{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":73291,"databundleVersionId":8930475,"sourceType":"competition"}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# <font color=#025dfa> Derin Öğrenim Classification ile Sigorta Çapraz Satışı - Insurance Cross-Selling with Deep Learning Classification","metadata":{}},{"cell_type":"markdown","source":"TR = Her yorum satırı kendisini üstündeki koda aittir. İlk olarak Türkçe, son olarak İngilizce yazıldı.\n\nEN = Each comment line belongs to the code above it. It was first written in Turkish and lastly in English.\n\nTR = Bu proje, sigorta sektöründe çapraz satış fırsatlarını belirlemek amacıyla derin öğrenme tabanlı bir sınıflandırma modeli geliştirmeyi hedeflemektedir. Müşterilerin demografik verileri, mevcut poliçe türleri, satın alma geçmişi ve risk profilleri gibi çeşitli faktörler kullanılarak, hangi müşterilere ek sigorta ürünlerinin önerilebileceği sınıflandırılacaktır. Proje, sigorta şirketlerinin çapraz satış stratejilerini optimize ederek, müşteri memnuniyetini artırmayı ve şirketin gelirlerini maksimize etmeyi amaçlamaktadır.\n\nEN = This project aims to develop a deep learning-based classification model to identify cross-selling opportunities in the insurance industry. Using various factors such as customer demographic data, current policy types, purchase history and risk profiles, it will classify which customers can be offered additional insurance products. The project aims to increase customer satisfaction and maximize company revenues by optimizing insurance companies' cross-selling strategies.\n\nKaynak/Source = https://www.kaggle.com/competitions/playground-series-s4e7","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math\nimport nltk\nimport warnings\nwarnings.filterwarnings('ignore')\nimport re\nimport pickle\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom sklearn.preprocessing import StandardScaler,MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:27:45.084663Z","iopub.execute_input":"2024-09-30T15:27:45.085072Z","iopub.status.idle":"2024-09-30T15:27:59.342553Z","shell.execute_reply.started":"2024-09-30T15:27:45.085033Z","shell.execute_reply":"2024-09-30T15:27:59.341715Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"pd.set_option(\"display.max_columns\",None) \n# TR = En fazla kaç sütun olduğunu gösteriyor. \n# EN = It shows the maximum number of columns.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:27:59.344392Z","iopub.execute_input":"2024-09-30T15:27:59.344941Z","iopub.status.idle":"2024-09-30T15:27:59.349247Z","shell.execute_reply.started":"2024-09-30T15:27:59.344906Z","shell.execute_reply":"2024-09-30T15:27:59.348325Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv('/kaggle/input/playground-series-s4e7/train.csv')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:27:59.350744Z","iopub.execute_input":"2024-09-30T15:27:59.351123Z","iopub.status.idle":"2024-09-30T15:28:21.239998Z","shell.execute_reply.started":"2024-09-30T15:27:59.351074Z","shell.execute_reply":"2024-09-30T15:28:21.239088Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## <font color=#FFD700> EDA Keşif Amaçlı Veri Analizi - EDA - Exploratory Data Analysis ","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.241334Z","iopub.execute_input":"2024-09-30T15:28:21.241757Z","iopub.status.idle":"2024-09-30T15:28:21.269717Z","shell.execute_reply.started":"2024-09-30T15:28:21.241703Z","shell.execute_reply":"2024-09-30T15:28:21.268880Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n0   0    Male   21                1         35.0                   0   \n1   1    Male   43                1         28.0                   0   \n2   2  Female   25                1         14.0                   1   \n3   3  Female   35                1          1.0                   0   \n4   4  Female   36                1         15.0                   1   \n\n  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n0    1-2 Year            Yes         65101.0                 124.0      187   \n1   > 2 Years            Yes         58911.0                  26.0      288   \n2    < 1 Year             No         38043.0                 152.0      254   \n3    1-2 Year            Yes          2630.0                 156.0       76   \n4    1-2 Year             No         31951.0                 152.0      294   \n\n   Response  \n0         0  \n1         1  \n2         0  \n3         0  \n4         0  ","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>id</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Vehicle_Age</th>\n      <th>Vehicle_Damage</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>65101.0</td>\n      <td>124.0</td>\n      <td>187</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>Male</td>\n      <td>43</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>&gt; 2 Years</td>\n      <td>Yes</td>\n      <td>58911.0</td>\n      <td>26.0</td>\n      <td>288</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>Female</td>\n      <td>25</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>38043.0</td>\n      <td>152.0</td>\n      <td>254</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Female</td>\n      <td>35</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>2630.0</td>\n      <td>156.0</td>\n      <td>76</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Female</td>\n      <td>36</td>\n      <td>1</td>\n      <td>15.0</td>\n      <td>1</td>\n      <td>1-2 Year</td>\n      <td>No</td>\n      <td>31951.0</td>\n      <td>152.0</td>\n      <td>294</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.sample(5)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.271910Z","iopub.execute_input":"2024-09-30T15:28:21.272221Z","iopub.status.idle":"2024-09-30T15:28:21.736546Z","shell.execute_reply.started":"2024-09-30T15:28:21.272186Z","shell.execute_reply":"2024-09-30T15:28:21.735547Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"              id  Gender  Age  Driving_License  Region_Code  \\\n8430704  8430704    Male   47                1          8.0   \n7906959  7906959  Female   24                1         28.0   \n3171090  3171090    Male   23                1         47.0   \n781          781    Male   64                1         21.0   \n1422845  1422845    Male   28                1         36.0   \n\n         Previously_Insured Vehicle_Age Vehicle_Damage  Annual_Premium  \\\n8430704                   0    1-2 Year            Yes         27505.0   \n7906959                   1    < 1 Year             No         33756.0   \n3171090                   0    < 1 Year            Yes         31593.0   \n781                       1    1-2 Year            Yes          2630.0   \n1422845                   1    < 1 Year             No         28990.0   \n\n         Policy_Sales_Channel  Vintage  Response  \n8430704                   4.0      225         0  \n7906959                 152.0      216         0  \n3171090                 160.0       86         0  \n781                     124.0       91         0  \n1422845                 152.0      186         0  ","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>id</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Vehicle_Age</th>\n      <th>Vehicle_Damage</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8430704</th>\n      <td>8430704</td>\n      <td>Male</td>\n      <td>47</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>27505.0</td>\n      <td>4.0</td>\n      <td>225</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7906959</th>\n      <td>7906959</td>\n      <td>Female</td>\n      <td>24</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>33756.0</td>\n      <td>152.0</td>\n      <td>216</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3171090</th>\n      <td>3171090</td>\n      <td>Male</td>\n      <td>23</td>\n      <td>1</td>\n      <td>47.0</td>\n      <td>0</td>\n      <td>&lt; 1 Year</td>\n      <td>Yes</td>\n      <td>31593.0</td>\n      <td>160.0</td>\n      <td>86</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>781</th>\n      <td>781</td>\n      <td>Male</td>\n      <td>64</td>\n      <td>1</td>\n      <td>21.0</td>\n      <td>1</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>2630.0</td>\n      <td>124.0</td>\n      <td>91</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1422845</th>\n      <td>1422845</td>\n      <td>Male</td>\n      <td>28</td>\n      <td>1</td>\n      <td>36.