3.Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "José Delgado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/training.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Features and Targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Machine Failure', axis=1), df['Machine Failure'], test_size=0.3, random_state=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rebalance Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NzfHhw4crMTFRwcHBcjgceuihh+R0OtW1a1dJUq9evRQdHa37779f06dPV25uriZNmqSEhATzFtCoUaP0wgsvaMKECRo2bJjWrFmjJUuWKDWVLy4AAKhkgLkQM2fOlJeXl+Lj41VcXKzY2Fi9+OKL5nZvb28tX75co0ePltPplL+/v4YMGaKpU6eaNc2bN1dqaqrGjRun2bNn6+qrr9b8+fMVGxtb1e0CAAALuqj3wFjBhT5HXhNxibl24RJz7cL3u3apjd/vy/oeGAAAAE8iwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMupVICZO3euOnToIIfDIYfDIafTqRUrVpjbu3fvLpvN5raMGjXK7Rg5OTmKi4tTvXr1FBISovHjx6u0tNStZt26derUqZPsdruioqKUkpJy8WcIAABqHJ/KFF999dV66qmndM0118gwDC1YsEADBgzQ1q1b1bZtW0nSgw8+qKlTp5r71KtXz/xzWVmZ4uLiFBYWpo0bN+rQoUMaPHiw6tSpo2nTpkmSsrOzFRcXp1GjRmnhwoVKT0/XiBEj1KRJE8XGxlbFOQMAAIurVIDp37+/2/qTTz6puXPn6rPPPjMDTL169RQWFnbG/T/66CPt2rVLq1evVmhoqK6//no9/vjjmjhxoqZMmSJfX1/NmzdPzZs314wZMyRJbdq00aeffqqZM2cSYAAAgKRLmANTVlamt99+W8ePH5fT6TTHFy5cqEaNGqldu3ZKSkrSzz//bG7LyMhQ+/btFRoaao7FxsbK5XJp586dZk3Pnj3dPis2NlYZGRnn7Ke4uFgul8ttAQAANVOlrsBI0vbt2+V0OnXy5EnVr19fy5YtU3R0tCTpnnvuUWRkpMLDw/Xll19q4sSJ2r17t5YuXSpJys3NdQsvksz13Nzcc9a4XC6dOHFCfn5+Z+wrOTlZjz32WGVPBwAAWFClA0yrVq2UlZWlwsJC/fe//9WQIUO0fv16RUdHa+TIkWZd+/bt1aRJE/Xo0UP79u1Ty5Ytq7Tx30pKSlJiYqK57nK5FBERcVk/EwAAeEalbyH5+voqKipKMTExSk5O1nXXXafZs2efsbZLly6SpL1790qSwsLClJeX51ZTsV4xb+ZsNQ6H46xXXyTJbrebT0dVLAAAoGa65PfAlJeXq7i4+IzbsrKyJElNmjSRJDmdTm3fvl35+flmTVpamhwOh3kbyul0Kj093e04aWlpbvNsAABA7VapW0hJSUnq06ePmjZtqmPHjmnRokVat26dVq1apX379mnRokXq27evGjZsqC+//FLjxo1Tt27d1KFDB0lSr169FB0drfvvv1/Tp09Xbm6uJk2apISEBNntdknSqFGj9MILL2jChAkaNmyY1qxZoyVLlig1NbXqzx4AAFhSpQJMfn6+Bg8erEOHDikwMFAdOnTQqlWr9Ic//EEHDhzQ6tWrNWvWLB0/flwRERGKj4/XpEmTzP29vb21fPlyjR49Wk6nU/7+/hoyZIjbe2OaN2+u1NRUjRs3TrNnz9bVV1+t+fPn8wg1AAAwVSrAvPLKK2fdFhERofXr15/3GJGRkfrwww/PWdO9e3dt3bq1Mq0BAIBahN9CAgAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAllOpADN37lx16NBBDodDDodDTqdTK1asMLefPHlSCQkJatiwoerXr6/4+Hjl5eW5HSMnJ0dxcXGqV6+eQkJCNH78eJWWlrrVrFu3Tp06dZLdbldUVJRSUlIu/gwBAECNU6kAc/XVV+upp55SZmamPv/8c912220aMGCAdu7cKUkaN26cPvjgA73zzjtav369Dh48qDvuuMPcv6ysTHFxcSopKdHGjRu1YMECpaSkaPLkyWZNdna24uLidOuttyorK0tjx47ViBEjtGrVqio6ZQAAYHU2wzCMSzlAcHCwnn76ad15551q3LixFi1apDvvvFOS9PXXX6tNmzbKyMhQ165dtWLFCvXr108HDx5UaGioJGnevHmaOHGiDh8+LF9fX02cOFGpqanasWOH+RmDBg1SQUGBVq5cedY+iouLVVxcbK67XC5FRESosLBQDofjUk7Rcpr9I9XTLeAK2v9UnKdbwBXE97t2qY3fb5fLpcDAwPP++33Rc2DKysr09ttv6/jx43I6ncrMzNSpU6fUs2dPs6Z169Zq2rSpMjIyJEkZGRlq3769GV4kKTY2Vi6Xy7yKk5GR4XaMipqKY5xNcnKyAgMDzSUiIuJiTw0AAFRzlQ4w27dvV/369WW32zVq1CgtW7ZM0dHRys3Nla+vr4KCgtzqQ0NDlZubK0nKzc11Cy8V2yu2navG5XLpxIkTZ+0rKSlJhYWF5nLgwIHKnhoAALAIn8ru0KpVK2VlZamwsFD//e9/NWTIEK1fv/5y9FYpdrtddrvd020AAIAroNIBxtfXV1FRUZKkmJgYbdmyRbNnz9Zdd92lkpISFRQUuF2FycvLU1hYmCQpLCxMmzdvdjtexVNKv6757ZNLeXl5cjgc8vPzq2y7AACgBrrk98CUl5eruLhYMTExqlOnjtLT081tu3fvVk5OjpxOpyTJ6XRq+/btys/PN2vS0tLkcDgUHR1t1vz6GBU1FccAAACo1BWYpKQk9enTR02bNtWxY8e0aNEirVu3TqtWrVJgYKCGDx+uxMREBQcHy+Fw6KGHHpLT6VTXrl0lSb169VJ0dLTuv/9+TZ8+Xbm5uZo0aZISEhLM2z+jRo3SCy+8oAkTJmjYsGFas2aNlixZotRUZt4DAIBfVCrA5Ofna/DgwTp06JACAwPVoUMHrVq1Sn/4wx8kSTNnzpSXl5fi4+NVXFys2NhYvfjii+b+3t7eWr58uUaPHi2n0yl/f38NGTJEU6dONWuaN2+u1NRUjRs3TrNnz9bVV1+t+fPnKzY2topOGQAAWN0lvwemurrQ58hrIt4TUbvUxvdE1GZ8v2uX2vj9vuzvgQEAAPAUAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALCcSgWY5ORk3XDDDQoICFBISIgGDhyo3bt3u9V0795dNpvNbRk1apRbTU5OjuLi4lSvXj2FhIRo/PjxKi0tdatZt26dOnXqJLvdrqioKKWkpFzcGQIAgBqnUgFm/fr1SkhI0Geffaa0tDSdOnVKvXr10vHjx93qHnzwQR06dMhcpk+fbm4rKytTXFycSkpKtHHjRi1YsEApKSmaPHmyWZOdna24uDjdeuutysrK0tixYzVixAitWrXqEk8XAADUBD6VKV65cqXbekpKikJCQpSZmalu3bqZ4/Xq1VNYWNgZj/HRRx9p165dWr16tUJDQ3X99dfr8ccf18SJEzVlyhT5+vpq3rx5at68uWbMmCFJatOmjT799FPNnDlTsbGxlT1HAABQw1zSHJjCwkJJUnBwsNv4woUL1ahRI7Vr105JSUn6+eefzW0ZGRlq3769QkNDzbHY2Fi5XC7t3LnTrOnZs6fbMWNjY5WRkXHWXoqLi+VyudwWAABQM1XqCsyvlZeXa+zYsbrxxhvVrl07c/yee+5RZGSkwsPD9eWXX2rixInavXu3li5dKknKzc11Cy+SzPXc3Nxz1rhcLp04cUJ+fn6n9ZOcnKzHHnvsYk8HAABYyEUHmISEBO3YsUOffvqp2/jIkSPNP7dv315NmjRRjx49tG/fPrVs2fLiOz2PpKQkJSYmmusul0sRERGX7fMAAIDnXNQtpDFjxmj58uVau3atrr766nPWdunSRZK0d+9eSVJYWJjy8vLcairWK+bNnK3G4XCc8eqLJNntdjkcDrcFAADUTJUKMIZhaMyYMVq2bJnWrFmj5s2bn3efrKwsSVKTJk0kSU6nU9u3b1d+fr5Zk5aWJofDoejoaLMmPT3d7ThpaWlyOp2VaRcAANRQlQowCQkJevPNN7Vo0SIFBAQoNzdXubm5OnHihCRp3759evzxx5WZman9+/fr/fff1+DBg9WtWzd16NBBktSrVy9FR0fr/vvv17Zt27Rq1SpNmjRJCQkJstvtkqRRo0bp22+/1YQJE/T111/rxRdf1JIlSzRu3LgqPn0AAGBFlQowc+fOVWFhobp3764mTZqYy+LFiyVJvr6+Wr16tXr16qXWrVvrkUceUXx8vD744APzGN7e3lq+fLm8vb3ldDp13333afDgwZo6dapZ07x5c6WmpiotLU3XXXedZsyYofnz5/MINQAAkFTJSbyGYZxze0REhNavX3/e40RGRurDDz88Z0337t21devWyrQHAABqCX4LCQAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWE6lAkxycrJuuOEGBQQEKCQkRAMHDtTu3bvdak6ePKmEhAQ1bNhQ9evXV3x8vPLy8txqcnJyFBcXp3r16ikkJETjx49XaWmpW826devUqVMn2e12RUVFKSUl5eLOEAAA1DiVCjDr169XQkKCPvvsM6WlpenUqVPq1auXjh8/btaMGzdOH3zwgd555x2tX79eBw8e1B133GFuLysrU1xcnEpKSrRx40YtWLBAKSkpmjx5slmTnZ2tuLg43XrrrcrKytLYsWM1YsQIrVq1qgpOGQAAWJ3NMAzjYnc+fPiwQkJCtH79enXr1k2FhYVq3LixFi1apDvvvFOS9PXXX6tNmzbKyMhQ165dtWLFCvXr108HDx5UaGioJGnevHmaOHGiDh8+LF9fX02cOFGpqanasWOH+VmDBg1SQUGBVq5cecZeiouLVVxcbK67XC5FRESosLBQDofjYk/Rkpr9I9XTLeAK2v9UnKdbwBXE97t2qY3fb5fLpcDAwPP++31Jc2AKCwslScHBwZKkzMxMnTp1Sj179jRrWrduraZNmyojI0OSlJGRofbt25vhRZJiY2Plcrm0c+dOs+bXx6ioqTjGmSQnJyswMNBcIiIiLuXUAABANXbRAaa8vFxjx47VjTfeqHbt2kmScnNz5evrq6CgILfa0NBQ5ebmmjW/Di8V2yu2navG5XLpxIkTZ+wnKSlJhYWF5nLgwIGLPTUAAFDN+VzsjgkJCdqxY4c+/fTTquznotntdtntdk+3AQAAroCLugIzZswYLV++XGvXrtXVV19tjoeFhamkpEQFBQVu9Xl5eQoLCzNrfvtUUsX6+WocDof8/PwupmUAAFCDVCrAGIahMWPGaNmyZVqzZo2aN2/utj0mJkZ16tRRenq6ObZ7927l5OTI6XRKkpxOp7Zv3678/HyzJi0tTQ6HQ9HR0WbNr49RUVNxDAAAULtV6hZSQkKCFi1apP/9738KCAgw56wEBgbKz89PgYGBGj58uBITExUcHCyHw6GHHnpITqdTXbt2lST16tVL0dHRuv/++zV9+nTl5uZq0qRJSkhIMG8BjRo1Si+88IImTJigYcOGac2aNVqyZIlSU5l9DwAAKnkFZu7cuSosLFT37t3VpEkTc1m8eLFZM3PmTPXr10/x8fHq1q2bwsLCtHTpUnO7t7e3li9fLm9vbzmdTt13330aPHiwpk6datY0b95cqampSktL03XXXacZM2Zo/vz5io2NrYJTBgAAVndJ74Gpzi70OfKaiPdE1C618T0RtRnf79qlNn6/r8h7YAAAADyBAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyn0gHm448/Vv/+/RUeHi6bzab33nvPbfvQoUNls9nclt69e7vVHD16VPfee68cDoeCgoI0fPhwFRUVudV8+eWXuvnmm1W3bl1FRERo+vTplT87AABQI1U6wBw/flzXXXed5syZc9aa3r1769ChQ+by1ltvuW2/9957tXPnTqWlpWn58uX6+OOPNXLkSHO7y+VSr169FBkZqczMTD399NOaMmWKXn755cq2CwAAaiCfyu7Qp08f9enT55w1drtdYWFhZ9z21VdfaeXKldqyZYs6d+4sSXr++efVt29fPfPMMwoPD9fChQtVUlKiV199Vb6+vmrbtq2ysrL07LPPugUdAABQO12WOTDr1q1TSEiIWrVqpdGjR+vIkSPmtoyMDAUFBZnhRZJ69uwpLy8vbdq0yazp1q2bfH19zZrY2Fjt3r1bP/300xk/s7i4WC6Xy20BAAA1U5UHmN69e+v1119Xenq6/v3vf2v9+vXq06ePysrKJEm5ubkKCQlx28fHx0fBwcHKzc01a0JDQ91qKtYran4rOTlZgYGB5hIREVHVpwYAAKqJSt9COp9BgwaZf27fvr06dOigli1bat26derRo0dVf5wpKSlJiYmJ5rrL5SLEAABQQ132x6hbtGihRo0aae/evZKksLAw5efnu9WUlpbq6NGj5ryZsLAw5eXludVUrJ9tbo3dbpfD4XBbAABAzXTZA8z333+vI0eOqEmTJpIkp9OpgoICZWZmmjVr1qxReXm5unTpYtZ8/PHHOnXqlFmTlpamVq1aqUGDBpe7ZQAAUM1VOsAUFRUpKytLWVlZkqTs7GxlZWUpJydHRUVFGj9+vD777DPt379f6enpGjBggKKiohQbGytJatOmjXr37q0HH3xQmzdv1oYNGzRmzBgNGjRI4eHhkqR77rlHvr6+Gj58uHbu3KnFixdr9uzZbreIAABA7VXpAPP555+rY8eO6tixoyQpMTFRHTt21OTJk+Xt7a0vv/xSf/zjH3Xttddq+PDhiomJ0SeffCK73W4eY+HChWrdurV69Oihvn376qabbnJ7x0tgYKA++ugjZWdnKyYmRo888ogmT57MI9QAAEDSRUzi7d69uwzDOOv2VatWnfcYwcHBWrRo0TlrOnTooE8++aSy7QEAgFqA30ICAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWU+kA8/HHH6t///4KDw+XzWbTe++957bdMAxNnjxZTZo0kZ+fn3r27Kk9e/