Replacing nans with mode and recomputing corr

EDA.ipynb

@@ -3858,6 +3858,40 @@
     "print(\"------SIT_TTO 3: ALTA-----\")\n",
     "find_diff(3, corr_mats[2][0], corr_mats[2][1])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### PCA (in progress)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ==============================================================================\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standarize data to have mean 0 and sd 1, since variables have different dispersions -> PCA\n",
+    "pca_pipe = make_pipeline(StandardScaler(), PCA())\n",
+    "pca_pipe.fit(bd)\n",
+    "\n",
+    "# Se extrae el modelo entrenado del pipeline\n",
+    "modelo_pca = pca_pipe.named_steps['pca']"
+   ]
   }
  ],
  "metadata": {