From ef601f2dd8d9ea23fab45c8566afcaf93d098bfd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Diego=20Crist=C3=B3bal=20Herreros?=
 <diego.cristobal@teamcmp.com>
Date: Sat, 4 Feb 2023 23:32:52 +0100
Subject: [PATCH] Feat: add Makefile and clean lebron data to predict points

---
 .gitignore    |    2 +
 Makefile      |    9 +
 entrega.ipynb | 1221 ++++++++++++++++++++++++++++++++++++++++++++-----
 3 files changed, 1109 insertions(+), 123 deletions(-)
 create mode 100644 Makefile

diff --git a/.gitignore b/.gitignore
index defdce7..beae844 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,8 @@
 # Fichero con las variables de kaggle
 .env
 
+# Variables Makefile
+Makefile.local
 # Dataframes en local
 dataframes/*
 
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..e483e8c
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,9 @@
+include Makefile.local
+
+deps:
+	@echo "Installing dependencies..."
+	@pip install -r requirements.txt
+
+dev: deps
+	@echo "Starting development server..."
+	@jupyter notebook entrega.ipynb
\ No newline at end of file
diff --git a/entrega.ipynb b/entrega.ipynb
index d465678..ed6eb93 100644
--- a/entrega.ipynb
+++ b/entrega.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,9 +43,10 @@
     "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
     "from sklearn.gaussian_process import GaussianProcessClassifier\n",
     "from sklearn.gaussian_process.kernels import RBF\n",
-    "from sklearn.metrics import RocCurveDisplay\n",
+    "from sklearn.metrics import RocCurveDisplay, mean_absolute_error, mean_absolute_percentage_error, r2_score\n",
     "import warnings\n",
     "import random\n",
+    "import kaggle\n",
     "warnings.filterwarnings('ignore')"
    ]
   },
@@ -58,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 313,
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 314,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 315,
    "metadata": {},
    "outputs": [
     {
@@ -949,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 316,
    "metadata": {},
    "outputs": [
     {
@@ -1065,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 317,
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 318,
    "metadata": {},
    "outputs": [
     {
@@ -1686,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 319,
    "metadata": {},
    "outputs": [
     {
@@ -2105,7 +2106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 320,
    "metadata": {},
    "outputs": [
     {
@@ -2180,7 +2181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 321,
    "metadata": {},
    "outputs": [
     {
@@ -2474,7 +2475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 322,
    "metadata": {
     "scrolled": true
    },
@@ -2660,7 +2661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 323,
    "metadata": {},
    "outputs": [
     {
@@ -2669,7 +2670,7 @@
        "<AxesSubplot: >"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 323,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2690,7 +2691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 324,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2699,7 +2700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 325,
    "metadata": {
     "scrolled": true
    },
@@ -2731,7 +2732,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 325,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2749,7 +2750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 326,
    "metadata": {
     "scrolled": false
    },
@@ -2795,7 +2796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 327,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2818,7 +2819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 328,
    "metadata": {
     "scrolled": false
    },
@@ -3445,7 +3446,7 @@
        "[20 rows x 21 columns]"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 328,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3463,7 +3464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 329,
    "metadata": {
     "scrolled": true
    },
@@ -3474,7 +3475,7 @@
        "<AxesSubplot: title={'center': 'Matriz de correlación'}>"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 329,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3504,7 +3505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 330,
    "metadata": {
     "scrolled": true
    },
@@ -3537,7 +3538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 331,
    "metadata": {
     "scrolled": true
    },
@@ -3545,10 +3546,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f74d577ee30>"
+       "<matplotlib.legend.Legend at 0x7f00c8903ca0>"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 331,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3588,7 +3589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 332,
    "metadata": {
     "scrolled": false
    },
@@ -3669,7 +3670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 333,
    "metadata": {
     "scrolled": true
    },
