KNN_DT_Tasks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10090c31",
   "metadata": {},
   "source": [
    "<h1>Task1\n",
    "<h2>Handwritten Digits Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a0a04de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
       "       [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
       "       [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
       "       [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
       "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
       "       [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
       "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
       "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "import numpy as np\n",
    "\n",
    "data = load_digits()\n",
    "X, y = data.data, data.target\n",
    "\n",
    "X[0, :].reshape([8,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c2996e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 4, sharey=True, figsize=(16,6))\n",
    "for i in range(4):\n",
    "    axes[i].imshow(X[i, :].reshape([8, 8]), cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da7061e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_holdout, y_train, y_holdout = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=17\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82fb4a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('knn', KNeighborsClassifier(n_neighbors=10))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "tree = DecisionTreeClassifier(max_depth=5, random_state=17)\n",
    "knn_pipe = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"knn\", KNeighborsClassifier(n_neighbors=10))]\n",
    ")\n",
    "\n",
    "tree.fit(X_train, y_train)\n",
    "knn_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14fb82cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.975925925925926, 0.6666666666666666)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "\n",
    "tree_pred = tree.predict(X_holdout)\n",
    "knn_pred = knn_pipe.predict(X_holdout)\n",
    "accuracy_score(y_holdout, knn_pred), accuracy_score(\n",
    "    y_holdout, tree_pred\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a935bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 99 candidates, totalling 495 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=DecisionTreeClassifier(max_depth=5, random_state=17),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': [1, 2, 3, 5, 10, 20, 25, 30, 40, 50, 64],\n",
       "                         'max_features': [1, 2, 3, 5, 10, 20, 30, 50, 64]},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_params = {\n",
    "    \"max_depth\": [1, 2, 3, 5, 10, 20, 25, 30, 40, 50, 64],\n",
    "    \"max_features\": [1, 2, 3, 5, 10, 20, 30, 50, 64]\n",
    "}\n",
    "\n",
    "tree_grid = GridSearchCV(tree, tree_params, cv=5, n_jobs=-1, verbose=True)\n",
    "\n",
    "tree_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "32339caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 10, 'max_features': 50}, 0.8568203376968316)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_params_, tree_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3815483b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9864858028204642"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(\n",
    "    cross_val_score(KNeighborsClassifier(n_neighbors=1), X_train, y_train, cv=5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5616d7b",
   "metadata": {},
   "source": [
    "<h1> Task 2\n",
    "<h2> Toy dataset “Will They? Won’t They?”\n",
    "<h3> Person A is deciding whether they will go on a second date with Person B. It will depend on their looks, eloquence, alcohol consumption (only for example), and how much money was spent on the first date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "948b13dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "import collections\n",
    "from io import StringIO\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from ipywidgets import Image\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "138be918",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_df(dic, feature_list):\n",
    "    out = pd.DataFrame(dic)\n",
    "    out = pd.concat([out, pd.get_dummies(out[feature_list])], axis=1)\n",
    "    out.drop(feature_list, axis=1, inplace=True)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e81ab86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def intersect_features(train, test):\n",
    "    common_feat = list(set(train.keys()) & set(test.keys()))\n",
    "    return train[common_feat], test[common_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89bb756b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"Looks\", \"Alcoholic_beverage\", \"Eloquence\", \"Money_spent\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e6695805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Will_go</th>\n",
       "      <th>Looks_handsome</th>\n",
       "      <th>Looks_repulsive</th>\n",
       "      <th>Alcoholic_beverage_no</th>\n",
       "      <th>Alcoholic_beverage_yes</th>\n",
       "      <th>Eloquence_average</th>\n",
       "      <th>Eloquence_high</th>\n",
       "      <th>Eloquence_low</th>\n",
       "      <th>Money_spent_little</th>\n",
       "      <th>Money_spent_lots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Will_go  Looks_handsome  Looks_repulsive  Alcoholic_beverage_no  \\\n",
       "0        0               1                0                      0   \n",
       "1        1               1                0                      0   \n",
       "2        0               1                0                      1   \n",
       "3        1               0                1                      1   \n",
       "4        1               0                1                      0   \n",
       "5        0               0                1                      0   \n",
       "6        0               1                0                      0   \n",
       "\n",
       "   Alcoholic_beverage_yes  Eloquence_average  Eloquence_high  Eloquence_low  \\\n",
       "0                       1                  0               1              0   \n",
       "1                       1                  0               0              1   \n",