a41Rw9elT33nuvHA6HgoKCNHz4cBUVFbnVfPnll7r55ptVt25dRUREaPr06ZU/OwAAUCNVOsAcP35c1113nebMmXPG7dOnT9dzzz2nefPmadOmTfL391dsbKxOnjxp1tx7773auXOn0tLStHz5cn388ccaOXKkud3lcqlXr16KjIxUZmamnn76aU2ZMkUvv/zyRZwiAACoaXwqu0OfPn3Up0+fM24zDEOzZs3SpEmTNGDAAEnS66+/rtDQUL333nsaNGiQvvrqK61cuVJbtmxR586dJUnPP/+8+vbtq2eeeUbh4eFauHChSkpK9Oqrr8rX11dt27ZVVlaWnn32Wbeg82vFxcUqLi42110uV2VPDQAAWESVzoHJzs5Wbm6uevbsaY4FBgaqS5cuysjIkCRlZGQoKCjIDC+S1LNnT3l5eWnTpk1mTbdu3eTr62vWxMbGavfu3frpp5/O+NnJyckKDAw0l4iIiKo8NQAAUI1UaYDJzc2VJIWGhrqNh4aGmttyc3MVEhLitt3Hx0fBwcFuNWc6xq8/47eSkpJUWFhoLgcOHLj0EwIAANVSpW8hVVd2u112u93TbQAAgCugSq/AhIWFSZLy8vLcxvPy8sxtYWFhys/Pd9teWlqqo0ePutWc6Ri//gwAAFB7VWmAad68ucLCwpSenm6OuVwubdq0SU6nU5LkdDpVUFCgzMxMs2bNmjUqLy9Xly5dzJqPP/5Yp06dMmvS0tLUqlUrNWjQoCpbBgAAFlTpAFNUVKSsrCxlZWVJ+mXiblZWlnJycmSz2TR27Fg98cQTev/997V9+3YNHjxY4eHhGjhwoCSpTZs26t27tx588EFt3rxZGzZs0JgxYzRo0CCFh4dLku655x75+vpq+PDh2rlzpxYvXqzZs2crMTGxyk4cAABYV6XnwHz++ee69dZbzfWKUDFkyBClpKRowoQJOn78uEaOHKmCggLddNNNWrlyperWrWvus3DhQo0ZM0Y9evSQl5eX4uPj9dxzz5nbAwMD9dFHHykhIUExMTFq1KiRJk+efNZHqAEAQO1iMwzD8HQTl4PL5VJgYKAKCwvlcDg83c4V1ewfqZ5uAVfQ/qfiPN0CriC+37VLbfx+X+i/3/wWEgAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsJwqDzBTpkyRzWZzW1q3bm1uP3nypBISEtSwYUPVr19f8fHxysvLcztGTk6O4uLiVK9ePYWEhGj8+PEqLS2t6lYBAIBF+VyOg7Zt21arV6/+/x/i8/8/Zty4cUpNTdU777yjwMBAjRkzRnfccYc2bNggSSorK1NcXJzCwsK0ceNGHTp0SIMHD1adOnU0bdq0y9EuAACwmMsSYHx8fBQWFnbaeGFhoV555RUtWrRIt912myTptddeU5s2bfTZZ5+pa9eu+uijj7Rr1y6tXr1aoaGhuv766/X4449r4sSJmjJlinx9fS9HywAAwEIuyxyYPXv2KDw8XC1atNC9996rnJwcSVJmZqZOnTqlnj17mrWtW7dW06ZNlZGRIUnKyMhQ+/btFRoaatbExsbK5XJp586dZ/3M4uJiuVwutwUAANRMVR5gunTpopSUFK1cuVJz585Vdna2br75Zh07dky5ubny9fVVUFCQ2z6hoaHKzc2VJOXm5rqFl4rtFdvOJjk5WYGBgeYSERFRtScGAACqjSq/hdSnTx/zzx06dFCXLl0UGRmpJUuWyM/Pr6o/zpSUlKTExERz3eVyEWIAAKihLvtj1EFBQbr22mu1d+9ehYWFqaSkRAUFBW41eXl55pyZsLCw055Kqlg/07yaCna7XQ6Hw20BAAA102UPMEVFRdq3b5+aNGmimJgY1alTR+np6eb23bt3KycnR06nU5LkdDq1fft25efnmzVpaWlyOByKjo6+3O0CAAALqPJbSH//+9/Vv39/RUZG6uDBg3r00Ufl7e2tu+++W4GBgRo+fLgSExMVHBwsh8Ohhx56SE6nU127dpUk9erVS9HR0br//vs1ffp05ebmatKkSUpISJDdbq/qdgEAgAVVeYD5/vvvdffdd+vIkSNq3LixbrrpJn322Wdq3LixJGnmzJny8vJSfHy8iouLFRsbqxdffNHc39vbW8uXL9fo0aPldDrl7++vIUOGaOrUqVXdKgAAsKgqDzBvv/32ObfXrVtXc+bM0Zw5c85aExkZqQ8//LCqWwMAADUEv4UEAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAsp1oHmDlz5qhZs2aqW7euunTpos2bN3u6JQAAUA1U2wCzePFiJSYm6tFHH9UXX3yh6667TrGxscrPz/d0awAAwMOqbYB59tln9eCDD+qBBx5QdHS05s2bp3r16unVV1/1dGsAAMDDfDzdwJmUlJQoMzNTSUlJ5piXl5d69uypjIyMM+5TXFys4uJic72wsFCS5HK5Lm+z1VB58c+ebgFXUG3833htxve7dqmN3++KczYM45x11TLA/PjjjyorK1NoaKjbeGhoqL7++usz7pOcnKzHHnvstPGIiIjL0iNQXQTO8nQHAC6X2vz9PnbsmAIDA8+6vVoGmIuRlJSkxMREc728vFxHjx5Vw4YNZbPZPNgZrgSXy6WIiAgdOHBADofD0+0AqEJ8v2sXwzB07NgxhYeHn7OuWgaYRo0aydvbW3l5eW7jeXl5CgsLO+M+drtddrvdbSwoKOhytYhqyuFw8B84oIbi+117nOvKS4VqOYnX19dXMTExSk9PN8fKy8uVnp4up9Ppwc4AAEB1UC2vwEhSYmKihgwZos6dO+t3v/udZs2apePHj+uBBx7wdGsAAMDDqm2Aueuuu3T48GFNnjxZubm5uv7667Vy5crTJvYC0i+3EB999NHTbiMCsD6+3zgTm3G+55QAAACqmWo5BwYAAOBcCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByqu2L7IDzee655844brPZVLduXUVFRalbt27y9va+wp0BqColJSXKzs5Wy5Yt5ePDP1n4/3iRHSyrefPmOnz4sH7++Wc1aNBAkvTTTz+pXr16ql+/vvLz89WiRQutXbtWERERHu4WQGX8/PPPeuihh7RgwQJJ0jfffKMWLVrooYce0lVXXaV//OMfHu4QnsYtJFjWtGnTdMMNN2jPnj06cuSIjhw5om+++UZdunTR7NmzlZOTo7CwMI0bN87TrQKopKSkJG3btk3r1q1T3bp1zfGePXtq8eLFHuwM1QVXYGBZLVu21Lvvvqvrr7/ebXzr1q2Kj4/Xt99+q40bNyo+Pl6HDh3yTJMALkpkZKQWL16srl27KiAgQNu2bVOLFi20d+9ederUSS6Xy9MtwsO4AgPLOnTokEpLS08bLy0tVW5uriQpPDxcx44du9KtAbhEhw8fVkhIyGnjx48fl81m80BHqG4IMLCsW2+9VX/5y1+0detWc2zr1q0aPXq0brvtNknS9u3b1bx5c0+1COAide7cWampqeZ6RWiZP3++nE6np9pCNcKUbljWK6+8ovvvv18xMTGqU6eOpF+uvvTo0UOvvPKKJKl+/fqaMWOGJ9sEcBGmTZumPn36aNeuXSotLdXs2bO1a9cubdy4UevXr/d0e6gGmAMDy/v666/1zTffSJJatWqlVq1aebgjAFXh22+/VXJysrZt26aioiJ16tRJEydOVPv27T3dGqoBAgwAoFo5deqU/vKXv+j//u//uAWMsyLAwLLKysqUkpKi9PR05efnq7y83G37mjVrPNQZgEsVGBiorKwsAgzOijkwsKyHH35YKSkpiouLU7t27XgyAahBBg4cqPfee4/3OOGsCDCwrLfffltLlixR3759Pd0KgCp2zTXXaOrUqdqwYYNiYmLk7+/vtv1vf/ubhzpDdcEtJFhWeHi41q1bp2uvvdbTrQCoYue6dWSz2fTtt99ewW5QHRFgYFkzZszQt99+qxdeeIHbRwBQyxBgYFm333671q5dq+DgYLVt29Z8F0yFpUuXeqgzAMDlxhwYWFZQUJBuv/12T7cB4DIYNmzYObe/+uqrV6gTVFcEGFjWa6+95ukWAFwmP/30k9v6qVOntGPHDhUUFJg/FYLajQADAKh2li1bdtpYeXm5Ro8erZYtW3qgI1Q3zIGBpXTq1Enp6elq0KCBOnbseM7Ju1988cUV7AzAlbB79251795dhw4d8nQr8DCuwMBSBgwYILvdLumXF10BqF327dun0tJST7eBaoArMACAaicxMdFt3TAMHTp0SKmpqRoyZIheeOEFD3WG6oIAA8srKSk5428hNW3a1EMdAbhUt956q9u6l5eXGjdurNtuu03Dhg2Tjw83EGo7Agws65tvvtHw4cO1ceNGt3HDMGSz2VRWVuahzgAAlxsRFpb1wAMPyMfHR8uXL1eTJk14Gy8A1CJcgYFl+fv7KzMzU61bt/Z0KwCqwPmeLPw1njIEV2BgWdHR0frxxx893QaAKsKThagMrsDAstasWaNJkyZp2rRpat++/Wm/heRwODzUGQDgciPAwLK8vLwk6bRLzkziBYCaj1tIsKy1a9d6ugUAVSg4OFjffPONGjVqpAYNGpxzPszRo0evYGeojggwsKxbbrnF0y0AqEIzZ85UQECAJGnWrFmebQbVHreQYGkFBQXavHnzGV9kN3jwYA91BQC43AgwsKwPPvhA9957r4qKiuRwONwuN9tsNi4xAzXEyZMnVVJS4jbGJH0QYGBZ1157rfr27atp06apXr16nm4HQBU6fvy4Jk6cqCVLlujIkSOnbWeSPrw83QBwsX744Qf97W9/I7wANdCECRO0Zs0azZ07V3a7XfPnz9djjz2m8PBwvf76655uD9UAAQaWFRsbq88//9zTbQC4DD744AO9+OKLio+Pl4+Pj26++WbzvU8LFy70dHuoBngKCZYVFxen8ePHa9euXWd8kd0f//hHD3UG4FIdPXpULVq0kPTLfJeKOW033XSTRo8e7cnWUE0QYGBZDz74oCRp6tSpp23jRXaAtbVo0ULZ2dlq2rSpWrdurSVLluh3v/udPvjgAwUFBXm6PVQD3EKCZZWXl591IbwA1vTtt9+qvLxcDzzwgLZt2yZJ+sc//qE5c+aobt26GjdunMaPH+/hLlEd8BQSAKDa8Pb21qFDhxQSEiJJuuuuu/Tcc8/p5MmTyszMVFRUlDp06ODhLlEdEGBgWWe6dfRrkydPvkKdAKgqXl5eys3NNQNMQECAtm3bZs6HASowBwaWtWzZMrf1U6dOKTs7Wz4+PmrZsiUBBgBqMAIMLGvr1q2njblcLg0dOlS33367BzoCcKlsNttpP+J4rh91RO3FLSTUONu3b1f//v21f/9+T7cCoJK8vLzUp08f2e12Sb+8D+a2226Tv7+/W93SpUs90R6qEa7AoMYpLCxUYWGhp9sAcBGGDBnitn7fffd5qBNUd1yBgWU999xzbuuGYejQoUN644031K1bN7311lse6gwAcLkRYGBZzZs3d1v38vJS48aNddtttykpKUkBAQEe6gwAcLlxCwmWlZ2dfdrYyZMnNWfOHF1zzTXKzc31QFcAgCuBN/HCcoqLi5WUlKTOnTvrxhtv1HvvvSdJeu2119SyZUvNnj1b48aN82yTAIDLiltIsJyJEyfqpZdeUs+ePbVx40YdPnxYDzzwgD777DP985//1J/+9Cd5e3t7uk0AwGXELSRYzjvvvKPXX39df/zjH7Vjxw516NBBpaWl2rZtG++LAIBagiswsBxfX19lZ2frqquukiT5+flp8+bNat++vYc7AwBcKcyBgeWUlZXJ19fXXPfx8VH9+vU92BEA4ErjFhIsxzAMDR061HxT58mTJzVq1Cje1AkAtQgBBpbDmzoBAMyBAQAAlsMcGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGABVbsqUKbr++uvPWdO9e3eNHTv2ivRzsZo1a6ZZs2aZ6zabzfztLQCeRYABaoGhQ4fKZrNp1KhRp21LSEiQzWbT0KFDr2hPS5cu1eOPP35ZP2P//v2y2WynLRf66P2WLVs0cuTIy9ojgIvDe2CAWiIiIkJvv/22Zs6cKT8/P0m/vARw0aJFatq06RXvJzg4+Ip91urVq9W2bVtzveL8z6dx48ZV2kdZWZlsNpu8vPj/jsCl4lsE1BKdOnVSRESE2xuKly5dqqZNm6pjx45utStXrtRNN92koKAgNWzYUP369dO+ffvcar7//nvdfffdCg4Olr+/vzp37qxNmza51bzxxhtq1qyZAgMDNWjQIB07dszc9ttbSM2aNdO0adM0bNgwBQQEqGnTpnr55ZfdjnfgwAH9+c9/VlBQkIKDgzVgwADt37//vOfesGFDhYWFmUtgYKD27dunAQMGKDQ0VPXr19cNN9yg1atXu+3321tIv7Zu3TrZbDYVFBSYY1lZWbLZbGZPKSkpCgoK0vvvv6/o6GjZ7Xbl5OSouLhYf//733XVVVfJ399fXbp00bp16857HgD+PwIMUIsMGzZMr732mrn+6quv6oEHHjit7vjx40pMTNTnn3+u9PR0eXl56fbbb1d5ebkkqaioSLfccot++OEHvf/++9q2bZsmTJhgbpekffv26b333tPy5cu1fPlyrV+/Xk899dQ5+5sxY4Y6d+6srVu36q9//atGjx6t3bt3S5JOnTql2NhYBQQE6JNPPtGGDRtUv3599e7dWyUlJZX+uygqKlLfvn2Vnp6urVu3qnfv3urfv79ycnIqfaxz+fnnn/Xvf/9b8+fP186dOxUSEqIxY8YoIyNDb7/9tr788kv96U9/Uu/evbVnz54q/WygRjMA1HhDhgwxBgwYYOTn5xt2u93Yv3+/sX//fqNu3brG4cOHjQEDBhhDhgw56/6HDx82JBnbt283DMMwXnrpJSMgIMA4cuTIGesfffRRo169eobL5TLHxo8fb3Tp0sVcv+WWW4yHH37YXI+MjDTuu+8+c728vNwICQkx5s6daxiGYbzxxhtGq1atjPLycrOmuLjY8PPzM1atWnXGPrKzsw1Jhp+fn+Hv728uX3zxxRnr27Ztazz//PNuPc2cOdNcl2QsW7bMMAzDWLt2rSHJ+Omnn8ztW7duNSQZ2dnZhmEYxmuvvWZIMrKyssya7777zvD29jZ++OEHt8/u0aOHkZSUdMa+AJyOOTBALdK4cWPFxcUpJSVFhmEoLi5OjRo1Oq1uz549mjx5sjZt2qQff/zRvLKSk5Ojdu3aKSsrSx07djznPJZmzZopICDAXG/SpIny8/PP2V+HDh3MP9tsNoWFhZn7bNu2TXv37nU7pvTLPJ7f3t76rcWLF6tNmzbmekREhIqKijRlyhSlpqbq0KFDKi0t1YkTJ6r8Coyvr6/beW3fvl1lZWW69tpr3eqKi4vVsGHDKv1soCYjwAC1zLBhwzRmzBhJ0pw5c85Y079/f0VGRuo///mPwsPDVV5ernbt2pm3ai5kEmydOnXc1m02m9stpsruU1RUpJiYGC1cuPC0/c432TYiIkJRUVFuYw8//LDS0tL0zDPPKCoqSn5+frrzzjsv+HZUxURc41c/J3fq1KnT6vz8/GSz2cz1oqIieXt7KzMzU97e3m619evXv6DPBkCAAWqdijkjNptNsbGxp20/cuSIdu/erf/85z+6+eabJUmffvqpW02HDh00f/58HT169Io9TdSpUyctXrxYISEhcjgcl3y8DRs2aOjQobr99tsl/RIsLmRCcIWK0HTo0CE1aNBA0i+TeM+nY8eOKisrU35+vvn3C6DymMQL1DLe3t766quvtGvXrtOuAEhSgwYN1LBhQ7388svau3ev1qxZo8TERLeau+++W2FhYRo4cKA2bNigb7/9Vu+++64yMjIuW9/33nuvGjVqpAEDBuiTTz5Rdna21q1bp7/97W/6/vvvK328a665RkuXLlVWVpa2bdume+6557xXiH4tKipKERERmjJlivbs2aPU1FTNmDHjvPtde+21uvfeezV48GAtXbpU2dnZ2rx5s5KTk5Wamlrp8wBqKwIMUAs5HI6zXsXw8vLS22+/rczMTLVr107jxo3T008/7Vbj6+urjz76SCEhIerbt6/at2+vp5566oyBqKrUq1dPH3/8sZo2bao77rhDbdq00fDhw3Xy5MmLuiLz7LPPqkGDBvr973+v/v37KzY2Vp06dbrg/evUqaO33npLX3/9tTp06KB///vfeuKJJy5o39dee02DBw/WI488olatWmngwIHasmWLR97HA1iVzfj1DVwAAAAL4AoMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwnP8HCSqI8zV6+BsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot(kind='bar', title='Count (target)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Reducing Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a PCA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_scaled_df = pd.DataFrame(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "      <td>7.918000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.130694e-16</td>\n",
       "      <td>4.486883e-18</td>\n",
       "      <td>2.871605e-17</td>\n",
       "      <td>-1.408881e-16</td>\n",
       "      <td>-1.094799e-16</td>\n",
       "      <td>6.461111e-17</td>\n",
       "      <td>4.935571e-17</td>\n",
       "      <td>7.179012e-18</td>\n",
       "      <td>-6.820062e-17</td>\n",
       "      <td>1.076852e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.920386e-16</td>\n",
       "      <td>-1.794753e-18</td>\n",
       "      <td>5.563734e-17</td>\n",
       "      <td>1.884491e-16</td>\n",
       "      <td>5.025309e-17</td>\n",
       "      <td>9.781404e-17</td>\n",
       "      <td>4.845833e-17</td>\n",
       "      <td>-7.179012e-17</td>\n",
       "      <td>1.722963e-16</td>\n",
       "      <td>2.388816e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "      <td>1.000063e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.475193e+00</td>\n",
       "      <td>-3.829305e+00</td>\n",
       "      <td>-3.555387e+00</td>\n",
       "      <td>-3.736258e+00</td>\n",
       "      <td>-3.645593e+00</td>\n",
       "      <td>-3.736258e+00</td>\n",
       "      <td>-4.193910e+00</td>\n",
       "      <td>-5.994339e+00</td>\n",
       "      <td>-3.727846e+00</td>\n",
       "      <td>-4.095714e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.948575e+00</td>\n",
       "      <td>-3.948575e+00</td>\n",
       "      <td>-3.527972e+00</td>\n",
       "      <td>-3.766304e+00</td>\n",
       "      <td>-3.948575e+00</td>\n",
       "      <td>-3.527972e+00</td>\n",
       "      <td>-3.519883e+00</td>\n",
       "      <td>-3.632377e+00</td>\n",
       "      <td>-3.789436e+00</td>\n",
       "      <td>-3.149074e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.464624e-01</td>\n",
       "      <td>-6.532652e-01</td>\n",
       "      <td>-6.690047e-01</td>\n",
       "      <td>-6.466453e-01</td>\n",
       "      <td>-6.470952e-01</td>\n",
       "      <td>-6.466453e-01</td>\n",
       "      <td>-6.571715e-01</td>\n",
       "      <td>-4.414820e-01</td>\n",
       "      <td>-6.525892e-01</td>\n",
       "      <td>-6.778362e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.540125e-01</td>\n",
       "      <td>-6.540125e-01</td>\n",
       "      <td>-6.538802e-01</td>\n",
       "      <td>-6.485973e-01</td>\n",
