Principal Components Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1928b3c",
   "metadata": {},
   "source": [
    "## Standard libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f217e22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8dad60",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9d51dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = pd.read_excel(\"C:/Users/bodin/School/STAT 5001/project/gl_project_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aad7425c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce data to include only games from the year 2000+\n",
    "data_reduced = full_data[full_data['date'] > 20000101]     #yyyymmdd\n",
    "\n",
    "# Only include variables specified in write-up\n",
    "data = data_reduced[['away_AB', 'away_H', 'away_2B', 'away_3B',\n",
    "             'away_HR', 'away_SH', 'away_SF', 'away_HBP', \n",
    "             'away_BB', 'away_IBB', 'away_K', 'away_SB', 'away_CS', \n",
    "             'away_GIDP', 'away_CI', 'away_score', \n",
    "             'home_AB', 'home_H', 'home_2B', 'home_3B', \n",
    "             'home_HR', 'home_SH', 'home_SF', 'home_HBP', \n",
    "             'home_BB', 'home_IBB', 'home_K', 'home_SB', 'home_CS', \n",
    "             'home_GIDP', 'home_CI', 'home_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bca2aaf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure no 'na' values\n",
    "data = data.dropna()\n",
    "\n",
    "# Add singles column as specified in our write-up\n",
    "data['away_1B'] = data['away_H'] - data['away_2B'] - data['away_3B'] - data['away_HR']\n",
    "data['home_1B'] = data['home_H'] - data['home_2B'] - data['home_3B'] - data['home_HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8828cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine away and home data columns to split into training & test sets\n",
    "data2 = pd.DataFrame()\n",
    "\n",
    "data2['AB'] = pd.concat([data['away_AB'], data['home_AB']], ignore_index=True)\n",
    "data2['H'] = pd.concat([data['away_H'], data['home_H']], ignore_index=True)\n",
    "data2['1B'] = pd.concat([data['away_1B'], data['home_1B']], ignore_index=True)\n",
    "data2['2B'] = pd.concat([data['away_2B'], data['home_2B']], ignore_index=True)\n",
    "data2['3B'] = pd.concat([data['away_3B'], data['home_3B']], ignore_index=True)\n",
    "data2['HR'] = pd.concat([data['away_HR'], data['home_HR']], ignore_index=True)\n",
    "data2['SH'] = pd.concat([data['away_SH'], data['home_SH']], ignore_index=True)\n",
    "data2['SF'] = pd.concat([data['away_SF'], data['home_SF']], ignore_index=True)\n",
    "data2['HBP'] = pd.concat([data['away_HBP'], data['home_HBP']], ignore_index=True)\n",
    "data2['BB'] = pd.concat([data['away_BB'], data['home_BB']], ignore_index=True)\n",
    "data2['IBB'] = pd.concat([data['away_IBB'], data['home_IBB']], ignore_index=True)\n",
    "data2['K'] = pd.concat([data['away_K'], data['home_K']], ignore_index=True)\n",
    "data2['SB'] = pd.concat([data['away_SB'], data['home_SB']], ignore_index=True)\n",
    "data2['CS'] = pd.concat([data['away_CS'], data['home_CS']], ignore_index=True)\n",
    "data2['GIDP'] = pd.concat([data['away_GIDP'], data['home_GIDP']], ignore_index=True)\n",
    "data2['CI'] = pd.concat([data['away_CI'], data['home_CI']], ignore_index=True)\n",
    "data2['SCORE'] = pd.concat([data['away_score'], data['home_score']], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aac23888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((110350, 17), (55175, 34))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape, data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1985bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AB',\n",
       " 'H',\n",
       " '1B',\n",
       " '2B',\n",
       " '3B',\n",
       " 'HR',\n",
       " 'SH',\n",
       " 'SF',\n",
       " 'HBP',\n",
       " 'BB',\n",
       " 'IBB',\n",
       " 'K',\n",
       " 'SB',\n",
       " 'CS',\n",
       " 'GIDP',\n",
       " 'CI',\n",
       " 'SCORE']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d07196ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data2.drop('SCORE', axis=1)\n",
    "y = data2['SCORE']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b8f48c",
   "metadata": {},
   "source": [
    "## Run Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "782206b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87110a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data\n",
    "np.random.seed(42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80576aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize data\n",
    "X_train_std = (X_train - np.mean(X_train, axis=0)) / np.std(X_train, axis=0)\n",
    "X_test_std = (X_test - np.mean(X_test, axis=0)) / np.std(X_test, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9118fa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Principal component analysis\n",
    "#n_components = min(X_train_std.shape[0], X_train_std.shape[1])\n",
    "n_components = 8 \n",
    "pca = PCA(n_components=n_components)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6cac0adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((88280, 8), (88280, 16))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca.shape, X_train_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dff4ef7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Number of Components: 1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "components_range = np.arange(1, 16)\n",
    "cv_scores = []\n",
    "\n",
    "for n_components in components_range:\n",
    "    pca = PCA(n_components = n_components)\n",
    "    X_train_pca = pca.fit_transform(X_train_std)\n",
    "    \n",
    "    model = LinearRegression()\n",
    "    \n",
    "    scores = cross_val_score(model, X_train_pca, y_train, cv=5)\n",
    "    cv_scores.append(np.mean(scores))\n",
    "\n",
    "\n",
    "# Plot the cross-validation scores\n",
    "plt.plot(components_range, cv_scores, marker='o')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cross-Validation Mean Squared Error')\n",
    "plt.title('Cross-Validation Scores for PCA Components')\n",
    "plt.show()\n",
    "\n",
    "# Choose the number of components with the lowest cross-validation error\n",
    "best_n_components = components_range[np.argmin(cv_scores)]\n",
    "print(f'Best Number of Components: {best_n_components}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85368b79",
   "metadata": {},
   "source": [
    "## Principal components regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afee9c5c",
   "metadata": {},
   "source": [
    "### 1 Component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "56dff418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 4.232229239345806\n",
