22-Train-Test-Split.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()\n",
    "X = data.data\n",
    "y = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "60 - 20 - 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_temp, y_train, y_temp  = train_test_split(X, y, train_size=0.6, random_state=42, stratify=y) # 100 => 60 - 40\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp) # 40 => 20 - 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((90, 4), (30, 4), (30, 4), (90,), (30,), (30,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_val.shape, X_test.shape, y_train.shape, y_val.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.DataFrame(X_train, columns=data.feature_names)\n",
    "df_train['set'] = 'train'\n",
    "df_train['target'] = y_train\n",
    "\n",
    "df_val = pd.DataFrame(X_val, columns=data.feature_names)\n",
    "df_val['set'] = 'val'\n",
    "df_val['target'] = y_val\n",
    "\n",
    "df_test = pd.DataFrame(X_test, columns=data.feature_names)\n",
    "df_test['set'] = 'test'\n",
    "df_test['target'] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = pd.concat([df_train, df_val, df_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "sns.countplot(data=df_combined, x='target', hue='set')\n",
    "plt.title('Distribution of Target across Train, Validation, and Test sets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0: 75\n",
    "1: 25\n",
    "\n",
    "\n",
    "Train:\n",
    "0: 75%\n",
    "1: 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "C_values = [0.001, 0.01, 0.1, 1, 50, 200]\n",
    "\n",
    "results = []\n",
    "\n",
    "for c in C_values:\n",
    "  model = LogisticRegression(C=c)\n",
    "  model.fit(X_train, y_train)\n",
    "  \n",
    "  y_val_pred = model.predict(X_val)\n",
    "  val_accuracy = accuracy_score(y_val, y_val_pred)\n",
    "  \n",
    "  y_train_pred = model.predict(X_train)\n",
    "  train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "  \n",
    "  results.append({\n",
    "    'C': c,\n",
    "    'validation_accuracy': val_accuracy,\n",
    "    'train_accuracy': train_accuracy\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>C</th>\n",
       "      <th>validation_accuracy</th>\n",
       "      <th>train_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.100</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.955556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.877778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.988889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>200.000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.744444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C  validation_accuracy  train_accuracy\n",
       "2    0.100             0.966667        0.955556\n",
       "3    1.000             0.966667        0.966667\n",
       "1    0.010             0.933333        0.877778\n",
       "4   50.000             0.933333        0.988889\n",
       "5  200.000             0.900000        1.000000\n",
       "0    0.001             0.733333        0.744444"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame(results)\n",
    "df_results.sort_values(by=\"validation_accuracy\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = LogisticRegression(C=0.1)\n",
    "final_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score: 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = final_model.predict(X_test)\n",
    "print(\"accuracy_score:\", accuracy_score(y_test, y_pred_test))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "base",
   "language": "python",
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   "file_extension": ".py",
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