{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Import library\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "import json\n", "import warnings\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder # Assuming these scalers/encoders are needed\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "from sklearn.pipeline import make_pipeline, Pipeline\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data Files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "with open('best_svm_model.pkl', 'rb') as file_1:\n", " list_cat_cols = pickle.load(file_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data Yang Sudah Dibuat Untuk Random State Data Inference" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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