       "2                       0                  1               0              0   \n",
       "3                       0                  1               0              0   \n",
       "4                       1                  0               0              1   \n",
       "5                       1                  0               1              0   \n",
       "6                       1                  1               0              0   \n",
       "\n",
       "   Money_spent_little  Money_spent_lots  \n",
       "0                   0                 1  \n",
       "1                   1                 0  \n",
       "2                   0                 1  \n",
       "3                   1                 0  \n",
       "4                   0                 1  \n",
       "5                   0                 1  \n",
       "6                   0                 1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = {}\n",
    "df_train[\"Looks\"] = [\n",
    "    \"handsome\",\n",
    "    \"handsome\",\n",
    "    \"handsome\",\n",
    "    \"repulsive\",\n",
    "    \"repulsive\",\n",
    "    \"repulsive\",\n",
    "    \"handsome\",\n",
    "]\n",
    "df_train[\"Alcoholic_beverage\"] = [\"yes\", \"yes\", \"no\", \"no\", \"yes\", \"yes\", \"yes\"]\n",
    "df_train[\"Eloquence\"] = [\"high\", \"low\", \"average\", \"average\", \"low\", \"high\", \"average\"]\n",
    "df_train[\"Money_spent\"] = [\"lots\", \"little\", \"lots\", \"little\", \"lots\", \"lots\", \"lots\"]\n",
    "df_train[\"Will_go\"] = LabelEncoder().fit_transform([\"+\", \"-\", \"+\", \"-\", \"-\", \"+\", \"+\"])\n",
    "\n",
    "df_train = create_df(df_train, features)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "82933546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Looks_handsome</th>\n",
       "      <th>Looks_repulsive</th>\n",
       "      <th>Alcoholic_beverage_no</th>\n",
       "      <th>Alcoholic_beverage_yes</th>\n",
       "      <th>Eloquence_average</th>\n",
       "      <th>Eloquence_high</th>\n",
       "      <th>Money_spent_little</th>\n",
       "      <th>Money_spent_lots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Looks_handsome  Looks_repulsive  Alcoholic_beverage_no  \\\n",
       "0               1                0                      1   \n",
       "1               1                0                      0   \n",
       "2               0                1                      0   \n",
       "\n",
       "   Alcoholic_beverage_yes  Eloquence_average  Eloquence_high  \\\n",
       "0                       0                  1               0   \n",
       "1                       1                  0               1   \n",
       "2                       1                  1               0   \n",
       "\n",
       "   Money_spent_little  Money_spent_lots  \n",
       "0                   0                 1  \n",
       "1                   1                 0  \n",
       "2                   0                 1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = {}\n",
    "df_test[\"Looks\"] = [\"handsome\", \"handsome\", \"repulsive\"]\n",
    "df_test[\"Alcoholic_beverage\"] = [\"no\", \"yes\", \"yes\"]\n",
    "df_test[\"Eloquence\"] = [\"average\", \"high\", \"average\"]\n",
    "df_test[\"Money_spent\"] = [\"lots\", \"little\", \"lots\"]\n",
    "df_test = create_df(df_test, features)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab990b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money_spent_little</th>\n",
       "      <th>Looks_handsome</th>\n",
       "      <th>Eloquence_average</th>\n",
       "      <th>Eloquence_high</th>\n",
       "      <th>Alcoholic_beverage_no</th>\n",
       "      <th>Money_spent_lots</th>\n",
       "      <th>Alcoholic_beverage_yes</th>\n",
       "      <th>Looks_repulsive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Money_spent_little  Looks_handsome  Eloquence_average  Eloquence_high  \\\n",
       "0                   0               1                  0               1   \n",
       "1                   1               1                  0               0   \n",
       "2                   0               1                  1               0   \n",
       "3                   1               0                  1               0   \n",
       "4                   0               0                  0               0   \n",
       "5                   0               0                  0               1   \n",
       "6                   0               1                  1               0   \n",
       "\n",
       "   Alcoholic_beverage_no  Money_spent_lots  Alcoholic_beverage_yes  \\\n",
       "0                      0                 1                       1   \n",
       "1                      0                 0                       1   \n",
       "2                      1                 1                       0   \n",
       "3                      1                 0                       0   \n",
       "4                      0                 1                       1   \n",
       "5                      0                 1                       1   \n",
       "6                      0                 1                       1   \n",
       "\n",
       "   Looks_repulsive  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                1  \n",
       "4                1  \n",
       "5                1  \n",
       "6                0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_train[\"Will_go\"]\n",
    "df_train, df_test = intersect_features(train=df_train, test=df_test)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5b550a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_holdout, y_train, y_holdout = train_test_split(\n",
    "    df_train, y, test_size=0.3, random_state=17\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e804e545",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=5, random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3af8f789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, random_state=17)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0c3542a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_pred = tree.predict(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "31195b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_pred"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}