diff --git a/Wine_prices_regressor.ipynb b/Wine_prices_regressor.ipynb
index 2dd4ff3..c18b171 100644
--- a/Wine_prices_regressor.ipynb
+++ b/Wine_prices_regressor.ipynb
@@ -1,18 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "code",
@@ -33,77 +19,73 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "df = pd.read_csv('wines_SPA.csv')\n",
-        "df = df.drop(columns=['winery','country','region','num_reviews'])"
-      ],
+      "execution_count": 38,
       "metadata": {
         "id": "CgyD63rD1XSO"
       },
-      "execution_count": 38,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv('wines_SPA.csv')\n",
+        "df = df.drop(columns=['winery','country','region','num_reviews'])"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "tn4dUcNoNOYc"
+      },
+      "outputs": [],
       "source": [
         "label_encoder = LabelEncoder()\n",
         "df['wine_encoded'] = label_encoder.fit_transform(df['wine'])\n",
         "label_encoder = LabelEncoder()\n",
         "df['type_encoded'] = label_encoder.fit_transform(df['type'])\n",
         "df = df.drop(columns=['wine','type','type_encoded'])"
-      ],
-      "metadata": {
-        "id": "tn4dUcNoNOYc"
-      },
-      "execution_count": 39,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "YBNiaBln1Z4S"
+      },
+      "outputs": [],
       "source": [
         "df['year'] = df['year'].replace('N.V.', np.NaN)\n",
         "imputer = SimpleImputer(strategy='most_frequent')\n",
         "object_columns = df.select_dtypes(include=['object']).columns\n",
         "df[object_columns] = imputer.fit_transform(df[object_columns])\n",
         "df['year'] = df['year'].astype(np.int64)"
-      ],
-      "metadata": {
-        "id": "YBNiaBln1Z4S"
-      },
-      "execution_count": 40,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "Gg_EbXvA57T6"
+      },
+      "outputs": [],
       "source": [
         "numerical_columns = df.select_dtypes(include=['number']).columns\n",
         "imputer = SimpleImputer(strategy='mean')\n",
         "df[numerical_columns] = imputer.fit_transform(df[numerical_columns])"
-      ],
-      "metadata": {
-        "id": "Gg_EbXvA57T6"
-      },
-      "execution_count": 41,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "null_values = df.isnull().sum()\n",
-        "print(null_values)"
-      ],
+      "execution_count": 42,
       "metadata": {
-        "id": "6s2x1o4-2geb",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "6s2x1o4-2geb",
         "outputId": "507e6283-7434-48fd-f0e4-236b726e7420"
       },
-      "execution_count": 42,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "year            0\n",
             "rating          0\n",
@@ -114,14 +96,15 @@
             "dtype: int64\n"
           ]
         }
+      ],
+      "source": [
+        "null_values = df.isnull().sum()\n",
+        "print(null_values)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df = (df-df.mean())/df.std()\n",
-        "df.head()"
-      ],
+      "execution_count": 43,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -130,19 +113,14 @@
         "id": "ADKoKFDxHkjT",
         "outputId": "91fc7e24-8544-48d3-9cda-cd83bcd40f89"
       },
-      "execution_count": 43,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "       year    rating     price      body   acidity  wine_encoded\n",
-              "0 -0.058537  5.465322  6.217909  1.570221  0.234120      1.257290\n",
-              "1  0.674395  5.465322  1.685354 -0.295595 -4.151132      1.516454\n",
-              "2 -0.644882  4.618073  1.761506  1.570221  0.234120      1.339359\n",
-              "3 -2.110745  4.618073  4.209086  1.570221  0.234120      1.339359\n",
-              "4 -2.550504  4.618073  4.775073  1.570221  0.234120      1.339359"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7500,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -15.156923529167317,\n        \"max\": 1.1141534449354134,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -12.371784227293878,\n          -0.058536787432350995,\n          -10.46616259969626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.4654222033697347,\n        \"max\": 5.4653218929678875,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.618072736348223,\n          1.229076109869587,\n          5.4653218929678875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.36650066476386006,\n        \"max\": 20.344850992307457,\n        \"num_unique_values\": 1292,\n        \"samples\": [\n          0.7176547172640182,\n          0.8639734609276789,\n          -0.20859921808601192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999999,\n        \"min\": -4.027228554610704,\n        \"max\": 1.5702212761775227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.2955953340852197,\n          -4.027228554610704,\n          -2.1614119443479622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -8.536383221145876,\n        \"max\": 0.23412021492594282,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -4.1511315031099665,\n          1.9474429704561965e-15,\n          0.23412021492594282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wine_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.021135657334372,\n        \"max\": 1.6330782930157417,\n        \"num_unique_values\": 847,\n        \"samples\": [\n          -1.4207388026078167,\n          1.196818707926662,\n          0.3847711634044144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-a855a496-d10c-4e13-a595-7944a6b5a3aa\" class=\"colab-df-container\">\n",
