y_train).value_counts())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rezIZvvO6lco",
"outputId": "b8423106-95a2-4776-f984-4124516135d0"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Balanced class distribution:\n",
"target\n",
"0 634\n",
"1 634\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Dimensionality Reduction\n",
"pca = PCA(n_components=5)\n",
"X_train_pca = pca.fit_transform(X_train)\n",
"X_test_pca = pca.transform(X_test)"
],
"metadata": {
"id": "aLsm723Z6nNP"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Feature Selection\n",
"rf = RandomForestClassifier(random_state=42)\n",
"rfe = RFECV(rf, step=1, cv=5)\n",
"X_train_rfe = rfe.fit_transform(X_train_pca, y_train)\n",
"X_test_rfe = rfe.transform(X_test_pca)"
],
"metadata": {
"id": "aX7Ep9ha6od3"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Cross-validation using K-Fold\n",
"kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
"scores = cross_val_score(rf, X_train_rfe, y_train, cv=kf)\n",
"print(\"Cross-validation accuracy scores:\", scores)\n",
"print(\"Mean accuracy:\", scores.mean())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AHp4DG836qPs",
"outputId": "99e460a0-111e-456a-8ba4-af0aa96fc3e1"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cross-validation accuracy scores: [0.90551181 0.90944882 0.92519685 0.93280632 0.95652174]\n",
"Mean accuracy: 0.9258971087112136\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Hyperparameter tuning\n",
"param_dist = {'n_estimators': [50, 100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5, 10]}\n",
"random_search = RandomizedSearchCV(rf, param_distributions=param_dist, n_iter=10, cv=5, random_state=42)\n",
"random_search.fit(X_train_rfe, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 167
},
"id": "FNFyXwz_6tkn",
"outputId": "538cb05d-5dc0-42ef-c40c-2358170e5ec9"
},
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
" param_distributions={'max_depth': [None, 10, 20],\n",
" 'min_samples_split': [2, 5, 10],\n",
" 'n_estimators': [50, 100, 200]},\n",
" random_state=42)"
],
"text/html": [
"<style>#sk-container-id-3 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\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: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-3 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
" text-align: center;\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>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
" param_distributions={&#x27;max_depth&#x27;: [None, 10, 20],\n",
" &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
" &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
" random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
" param_distributions={&#x27;max_depth&#x27;: [None, 10, 20],\n",
" &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
" &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
" random_state=42)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=50, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=50, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"# Train the final model\n",
"best_model = random_search.best_estimator_\n",
"best_model.fit(X_train_rfe, y_train)\n",
"y_pred = best_model.predict(X_test_rfe)"
],
"metadata": {
"id": "vDmSe79A6u0s"
},
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Evaluate performance\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Final model accuracy:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e6mus0Rk6vn9",
"outputId": "5f27ae9e-8f48-402a-af3a-9834f60005a7"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Final model accuracy: 0.91\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import classification_report, roc_auc_score\n",
"print(classification_report(y_test, y_pred))\n",
"roc_auc = roc_auc_score(y_test, best_model.predict_proba(X_test_rfe)[:, 1])\n",
"print(\"ROC AUC Score:\", roc_auc)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w1EKLsju4DKm",
"outputId": "f64325c5-88fc-4a1f-81d7-9273d0a4feca"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.97 0.92 0.94 158\n",
" 1 0.74 0.88 0.80 42\n",
"\n",
" accuracy 0.91 200\n",
" macro avg 0.85 0.90 0.87 200\n",
"weighted avg 0.92 0.91 0.91 200\n",
"\n",
"ROC AUC Score: 0.9531344183242916\n"
]
}
]
}
]
}
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