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>28990.0</td>\n      <td>152.0</td>\n      <td>186</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.tail()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.738404Z","iopub.execute_input":"2024-09-30T15:28:21.738885Z","iopub.status.idle":"2024-09-30T15:28:21.752993Z","shell.execute_reply.started":"2024-09-30T15:28:21.738827Z","shell.execute_reply":"2024-09-30T15:28:21.752093Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"                id  Gender  Age  Driving_License  Region_Code  \\\n11504793  11504793    Male   48                1          6.0   \n11504794  11504794  Female   26                1         36.0   \n11504795  11504795  Female   29                1         32.0   \n11504796  11504796  Female   51                1         28.0   \n11504797  11504797    Male   25                1         28.0   \n\n          Previously_Insured Vehicle_Age Vehicle_Damage  Annual_Premium  \\\n11504793                   0    1-2 Year            Yes         27412.0   \n11504794                   0    < 1 Year            Yes         29509.0   \n11504795                   1    < 1 Year             No          2630.0   \n11504796                   0    1-2 Year            Yes         48443.0   \n11504797                   1    < 1 Year             No         32855.0   \n\n          Policy_Sales_Channel  Vintage  Response  \n11504793                  26.0      218         0  \n11504794                 152.0      115         1  \n11504795                 152.0      189         0  \n11504796                  26.0      274         1  \n11504797                 152.0      189         0  ","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>id</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Vehicle_Age</th>\n      <th>Vehicle_Damage</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11504793</th>\n      <td>11504793</td>\n      <td>Male</td>\n      <td>48</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>27412.0</td>\n      <td>26.0</td>\n      <td>218</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11504794</th>\n      <td>11504794</td>\n      <td>Female</td>\n      <td>26</td>\n      <td>1</td>\n      <td>36.0</td>\n      <td>0</td>\n      <td>&lt; 1 Year</td>\n      <td>Yes</td>\n      <td>29509.0</td>\n      <td>152.0</td>\n      <td>115</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11504795</th>\n      <td>11504795</td>\n      <td>Female</td>\n      <td>29</td>\n      <td>1</td>\n      <td>32.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>2630.0</td>\n      <td>152.0</td>\n      <td>189</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11504796</th>\n      <td>11504796</td>\n      <td>Female</td>\n      <td>51</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>48443.0</td>\n      <td>26.0</td>\n      <td>274</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11504797</th>\n      <td>11504797</td>\n      <td>Male</td>\n      <td>25</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>32855.0</td>\n      <td>152.0</td>\n      <td>189</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.754287Z","iopub.execute_input":"2024-09-30T15:28:21.754775Z","iopub.status.idle":"2024-09-30T15:28:21.762890Z","shell.execute_reply.started":"2024-09-30T15:28:21.754729Z","shell.execute_reply":"2024-09-30T15:28:21.761934Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"(11504798, 12)"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.764233Z","iopub.execute_input":"2024-09-30T15:28:21.764843Z","iopub.status.idle":"2024-09-30T15:28:21.786270Z","shell.execute_reply.started":"2024-09-30T15:28:21.764800Z","shell.execute_reply":"2024-09-30T15:28:21.785347Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 11504798 entries, 0 to 11504797\nData columns (total 12 columns):\n #   Column                Dtype  \n---  ------                -----  \n 0   id                    int64  \n 1   Gender                object \n 2   Age                   int64  \n 3   Driving_License       int64  \n 4   Region_Code           float64\n 5   Previously_Insured    int64  \n 6   Vehicle_Age           object \n 7   Vehicle_Damage        object \n 8   Annual_Premium        float64\n 9   Policy_Sales_Channel  float64\n 10  Vintage               int64  \n 11  Response              int64  \ndtypes: float64(3), int64(6), object(3)\nmemory usage: 1.0+ GB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isnull().sum().sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:21.787570Z","iopub.execute_input":"2024-09-30T15:28:21.788028Z","iopub.status.idle":"2024-09-30T15:28:24.973621Z","shell.execute_reply.started":"2024-09-30T15:28:21.787985Z","shell.execute_reply":"2024-09-30T15:28:24.972461Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"id                      0\nGender                  0\nAge                     0\nDriving_License         0\nRegion_Code             0\nPreviously_Insured      0\nVehicle_Age             0\nVehicle_Damage          0\nAnnual_Premium          0\nPolicy_Sales_Channel    0\nVintage                 0\nResponse                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:24.975101Z","iopub.execute_input":"2024-09-30T15:28:24.975544Z","iopub.status.idle":"2024-09-30T15:28:28.269588Z","shell.execute_reply.started":"2024-09-30T15:28:24.975497Z","shell.execute_reply":"2024-09-30T15:28:28.268643Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"                 id           Age  Driving_License   Region_Code  \\\ncount  1.150480e+07  1.150480e+07     1.150480e+07  1.150480e+07   \nmean   5.752398e+06  3.838356e+01     9.980220e-01  2.641869e+01   \nstd    3.321149e+06  1.499346e+01     4.443120e-02  1.299159e+01   \nmin    0.000000e+00  2.000000e+01     0.000000e+00  0.000000e+00   \n25%    2.876199e+06  2.400000e+01     1.000000e+00  1.500000e+01   \n50%    5.752398e+06  3.600000e+01     1.000000e+00  2.800000e+01   \n75%    8.628598e+06  4.900000e+01     1.000000e+00  3.500000e+01   \nmax    1.150480e+07  8.500000e+01     1.000000e+00  5.200000e+01   \n\n       Previously_Insured  Annual_Premium  Policy_Sales_Channel       Vintage  \\\ncount        1.150480e+07    1.150480e+07          1.150480e+07  1.150480e+07   \nmean         4.629966e-01    3.046137e+04          1.124254e+02  1.638977e+02   \nstd          4.986289e-01    1.645475e+04          5.403571e+01  7.997953e+01   \nmin          0.000000e+00    2.630000e+03          1.000000e+00  1.000000e+01   \n25%          0.000000e+00    2.527700e+04          2.900000e+01  9.900000e+01   \n50%          0.000000e+00    3.182400e+04          1.510000e+02  1.660000e+02   \n75%          1.000000e+00    3.945100e+04          1.520000e+02  2.320000e+02   \nmax          1.000000e+00    5.401650e+05          1.630000e+02  2.990000e+02   \n\n           Response  \ncount  1.150480e+07  \nmean   1.229973e-01  \nstd    3.284341e-01  \nmin    0.000000e+00  \n25%    0.000000e+00  \n50%    0.000000e+00  \n75%    0.000000e+00  \nmax    1.000000e+00  ","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>id</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n      <td>1.150480e+07</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.752398e+06</td>\n      <td>3.838356e+01</td>\n      <td>9.980220e-01</td>\n      <td>2.641869e+01</td>\n      <td>4.629966e-01</td>\n      <td>3.046137e+04</td>\n      <td>1.124254e+02</td>\n      <td>1.638977e+02</td>\n      <td>1.229973e-01</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.321149e+06</td>\n      <td>1.499346e+01</td>\n      <td>4.443120e-02</td>\n      <td>1.299159e+01</td>\n      <td>4.986289e-01</td>\n      <td>1.645475e+04</td>\n      <td>5.403571e+01</td>\n      <td>7.997953e+01</td>\n      <td>3.284341e-01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000e+00</td>\n      <td>2.000000e+01</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>2.630000e+03</td>\n      <td>1.000000e+00</td>\n      <td>1.000000e+01</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.876199e+06</td>\n      <td>2.400000e+01</td>\n      <td>1.000000e+00</td>\n      <td>1.500000e+01</td>\n      <td>0.000000e+00</td>\n      <td>2.527700e+04</td>\n      <td>2.900000e+01</td>\n      <td>9.900000e+01</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.752398e+06</td>\n      <td>3.600000e+01</td>\n      <td>1.000000e+00</td>\n      <td>2.800000e+01</td>\n      <td>0.000000e+00</td>\n      <td>3.182400e+04</td>\n      <td>1.510000e+02</td>\n      <td>1.660000e+02</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>8.628598e+06</td>\n      <td>4.900000e+01</td>\n      <td>1.000000e+00</td>\n      <td>3.500000e+01</td>\n      <td>1.000000e+00</td>\n      <td>3.945100e+04</td>\n      <td>1.520000e+02</td>\n      <td>2.320000e+02</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.150480e+07</td>\n      <td>8.500000e+01</td>\n      <td>1.000000e+00</td>\n      <td>5.200000e+01</td>\n      <td>1.000000e+00</td>\n      <td>5.401650e+05</td>\n      <td>1.630000e+02</td>\n      <td>2.990000e+02</td>\n      <td>1.000000e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.corr(numeric_only=True)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:28.270690Z","iopub.execute_input":"2024-09-30T15:28:28.270973Z","iopub.status.idle":"2024-09-30T15:28:30.974926Z","shell.execute_reply.started":"2024-09-30T15:28:28.270939Z","shell.execute_reply":"2024-09-30T15:28:30.973981Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"                            id       Age  Driving_License  Region_Code  \\\nid                    1.000000 -0.000088        -0.000045     0.000147   \nAge                  -0.000088  1.000000        -0.078519     0.037041   \nDriving_License      -0.000045 -0.078519         1.000000    -0.001329   \nRegion_Code           0.000147  0.037041        -0.001329     1.000000   \nPreviously_Insured    0.000217 -0.276248         0.013733    -0.022367   \nAnnual_Premium       -0.000205  0.056327        -0.007300    -0.001741   \nPolicy_Sales_Channel  0.000550 -0.591443         0.042941    -0.037606   \nVintage              -0.000182 -0.013293        -0.000697    -0.005537   \nResponse              0.000018  0.122134         0.009197     0.012816   \n\n                      Previously_Insured  Annual_Premium  \\\nid                              0.000217       -0.000205   \nAge                            -0.276248        0.056327   \nDriving_License                 0.013733       -0.007300   \nRegion_Code                    -0.022367       -0.001741   \nPreviously_Insured              1.000000        0.007665   \nAnnual_Premium                  0.007665        1.000000   \nPolicy_Sales_Channel            0.236838       -0.128559   \nVintage                         0.019431        0.003284   \nResponse                       -0.345930        0.032261   \n\n                      Policy_Sales_Channel   Vintage  Response  \nid                                0.000550 -0.000182  0.000018  \nAge                              -0.591443 -0.013293  0.122134  \nDriving_License                   0.042941 -0.000697  0.009197  \nRegion_Code                      -0.037606 -0.005537  0.012816  \nPreviously_Insured                0.236838  0.019431 -0.345930  \nAnnual_Premium                   -0.128559  0.003284  0.032261  \nPolicy_Sales_Channel              1.000000  0.008999 -0.152733  \nVintage                           0.008999  1.000000 -0.015177  \nResponse                         -0.152733 -0.015177  1.000000  ","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>id</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>id</th>\n      <td>1.000000</td>\n      <td>-0.000088</td>\n      <td>-0.000045</td>\n      <td>0.000147</td>\n      <td>0.000217</td>\n      <td>-0.000205</td>\n      <td>0.000550</td>\n      <td>-0.000182</td>\n      <td>0.000018</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>-0.000088</td>\n      <td>1.000000</td>\n      <td>-0.078519</td>\n      <td>0.037041</td>\n      <td>-0.276248</td>\n      <td>0.056327</td>\n      <td>-0.591443</td>\n      <td>-0.013293</td>\n      <td>0.122134</td>\n    </tr>\n    <tr>\n      <th>Driving_License</th>\n      <td>-0.000045</td>\n      <td>-0.078519</td>\n      <td>1.000000</td>\n      <td>-0.001329</td>\n      <td>0.013733</td>\n      <td>-0.007300</td>\n      <td>0.042941</td>\n      <td>-0.000697</td>\n      <td>0.009197</td>\n    </tr>\n    <tr>\n      <th>Region_Code</th>\n      <td>0.000147</td>\n      <td>0.037041</td>\n      <td>-0.001329</td>\n      <td>1.000000</td>\n      <td>-0.022367</td>\n      <td>-0.001741</td>\n      <td>-0.037606</td>\n      <td>-0.005537</td>\n      <td>0.012816</td>\n    </tr>\n    <tr>\n      <th>Previously_Insured</th>\n      <td>0.000217</td>\n      <td>-0.276248</td>\n      <td>0.013733</td>\n      <td>-0.022367</td>\n      <td>1.000000</td>\n      <td>0.007665</td>\n      <td>0.236838</td>\n      <td>0.019431</td>\n      <td>-0.345930</td>\n    </tr>\n    <tr>\n      <th>Annual_Premium</th>\n      <td>-0.000205</td>\n      <td>0.056327</td>\n      <td>-0.007300</td>\n      <td>-0.001741</td>\n      <td>0.007665</td>\n      <td>1.000000</td>\n      <td>-0.128559</td>\n      <td>0.003284</td>\n      <td>0.032261</td>\n    </tr>\n    <tr>\n      <th>Policy_Sales_Channel</th>\n      <td>0.000550</td>\n      <td>-0.591443</td>\n      <td>0.042941</td>\n      <td>-0.037606</td>\n      <td>0.236838</td>\n      <td>-0.128559</td>\n      <td>1.000000</td>\n      <td>0.008999</td>\n      <td>-0.152733</td>\n    </tr>\n    <tr>\n      <th>Vintage</th>\n      <td>-0.000182</td>\n      <td>-0.013293</td>\n      <td>-0.000697</td>\n      <td>-0.005537</td>\n      <td>0.019431</td>\n      <td>0.003284</td>\n      <td>0.008999</td>\n      <td>1.000000</td>\n      <td>-0.015177</td>\n    </tr>\n    <tr>\n      <th>Response</th>\n      <td>0.000018</td>\n      <td>0.122134</td>\n      <td>0.009197</td>\n      <td>0.012816</td>\n      <td>-0.345930</td>\n      <td>0.032261</td>\n      <td>-0.152733</td>\n      <td>-0.015177</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(11,11))\nsns.heatmap(df.corr(numeric_only=True),annot=True,cmap='Blues');","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:30.976229Z","iopub.execute_input":"2024-09-30T15:28:30.976540Z","iopub.status.idle":"2024-09-30T15:28:34.307895Z","shell.execute_reply.started":"2024-09-30T15:28:30.976507Z","shell.execute_reply":"2024-09-30T15:28:34.306965Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1100x1100 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"abs(df.corr(numeric_only=True)['Response']).sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:34.309093Z","iopub.execute_input":"2024-09-30T15:28:34.309466Z","iopub.status.idle":"2024-09-30T15:28:37.019503Z","shell.execute_reply.started":"2024-09-30T15:28:34.309429Z","shell.execute_reply":"2024-09-30T15:28:37.018605Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"Response                1.000000\nPreviously_Insured      0.345930\nPolicy_Sales_Channel    0.152733\nAge                     0.122134\nAnnual_Premium          