       "      <td>-6.540125e-01</td>\n",
       "      <td>-6.538802e-01</td>\n",
       "      <td>-6.161674e-01</td>\n",
       "      <td>-6.875806e-01</td>\n",
       "      <td>-7.345368e-01</td>\n",
       "      <td>-6.609682e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.198781e-02</td>\n",
       "      <td>-2.081976e-02</td>\n",
       "      <td>-1.384351e-02</td>\n",
       "      <td>1.717327e-03</td>\n",
       "      <td>5.053805e-03</td>\n",
       "      <td>1.717327e-03</td>\n",
       "      <td>-8.281096e-03</td>\n",
       "      <td>-2.383504e-01</td>\n",
       "      <td>-7.060073e-03</td>\n",
       "      <td>1.093127e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.226529e-03</td>\n",
       "      <td>-4.226529e-03</td>\n",
       "      <td>4.226529e-03</td>\n",
       "      <td>-2.216792e-02</td>\n",
       "      <td>-4.226529e-03</td>\n",
       "      <td>4.226529e-03</td>\n",
       "      <td>-2.054949e-01</td>\n",
       "      <td>1.493259e-02</td>\n",
       "      <td>2.128447e-01</td>\n",
       "      <td>-1.394482e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.499382e-01</td>\n",
       "      <td>6.672515e-01</td>\n",
       "      <td>6.584395e-01</td>\n",
       "      <td>6.570958e-01</td>\n",
       "      <td>6.472383e-01</td>\n",
       "      <td>6.570958e-01</td>\n",
       "      <td>6.553271e-01</td>\n",
       "      <td>2.894332e-01</td>\n",
       "      <td>6.398313e-01</td>\n",
       "      <td>6.483094e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.538802e-01</td>\n",
       "      <td>6.538802e-01</td>\n",
       "      <td>6.540125e-01</td>\n",
       "      <td>6.492926e-01</td>\n",
       "      <td>6.538802e-01</td>\n",
       "      <td>6.540125e-01</td>\n",
       "      <td>4.589944e-01</td>\n",
       "      <td>6.906328e-01</td>\n",
       "      <td>7.168882e-01</td>\n",
       "      <td>5.074106e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.790858e+00</td>\n",
       "      <td>3.899594e+00</td>\n",
       "      <td>3.878507e+00</td>\n",
       "      <td>3.978828e+00</td>\n",
       "      <td>4.151586e+00</td>\n",
       "      <td>3.978828e+00</td>\n",
       "      <td>3.867689e+00</td>\n",
       "      <td>1.126998e+01</td>\n",
       "      <td>4.065088e+00</td>\n",
       "      <td>4.238175e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.527972e+00</td>\n",
       "      <td>3.527972e+00</td>\n",
       "      <td>3.948575e+00</td>\n",
       "      <td>4.189567e+00</td>\n",
       "      <td>3.527972e+00</td>\n",
       "      <td>3.948575e+00</td>\n",
       "      <td>6.627203e+00</td>\n",
       "      <td>4.259685e+00</td>\n",
       "      <td>2.729922e+00</td>\n",
       "      <td>6.121168e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0             1             2             3             4    \\\n",
       "count  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03   \n",
       "mean   1.130694e-16  4.486883e-18  2.871605e-17 -1.408881e-16 -1.094799e-16   \n",
       "std    1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00   \n",
       "min   -3.475193e+00 -3.829305e+00 -3.555387e+00 -3.736258e+00 -3.645593e+00   \n",
       "25%   -6.464624e-01 -6.532652e-01 -6.690047e-01 -6.466453e-01 -6.470952e-01   \n",
       "50%   -2.198781e-02 -2.081976e-02 -1.384351e-02  1.717327e-03  5.053805e-03   \n",
       "75%    6.499382e-01  6.672515e-01  6.584395e-01  6.570958e-01  6.472383e-01   \n",
       "max    3.790858e+00  3.899594e+00  3.878507e+00  3.978828e+00  4.151586e+00   \n",
       "\n",
       "                5             6             7             8             9    \\\n",
       "count  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03   \n",
