Aviakompaniya yo'lovchilarining qoniqishi.ipynb

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1-5)\n","\n","`Departure/Arrival time convenient`: Ketish/Kelish vaqtining qoniqish darajasi\n","\n","`Ease of Online booking`: Onlayn bron qilishdan qoniqish darajasi\n","\n","`Gate location`: Darvoza joylashuvidan qoniqish darajasi\n","\n","`Food and drink`: Oziq-ovqat va ichimlikdan qoniqish darajasi\n","\n","`Online boarding`: Onlayn bortdan qoniqish darajasi\n","\n","`Seat comfort`: O'rindiqning qulayligidan qoniqish darajasi\n","\n","`Inflight entertainment`: Parvoz ichidagi o'yin-kulgidan qoniqish darajasi\n","\n","`On-board service`: Bort xizmatidan qoniqish darajasi\n","\n","`Leg room service`: oyoq xonasi xizmatidan qoniqish darajasi\n","\n","`Baggage handling`: bagajni tashishdan qoniqish darajasi\n","\n","`Check-in service`: Ro'yxatdan o'tish xizmatidan qoniqish darajasi\n","\n","`Inflight service`: Parvoz ichidagi xizmatdan qoniqish darajasi\n","\n","`Cleanliness`: Tozalikdan qoniqish darajasi\n","\n","`Departure Delay in Minutes`: jo‘nash vaqtida kechikish daqiqalari\n","\n","`Arrival Delay in Minutes`: yetib kelganda kechikish daqiqalari\n","\n","`Satisfaction`: Aviakompaniyaning qoniqish darajasi (qoniqish, neytral yoki norozilik)"],"metadata":{"id":"r799RdXkKPUz"}},{"cell_type":"markdown","source":["Kerakli kutubxonalarni yuklab olamiz"],"metadata":{"id":"YzVAkZbx0lKZ"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"aHFooDNJJmTH","executionInfo":{"status":"ok","timestamp":1711341585771,"user_tz":-300,"elapsed":4959,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","from sklearn.svm import SVC\n","from sklearn import metrics\n","import matplotlib.pyplot as plt\n","from xgboost import XGBClassifier\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import OrdinalEncoder\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split\n","from sklearn.tree import DecisionTreeClassifier, plot_tree"]},{"cell_type":"markdown","source":["`Kaggle`ga ulanib olamiz"],"metadata":{"id":"cVxDoTs56anY"}},{"cell_type":"code","source":["!pip install kaggle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y1g7e8FW6Z46","executionInfo":{"status":"ok","timestamp":1711341693630,"user_tz":-300,"elapsed":6728,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"61cb4ab5-4e1b-4094-8c4a-83e3de4e1bc2"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n","Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"]}]},{"cell_type":"markdown","source":["Qaysi fayl bilan ishlayotganimizni fayl bilan manzilini berib ketamiz"],"metadata":{"id":"ikGKe13O6p0s"}},{"cell_type":"code","source":["from google.colab import files\n","\n","files.upload()"],"metadata":{"id":"-dyuZGQg6g_S"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["1-o'rinda kaggle ga ulanib olamiz\n","\n","2-o'rinda biz `kaggle` profilimizdan yangi `kaggle.json` kalit qiymatni yuklab olamiz.\n","\n","3-o'rinda shu kalit bilan kaggleni ulab qo'yamiz\n","\n","4-o'rinda kaggle saytidan kerakli dataset nomini berib google colabga yuklab olamiz"],"metadata":{"id":"1fdXg7dI6vNS"}},{"cell_type":"code","source":["!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 ~/.kaggle/kaggle.json\n","\n","!kaggle competitions download -c aviakompaniya"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Efa84jz76_eo","executionInfo":{"status":"ok","timestamp":1711341832716,"user_tz":-300,"elapsed":2391,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"6a7ea460-d82a-4a16-a6bf-623aa788d978"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading aviakompaniya.zip to /content\n","\r  0% 0.00/244k [00:00<?, ?B/s]\n","\r100% 244k/244k [00:00<00:00, 130MB/s]\n"]}]},{"cell_type":"markdown","source":["shu yuklab olgan faylimizni `.zip`dan chiqarib olamiz"],"metadata":{"id":"4vZAKuH67E5k"}},{"cell_type":"code","source":["!unzip aviakompaniya.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sUE2hL7C7It2","executionInfo":{"status":"ok","timestamp":1711341884968,"user_tz":-300,"elapsed":351,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"ecfbf323-8f59-43fe-803b-5a3a4db6230d"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  aviakompaniya.zip\n","  inflating: sample_submission.csv   \n","  inflating: test_dataset.csv        \n","  inflating: train_dataset.csv       \n"]}]},{"cell_type":"code","source":["df1 = pd.read_csv(\"/content/sample_submission.csv\")"],"metadata":{"id":"b_Fs43bJuTBH","executionInfo":{"status":"ok","timestamp":1711341924038,"user_tz":-300,"elapsed":382,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["train_set = pd.read_csv(\"/content/train_dataset.csv\")"],"metadata":{"id":"MUdmR8lXywyL","executionInfo":{"status":"ok","timestamp":1711341925356,"user_tz":-300,"elapsed":5,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["test_set = pd.read_csv(\"/content/test_dataset.csv\")"],"metadata":{"id":"hu4e8lR0Jtj5","executionInfo":{"status":"ok","timestamp":1711341927010,"user_tz":-300,"elapsed":7,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["df1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"qr0iDHIrJ6uQ","executionInfo":{"status":"ok","timestamp":1711341930565,"user_tz":-300,"elapsed":353,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"db89240d-f713-43d3-e820-656322f84576"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        id  satisfaction\n","0        1             0\n","1        2             0\n","2        3             0\n","3        4             0\n","4        5             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Inflight entertainment  On-board service  \\\n","0                       2  ...                       4                 3   \n","1                       4  ...                       5                 5   \n","2                       4  ...                       1                 1   \n","3                       3  ...                       2                 4   \n","4                       3  ...                       4                 5   \n","\n","   Leg room service  Baggage handling  Checkin service  Inflight service  \\\n","0                 1                 3                4                 3   \n","1                 5                 5                3                 5   \n","2                 1                 1                1                 1   \n","3                 4                 5                5                 4   \n","4                 2                 5                3                 5   \n","\n","   Cleanliness  Departure Delay in Minutes  Arrival Delay in Minutes  \\\n","0            4                          10                       3.0   \n","1            3                           0                       0.0   \n","2            2                           0                      18.0   \n","3            2                           0                       0.0   \n","4            4                           0                      10.0   \n","\n","   satisfaction  \n","0             0  \n","1             1  \n","2             0  \n","3             1  \n","4             0  \n","\n","[5 rows x 24 columns]"],"text/html":["\n","  <div id=\"df-f8137735-a839-4bf6-9c57-a2f47e281188\" class=\"colab-df-container\">\n","    <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\" 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Seat comfort  Inflight entertainment  \\\n","0                       2  ...             5                       2   \n","1                       1  ...             4                       2   \n","2                       4  ...             5                       5   \n","3                       0  ...             1                       5   \n","4                       4  ...             5                       5   \n","\n","   On-board service  Leg room service  Baggage handling  Checkin service  \\\n","0                 4                 4                 3                5   \n","1                 2                 2                 2                4   \n","2                 5                 5                 5                4   \n","3                 4                 4                 1                3   \n","4                 4                 5                 4                5   \n","\n","   Inflight service  Cleanliness  Departure Delay in Minutes  \\\n","0                 1            5                         152   \n","1                 2            3                          60   \n","2                 5            4                           0   \n","3                 3            5                           0   \n","4                 5            5                         130   \n","\n","   Arrival Delay in Minutes  \n","0                     191.0  \n","1                      52.0  \n","2                       0.0  \n","3                       0.0  \n","4                     140.0  \n","\n","[5 rows x 23 columns]"],"text/html":["\n","  <div id=\"df-2ee98485-85e3-4a31-a3f5-d5acaa15eb21\" class=\"colab-df-container\">\n","    <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>id</th>\n","      <th>Gender</th>\n","      <th>Customer Type</th>\n","      <th>Age</th>\n","      <th>Type of Travel</th>\n","      <th>Class</th>\n","      <th>Flight Distance</th>\n","      <th>Inflight wifi service</th>\n","      <th>Departure/Arrival time convenient</th>\n","      <th>Ease of Online booking</th>\n","      <th>...</th>\n","      <th>Seat comfort</th>\n","      <th>Inflight entertainment</th>\n","      <th>On-board service</th>\n","      <th>Leg room service</th>\n","      <th>Baggage handling</th>\n","      <th>Checkin service</th>\n","      <th>Inflight service</th>\n","      <th>Cleanliness</th>\n","      <th>Departure Delay in Minutes</th>\n","      <th>Arrival Delay in Minutes</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Female</td>\n","      <td>Loyal Customer</td>\n","      <td>25</td>\n","      <td>Personal Travel</td>\n","      <td>Eco</td>\n","      <td>2704</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>152</td>\n","      <td>191.