0.032261\nVintage                 0.015177\nRegion_Code             0.012816\nDriving_License         0.009197\nid                      0.000018\nName: Response, dtype: float64"},"metadata":{}}]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"markdown","source":"## <font color=#C0C0C0> Gereksiz Verileri Silme İşlemi Yapıyoruz - We Delete Unnecessary Data","metadata":{}},{"cell_type":"code","source":"df=df.drop(['Gender','id'],axis=1)\ndf.head(1)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:37.025302Z","iopub.execute_input":"2024-09-30T15:28:37.025616Z","iopub.status.idle":"2024-09-30T15:28:37.487073Z","shell.execute_reply.started":"2024-09-30T15:28:37.025583Z","shell.execute_reply":"2024-09-30T15:28:37.486086Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"   Age  Driving_License  Region_Code  Previously_Insured Vehicle_Age  \\\n0   21                1         35.0                   0    1-2 Year   \n\n  Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  Response  \n0            Yes         65101.0                 124.0      187         0  ","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>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Vehicle_Age</th>\n      <th>Vehicle_Damage</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>21</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>Yes</td>\n      <td>65101.0</td>\n      <td>124.0</td>\n      <td>187</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"markdown","source":"## <font color='#8b1cc7'> Sütün İşimlerini Orjinal, Türkçe Ve İngilizce Versiyonda Göster  - Show the Processes of Milk in Original, Turkish and English Version","metadata":{}},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"markdown","source":"## <font color=#1cfc03> Aykırı Değer - Outlier","metadata":{}},{"cell_type":"code","source":"numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns\n\nnum_cols = len(numeric_cols)\nnum_plots_per_group = 9\nnum_groups = math.ceil(num_cols / num_plots_per_group)\n\nfor group in range(num_groups):\n    cols_in_group = numeric_cols[group * num_plots_per_group:(group + 1) * num_plots_per_group]\n    num_plots = len(cols_in_group)\n    rows = math.ceil(num_plots / 3)\n    fig, axs = plt.subplots(rows, 3, figsize=(15, 5 * rows))\n    axs = axs.flatten() if num_plots > 1 else [axs]\n\n    for i, col in enumerate(cols_in_group):\n        axs[i].boxplot(df[col].dropna())\n        axs[i].set_title(col)\n\n    for i in range(num_plots, len(axs)):\n        axs[i].set_visible(False)  \n\n    plt.suptitle(f'Group {group + 1} Boxplots', fontsize=16)\n    plt.tight_layout(rect=[0, 0, 1, 0.95])\n","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:37.488204Z","iopub.execute_input":"2024-09-30T15:28:37.488487Z","iopub.status.idle":"2024-09-30T15:28:51.067376Z","shell.execute_reply.started":"2024-09-30T15:28:37.488455Z","shell.execute_reply":"2024-09-30T15:28:51.066424Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1500 with 9 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns  \n# TR = Veri setindeki sayısal sütunları (float64 ve int64 türündeki sütunları) seçer  \n# EN = Selects numeric columns from the dataset (float64 and int64 types)\n\nfor col in numeric_cols:  \n    # TR = Her bir sayısal sütun için döngü başlatır  \n    # EN = Loops through each numeric column\n\n    percentile_85 = df[col].quantile(0.90)  \n    # TR = Sütundaki 85. yüzdelik dilim değerini hesaplar  \n    # EN = Calculates the 85th percentile value for the column\n\n    outlier_count = (df[col] > percentile_85).sum()  \n    # TR = 85. yüzdelik dilimden büyük olan değerlerin sayısını bulur  \n    # EN = Counts how many values are greater than the 85th percentile\n\n    total_rows = len(df)  \n    # TR = Veri setindeki toplam satır sayısını alır  \n    # EN = Gets the total number of rows in the dataset\n\n    if outlier_count < total_rows:  \n        # TR = Eğer aykırı değer sayısı toplam satır sayısından azsa  \n        # EN = If the outlier count is less than the total number of rows\n\n        df = df[df[col] <= percentile_85]  \n        # TR = 85. yüzdelik dilimden küçük veya eşit olan değerleri tutarak veri setini filtreler  \n        # EN = Filters the dataset to keep only values less than or equal to the 85th percentile","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:51.068584Z","iopub.execute_input":"2024-09-30T15:28:51.068885Z","iopub.status.idle":"2024-09-30T15:28:56.503263Z","shell.execute_reply.started":"2024-09-30T15:28:51.068853Z","shell.execute_reply":"2024-09-30T15:28:56.502400Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns\n\nnum_cols = len(numeric_cols)\nnum_plots_per_group = 9\nnum_groups = math.ceil(num_cols / num_plots_per_group)\n\nfor group in range(num_groups):\n    cols_in_group = numeric_cols[group * num_plots_per_group:(group + 1) * num_plots_per_group]\n    num_plots = len(cols_in_group)\n    rows = math.ceil(num_plots / 3)\n    fig, axs = plt.subplots(rows, 3, figsize=(15, 5 * rows))\n    axs = axs.flatten() if num_plots > 1 else [axs]\n\n    for i, col in enumerate(cols_in_group):\n        axs[i].boxplot(df[col].dropna())\n        axs[i].set_title(col)\n\n    for i in range(num_plots, len(axs)):\n        axs[i].set_visible(False)  \n\n    plt.suptitle(f'Group {group + 1} Boxplots', fontsize=16)\n    plt.tight_layout(rect=[0, 0, 1, 0.95])","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:28:56.504498Z","iopub.execute_input":"2024-09-30T15:28:56.504827Z","iopub.status.idle":"2024-09-30T15:29:09.113767Z","shell.execute_reply.started":"2024-09-30T15:28:56.504793Z","shell.execute_reply":"2024-09-30T15:29:09.112530Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1500 with 9 Axes>","image/png":"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<font color='#ff4d00'> Boşluk Varsa Doldurmaya, Düzeltilecek Kısım Varsa Düzeltmeye Başladık - If there is a gap, we started to fill it and if there is a part to be corrected, we started to correct it.","metadata":{}},{"cell_type":"code","source":"#for column in df.columns:\n    # TR = Verideki her bir sütun için döngü başlatılıyor  \n    # EN = Loops through each column in the dataframe\n\n#    print(f\"\\nSütun: {column}\")  \n    # TR = Sütun ismi ekrana yazdırılıyor  \n    # EN = Prints the column name\n\n#    unique = df[column].dropna().unique()  \n    # TR = NaN değerleri düşürerek benzersiz değerler elde ediliyor  \n    # EN = Gets the unique values after dropping NaN values\n\n#    if pd.api.types.is_numeric_dtype(df[column]):  \n        # TR = Eğer sütundaki değerler sayısal ise, değerler sıralanıyor  \n        # EN = If the column is of numeric type, the unique values are sorted\n#        unique = sorted(unique)\n        \n#    nunique = len(unique)  # Benzersiz değerlerin sayısını hesapla\n    # TR = Benzersiz değerlerin sayısını hesaplar  \n    # EN = Calculates the number of unique values\n\n#    print(f\"Nunique Değerler: {nunique}\")  \n    # TR = Benzersiz değerlerin sayısını ekrana yazdırır  \n    # EN = Prints the number of unique values\n\n#    print(f\"Unique Değerler: {unique}\")  \n    # TR = Benzersiz değerler ekrana yazdırılıyor  \n    # EN = Prints the unique values\n","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:09.115639Z","iopub.execute_input":"2024-09-30T15:29:09.116130Z","iopub.status.idle":"2024-09-30T15:29:12.529835Z","shell.execute_reply.started":"2024-09-30T15:29:09.116082Z","shell.execute_reply":"2024-09-30T15:29:12.528914Z"},"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true,"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"\nSütun: Age\nNunique Değerler: 42\nUnique Değerler: [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]\n\nSütun: Driving_License\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n\nSütun: Region_Code\nNunique Değerler: 47\nUnique Değerler: [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0]\n\nSütun: Previously_Insured\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n\nSütun: Vehicle_Age\nNunique Değerler: 3\nUnique Değerler: ['< 1 Year' '1-2 Year' '> 2 Years']\n\nSütun: Vehicle_Damage\nNunique Değerler: 2\nUnique Değerler: ['No' 'Yes']\n\nSütun: Annual_Premium\nNunique Değerler: 31259\nUnique Değerler: [2630.0, 2642.0, 2730.0, 2743.0, 2766.0, 2776.0, 2783.0, 2785.0, 2789.0, 2792.0, 2796.0, 2820.0, 2830.0, 2864.0, 2883.0, 2886.0, 2898.0, 2905.0, 2909.0, 2914.0, 2921.0, 2939.0, 2958.0, 3022.0, 3023.0, 3030.0, 3031.0, 3041.0, 3048.0, 3086.0, 3142.0, 3150.0, 3189.0, 3224.0, 3230.0, 3260.0, 3327.0, 3330.0, 3344.0, 3364.0, 3407.0, 3416.0, 3419.0, 3425.0, 3426.0, 3435.0, 3438.0, 3465.0, 3468.0, 3501.0, 3517.0, 3553.0, 3565.0, 3569.0, 3572.0, 3587.0, 3600.0, 3615.0, 3619.0, 3623.0, 3624.0, 3628.0, 3630.0, 3645.0, 3660.0, 3675.0, 3724.0, 3734.0, 3743.0, 3747.0, 3770.0, 3796.0, 3800.0, 3801.0, 3805.0, 3810.0, 3837.0, 3847.0, 3854.0, 3861.0, 3874.0, 3897.0, 3900.0, 3902.0, 3903.0, 3905.0, 3909.0, 3922.0, 3931.0, 3943.0, 3968.0, 3972.0, 3973.0, 4023.0, 4029.0, 4035.0, 4038.0, 4040.0, 4049.0, 4052.0, 4054.0, 4059.0, 4061.0, 4081.0, 4097.0, 4110.0, 4225.0, 4260.0, 4301.0, 4328.0, 4332.0, 4364.0, 4401.0, 4419.0, 4469.0, 4517.0, 4518.0, 4542.0, 4570.0, 4580.0, 4596.0, 4663.0, 4700.0, 4723.0, 4741.0, 4742.0, 4745.0, 4760.0, 4837.0, 4862.0, 4863.0, 4892.0, 4896.0, 4902.0, 4903.0, 4937.0, 4963.0, 5008.0, 5051.0, 5062.0, 5065.0, 5066.0, 5092.0, 5100.0, 5315.0, 5316.0, 5321.0, 5323.0, 5324.0, 5400.0, 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48257.0, 48258.0, 48259.0, 48261.0, 48262.0, 48263.0, 48264.0, 48265.0, 48266.0, 48267.0, 48269.0, 48270.0, 48271.0, 48272.0, 48273.0, 48274.0, 48275.0, 48276.0, 48277.0, 48278.0, 48279.0, 48280.0, 48281.0, 48282.0, 48283.0, 48284.0, 48285.0, 48286.0, 48287.0, 48288.0, 48289.0, 48291.0, 48292.0, 48293.0, 48294.0, 48295.0, 48296.0, 48297.0, 48298.0, 48299.0, 48300.0, 48301.0, 48302.0, 48303.0, 48304.0, 48305.0, 48306.0, 48307.0, 48308.0, 48309.0, 48310.0, 48311.0, 48312.0, 48313.0, 48314.0, 48315.0, 48317.0, 48318.0, 48319.0, 48320.0, 48321.0, 48322.0, 48323.0, 48324.0, 48325.0, 48326.0, 48327.0, 48328.0, 48329.0, 48330.0, 48331.0, 48332.0, 48333.0, 48334.0, 48335.0, 48336.0, 48337.0, 48338.0, 48339.0, 48340.0, 48341.0, 48342.0, 48343.0, 48344.0, 48345.0, 48346.0, 48347.0, 48350.0, 48351.0, 48352.0, 48353.0, 48354.0, 48355.0, 48356.0, 48357.0, 48358.0, 48359.0, 48360.0, 48361.0, 48362.0, 48363.0, 48364.0, 48365.0, 48366.0, 48367.0, 48368.0, 48369.0, 48370.0, 48371.0, 48372.0, 48373.0, 48374.0, 48375.0, 48376.0, 48377.0, 48378.0, 48379.0, 48380.0, 48381.0, 48382.0, 48383.0, 48384.0, 48386.0, 48387.0, 48388.0, 48389.0, 48390.0, 48391.0, 48392.0, 48393.0, 48394.0, 48395.0, 48396.0, 48397.0, 48398.0, 48399.0, 48400.0, 48401.0, 48402.0, 48403.0, 48404.0, 48405.0, 48406.0, 48407.0, 48408.0, 48409.0, 48410.0, 48411.0, 48412.0, 48414.0, 48415.0, 48416.0, 48417.0, 48418.0, 48419.0, 48420.0, 48421.0, 48422.0, 48423.0, 48424.0, 48425.0, 48426.0, 48427.0, 48428.0, 48429.0, 48430.0, 48431.0, 48432.0, 48433.0, 48434.0, 48435.0, 48436.0, 48437.0, 48438.0, 48439.0, 48440.0, 48441.0, 48442.0, 48443.0, 48444.0, 48445.0, 48446.0, 48447.0, 48448.0, 48449.0, 48450.0, 48451.0, 48452.0, 48453.0, 48454.0, 48455.0, 48456.0, 48457.0, 48458.0, 48459.0, 48460.0, 48461.0, 48462.0, 48463.0, 48464.0, 48465.0, 48466.0, 48467.0, 48469.0, 48470.0, 48471.0, 48472.0, 48473.0, 48474.0, 48475.0, 48476.0, 48477.0, 48478.0, 48479.0, 48480.0, 48481.0, 48482.0, 48483.0, 48484.0, 48485.0, 48486.0, 48488.0, 48489.0, 48490.0, 48491.0, 48492.0, 48493.0, 48494.0, 48495.0, 48496.0, 48497.0, 48498.0, 48499.0, 48500.0, 48501.0, 48502.0, 48503.0, 48504.0, 48505.0, 48506.0, 48507.0, 48509.0, 48511.0, 48512.0, 48513.0, 48514.0, 48515.0, 48516.0, 48517.0, 48518.0, 48519.0, 48520.0, 48521.0, 48522.0, 48523.0, 48524.0, 48525.0, 48526.0, 48527.0, 48528.0, 48529.0, 48530.0, 48531.0, 48532.0, 48533.0, 48534.0, 48535.0, 48536.0, 48537.0, 48538.0, 48539.0, 48540.0, 48541.0, 48542.0, 48543.0, 48544.0, 48545.0, 48546.0, 48547.0, 48548.0, 48549.0, 48550.0, 48551.0, 48552.0, 48553.0, 48554.0, 48555.0, 48556.0, 48557.0, 48558.0, 48559.0, 48560.0, 48561.0, 48562.0, 48563.0, 48564.0, 48565.0, 48566.0, 48567.0, 48568.0, 48569.0, 48570.0, 48571.0, 48572.0, 48573.0, 48574.0, 48575.0, 48576.0, 48577.0, 48578.0, 48579.0, 48580.0, 48581.0, 48582.0, 48583.0, 48584.0, 48585.0, 48586.0, 48587.0, 48588.0, 48589.0, 48590.0, 48591.0, 48592.0, 48593.0, 48594.0, 48595.0, 48596.0, 48597.0, 48598.0, 48599.0, 48600.0, 48601.0, 48602.0, 48603.0, 48604.0, 48605.0, 48606.0, 48607.0, 48608.0, 48609.0, 48610.0, 48611.0, 48612.0, 48613.0, 48614.0, 48615.0, 48616.0, 48617.0, 48618.0, 48619.0, 48620.0, 48621.0, 48622.0, 48623.0, 48624.0, 48625.0, 48626.0, 48627.0, 48628.0, 48629.0, 48630.0, 48631.0, 48632.0, 48633.0, 48634.0, 48635.0, 48636.0, 48637.0, 48638.0, 48639.0, 48640.0, 48641.0, 48642.0, 48643.0, 48644.0, 48645.0, 48646.0, 48647.0, 48648.0, 48649.0, 48650.0, 48651.0, 48654.0, 48655.0, 48656.0, 48657.0, 48658.0, 48659.0, 48660.0, 48661.0, 48662.0, 48663.0, 48664.0, 48665.0, 48666.0, 48667.0, 48668.0, 48669.0, 48670.0, 48671.0, 48672.0, 48673.0, 48674.0, 48675.0, 48676.0, 48677.0, 48678.0, 48679.0, 48680.0, 48681.0, 48682.0, 48683.0, 48684.0, 48685.0, 48686.0, 48687.0, 48688.0, 48689.0, 48690.0, 48691.0, 48692.0, 48693.0, 48694.0, 48695.0, 48696.0, 48697.0, 48699.0, 48700.0, 48701.0, 48702.0, 48703.0, 48704.0, 48705.0, 48706.0, 48707.0, 48708.0, 48709.0, 48710.0, 48711.0, 48712.0, 48713.0, 48714.0, 48715.0, 48716.0, 48717.0, 48718.0, 48719.0, 48720.0, 48721.0, 48722.0, 48723.0, 48724.0, 48725.0, 48726.0, 48727.0, 48728.0, 48729.0, 48730.0, 48731.0, 48732.0, 48733.0, 48734.0, 48735.0, 48736.0, 48737.0, 48738.0, 48739.0, 48740.0, 48741.0]\n\nSütun: Policy_Sales_Channel\nNunique Değerler: 146\nUnique Değerler: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 68.0, 69.0, 70.0, 71.0, 73.0, 74.0, 75.0, 76.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 145.0, 146.0, 147.0, 148.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0]\n\nSütun: Vintage\nNunique Değerler: 262\nUnique Değerler: [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271]\n\nSütun: Response\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isnull().sum().sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:12.531070Z","iopub.execute_input":"2024-09-30T15:29:12.531403Z","iopub.status.idle":"2024-09-30T15:29:13.867675Z","shell.execute_reply.started":"2024-09-30T15:29:12.531369Z","shell.execute_reply":"2024-09-30T15:29:13.866660Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"Age                     0\nDriving_License         0\nRegion_Code             0\nPreviously_Insured      0\nVehicle_Age             0\nVehicle_Damage          0\nAnnual_Premium          0\nPolicy_Sales_Channel    0\nVintage                 0\nResponse                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df['Vehicle_Damage']=df['Vehicle_Damage'].map({'Yes':0,'No':1})\ndf['Vehicle_Age']=df['Vehicle_Age'].map({'< 1 Year': 0, '1-2 Year': 1, '> 2 Years': 2})","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:13.868838Z","iopub.execute_input":"2024-09-30T15:29:13.869144Z","iopub.status.idle":"2024-09-30T15:29:15.272370Z","shell.execute_reply.started":"2024-09-30T15:29:13.869112Z","shell.execute_reply":"2024-09-30T15:29:15.271327Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"code","source":"df.isnull().sum().sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:15.273599Z","iopub.execute_input":"2024-09-30T15:29:15.273913Z","iopub.status.idle":"2024-09-30T15:29:15.348565Z","shell.execute_reply.started":"2024-09-30T15:29:15.273880Z","shell.execute_reply":"2024-09-30T15:29:15.347594Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"Age                     