       "mean   6.461111e-17  4.935571e-17  7.179012e-18 -6.820062e-17  1.076852e-16   \n",
       "std    1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00   \n",
       "min   -3.736258e+00 -4.193910e+00 -5.994339e+00 -3.727846e+00 -4.095714e+00   \n",
       "25%   -6.466453e-01 -6.571715e-01 -4.414820e-01 -6.525892e-01 -6.778362e-01   \n",
       "50%    1.717327e-03 -8.281096e-03 -2.383504e-01 -7.060073e-03  1.093127e-02   \n",
       "75%    6.570958e-01  6.553271e-01  2.894332e-01  6.398313e-01  6.483094e-01   \n",
       "max    3.978828e+00  3.867689e+00  1.126998e+01  4.065088e+00  4.238175e+00   \n",
       "\n",
       "       ...           92            93            94            95   \\\n",
       "count  ...  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03   \n",
       "mean   ...  1.920386e-16 -1.794753e-18  5.563734e-17  1.884491e-16   \n",
       "std    ...  1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00   \n",
       "min    ... -3.948575e+00 -3.948575e+00 -3.527972e+00 -3.766304e+00   \n",
       "25%    ... -6.540125e-01 -6.540125e-01 -6.538802e-01 -6.485973e-01   \n",
       "50%    ... -4.226529e-03 -4.226529e-03  4.226529e-03 -2.216792e-02   \n",
       "75%    ...  6.538802e-01  6.538802e-01  6.540125e-01  6.492926e-01   \n",
       "max    ...  3.527972e+00  3.527972e+00  3.948575e+00  4.189567e+00   \n",
       "\n",
       "                96            97            98            99            100  \\\n",
       "count  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03  7.918000e+03   \n",
       "mean   5.025309e-17  9.781404e-17  4.845833e-17 -7.179012e-17  1.722963e-16   \n",
       "std    1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00  1.000063e+00   \n",
       "min   -3.948575e+00 -3.527972e+00 -3.519883e+00 -3.632377e+00 -3.789436e+00   \n",
       "25%   -6.540125e-01 -6.538802e-01 -6.161674e-01 -6.875806e-01 -7.345368e-01   \n",
       "50%   -4.226529e-03  4.226529e-03 -2.054949e-01  1.493259e-02  2.128447e-01   \n",
       "75%    6.538802e-01  6.540125e-01  4.589944e-01  6.906328e-01  7.168882e-01   \n",
       "max    3.527972e+00  3.948575e+00  6.627203e+00  4.259685e+00  2.729922e+00   \n",
       "\n",
       "                101  \n",
       "count  7.918000e+03  \n",
       "mean   2.388816e-15  \n",
       "std    1.000063e+00  \n",
       "min   -3.149074e+00  \n",
       "25%   -6.609682e-01  \n",
       "50%   -1.394482e-01  \n",
       "75%    5.074106e-01  \n",
       "max    6.121168e+00  \n",
       "\n",
       "[8 rows x 102 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_scaled_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x3000 with 110 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_scaled_df.hist(figsize=(30, 30), bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_pca = {}\n",
    "for component in [*range(2, len(X_train.columns) - 1)]:\n",
    "    pca = PCA(n_components=component)\n",
    "    pca.fit(scaled_data)\n",
    "    fit_pca[component] = pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13857708, 0.10187388, 0.07224793, 0.06127823, 0.04872047])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[5].explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Components using a Scree Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.60755735, -0.46146099,  0.25417277,  0.04628206,  0.60076818,\n",
       "        0.04628206, -0.13907527, -0.08320706, -0.20278523,  0.25039446,\n",
       "        2.50544027, -1.46843135,  0.25039446,  4.54580759,  0.30928566,\n",
       "        0.25039446,  2.50544027, -1.46843135,  0.25039446,  4.54580759,\n",
       "        0.30928566,  0.52725576, -0.56457519, -0.26616693, -0.18314544,\n",