      "Explained Variance Ratio: [0.18945211]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=1)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_pca, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_pca)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Optionally, you can inspect the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(f'Explained Variance Ratio: {explained_variance_ratio}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d4f098b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.583548219102603"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d1ec45",
   "metadata": {},
   "source": [
    "### 2 Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4f76e965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 4.185261282993794\n",
      "Explained Variance Ratio: [0.1894519  0.08664394]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.588169870710239"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_pca, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_pca)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Optionally, you can inspect the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(f'Explained Variance Ratio: {explained_variance_ratio}')\n",
    "\n",
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49831e15",
   "metadata": {},
   "source": [
    "### 8 Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5f7e7c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 3.1258765324126028\n",
      "Explained Variance Ratio: [0.18945211 0.08690096 0.08104141 0.06757941 0.06501706 0.0639633\n",
      " 0.06230393 0.0616919 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6924134362367786"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=8)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_pca, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_pca)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Optionally, you can inspect the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(f'Explained Variance Ratio: {explained_variance_ratio}')\n",
    "\n",
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5616760",
   "metadata": {},
   "source": [
    "### 15 Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d380253d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2.2750524555124167\n",
      "Explained Variance Ratio: [0.18945211 0.08690096 0.08104141 0.06757941 0.06501706 0.0639633\n",
      " 0.06230393 0.0616919  0.06006351 0.05784864 0.05678729 0.05129051\n",
      " 0.0430651  0.03778218 0.01521271]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7761346105912748"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=15)\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_pca, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_pca)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Optionally, you can inspect the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(f'Explained Variance Ratio: {explained_variance_ratio}')\n",
    "\n",
    "model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cf6a24",
   "metadata": {},
   "source": [
    "## Find best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22c7ea32",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91e148c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a PCA regression model with a linear regression estimator\n",
    "pca_model = Pipeline([\n",
    "    ('pca', PCA()),\n",
    "    ('regression', LinearRegression())\n",
    "])\n",
    "\n",
    "# Set up parameter grid for grid search\n",
    "param_grid = {\n",
    "    'pca__n_components': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
    "                         10, 11, 12, 13, 14, 15, 16\n",
    "                         ],  # Example values for the number of components\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe2cca85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;pca&#x27;, PCA()),\n",
       "                                       (&#x27;regression&#x27;, LinearRegression())]),\n",
       "             param_grid={&#x27;pca__n_components&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                               11, 12, 13, 14, 15, 16]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;pca&#x27;, PCA()),\n",
       "                                       (&#x27;regression&#x27;, LinearRegression())]),\n",
       "             param_grid={&#x27;pca__n_components&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                               11, 12, 13, 14, 15, 16]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pca&#x27;, PCA()), (&#x27;regression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('pca', PCA()),\n",
       "                                       ('regression', LinearRegression())]),\n",
       "             param_grid={'pca__n_components': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                               11, 12, 13, 14, 15, 16]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create GridSearchCV object\n",
    "grid_search = GridSearchCV(pca_model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "# Fit the model to the training data\n",
    "grid_search.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "12173978",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Access the linear regression estimator from the best model\n",
    "best_regressor = grid_search.best_estimator_.named_steps['regression']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d68e8d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'pca__n_components': 15}\n",
      "Mean Squared Error on Test Set: 2.2750524555124167\n"
     ]
    }
   ],
   "source": [
    "# Get the best parameters and best cross-validated score\n",
    "best_params = grid_search.best_params_\n",
    "print(f'Best Parameters: {best_params}')\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = grid_search.predict(X_test_std)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "print(f'Mean Squared Error on Test Set: {mse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e4f73d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted vs actual values\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('PLS Regression: Actual vs Predicted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8b03a29e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Coefficients:\n",
      "Intercept: 4.55642274580879\n",
      "Coefficients: [ 1.3741524  -0.17604682  0.11258342  0.38051764 -0.56835614  0.23240342\n",
      "  0.2462425  -0.65954217  0.33430727 -0.46814712  0.28983752 -0.04653517\n",
      " -0.29509363 -0.33634182 -1.16025251]\n"
     ]
    }
   ],
   "source": [
    "# Display the coefficients\n",
    "coefficients = best_regressor.coef_\n",
    "intercept = best_regressor.intercept_\n",
    "\n",
    "print(\"Linear Regression Coefficients:\")\n",
    "print(\"Intercept:\", intercept)\n",
    "print(\"Coefficients:\", coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cbceeb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}