@@ -3703,7 +3704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 334,
    "metadata": {
     "scrolled": true
    },
@@ -3737,7 +3738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 335,
    "metadata": {},
    "outputs": [
     {
@@ -3769,7 +3770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 336,
    "metadata": {},
    "outputs": [
     {
@@ -3801,7 +3802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 337,
    "metadata": {
     "scrolled": true
    },
@@ -3820,7 +3821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 338,
    "metadata": {
     "scrolled": true
    },
@@ -4020,7 +4021,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 338,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4039,7 +4040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 339,
    "metadata": {},
    "outputs": [
     {
@@ -4112,7 +4113,7 @@
        "4    2022-12-21  22200468    2022"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 339,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4131,7 +4132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 340,
    "metadata": {},
    "outputs": [
     {
@@ -4322,7 +4323,7 @@
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 340,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4341,7 +4342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 341,
    "metadata": {},
    "outputs": [
     {
@@ -4574,7 +4575,7 @@
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 341,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4593,7 +4594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 342,
    "metadata": {
     "scrolled": false
    },
@@ -4604,13 +4605,13 @@
        "<AxesSubplot: xlabel='PTS', ylabel='SEASON'>"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 342,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4634,7 +4635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 343,
    "metadata": {},
    "outputs": [
     {
@@ -4643,7 +4644,7 @@
        "<AxesSubplot: xlabel='FG_PCT', ylabel='SEASON'>"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 343,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -4672,7 +4673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 344,
    "metadata": {
     "scrolled": true
    },
@@ -4683,7 +4684,7 @@
        "<AxesSubplot: xlabel='AST', ylabel='SEASON'>"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 344,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -4711,7 +4712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 345,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4725,7 +4726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 346,
    "metadata": {
     "scrolled": true
    },
@@ -4932,7 +4933,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 346,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4950,7 +4951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 347,
    "metadata": {},
    "outputs": [
     {
@@ -4980,7 +4981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 348,
    "metadata": {
     "scrolled": true
    },
@@ -5178,7 +5179,7 @@
        "4      47.0               0  "
       ]
      },
-     "execution_count": 180,
+     "execution_count": 348,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5197,7 +5198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5213,7 +5214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 350,
    "metadata": {},
    "outputs": [
     {
@@ -5222,7 +5223,7 @@
        "(542, 20)"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 350,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5240,7 +5241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 351,
    "metadata": {
     "scrolled": false
    },
@@ -5267,7 +5268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 352,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5284,7 +5285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 353,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5301,7 +5302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 354,
    "metadata": {
     "scrolled": true
    },
@@ -5457,7 +5458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 355,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5476,7 +5477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 356,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5491,7 +5492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 357,
    "metadata": {
     "scrolled": true
    },
@@ -5622,7 +5623,7 @@
        "234       0.592593       0.552632  "
       ]
      },
-     "execution_count": 189,
+     "execution_count": 357,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5640,7 +5641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 358,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5661,7 +5662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 359,
    "metadata": {},
    "outputs": [
     {
@@ -5708,7 +5709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 360,
    "metadata": {},
    "outputs": [
     {