0\nDriving_License         0\nRegion_Code             0\nPreviously_Insured      0\nVehicle_Age             0\nVehicle_Damage          0\nAnnual_Premium          0\nPolicy_Sales_Channel    0\nVintage                 0\nResponse                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:15.349737Z","iopub.execute_input":"2024-09-30T15:29:15.350100Z","iopub.status.idle":"2024-09-30T15:29:15.359180Z","shell.execute_reply.started":"2024-09-30T15:29:15.350066Z","shell.execute_reply":"2024-09-30T15:29:15.358251Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 7222760 entries, 2 to 11504797\nData columns (total 10 columns):\n #   Column                Dtype  \n---  ------                -----  \n 0   Age                   int64  \n 1   Driving_License       int64  \n 2   Region_Code           float64\n 3   Previously_Insured    int64  \n 4   Vehicle_Age           int64  \n 5   Vehicle_Damage        int64  \n 6   Annual_Premium        float64\n 7   Policy_Sales_Channel  float64\n 8   Vintage               int64  \n 9   Response              int64  \ndtypes: float64(3), int64(7)\nmemory usage: 606.2 MB\n","output_type":"stream"}]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"markdown","source":"## <font color=#e0c510> Verileri Görşelleştirme - Visualizing Data me","metadata":{}},{"cell_type":"code","source":"#for column in df.columns:\n    # TR = Verideki her bir sütun için döngü başlatılıyor  \n    # EN = Loops through each column in the dataframe\n\n#    print(f\"\\nSütun: {column}\")  \n    # TR = Sütun ismi ekrana yazdırılıyor  \n    # EN = Prints the column name\n\n#    unique = df[column].dropna().unique()  \n    # TR = NaN değerleri düşürerek benzersiz değerler elde ediliyor  \n    # EN = Gets the unique values after dropping NaN values\n\n#    if pd.api.types.is_numeric_dtype(df[column]):  \n        # TR = Eğer sütundaki değerler sayısal ise, değerler sıralanıyor  \n        # EN = If the column is of numeric type, the unique values are sorted\n#        unique = sorted(unique)\n        \n#    nunique = len(unique)  # Benzersiz değerlerin sayısını hesapla\n    # TR = Benzersiz değerlerin sayısını hesaplar  \n    # EN = Calculates the number of unique values\n\n#    print(f\"Nunique Değerler: {nunique}\")  \n    # TR = Benzersiz değerlerin sayısını ekrana yazdırır  \n    # EN = Prints the number of unique values\n\n#    print(f\"Unique Değerler: {unique}\")  \n    # TR = Benzersiz değerler ekrana yazdırılıyor  \n    # EN = Prints the unique values\n","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:15.360635Z","iopub.execute_input":"2024-09-30T15:29:15.361536Z","iopub.status.idle":"2024-09-30T15:29:16.193971Z","shell.execute_reply.started":"2024-09-30T15:29:15.361500Z","shell.execute_reply":"2024-09-30T15:29:16.193032Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"\nSütun: Age\nNunique Değerler: 42\nUnique Değerler: [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]\n\nSütun: Driving_License\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n\nSütun: Region_Code\nNunique Değerler: 47\nUnique Değerler: [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0]\n\nSütun: Previously_Insured\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n\nSütun: Vehicle_Age\nNunique Değerler: 3\nUnique Değerler: [0, 1, 2]\n\nSütun: Vehicle_Damage\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n\nSütun: Annual_Premium\nNunique Değerler: 31259\nUnique Değerler: [2630.0, 2642.0, 2730.0, 2743.0, 2766.0, 2776.0, 2783.0, 2785.0, 2789.0, 2792.0, 2796.0, 2820.0, 2830.0, 2864.0, 2883.0, 2886.0, 2898.0, 2905.0, 2909.0, 2914.0, 2921.0, 2939.0, 2958.0, 3022.0, 3023.0, 3030.0, 3031.0, 3041.0, 3048.0, 3086.0, 3142.0, 3150.0, 3189.0, 3224.0, 3230.0, 3260.0, 3327.0, 3330.0, 3344.0, 3364.0, 3407.0, 3416.0, 3419.0, 3425.0, 3426.0, 3435.0, 3438.0, 3465.0, 3468.0, 3501.0, 3517.0, 3553.0, 3565.0, 3569.0, 3572.0, 3587.0, 3600.0, 3615.0, 3619.0, 3623.0, 3624.0, 3628.0, 3630.0, 3645.0, 3660.0, 3675.0, 3724.0, 3734.0, 3743.0, 3747.0, 3770.0, 3796.0, 3800.0, 3801.0, 3805.0, 3810.0, 3837.0, 3847.0, 3854.0, 3861.0, 3874.0, 3897.0, 3900.0, 3902.0, 3903.0, 3905.0, 3909.0, 3922.0, 3931.0, 3943.0, 3968.0, 3972.0, 3973.0, 4023.0, 4029.0, 4035.0, 4038.0, 4040.0, 4049.0, 4052.0, 4054.0, 4059.0, 4061.0, 4081.0, 4097.0, 4110.0, 4225.0, 4260.0, 4301.0, 4328.0, 4332.0, 4364.0, 4401.0, 4419.0, 4469.0, 4517.0, 4518.0, 4542.0, 4570.0, 4580.0, 4596.0, 4663.0, 4700.0, 4723.0, 4741.0, 4742.0, 4745.0, 4760.0, 4837.0, 4862.0, 4863.0, 4892.0, 4896.0, 4902.0, 4903.0, 4937.0, 4963.0, 5008.0, 5051.0, 5062.0, 5065.0, 5066.0, 5092.0, 5100.0, 5315.0, 5316.0, 5321.0, 5323.0, 5324.0, 5400.0, 5435.0, 5487.0, 5566.0, 5578.0, 5744.0, 5753.0, 5823.0, 5850.0, 5869.0, 5881.0, 5892.0, 5897.0, 5961.0, 5984.0, 6061.0, 6096.0, 6098.0, 6159.0, 6217.0, 6340.0, 7166.0, 7670.0, 7937.0, 8702.0, 8739.0, 8981.0, 9792.0, 10148.0, 10576.0, 10921.0, 10922.0, 11020.0, 11219.0, 11457.0, 11555.0, 11616.0, 11670.0, 11924.0, 12073.0, 12138.0, 12228.0, 12229.0, 12297.0, 12298.0, 12351.0, 12368.0, 12390.0, 12506.0, 12582.0, 12708.0, 12721.0, 12755.0, 12756.0, 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48310.0, 48311.0, 48312.0, 48313.0, 48314.0, 48315.0, 48317.0, 48318.0, 48319.0, 48320.0, 48321.0, 48322.0, 48323.0, 48324.0, 48325.0, 48326.0, 48327.0, 48328.0, 48329.0, 48330.0, 48331.0, 48332.0, 48333.0, 48334.0, 48335.0, 48336.0, 48337.0, 48338.0, 48339.0, 48340.0, 48341.0, 48342.0, 48343.0, 48344.0, 48345.0, 48346.0, 48347.0, 48350.0, 48351.0, 48352.0, 48353.0, 48354.0, 48355.0, 48356.0, 48357.0, 48358.0, 48359.0, 48360.0, 48361.0, 48362.0, 48363.0, 48364.0, 48365.0, 48366.0, 48367.0, 48368.0, 48369.0, 48370.0, 48371.0, 48372.0, 48373.0, 48374.0, 48375.0, 48376.0, 48377.0, 48378.0, 48379.0, 48380.0, 48381.0, 48382.0, 48383.0, 48384.0, 48386.0, 48387.0, 48388.0, 48389.0, 48390.0, 48391.0, 48392.0, 48393.0, 48394.0, 48395.0, 48396.0, 48397.0, 48398.0, 48399.0, 48400.0, 48401.0, 48402.0, 48403.0, 48404.0, 48405.0, 48406.0, 48407.0, 48408.0, 48409.0, 48410.0, 48411.0, 48412.0, 48414.0, 48415.0, 48416.0, 48417.0, 48418.0, 48419.0, 48420.0, 48421.0, 48422.0, 48423.0, 48424.0, 48425.0, 48426.0, 48427.0, 48428.0, 48429.0, 48430.0, 48431.0, 48432.0, 48433.0, 48434.0, 48435.0, 48436.0, 48437.0, 48438.0, 48439.0, 48440.0, 48441.0, 48442.0, 48443.0, 48444.0, 48445.0, 48446.0, 48447.0, 48448.0, 48449.0, 48450.0, 48451.0, 48452.0, 48453.0, 48454.0, 48455.0, 48456.0, 48457.0, 48458.0, 48459.0, 48460.0, 48461.0, 48462.0, 48463.0, 48464.0, 48465.0, 48466.0, 48467.0, 48469.0, 48470.0, 48471.0, 48472.0, 48473.0, 48474.0, 48475.0, 48476.0, 48477.0, 48478.0, 48479.0, 48480.0, 48481.0, 48482.0, 48483.0, 48484.0, 48485.0, 48486.0, 48488.0, 48489.0, 48490.0, 48491.0, 48492.0, 48493.0, 48494.0, 48495.0, 48496.0, 48497.0, 48498.0, 48499.0, 48500.0, 48501.0, 48502.0, 48503.0, 48504.0, 48505.0, 48506.0, 48507.0, 48509.0, 48511.0, 48512.0, 48513.0, 48514.0, 48515.0, 48516.0, 48517.0, 48518.0, 48519.0, 48520.0, 48521.0, 48522.0, 48523.0, 48524.0, 48525.0, 48526.0, 48527.0, 48528.0, 48529.0, 48530.0, 48531.0, 48532.0, 48533.0, 48534.0, 48535.0, 48536.0, 48537.0, 48538.0, 48539.0, 48540.0, 48541.0, 48542.0, 48543.0, 48544.0, 48545.0, 48546.0, 48547.0, 48548.0, 48549.0, 48550.0, 48551.0, 48552.0, 48553.0, 48554.0, 48555.0, 48556.0, 48557.0, 48558.0, 48559.0, 48560.0, 48561.0, 48562.0, 48563.0, 48564.0, 48565.0, 48566.0, 48567.0, 48568.0, 48569.0, 48570.0, 48571.0, 48572.0, 48573.0, 48574.0, 48575.0, 48576.0, 48577.0, 48578.0, 48579.0, 48580.0, 48581.0, 48582.0, 48583.0, 48584.0, 48585.0, 48586.0, 48587.0, 48588.0, 48589.0, 48590.0, 48591.0, 48592.0, 48593.0, 48594.0, 48595.0, 48596.0, 48597.0, 48598.0, 48599.0, 48600.0, 48601.0, 48602.0, 48603.0, 48604.0, 48605.0, 48606.0, 48607.0, 48608.0, 48609.0, 48610.0, 48611.0, 48612.0, 48613.0, 48614.0, 48615.0, 