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Female</td>\n","      <td>Loyal Customer</td>\n","      <td>49</td>\n","      <td>Business travel</td>\n","      <td>Business</td>\n","      <td>1623</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>60</td>\n","      <td>52.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Male</td>\n","      <td>Loyal Customer</td>\n","      <td>51</td>\n","      <td>Business travel</td>\n","      <td>Business</td>\n","      <td>338</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>...</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Male</td>\n","      <td>Loyal Customer</td>\n","      <td>34</td>\n","      <td>Business travel</td>\n","      <td>Eco Plus</td>\n","      <td>95</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>0</td>\n","      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In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  train_set.corrwith(train_set['satisfaction']).abs().sort_values(ascending=False)\n"]},{"output_type":"execute_result","data":{"text/plain":["satisfaction                         1.000000\n","Online boarding                      0.504986\n","Inflight entertainment               0.400900\n","Seat comfort                         0.342294\n","On-board service                     0.326426\n","Leg room service                     0.324368\n","Cleanliness                          0.310338\n","Flight Distance                      0.298703\n","Inflight wifi service                0.277395\n","Baggage handling                     0.247965\n","Inflight service                     0.240584\n","Checkin service                      0.232359\n","Food and drink                       0.207854\n","Ease of Online booking               0.164662\n","Age                                  0.144302\n","Departure/Arrival time convenient    0.067638\n","Arrival Delay in Minutes             0.048650\n","Departure Delay in Minutes           0.042960\n","Gate location                        0.007169\n","id                                   0.002931\n","dtype: float64"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["`train_set` jadvalini matnlarni sonlar ko'rinishiga o'ktazamiz"],"metadata":{"id":"HGJBlvmT08oM"}},{"cell_type":"code","source":["train_columns = ['Gender', 'Customer Type', 'Type of Travel', 'Class']\n","\n","train_set[train_columns] = train_set[train_columns].replace({\"Male\":1, \"Female\":0,\n","                                                             \"Loyal Customer\":1, \"disloyal Customer\":0,\n","                                                             \"Personal Travel\":1, \"Business travel\":0,\n","                                                             \"Eco\":0, \"Eco Plus\":1, \"Business\":2})"],"metadata":{"id":"rjnJ5pbyQoze","executionInfo":{"status":"ok","timestamp":1711341953870,"user_tz":-300,"elapsed":361,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":["`NaN` qiymatlarni 0 bilan to'ldiramiz"],"metadata":{"id":"qBWIAbcD1Ekg"}},{"cell_type":"code","source":["train_set['Arrival Delay in Minutes'] = train_set['Arrival Delay in Minutes'].fillna(0)"],"metadata":{"id":"aNJ_pq3aTGdq","executionInfo":{"status":"ok","timestamp":1711341956622,"user_tz":-300,"elapsed":6,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["------------\n","\n","ML ga tayyorgarlik\n","-----------------"],"metadata":{"id":"-m06UpHmT7Ml"}},{"cell_type":"markdown","source":["train_set jadvalimizni ikki qismga bo'lib olamiz"],"metadata":{"id":"cvao7gIO1J5Y"}},{"cell_type":"code","source":["X = train_set.drop([\"satisfaction\", \"id\"], axis=1)\n","Y = train_set['satisfaction']"],"metadata":{"id":"7nB_IikUTwtR","executionInfo":{"status":"ok","timestamp":1711341960140,"user_tz":-300,"elapsed":361,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["`satisfaction` ustuni yo'q bo'lgan jadvalimizni standartlashtirib olamiz"],"metadata":{"id":"i6aRn0CC1QPd"}},{"cell_type":"code","source":["scaler = StandardScaler()\n","X = scaler.fit_transform(X)"],"metadata":{"id":"tjrvtiByT5u0","executionInfo":{"status":"ok","timestamp":1711341961989,"user_tz":-300,"elapsed":313,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["Endi X va Y jadvallarini qismlarga bo'lib modelimizni o'qitib test qilishimiz ham mumkin"],"metadata":{"id":"xlkhMVOW1ZiD"}},{"cell_type":"code","source":["X_train, X_test, Y_train, Y_test=train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=0)"],"metadata":{"id":"Z0ETXpcZUDQz","executionInfo":{"status":"ok","timestamp":1711341963697,"user_tz":-300,"elapsed":4,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":["Machine