@@ -5739,7 +5740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 361,
    "metadata": {},
    "outputs": [
     {
@@ -5748,7 +5749,7 @@
        "0.7361963190184049"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 361,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5768,14 +5769,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 362,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.7914110429447853\n"
+      "Accuracy: 0.7975460122699386\n"
      ]
     }
    ],
@@ -5799,7 +5800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 363,
    "metadata": {
     "scrolled": true
    },
@@ -5810,13 +5811,13 @@
        "<AxesSubplot: >"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 363,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -5839,7 +5840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 364,
    "metadata": {},
    "outputs": [
     {
@@ -5848,12 +5849,12 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.71      0.74      0.73        61\n",
-      "           1       0.84      0.82      0.83       102\n",
+      "           0       0.73      0.74      0.73        61\n",
+      "           1       0.84      0.83      0.84       102\n",
       "\n",
-      "    accuracy                           0.79       163\n",
-      "   macro avg       0.78      0.78      0.78       163\n",
-      "weighted avg       0.79      0.79      0.79       163\n",
+      "    accuracy                           0.80       163\n",
+      "   macro avg       0.78      0.79      0.78       163\n",
+      "weighted avg       0.80      0.80      0.80       163\n",
       "\n"
      ]
     }
@@ -5871,7 +5872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 365,
    "metadata": {
     "scrolled": true
    },
@@ -5906,7 +5907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 366,
    "metadata": {},
    "outputs": [
     {
@@ -5938,7 +5939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 367,
    "metadata": {
     "scrolled": false
    },
@@ -5970,7 +5971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 368,
    "metadata": {
     "scrolled": true
    },
@@ -6160,7 +6161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 369,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6176,7 +6177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 370,
    "metadata": {},
    "outputs": [
     {
@@ -6200,7 +6201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 371,
    "metadata": {},
    "outputs": [
     {
@@ -6232,7 +6233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 372,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6248,7 +6249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 373,
    "metadata": {},
    "outputs": [
     {
@@ -6272,16 +6273,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 374,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f751aad4940>"
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f00b10f4c40>"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 374,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6303,7 +6304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 375,
    "metadata": {
     "scrolled": true
    },
@@ -6337,7 +6338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 376,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6360,7 +6361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 377,
    "metadata": {
     "scrolled": false
    },
@@ -6369,19 +6370,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dummy          | score = 0.479 | time = 0.000s/0.000s\n",
-      "KNN(3)         | score = 0.742 | time = 0.001s/0.005s\n",
-      "RBF SVM        | score = 0.755 | time = 0.004s/0.002s\n",
-      "Decision Tree  | score = 0.724 | time = 0.002s/0.001s\n",
-      "Random Forest  | score = 0.736 | time = 0.012s/0.002s\n",
-      "Neural Net     | score = 0.810 | time = 0.183s/0.001s\n",
+      "Dummy          | score = 0.429 | time = 0.001s/0.001s\n",
+      "KNN(3)         | score = 0.742 | time = 0.001s/0.004s\n",
+      "RBF SVM        | score = 0.755 | time = 0.005s/0.002s\n",
+      "Decision Tree  | score = 0.718 | time = 0.002s/0.001s\n",
+      "Random Forest  | score = 0.761 | time = 0.015s/0.002s\n",
+      "Neural Net     | score = 0.804 | time = 0.203s/0.001s\n",
       "AdaBoost       | score = 0.779 | time = 0.072s/0.007s\n",
       "Naive Bayes    | score = 0.804 | time = 0.001s/0.001s\n",
       "QDA            | score = 0.816 | time = 0.001s/0.001s\n",
       "Linear SVC     | score = 0.804 | time = 0.003s/0.001s\n",
-      "Linear SVM     | score = 0.804 | time = 0.003s/0.002s\n",
-      "Gaussian Proc  | score = 0.798 | time = 1.518s/0.004s\n",
-      "LogisticRegr   | score = 0.816 | time = 0.004s/0.006s\n"
+      "Linear SVM     | score = 0.804 | time = 0.004s/0.002s\n",
+      "Gaussian Proc  | score = 0.798 | time = 0.956s/0.005s\n",
+      "LogisticRegr   | score = 0.816 | time = 0.004s/0.001s\n"
      ]
     }
    ],
@@ -6410,7 +6411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 378,
