diff --git a/lab7/main.ipynb b/lab7/main.ipynb new file mode 100644 index 0000000..6918dc7 --- /dev/null +++ b/lab7/main.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: numpy in c:\\software\\python3\\lib\\site-packages (1.24.2)\n", + "Requirement already satisfied: pandas in c:\\software\\python3\\lib\\site-packages (1.5.3)\n", + "Requirement already satisfied: sklearn in \\\\files\\students\\s478831\\.appdata\\python\\python310\\site-packages (0.0.post4)\n", + "Requirement already satisfied: xgboost in \\\\files\\students\\s478831\\.appdata\\python\\python310\\site-packages (1.7.5)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\software\\python3\\lib\\site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\software\\python3\\lib\\site-packages (from pandas) (2022.7.1)\n", + "Requirement already satisfied: scipy in c:\\software\\python3\\lib\\site-packages (from xgboost) (1.10.1)\n", + "Requirement already satisfied: six>=1.5 in c:\\software\\python3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" + ] + } + ], + "source": [ + "!pip install numpy pandas sklearn xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import os, sys\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from xgboost import XGBClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 24 columns
\n", + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n", + " predictor=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n", + " predictor=None, random_state=None, ...)