Learning\n","--------"],"metadata":{"id":"6Yfdx_hHULiZ"}},{"cell_type":"code","source":["# Modelni yaratamiz (training)\n","XGB_model = XGBClassifier()\n","XGB_model.fit(X_train, Y_train)\n","\n","# Modelni baholaymiz\n","Y_pred = XGB_model.predict(X_test)\n","print(metrics.classification_report(Y_test, Y_pred))\n","print(\"Model aniqligi:\", metrics.accuracy_score(Y_test,Y_pred))\n","\n","# confusion matrix - matritsa ko'rinishida xatolikni ko'rish\n","conf_mat = metrics.confusion_matrix(Y_test, Y_pred)\n","sns.heatmap(conf_mat, annot=True,fmt=\"g\")\n","plt.show()\n","\n","# ROC curve - grafik ko'rinishida xatolikni ko'rish\n","fpr, tpr, thresholds = metrics.roc_curve(Y_test, Y_pred)\n","roc_auc = metrics.auc(fpr, tpr)\n","display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='ROC curve')\n","display.plot()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"HGao52dCUMUJ","executionInfo":{"status":"ok","timestamp":1711341966759,"user_tz":-300,"elapsed":942,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"40602664-e7ae-47c4-e2c7-b7d95d8b3acf"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.95      0.95      1000\n","           1       0.95      0.94      0.95      1000\n","\n","    accuracy                           0.95      2000\n","   macro avg       0.95      0.95      0.95      2000\n","weighted avg       0.95      0.95      0.95      2000\n","\n","Model aniqligi: 0.9485\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["---------\n","Test_set\n","----------"],"metadata":{"id":"GwugGk6tU00u"}},{"cell_type":"markdown","source":["Endigi navbat `test_set` jadvalimizga.\n","\n","uni qiymatlarini matndan songa o'tkazib olamiz"],"metadata":{"id":"bTCnWryn11W3"}},{"cell_type":"code","source":["test_columns = ['Gender', 'Customer Type', 'Type of Travel', 'Class']\n","\n","test_set[train_columns] = test_set[train_columns].replace({\"Male\":1, \"Female\":0,\n","                                                             \"Loyal Customer\":1, \"disloyal Customer\":0,\n","                                                             \"Personal Travel\":1, \"Business travel\":0,\n","                                                             \"Eco\":0, \"Eco Plus\":1, \"Business\":2})"],"metadata":{"id":"yHD10qYsU0Vj","executionInfo":{"status":"ok","timestamp":1711341973757,"user_tz":-300,"elapsed":8,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["test_set['Arrival Delay in Minutes'] = test_set['Arrival Delay in Minutes'].fillna(0)"],"metadata":{"id":"rx6jaoMWU_xz","executionInfo":{"status":"ok","timestamp":1711341976284,"user_tz":-300,"elapsed":413,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["# X ni tayyorlash\n","X1_test = test_set.copy()"],"metadata":{"id":"EWAsl6CDdRDZ","executionInfo":{"status":"ok","timestamp":1711341977786,"user_tz":-300,"elapsed":7,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":["Bizga `X1_test` jadvalimizda `id` ustuni kerak emas"],"metadata":{"id":"yo0h7isu2AEx"}},{"cell_type":"code","source":["X1_test = X1_test.drop(\"id\", axis=1)"],"metadata":{"id":"-iKunz-DymeP","executionInfo":{"status":"ok","timestamp":1711341979868,"user_tz":-300,"elapsed":632,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["# Test datasetni ham standartlashtirish\n","X1_test = scaler.transform(X1_test)"],"metadata":{"id":"BL42W80vdnJg","executionInfo":{"status":"ok","timestamp":1711341981715,"user_tz":-300,"elapsed":335,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["# Test datani ishlatib 'satisfaction' ustunini bashorat qilish\n","Y_test_pred = XGB_model.predict(X1_test)"],"metadata":{"id":"QGTxCdK-dQ1L","executionInfo":{"status":"ok","timestamp":1711341983169,"user_tz":-300,"elapsed":5,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":["bashorat qilingan qiymatlarni jadval ko'rinishiga keltirib olamiz"],"metadata":{"id":"aejZn-d62Im5"}},{"cell_type":"code","source":["Y_test_pred = pd.DataFrame(Y_test_pred)"],"metadata":{"id":"zZUPJxvWdtsi","executionInfo":{"status":"ok","timestamp":1711341986038,"user_tz":-300,"elapsed":429,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}}},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":["bashorat qilinishi kerak bo'lgan qiymat"],"metadata":{"id":"onqZRKIz2N9E"}},{"cell_type":"code","source":["Y_test_pred"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"4OQnYGvGkB4I","executionInfo":{"status":"ok","timestamp":1711341987660,"user_tz":-300,"elapsed":349,"user":{"displayName":"Baxtiyor Pardaboyev","userId":"09965368857187910214"}},"outputId":"3e1d9fbb-3419-4895-828f-b88e20d9f7d3"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      0\n","0     0\n","1     1\n","2     1\n","3     1\n","4     1\n","...  ..\n","3995  1\n","3996  0\n","3997  0\n","3998  1\n","3999  0\n","\n","[4000 rows x 1 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