48616.0, 48617.0, 48618.0, 48619.0, 48620.0, 48621.0, 48622.0, 48623.0, 48624.0, 48625.0, 48626.0, 48627.0, 48628.0, 48629.0, 48630.0, 48631.0, 48632.0, 48633.0, 48634.0, 48635.0, 48636.0, 48637.0, 48638.0, 48639.0, 48640.0, 48641.0, 48642.0, 48643.0, 48644.0, 48645.0, 48646.0, 48647.0, 48648.0, 48649.0, 48650.0, 48651.0, 48654.0, 48655.0, 48656.0, 48657.0, 48658.0, 48659.0, 48660.0, 48661.0, 48662.0, 48663.0, 48664.0, 48665.0, 48666.0, 48667.0, 48668.0, 48669.0, 48670.0, 48671.0, 48672.0, 48673.0, 48674.0, 48675.0, 48676.0, 48677.0, 48678.0, 48679.0, 48680.0, 48681.0, 48682.0, 48683.0, 48684.0, 48685.0, 48686.0, 48687.0, 48688.0, 48689.0, 48690.0, 48691.0, 48692.0, 48693.0, 48694.0, 48695.0, 48696.0, 48697.0, 48699.0, 48700.0, 48701.0, 48702.0, 48703.0, 48704.0, 48705.0, 48706.0, 48707.0, 48708.0, 48709.0, 48710.0, 48711.0, 48712.0, 48713.0, 48714.0, 48715.0, 48716.0, 48717.0, 48718.0, 48719.0, 48720.0, 48721.0, 48722.0, 48723.0, 48724.0, 48725.0, 48726.0, 48727.0, 48728.0, 48729.0, 48730.0, 48731.0, 48732.0, 48733.0, 48734.0, 48735.0, 48736.0, 48737.0, 48738.0, 48739.0, 48740.0, 48741.0]\n\nSütun: Policy_Sales_Channel\nNunique Değerler: 146\nUnique Değerler: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 68.0, 69.0, 70.0, 71.0, 73.0, 74.0, 75.0, 76.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 145.0, 146.0, 147.0, 148.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0]\n\nSütun: Vintage\nNunique Değerler: 262\nUnique Değerler: [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271]\n\nSütun: Response\nNunique Değerler: 2\nUnique Değerler: [0, 1]\n","output_type":"stream"}]},{"cell_type":"raw","source":"int_columns = df.select_dtypes(include=['int64']).columns\nfloat_columns = df.select_dtypes(include=['float64']).columns\nobject_columns = df.select_dtypes(include=['object']).columns","metadata":{}},{"cell_type":"code","source":"int_columns = df.select_dtypes(include=['int64']).columns\n# TR = İnt veri türündeki sütunları alır  \n# EN = Selects columns with int data type\n\ncolumns = [col for col in df.columns if col in int_columns] \n# TR = (in df.columns) df DataFrame içindeki (col for col)her sütuna bak.\n# TR = (if col in object_columns) istenilen kolonlara bakar.\n\n# EN = (in df.columns) Look at each column in df DataFrame (col for col).\n# EN = (if col in object_columns) looks at the desired columns.\n\nnum_columns = len(columns)\n# TR = sütun sayısını okuyup, num_columns değişkenine atadım.\n# EN = I read the number of columns and assigned it to the num_columns variable.\n\nfig, axes = plt.subplots(nrows=(num_columns // 3) + 1, ncols=3, figsize=(18, (num_columns // 3 + 1) * 5))\n# TR = nrows=(num_columns // 3) + 1: Bu, oluşturulacak alt grafiklerin (subplot'ların) satır sayısını belirler.\n# TR = num_columns // 3, toplam sütun sayısının 3'e bölünmesiyle elde edilen tam sayı değerdir. + 1 ise, sütun sayısı 3'e tam bölünmüyorsa ekstra bir satır ekler.\n# TR = ncols=3: Bu, her satırda kaç tane alt grafik olacağını belirler. Bu örnekte, her satırda 3 alt grafik olacak şekilde ayarlanmıştır.  \n# TR = figsize=(18, (num_columns // 3 + 1) * 5): Bu, oluşturulacak figürün (grafiklerin tümünün bulunduğu büyük alanın) boyutlarını ayarlar.\n# TR = figsize parametresi, genişlik ve yükseklik değerlerini (18, (num_columns // 3 + 1) * 5) şeklinde alır. \n# TR = Genişlik 18 inç, yükseklik ise her satır için 5 inç olacak şekilde hesaplanır.\n\n# EN = nrows=(num_columns // 3) + 1: This determines the number of rows of subplots to be created.\n# EN = num_columns // 3 is the integer value obtained by dividing the total number of columns by 3. + 1 adds an extra row if the column count is not perfectly divisible by 3.\n# EN = ncols=3: This determines how many subplots will be in each row. In this example, there will be 3 subplots per row.\n# EN = figsize=(18, (num_columns // 3 + 1) * 5): This sets the size of the figure (the large area containing all the plots).\n# EN = The figsize parameter takes the width and height values as (18, (num_columns // 3 + 1) * 5).\n# EN = The width is 18 inches, and the height is calculated as 5 inches per row.\n\n\nfor i, column in enumerate(columns):  \n    row = i // 3  \n    # TR = Her sütunun hangi satırda gösterileceğini hesaplar (3 sütundan birinde)  \n    # EN = Calculates which row each column should be displayed in (one of three columns)\n\n    col = i % 3  \n    # TR = Her sütunun hangi sütunda gösterileceğini hesaplar (0'dan 2'ye kadar)  \n    # EN = Calculates which column each plot should be placed in (from 0 to 2)\n\n    sns.countplot(data=df, x=column, ax=axes[row, col],palette='bright')\n    # TR = Kategorik verilerin her bir sınıfındaki gözlem sayısını görselleştirmek için kullanılır ve her kategorinin frekansını çubuklarla gösterir.\n    # EN = It is used to visualize the number of observations in each class of categorical data and shows the frequency of each category with bars.\n\n    # TR = (data=df) Veriyi df adlı DataFrame alacak.\n    # TR = (x=column) column değişkenindeki sütunları alıp yatay eksenine eşitleyecek\n    # TR = (ax=axes[row, col]) Tablonun yukarıdan ve soldan kaçıncı sırada duracağını beliler\n\n    # EN = (data=df) Will take the data from the DataFrame named df.\n    # EN = (x=column) Will take the columns in the column variable and assign them to the x-axi\n    # EN = (ax=axes[row, col]) Specifies the position in the grid, indicating which row and column it will be placed in.\n\n    axes[row, col].set_title(f'Countplot of {column}')  \n    # TR = Grafiğin başlığını, ilgili sütun adı ile ayarlar  \n    # EN = Sets the title of the plot to the name of the column\n\n    axs = axs.flatten() if num_plots > 1 else [axs]  \n    # TR = Grafik pencerelerini düzleştirir (eğer tek değilse)  \n    # EN = Flattens the plot axes (if there’s more than one)\n\n\nfor j in range(i+1, len(axes.flat)):  \n    # TR = Kullanılmayan alt grafikler (subplots) siliniyor  \n    # EN = Removing unused subplots\n\n    fig.delaxes(axes.flat[j])  \n    # TR = Belirtilen alt grafiği (subplot) siler  \n    # EN = Deletes the specified subplot from the figure\n\nplt.tight_layout()  \n# TR = Grafiklerin düzenli görünmesi için yerleşim ayarlarını yapar  \n# EN = Adjusts the layout for a tidy appearance\n\nplt.show()  \n# TR = Grafiği ekranda gösterir  \n# EN = Displays the plot on the screen","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:16.195242Z","iopub.execute_input":"2024-09-30T15:29:16.195551Z","iopub.status.idle":"2024-09-30T15:29:23.798143Z","shell.execute_reply.started":"2024-09-30T15:29:16.195518Z","shell.execute_reply":"2024-09-30T15:29:23.797221Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x1500 with 7 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= df.select_dtypes(include=['float64']).columns\n# TR = Float veri türündeki sütunları alır  \n# EN = Selects columns with float data type\n\ncolumns = [col for col in df.columns if col in float_columns] \n# TR = (in df.columns) df DataFrame içindeki (col for col)her sütuna bak.\n# TR = (if col in object_columns) istenilen kolonlara bakar.\n\n# EN = (in df.columns) Look at each column in df DataFrame (col for col).\n# EN = (if col in object_columns) looks at the desired columns.\n\nnum_columns = len(columns)\n# TR = sütun sayısını okuyup, num_columns değişkenine atadım.\n# EN = I read the number of columns and assigned it to the num_columns variable.\n\nfig, axes = plt.subplots(nrows=(num_columns // 3) + 1, ncols=3, figsize=(18, (num_columns // 3 + 1) * 5))\n# TR = nrows=(num_columns // 3) + 1: Bu, oluşturulacak alt grafiklerin (subplot'ların) satır sayısını belirler.\n# TR = num_columns // 3, toplam sütun sayısının 3'e bölünmesiyle elde edilen tam sayı değerdir. + 1 ise, sütun sayısı 3'e tam bölünmüyorsa ekstra bir satır ekler.