       "       -0.74803427, -0.83818552, -0.74944515, -0.71532088, -0.86932589,\n",
       "       -0.79433775, -0.69173847, -0.84727016, -0.78759887, -0.67410582,\n",
       "       -0.81487775, -0.7670534 ,  0.67569003, -0.91924892, -1.51348988,\n",
       "       -1.75595945,  0.60076818,  1.73118082, -1.65143778, -0.22443118,\n",
       "       -1.47158839,  0.60076818, -0.33243297,  0.06100728,  0.52725576,\n",
       "       -0.91924892, -1.21897029, -1.21897029, -0.13392128, -0.29657103,\n",
       "       -0.1986071 , -0.33720001, -0.51744822, -0.0310227 , -0.15489966,\n",
       "       -0.09319092, -0.34120098,  0.05557692, -0.12746693, -0.09679982,\n",
       "       -2.18980976, -2.18980976, -2.18980976, -2.18980976, -2.18980976,\n",
       "       -2.18980976, -2.18980976, -2.18980976, -2.18980976, -2.18980976,\n",
       "       -2.18980976, -2.18980976, -2.18980976,  0.44649088,  0.42021961,\n",
       "        0.13853035,  0.13853035,  0.13853035,  0.13853035,  0.13853035,\n",
       "        0.13853035,  0.13853035, -2.22698298,  0.42021961, -1.28498656,\n",
       "       -0.81300456,  1.80486279,  1.56085249,  1.56085249, -1.56085249,\n",
       "       -0.15489966,  1.56085249, -1.56085249,  0.37863721, -1.46843135,\n",
       "       -2.18980976, -0.79433775])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[5].transform(scaled_data)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-7.57340169, -3.74405531,  1.41302935, -1.81733639,  3.04716016])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[5].transform(scaled_data)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fit_pca[5].transform(scaled_data)[0], fit_pca[5].transform(scaled_data)[1], 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fit_pca[100].explained_variance_ratio_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Explained Variance')\n",
    "plt.plot(fit_pca[100].explained_variance_ratio_.cumsum())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13857708, 0.24045096, 0.31269889, 0.37397711, 0.42269759,\n",
       "       0.46250901, 0.49338716, 0.52346789, 0.55194668, 0.57710583,\n",
       "       0.60072411, 0.62341834, 0.64396137, 0.66383667, 0.68352621,\n",
       "       0.70257311, 0.72128001, 0.73821852, 0.75144862, 0.7629671 ,\n",
       "       0.77414748, 0.78520518, 0.79584362, 0.80634498, 0.8167538 ,\n",
       "       0.82709278, 0.83734067, 0.84747346, 0.85732757, 0.86710263,\n",
       "       0.87679328, 0.88629523, 0.89552189, 0.90463863, 0.91368026,\n",
       "       0.92255939, 0.93133808, 0.93992703, 0.94826732, 0.9564688 ,\n",
       "       0.96436847, 0.9704765 , 0.97524227, 0.97916972, 0.98280313,\n",
       "       0.98620303, 0.98915696, 0.99177165, 0.99434618, 0.99663597,\n",
       "       0.99810642, 0.99909375, 0.99999965, 0.99999982, 0.99999993,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[100].explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.7629671027006212)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[20].explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8671026293604951)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[30].explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9564687956555192)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[40].explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9966359706366549)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[50].explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9828031275198726)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pca[45].explained_variance_ratio_.sum()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "3.13.0",
   "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}