    "metadata": {
     "scrolled": true
    },
@@ -6451,16 +6452,16 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.951182</td>\n",
-       "      <td>0.999025</td>\n",
-       "      <td>0.353708</td>\n",
-       "      <td>0.74379</td>\n",
-       "      <td>0.550416</td>\n",
-       "      <td>0.641651</td>\n",
-       "      <td>0.138385</td>\n",
-       "      <td>0.791221</td>\n",
-       "      <td>0.535437</td>\n",
-       "      <td>0.404599</td>\n",
+       "      <td>0.484673</td>\n",
+       "      <td>0.426222</td>\n",
+       "      <td>0.366168</td>\n",
+       "      <td>0.619539</td>\n",
+       "      <td>0.806304</td>\n",
+       "      <td>0.69534</td>\n",
+       "      <td>0.041175</td>\n",
+       "      <td>0.752383</td>\n",
+       "      <td>0.328081</td>\n",
+       "      <td>0.438076</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6468,22 +6469,22 @@
       ],
       "text/plain": [
        "   FG_PCT_home_norm  FT_PCT_home_norm  FG3_PCT_home_norm  AST_home_norm  \\\n",
-       "0          0.951182          0.999025           0.353708        0.74379   \n",
+       "0          0.484673          0.426222           0.366168       0.619539   \n",
        "\n",
        "   REB_home_norm  FG_PCT_away_norm  FT_PCT_away_norm  FG3_PCT_away_norm  \\\n",
-       "0       0.550416          0.641651          0.138385           0.791221   \n",
+       "0       0.806304           0.69534          0.041175           0.752383   \n",
        "\n",
        "   AST_away_norm  REB_away_norm  \n",
-       "0       0.535437       0.404599  "
+       "0       0.328081       0.438076  "
       ]
      },
-     "execution_count": 210,
+     "execution_count": 378,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "prediccion= pd.DataFrame()                                                   \n",
+    "prediccion = pd.DataFrame()                                                   \n",
     "\n",
     "for i in variables_elegidas_norm: \n",
     "    prediccion[i]= [random.uniform(0,1)] \n",
@@ -6500,7 +6501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 379,
    "metadata": {},
    "outputs": [
     {
@@ -6517,6 +6518,980 @@
     "else:\n",
     "    print(\"El resultado ha sido 1. Gana el equipo visitante\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos llegar a la conclusión de que despues de varios modelos entrenados, la precisión más alta ha sido el modelo de regresión logistica."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahora vamos a predecir los puntos de Lebron James en un partido con un modelo de regresión"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>GAME_ID</th>\n",
+       "      <th>TEAM_ABBREVIATION</th>\n",
+       "      <th>MIN</th>\n",
+       "      <th>FGM</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>FG_PCT</th>\n",
+       "      <th>FG3M</th>\n",
+       "      <th>FG3A</th>\n",
+       "      <th>FG3_PCT</th>\n",
+       "      <th>FTM</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DREB</th>\n",
+       "      <th>REB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TO</th>\n",
+       "      <th>PF</th>\n",
+       "      <th>PTS</th>\n",
+       "      <th>GAME_DATE_EST</th>\n",
+       "      <th>SEASON</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22200475</td>\n",
+       "      <td>LAL</td>\n",
+       "      <td>34:00</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2022-12-21</td>\n",
+       "      <td>2022.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22200451</td>\n",
+       "      <td>LAL</td>\n",
+       "      <td>36:33</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.542</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2022-12-18</td>\n",
+       "      <td>2022.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22200437</td>\n",
+       "      <td>LAL</td>\n",
+       "      <td>35:56</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.619</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2022-12-16</td>\n",
+       "      <td>2022.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>22200413</td>\n",
+       "      <td>LAL</td>\n",
+       "      <td>42:47</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.273</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2022-12-13</td>\n",
+       "      <td>2022.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22200396</td>\n",
+       "      <td>LAL</td>\n",
+       "      <td>36:52</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.583</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2022-12-11</td>\n",
+       "      <td>2022.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    GAME_ID TEAM_ABBREVIATION    MIN   FGM   FGA  FG_PCT  FG3M  FG3A  FG3_PCT  \\\n",
+       "0  22200475               LAL  34:00  11.0  21.0   0.524   0.0   4.0    0.000   \n",
+       "1  22200451               LAL  36:33  13.0  24.0   0.542   1.0   4.0    0.250   \n",
