From a2bcd385b9815a7dafc449a07c0f7a98873b15b1 Mon Sep 17 00:00:00 2001
From: Jakub Rogalski <kubarogalski4@wp.pl>
Date: Fri, 3 Nov 2023 11:01:22 +0100
Subject: [PATCH] Grupa E - aktualizacja

---
 dev-0/out.tsv    | 924 +++++++++++++++++++++++------------------------
 mieszkania.ipynb |  33 +-
 test-A/out.tsv   | 836 +++++++++++++++++++++---------------------
 3 files changed, 897 insertions(+), 896 deletions(-)

diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index 6a37ddc..d23852e 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
@@ -1,462 +1,462 @@
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diff --git a/mieszkania.ipynb b/mieszkania.ipynb
index 5a19868..c2ad7f2 100644
--- a/mieszkania.ipynb
+++ b/mieszkania.ipynb
@@ -11,7 +11,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -24,13 +24,13 @@
         {
           "data": {
             "text/html": [
-              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+              "<style>#sk-container-id-1 {color: black;}#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>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
             ],
             "text/plain": [
               "LinearRegression()"
             ]
           },
-          "execution_count": 4,
+          "execution_count": 1,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -39,10 +39,11 @@
         "import pandas as pd\n",
         "from sklearn.linear_model import LinearRegression\n",
         "from sklearn.preprocessing import MinMaxScaler\n",
+        "import matplotlib.pyplot as plt\n",
         "\n",
         "data = pd.read_csv('train/train.tsv', delimiter='\\t', header=None)\n",
         "\n",
-        "X = data.iloc[:, [6, 8]]\n",
+        "X = data.iloc[:, [4, 6, 8]]\n",
         "X = X.apply(pd.to_numeric, errors='coerce')\n",
         "X = X.fillna(11)\n",
         "\n",
@@ -69,7 +70,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -81,9 +82,9 @@
       "source": [
         "\n",
         "dane = pd.read_csv('dev-0/in.tsv', delimiter='\\t', header=None)\n",
-        "dane = dane[[5,7]]\n",
+        "dane = dane[[5,7,9]]\n",
         "dane = dane.apply(pd.to_numeric, errors='coerce')\n",
-        "dane = dane.fillna(11)\n",
+        "dane = dane.fillna(10)\n",
         "#print(dane[[5, 7]])\n",
         "scaler = MinMaxScaler()\n",
         "scaler.fit(dane)\n",
@@ -102,9 +103,9 @@
         "    file.writelines(lines)\n",
         "\n",
         "dane2 = pd.read_csv('Test-A/in.tsv', delimiter='\\t', header=None)\n",
-        "dane2 = dane2[[5,7]]\n",
+        "dane2 = dane2[[5,7,9]]\n",
         "dane2 = dane2.apply(pd.to_numeric, errors='coerce')\n",
-        "dane2 = dane2.fillna(11)\n",
+        "dane2 = dane2.fillna(10)\n",
         "scaler = MinMaxScaler()\n",
         "scaler.fit(dane2)\n",
         "dane2_normalized = scaler.transform(dane2)\n",
@@ -124,21 +125,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Średni Błąd Kwadratowy (MSE): 23708536181.88\n",
-            "Średni Błąd Bezwzględny (MAE): 75896.46\n",
-            "R-kwadrat (R2): 0.67\n"
+            "Średni Błąd Kwadratowy (MSE): 9010312188.80\n",
+            "Średni Błąd Bezwzględny (MAE): 37150.20\n",
+            "R-kwadrat (R2): 0.87\n"
           ]
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -164,13 +165,13 @@
         "r2 = r2_score(y, y_pred)\n",
         "print(f\"R-kwadrat (R2): {r2:.2f}\")\n",
         "\n",
-        "import matplotlib.pyplot as plt\n",
+        "\n",
         " \n",
         "plt.scatter(y, y_pred, label=\"Dane\")\n",
         "\n",
         "plt.xlabel(\"Rzeczywiste wartości\")\n",
         "plt.ylabel(\"Przewidywane wartości\")\n",
-        "plt.title(\"Wykres predykcji vs. rzeczywistość z linią regresji\")\n",
+        "plt.title(\"Wykres predykcji vs. rzeczywistość\")\n",
         "plt.legend()\n",
         "plt.show()"
       ]
diff --git a/test-A/out.tsv b/test-A/out.tsv
index 982d611..8db6a9b 100644
--- a/test-A/out.tsv
+++ b/test-A/out.tsv
@@ -1,418 +1,418 @@
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