@@ -431,28 +409,28 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7500,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -15.156923529167317,\n        \"max\": 1.1141534449354134,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -12.371784227293878,\n          -0.058536787432350995,\n          -10.46616259969626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.4654222033697347,\n        \"max\": 5.4653218929678875,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.618072736348223,\n          1.229076109869587,\n          5.4653218929678875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.36650066476386006,\n        \"max\": 20.344850992307457,\n        \"num_unique_values\": 1292,\n        \"samples\": [\n          0.7176547172640182,\n          0.8639734609276789,\n          -0.20859921808601192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999999,\n        \"min\": -4.027228554610704,\n        \"max\": 1.5702212761775227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.2955953340852197,\n          -4.027228554610704,\n          -2.1614119443479622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -8.536383221145876,\n        \"max\": 0.23412021492594282,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -4.1511315031099665,\n          1.9474429704561965e-15,\n          0.23412021492594282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wine_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.021135657334372,\n        \"max\": 1.6330782930157417,\n        \"num_unique_values\": 847,\n        \"samples\": [\n          -1.4207388026078167,\n          1.196818707926662,\n          0.3847711634044144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "       year    rating     price      body   acidity  wine_encoded\n",
+              "0 -0.058537  5.465322  6.217909  1.570221  0.234120      1.257290\n",
+              "1  0.674395  5.465322  1.685354 -0.295595 -4.151132      1.516454\n",
+              "2 -0.644882  4.618073  1.761506  1.570221  0.234120      1.339359\n",
+              "3 -2.110745  4.618073  4.209086  1.570221  0.234120      1.339359\n",
+              "4 -2.550504  4.618073  4.775073  1.570221  0.234120      1.339359"
+            ]
           },
+          "execution_count": 43,
           "metadata": {},
-          "execution_count": 43
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df = (df-df.mean())/df.std()\n",
+        "df.head()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "Q1 = df.quantile(0.25)\n",
-        "Q3 = df.quantile(0.75)\n",
-        "IQR = Q3 - Q1\n",
-        "lower_bound = Q1 - 1.5 * IQR\n",
-        "upper_bound = Q3 + 1.5 * IQR\n",
-        "df_no_outliers = df[~((df < lower_bound) | (df > upper_bound)).any(axis=1)]\n",
-        "print(\"Number of outliers removed:\", len(df) - len(df_no_outliers))\n"
-      ],
+      "execution_count": 44,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -460,38 +438,40 @@
         "id": "my1dLu6KWhMQ",
         "outputId": "97454231-d071-4c37-8eb6-59edc84cd5b8"
       },
-      "execution_count": 44,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Number of outliers removed: 4473\n"
           ]
         }
+      ],
+      "source": [
+        "Q1 = df.quantile(0.25)\n",
+        "Q3 = df.quantile(0.75)\n",
+        "IQR = Q3 - Q1\n",
+        "lower_bound = Q1 - 1.5 * IQR\n",
+        "upper_bound = Q3 + 1.5 * IQR\n",
+        "df_no_outliers = df[~((df < lower_bound) | (df > upper_bound)).any(axis=1)]\n",
+        "print(\"Number of outliers removed:\", len(df) - len(df_no_outliers))\n"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "y = df.iloc[:, -4].values\n",
-        "X = df.drop(columns=['price'])"
-      ],
+      "execution_count": 45,
       "metadata": {
         "id": "A2uWY65wH0N4"
       },
-      "execution_count": 45,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "y = df.iloc[:, -4].values\n",
+        "X = df.drop(columns=['price'])"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "corr = df.corr()\n",
-        "plt.figure(figsize=(10, 8))\n",
-        "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
-        "plt.title('Correlation Matrix Heatmap')\n",
-        "plt.show()"
-      ],
+      "execution_count": 46,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -500,25 +480,29 @@
         "id": "LSmhssJIVvVa",
         "outputId": "a991624d-8c16-447b-dcb9-6e342f43d2fb"
       },
-      "execution_count": 46,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 1000x800 with 2 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "corr = df.corr()\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
+        "plt.title('Correlation Matrix Heatmap')\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df"
-      ],
+      "execution_count": 47,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -527,27 +511,14 @@
         "id": "Tz_3OfCxDsz-",
         "outputId": "f600d614-fc10-487f-fc74-1e3f8ac26f3d"
       },
-      "execution_count": 47,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "          year    rating     price      body   acidity  wine_encoded\n",
-              "0    -0.058537  5.465322  6.217909  1.570221  0.234120      1.257290\n",