+       "2  22200437               LAL  35:56  13.0  21.0   0.619   1.0   4.0    0.250   \n",
+       "3  22200413               LAL  42:47  14.0  25.0   0.560   3.0  11.0    0.273   \n",
+       "4  22200396               LAL  36:52  14.0  24.0   0.583   2.0   6.0    0.333   \n",
+       "\n",
+       "   FTM  ...  DREB  REB   AST  STL  BLK   TO   PF   PTS  GAME_DATE_EST  SEASON  \n",
+       "0  9.0  ...   6.0  6.0  11.0  0.0  2.0  2.0  0.0  31.0     2022-12-21  2022.0  \n",
+       "1  6.0  ...   6.0  7.0   9.0  0.0  1.0  0.0  0.0  33.0     2022-12-18  2022.0  \n",
+       "2  3.0  ...   8.0  9.0   4.0  2.0  0.0  2.0  2.0  30.0     2022-12-16  2022.0  \n",
+       "3  2.0  ...   9.0  9.0   9.0  2.0  1.0  4.0  4.0  33.0     2022-12-13  2022.0  \n",
+       "4  5.0  ...   4.0  5.0   5.0  1.0  0.0  1.0  1.0  35.0     2022-12-11  2022.0  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 380,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_totales_lebron.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Borramos las columnas que no son relevantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "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>FGM</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>FG_PCT</th>\n",
+       "      <th>FG3M</th>\n",
+       "      <th>FG3A</th>\n",
+       "      <th>FG3_PCT</th>\n",
+       "      <th>FTM</th>\n",
+       "      <th>FTA</th>\n",
+       "      <th>FT_PCT</th>\n",
+       "      <th>OREB</th>\n",
+       "      <th>DREB</th>\n",
+       "      <th>REB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TO</th>\n",
+       "      <th>PF</th>\n",
+       "      <th>PTS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.542</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.619</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.273</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.583</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    FGM   FGA  FG_PCT  FG3M  FG3A  FG3_PCT  FTM   FTA  FT_PCT  OREB  DREB  \\\n",
+       "0  11.0  21.0   0.524   0.0   4.0    0.000  9.0  10.0    0.90   0.0   6.0   \n",
+       "1  13.0  24.0   0.542   1.0   4.0    0.250  6.0   8.0    0.75   1.0   6.0   \n",
+       "2  13.0  21.0   0.619   1.0   4.0    0.250  3.0   4.0    0.75   1.0   8.0   \n",
+       "3  14.0  25.0   0.560   3.0  11.0    0.273  2.0   4.0    0.50   0.0   9.0   \n",
+       "4  14.0  24.0   0.583   2.0   6.0    0.333  5.0   5.0    1.00   1.0   4.0   \n",
+       "\n",
+       "   REB   AST  STL  BLK   TO   PF   PTS  \n",
+       "0  6.0  11.0  0.0  2.0  2.0  0.0  31.0  \n",
+       "1  7.0   9.0  0.0  1.0  0.0  0.0  33.0  \n",
+       "2  9.0   4.0  2.0  0.0  2.0  2.0  30.0  \n",
+       "3  9.0   9.0  2.0  1.0  4.0  4.0  33.0  \n",
+       "4  5.0   5.0  1.0  0.0  1.0  1.0  35.0  "
+      ]
+     },
+     "execution_count": 381,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_totales_lebron = stats_totales_lebron.drop(['GAME_ID', 'TEAM_ABBREVIATION', 'MIN', 'GAME_DATE_EST', 'SEASON'], axis=1)\n",
+    "stats_totales_lebron.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vemos las columnas del dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 382,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A', 'FG3_PCT', 'FTM', 'FTA',\n",
+       "       'FT_PCT', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TO', 'PF',\n",
+       "       'PTS'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 382,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_totales_lebron.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 383,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables_escogidas = ['FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A', 'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TO', 'PF']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Añadimos las variables escogidas a la X y en la y el target de los puntos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_lebron = stats_totales_lebron[variables_escogidas]\n",
+    "y_lebron = stats_totales_lebron['PTS']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hacemos el split entre train y test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_lebron, \n",
+    "    y_lebron, \n",
+    "    test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vemos las 8 variables más significativas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1216, 8)\n",
+      "['FGM' 'FGA' 'FG_PCT' 'FG3M' 'FG3A' 'FG3_PCT' 'FTM' 'FTA']\n",
+      "Variable FGM: 4876.1375\n",
+      "Variable FGA: 1354.2733\n",
+      "Variable FG_PCT: 418.2969\n",
+      "Variable FG3M: 400.6381\n",
+      "Variable FG3A: 293.5535\n",
+      "Variable FG3_PCT: 168.7573\n",
+      "Variable FTM: 387.3912\n",
+      "Variable FTA: 373.7594\n",
+      "Variable FT_PCT: 41.2125\n",
+      "Variable OREB: 34.1568\n",
+      "Variable DREB: 45.1225\n",
+      "Variable REB: 69.6783\n",
+      "Variable AST: 0.8797\n",