-              "1     0.674395  5.465322  1.685354 -0.295595 -4.151132      1.516454\n",
-              "2    -0.644882  4.618073  1.761506  1.570221  0.234120      1.339359\n",
-              "3    -2.110745  4.618073  4.209086  1.570221  0.234120      1.339359\n",
-              "4    -2.550504  4.618073  4.775073  1.570221  0.234120      1.339359\n",
-              "...        ...       ...       ...       ...       ...           ...\n",
-              "7495  0.381222 -0.465422 -0.266804 -0.295595  0.234120      0.652574\n",
-              "7496  0.674395 -0.465422 -0.288220 -0.295595  0.234120     -0.120599\n",
-              "7497  0.527808 -0.465422 -0.237075 -0.295595  0.234120     -0.716676\n",
-              "7498 -0.351709 -0.465422  0.029292  1.570221  0.234120     -0.543900\n",
-              "7499  0.381222 -0.465422 -0.189322  1.570221  0.234120      0.553228\n",
-              "\n",
-              "[7500 rows x 6 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7500,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -15.156923529167317,\n        \"max\": 1.1141534449354134,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -12.371784227293878,\n          -0.058536787432350995,\n          -10.46616259969626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.4654222033697347,\n        \"max\": 5.4653218929678875,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.618072736348223,\n          1.229076109869587,\n          5.4653218929678875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.36650066476386006,\n        \"max\": 20.344850992307457,\n        \"num_unique_values\": 1292,\n        \"samples\": [\n          0.7176547172640182,\n          0.8639734609276789,\n          -0.20859921808601192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999999,\n        \"min\": -4.027228554610704,\n        \"max\": 1.5702212761775227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.2955953340852197,\n          -4.027228554610704,\n          -2.1614119443479622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -8.536383221145876,\n        \"max\": 0.23412021492594282,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -4.1511315031099665,\n          1.9474429704561965e-15,\n          0.23412021492594282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wine_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.021135657334372,\n        \"max\": 1.6330782930157417,\n        \"num_unique_values\": 847,\n        \"samples\": [\n          -1.4207388026078167,\n          1.196818707926662,\n          0.3847711634044144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-1db1ee08-1636-4e4f-a188-2c65d20ee07e\" class=\"colab-df-container\">\n",
@@ -946,49 +917,47 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7500,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -15.156923529167317,\n        \"max\": 1.1141534449354134,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -12.371784227293878,\n          -0.058536787432350995,\n          -10.46616259969626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.4654222033697347,\n        \"max\": 5.4653218929678875,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.618072736348223,\n          1.229076109869587,\n          5.4653218929678875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.36650066476386006,\n        \"max\": 20.344850992307457,\n        \"num_unique_values\": 1292,\n        \"samples\": [\n          0.7176547172640182,\n          0.8639734609276789,\n          -0.20859921808601192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999999,\n        \"min\": -4.027228554610704,\n        \"max\": 1.5702212761775227,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.2955953340852197,\n          -4.027228554610704,\n          -2.1614119443479622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -8.536383221145876,\n        \"max\": 0.23412021492594282,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -4.1511315031099665,\n          1.9474429704561965e-15,\n          0.23412021492594282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wine_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.021135657334372,\n        \"max\": 1.6330782930157417,\n        \"num_unique_values\": 847,\n        \"samples\": [\n          -1.4207388026078167,\n          1.196818707926662,\n          0.3847711634044144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "          year    rating     price      body   acidity  wine_encoded\n",
+              "0    -0.058537  5.465322  6.217909  1.570221  0.234120      1.257290\n",
+              "1     0.674395  5.465322  1.685354 -0.295595 -4.151132      1.516454\n",
+              "2    -0.644882  4.618073  1.761506  1.570221  0.234120      1.339359\n",
+              "3    -2.110745  4.618073  4.209086  1.570221  0.234120      1.339359\n",
+              "4    -2.550504  4.618073  4.775073  1.570221  0.234120      1.339359\n",
+              "...        ...       ...       ...       ...       ...           ...\n",
+              "7495  0.381222 -0.465422 -0.266804 -0.295595  0.234120      0.652574\n",
+              "7496  0.674395 -0.465422 -0.288220 -0.295595  0.234120     -0.120599\n",
+              "7497  0.527808 -0.465422 -0.237075 -0.295595  0.234120     -0.716676\n",
+              "7498 -0.351709 -0.465422  0.029292  1.570221  0.234120     -0.543900\n",