+      "Variable STL: 11.7895\n",
+      "Variable BLK: 14.7718\n",
+      "Variable TO: 2.2874\n",
+      "Variable PF: 27.1240\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import SelectKBest, chi2, f_regression\n",
+    "\n",
+    "# Seleccionamos las 8 mejores variables\n",
+    "selector = SelectKBest(f_regression, k=8)\n",
+    "\n",
+    "X_select = selector.fit_transform(X_train, y_train)\n",
+    "\n",
+    "print(X_select.shape)\n",
+    "\n",
+    "print(selector.get_feature_names_out())\n",
+    "\n",
+    "for var, value in zip(selector.feature_names_in_, selector.scores_):\n",
+    "    print('Variable %s: %.4f' % (var, value))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalizamos las variables de X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 387,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_lebron = prep.MinMaxScaler()\n",
+    "X = scaler_lebron.fit_transform(X_train[variables_escogidas])\n",
+    "variables_escogidas_norm = ['FGM_norm', 'FGA_norm', 'FG_PCT_norm', 'FG3M_norm', 'FG3A_norm', 'FG3_PCT_norm', 'FTM_norm', 'FTA_norm', 'FT_PCT_norm', 'OREB_norm', 'DREB_norm', 'REB_norm', 'AST_norm', 'STL_norm', 'BLK_norm', 'TO_norm', 'PF_norm']\n",
+    "# Creamos columnas con datos normalizados\n",
+    "X_train[variables_escogidas_norm] = X\n",
+    "# Borramos las variables no normalizadas\n",
+    "X_train = X_train.select_dtypes(include = 'number').drop(variables_escogidas, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 388,
+   "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>FGM_norm</th>\n",
+       "      <th>FGA_norm</th>\n",
+       "      <th>FG_PCT_norm</th>\n",
+       "      <th>FG3M_norm</th>\n",
+       "      <th>FG3A_norm</th>\n",
+       "      <th>FG3_PCT_norm</th>\n",
+       "      <th>FTM_norm</th>\n",
+       "      <th>FTA_norm</th>\n",
+       "      <th>FT_PCT_norm</th>\n",
+       "      <th>OREB_norm</th>\n",
+       "      <th>DREB_norm</th>\n",
+       "      <th>REB_norm</th>\n",
+       "      <th>AST_norm</th>\n",
+       "      <th>STL_norm</th>\n",
+       "      <th>BLK_norm</th>\n",
+       "      <th>TO_norm</th>\n",
+       "      <th>PF_norm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.257143</td>\n",
+       "      <td>0.393617</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.3750</td>\n",
+       "      <td>0.421053</td>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.457143</td>\n",
+       "      <td>0.684397</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.6250</td>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1723</th>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.657143</td>\n",
+       "      <td>0.635934</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.5625</td>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>0.315789</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1359</th>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.904255</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.384615</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.485714</td>\n",
+       "      <td>0.768322</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.3125</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.210526</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FGM_norm  FGA_norm  FG_PCT_norm  FG3M_norm  FG3A_norm  FG3_PCT_norm  \\\n",
+       "287       0.20  0.257143     0.393617      0.000   0.307692         0.000   \n",
+       "712       0.55  0.457143     0.684397      0.125   0.076923         1.000   \n",
+       "1723      0.70  0.657143     0.635934      0.625   0.615385         0.625   \n",
+       "1359      0.65  0.400000     0.904255      0.500   0.384615         0.800   \n",
+       "591       0.65  0.485714     0.768322      0.125   0.230769         0.333   \n",
+       "\n",
+       "      FTM_norm  FTA_norm  FT_PCT_norm  OREB_norm  DREB_norm  REB_norm  \\\n",
+       "287   0.157895  0.166667        0.750   0.285714     0.3750  0.421053   \n",
+       "712   0.368421  0.333333        0.875   0.142857     0.6250  0.578947   \n",
+       "1723  0.263158  0.333333        0.625   0.142857     0.5625  0.526316   \n",
+       "1359  0.157895  0.166667        0.750   0.428571     0.2500  0.368421   \n",
+       "591   0.473684  0.458333        0.818   0.000000     0.3125  0.263158   \n",
+       "\n",
+       "      AST_norm  STL_norm  BLK_norm   TO_norm   PF_norm  \n",
+       "287   0.578947  0.142857       0.6  0.181818  0.333333  \n",
+       "712   0.157895  0.428571       0.2  0.454545  0.166667  \n",
+       "1723  0.315789  0.000000       0.0  0.000000  0.166667  \n",
+       "1359  0.157895  0.000000       0.2  0.363636  0.166667  \n",
+       "591   0.210526  0.285714       0.4  0.090909  0.666667  "
+      ]
+     },
+     "execution_count": 388,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 389,
+   "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>FGM_norm</th>\n",
+       "      <th>FGA_norm</th>\n",