+              "7499  0.381222 -0.465422 -0.189322  1.570221  0.234120      0.553228\n",
+              "\n",
+              "[7500 rows x 6 columns]"
+            ]
           },
+          "execution_count": 47,
           "metadata": {},
-          "execution_count": 47
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "R9sOziuD3w8l"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "def train_test_split(df, price, test_size=0.2):\n",
-        "    df = df.sample(frac=1, random_state=1)\n",
-        "    len_data = len(df)\n",
-        "    test_index = int(len_data * test_size)\n",
-        "    X_train = df.iloc[test_index:]\n",
-        "    X_test = df.iloc[:test_index].drop(columns=[price])\n",
-        "    y_train = X_train[price]\n",
-        "    y_test = df.iloc[:test_index][price]\n",
-        "    X_train = X_train.drop(columns=[price])\n",
-        "    return X_train, y_train, X_test, y_test\n",
-        "\n",
-        "X_train, y_train, X_test, y_test = train_test_split(df, 'price')\n",
-        "print(\"X_train:\", X_train)\n",
-        "print(\"y_train:\", y_train)\n",
-        "print(\"X_test:\", X_test)\n",
-        "print(\"y_test:\", y_test)\n"
-      ],
+      "execution_count": 50,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -996,11 +965,10 @@
         "id": "YEHKuIZ1-hlx",
         "outputId": "080c1e8f-3b2b-4095-c29f-4a19a8a2f33b"
       },
-      "execution_count": 50,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "X_train:           year    rating      body       acidity  wine_encoded\n",
             "1743 -0.058537  0.381827 -4.027229  2.341202e-01     -1.684222\n",
@@ -1056,13 +1024,29 @@
             "Name: price, Length: 1500, dtype: float64\n"
           ]
         }
+      ],
+      "source": [
+        "def train_test_split(df, price, test_size=0.2):\n",
+        "    df = df.sample(frac=1, random_state=1)\n",
+        "    len_data = len(df)\n",
+        "    test_index = int(len_data * test_size)\n",
+        "    X_train = df.iloc[test_index:]\n",
+        "    X_test = df.iloc[:test_index].drop(columns=[price])\n",
+        "    y_train = X_train[price]\n",
+        "    y_test = df.iloc[:test_index][price]\n",
+        "    X_train = X_train.drop(columns=[price])\n",
+        "    return X_train, y_train, X_test, y_test\n",
+        "\n",
+        "X_train, y_train, X_test, y_test = train_test_split(df, 'price')\n",
+        "print(\"X_train:\", X_train)\n",
+        "print(\"y_train:\", y_train)\n",
+        "print(\"X_test:\", X_test)\n",
+        "print(\"y_test:\", y_test)\n"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
-      ],
+      "execution_count": 51,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1070,19 +1054,26 @@
         "id": "KbREY6QFFq0Q",
         "outputId": "20f08f89-19b0-4d2c-9231-d0201e04fb43"
       },
-      "execution_count": 51,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "(6000, 5) (1500, 5) (6000,) (1500,)\n"
           ]
         }
+      ],
+      "source": [
+        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ph02lNwN359r"
+      },
+      "outputs": [],
       "source": [
         "from sklearn.linear_model import LinearRegression, Lasso, Ridge, BayesianRidge\n",
         "from sklearn.ensemble import GradientBoostingRegressor\n",
@@ -1091,15 +1082,15 @@
         "from sklearn.neighbors import KNeighborsRegressor\n",
         "from sklearn.ensemble import RandomForestRegressor\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
-      ],
-      "metadata": {
-        "id": "Ph02lNwN359r"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5h5SA67U4AiZ"
+      },
+      "outputs": [],
       "source": [
         "models = {}\n",
         "def train_validate_predict(regressor, x_train, y_train, x_test, y_test, index):\n",
@@ -1110,15 +1101,15 @@
         "\n",
         "    r2 = r2_score(y_test, y_pred)\n",
         "    models[index] = r2"
-      ],
-      "metadata": {
-        "id": "5h5SA67U4AiZ"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jGjhp_RK4EK4"
+      },
+      "outputs": [],
       "source": [
         "model_list = [LinearRegression, Lasso, Ridge, BayesianRidge, DecisionTreeRegressor, LinearSVR, KNeighborsRegressor,\n",
         "              RandomForestRegressor,GradientBoostingRegressor]\n",
@@ -1129,18 +1120,11 @@
         "for regressor in model_list:\n",
         "    train_validate_predict(regressor(), X_train, y_train, X_test, y_test, model_names[index])\n",
         "    index+=1"
-      ],
-      "metadata": {
-        "id": "jGjhp_RK4EK4"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "models"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1148,10 +1132,8 @@
         "id": "IJ-63Ovl4IT_",
         "outputId": "2fec7103-4f3d-4ff8-d3e6-617c53c44fc9"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'Linear Regression': 0.1951597654962578,\n",
@@ -1165,10 +1147,30 @@
               " 'GradientBosstingRegressor': 0.8083165276847154}"
             ]
           },
+          "execution_count": 116,
           "metadata": {},
-          "execution_count": 116
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "models"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "myenv",
+      "language": "python",
+      "name": "myenv"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.11.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}