+       "      <th>FG_PCT_norm</th>\n",
+       "      <th>FG3M_norm</th>\n",
+       "      <th>FG3A_norm</th>\n",
+       "      <th>FG3_PCT_norm</th>\n",
+       "      <th>FTM_norm</th>\n",
+       "      <th>FTA_norm</th>\n",
+       "      <th>FT_PCT_norm</th>\n",
+       "      <th>OREB_norm</th>\n",
+       "      <th>DREB_norm</th>\n",
+       "      <th>REB_norm</th>\n",
+       "      <th>AST_norm</th>\n",
+       "      <th>STL_norm</th>\n",
+       "      <th>BLK_norm</th>\n",
+       "      <th>TO_norm</th>\n",
+       "      <th>PF_norm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.457143</td>\n",
+       "      <td>0.621749</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.384615</td>\n",
+       "      <td>0.400</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.778</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1506</th>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.556738</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.210526</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950</th>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.371429</td>\n",
+       "      <td>0.665485</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.208333</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.1875</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1005</th>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.628571</td>\n",
+       "      <td>0.614657</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>0.541667</td>\n",
+       "      <td>0.769</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.3125</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.644208</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.1875</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.210526</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FGM_norm  FGA_norm  FG_PCT_norm  FG3M_norm  FG3A_norm  FG3_PCT_norm  \\\n",
+       "482       0.50  0.457143     0.621749      0.250   0.384615         0.400   \n",
+       "1506      0.40  0.400000     0.556738      0.125   0.230769         0.333   \n",
+       "950       0.45  0.371429     0.665485      0.250   0.307692         0.500   \n",
+       "1005      0.65  0.628571     0.614657      0.125   0.692308         0.111   \n",
+       "705       0.60  0.542857     0.644208      0.000   0.153846         0.000   \n",
+       "\n",
+       "      FTM_norm  FTA_norm  FT_PCT_norm  OREB_norm  DREB_norm  REB_norm  \\\n",
+       "482   0.368421  0.375000        0.778   0.142857     0.2500  0.263158   \n",
+       "1506  0.052632  0.083333        0.500   0.000000     0.2500  0.210526   \n",
+       "950   0.157895  0.208333        0.600   0.000000     0.1875  0.157895   \n",
+       "1005  0.526316  0.541667        0.769   0.000000     0.3125  0.263158   \n",
+       "705   0.105263  0.166667        0.500   0.000000     0.1875  0.157895   \n",
+       "\n",
+       "      AST_norm  STL_norm  BLK_norm   TO_norm   PF_norm  \n",
+       "482   0.263158  0.000000       0.2  0.363636  0.666667  \n",
+       "1506  0.368421  0.428571       0.0  0.272727  0.333333  \n",
+       "950   0.473684  0.285714       0.0  0.363636  0.166667  \n",
+       "1005  0.473684  0.285714       0.2  0.363636  0.500000  \n",
+       "705   0.210526  0.428571       0.2  0.363636  0.500000  "
+      ]
+     },
+     "execution_count": 389,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = scaler_lebron.transform(X_test[variables_escogidas])\n",
+    "X_test[variables_escogidas_norm] = X\n",
+    "X_test = X_test.select_dtypes(include = 'number').drop(variables_escogidas, axis = 1)\n",
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Una vez normalizadas las columnas de X en train y test, probamos un modelo de arbol de decisiones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 390,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Regresión\n",
+      "----------\n",
+      "MAE en test: 0.912\n",
+      "MAPE en test: 0.036\n",
+      "\n",
+      "R Squared Score is: 0.9654147310205599\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "# Los inicialiamos con sus parámetros por defecto (salvo la semilla)\n",
+    "tree_reg = DecisionTreeRegressor(random_state = 42)\n",
+    "\n",
+    "# Los entrenamos...\n",
+    "tree_reg.fit(X_train, y_train)\n",
+    "\n",
+    "# Y los evaluamos en el conjunto de test\n",
+    "print('Regresión')\n",
+    "print('-'*10)\n",
+    "\n",
+    "preds = tree_reg.predict(X_test)\n",
+    "\n",
+    "mae_test = mean_absolute_error(y_test, preds)\n",
+    "mape_test = mean_absolute_percentage_error(y_test, preds)\n",
+    "\n",
+    "print('MAE en test: %.3f' % mae_test)\n",
+    "print('MAPE en test: %.3f' % mape_test)\n",
+    "print('')\n",
+    "print('R Squared Score is:', r2_score(y_test, preds))"
+   ]
   }
  ],
  "metadata": {
@@ -6535,7 +7510,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.13"
   },
   "vscode": {
    "interpreter": {