\n# TR = ncols=3: Bu, her satırda kaç tane alt grafik olacağını belirler. Bu örnekte, her satırda 3 alt grafik olacak şekilde ayarlanmıştır.  \n# TR = figsize=(18, (num_columns // 3 + 1) * 5): Bu, oluşturulacak figürün (grafiklerin tümünün bulunduğu büyük alanın) boyutlarını ayarlar.\n# TR = figsize parametresi, genişlik ve yükseklik değerlerini (18, (num_columns // 3 + 1) * 5) şeklinde alır. \n# TR = Genişlik 18 inç, yükseklik ise her satır için 5 inç olacak şekilde hesaplanır.\n\n# EN = nrows=(num_columns // 3) + 1: This determines the number of rows of subplots to be created.\n# EN = num_columns // 3 is the integer value obtained by dividing the total number of columns by 3. + 1 adds an extra row if the column count is not perfectly divisible by 3.\n# EN = ncols=3: This determines how many subplots will be in each row. In this example, there will be 3 subplots per row.\n# EN = figsize=(18, (num_columns // 3 + 1) * 5): This sets the size of the figure (the large area containing all the plots).\n# EN = The figsize parameter takes the width and height values as (18, (num_columns // 3 + 1) * 5).\n# EN = The width is 18 inches, and the height is calculated as 5 inches per row.\n\n\nfor i, column in enumerate(columns):  \n    row = i // 3  \n    # TR = Her sütunun hangi satırda gösterileceğini hesaplar (3 sütundan birinde)  \n    # EN = Calculates which row each column should be displayed in (one of three columns)\n\n    col = i % 3  \n    # TR = Her sütunun hangi sütunda gösterileceğini hesaplar (0'dan 2'ye kadar)  \n    # EN = Calculates which column each plot should be placed in (from 0 to 2)\n\n    sns.histplot(data=df,x=column, bins=20, ax=axes[row, col])  \n    # TR = Belirli bir değişkenin dağılımını görmek için kullanılır. Bu grafik, veri setindeki bir değişkenin frekanslarını veya yoğunluğunu analiz etmenizi sağlar.\n    # EN = It is used to see the distribution of a particular variable. This plot allows you to analyze the frequencies or density of a variable in the dataset.\n\n    # TR = (data=df) Veriyi df adlı DataFrame alacak.\n    # TR = (x=column) column değişkenindeki sütunları alıp yatay eksenine eşitleyecek\n    # TR = (bins=20) Veri ayarını 20 eşit genişlikte bölüme ayırmış olursunuz. \n    # TR = (ax=axes[row, col]) Tablonun yukarıdan ve soldan kaçıncı sırada duracağını beliler\n    \n    # EN = (data=df) Will take the data from the DataFrame named df.\n    # EN = (x=column) Will take the columns in the column variable and assign them to the x-axi\n    # EN = (bins=20) You will divide the data set into 20 equally wide sections.\n    # EN = (ax=axes[row, col]) Specifies the position in the grid, indicating which row and column it will be placed in.\n\n    axes[row, col].set_title(f'histplot of {column}')  \n    # TR = Grafiğin başlığını, ilgili sütun adı ile ayarlar  \n    # EN = Sets the title of the plot to the name of the column\n\n    axs = axs.flatten() if num_plots > 1 else [axs]  \n    # TR = Grafik pencerelerini düzleştirir (eğer tek değilse)  \n    # EN = Flattens the plot axes (if there’s more than one)\n\n\nfor j in range(i+1, len(axes.flat)):  \n    # TR = Kullanılmayan alt grafikler (subplots) siliniyor  \n    # EN = Removing unused subplots\n\n    fig.delaxes(axes.flat[j])  \n    # TR = Belirtilen alt grafiği (subplot) siler  \n    # EN = Deletes the specified subplot from the figure\n\nplt.tight_layout()  \n# TR = Grafiklerin düzenli görünmesi için yerleşim ayarlarını yapar  \n# EN = Adjusts the layout for a tidy appearance\n\nplt.show()  \n# TR = Grafiği ekranda gösterir  \n# EN = Displays the plot on the 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"},"metadata":{}}]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"markdown","source":"## <font color='#0F52BA'> Öznitelik Mühendisliği - Feature Engineering","metadata":{}},{"cell_type":"markdown","source":"### <font color=#007fff> Model - Modelling ","metadata":{}},{"cell_type":"code","source":"x=df.drop('Response',axis=1)\ny=df[['Response']]","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:34.233110Z","iopub.execute_input":"2024-09-30T15:29:34.233515Z","iopub.status.idle":"2024-09-30T15:29:34.440630Z","shell.execute_reply.started":"2024-09-30T15:29:34.233472Z","shell.execute_reply":"2024-09-30T15:29:34.439758Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"x=pd.get_dummies(x,drop_first=True) \n# Tr =  kategorik değişkenlerin sayısal değişkenlere dönüştürülmesi için kullanılır. \n# En = It is used to convert categorical variables into numerical variables.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:34.441780Z","iopub.execute_input":"2024-09-30T15:29:34.442092Z","iopub.status.idle":"2024-09-30T15:29:35.068460Z","shell.execute_reply.started":"2024-09-30T15:29:34.442059Z","shell.execute_reply":"2024-09-30T15:29:35.067430Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.20,random_state=42)\n# TR = modelimizi eğittik. \n# EN = We trained our model.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:35.069656Z","iopub.execute_input":"2024-09-30T15:29:35.069965Z","iopub.status.idle":"2024-09-30T15:29:36.511103Z","shell.execute_reply.started":"2024-09-30T15:29:35.069932Z","shell.execute_reply":"2024-09-30T15:29:36.510317Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"scaler = MinMaxScaler()\n# TR = Avantajları:\n# TR = Özellikle dağılımı belirli bir aralığa sığdırmak istediğinizde kullanışlıdır.\n# TR = Daha hızlı ve basit bir hesaplama yöntemi.\n# TR = Dezavantajları:\n# TR = Aykırı (outlier) değerlerden etkilenir. Aşırı büyük veya küçük değerler, ölçeklendirmeyi bozabilir.\n\n# EN = Advantages:\n# EN = Useful when you want to fit the distribution into a specific range.\n# EN = Faster and simpler calculation method.\n# EN = Disadvantages:\n# EN = Affected by outliers. Extremely large or small values can distort the scaling.\n\nx_train = scaler.fit_transform(x_train)\n# TR = Verileri ölçekleyerek, modelin daha hızlı ve etkili öğrenmesini sağlamak için tüm özellikleri aynı aralığa getiriyoruz.\n# EN = By scaling the data, we bring all the features into the same range to allow the model to learn faster and more effectively.\n\nx_test = scaler.transform(x_test)\n# TR = Test verilerini eğitimde kullanılan aynı ölçekle dönüştürerek modelin test verileri üzerinde doğru tahminler yapmasını sağlar.\n# EN = It enables the model to make accurate predictions on the test data by transforming the test data to the same scale used in training.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:36.512180Z","iopub.execute_input":"2024-09-30T15:29:36.512459Z","iopub.status.idle":"2024-09-30T15:29:37.231865Z","shell.execute_reply.started":"2024-09-30T15:29:36.512428Z","shell.execute_reply":"2024-09-30T15:29:37.231064Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"model=Sequential()\nmodel.add(Dense(2560,activation='relu',input_dim=x_train.shape[1]))\n# TR = Bu katman, tüm giriş nöronlarına bağlantı kurar ve her nöronun ağırlıklarını öğrenir. 56 nöron var.\n# Aktivasyon fonksiyonunu ReLU (Rectified Linear Unit) olarak ayarlar. ReLU fonksiyonu, negatif değerleri sıfıra dönüştürür ve pozitif değerleri olduğu gibi bırakır.\n# EN = This layer connects all input neurons and learns the weights of each neuron. There are 56 neurons.\n# Sets the activation function to ReLU (Rectified Linear Unit). The ReLU function converts negative values ​​to zero and leaves positive values ​​as is.\n\nmodel.add(BatchNormalization())\n# TR = Bu katman, modelin eğitim sürecini daha stabil hale getirmek için kullanılır.\n# EN = This layer is used to make the training process of the model more stable.\n\nmodel.add(Dropout(0.2))\n# TR = Derin öğrenme modelinde aşırı uyumu (overfitting) azaltmak için kullanılır. Genelde 0.2 ile 0.5 arasında olur.\n# EN = It is used to reduce overfitting in the deep learning model. It is generally between 0.2 and 0.5.\n\nmodel.add(Dense(128,activation='relu'))\nmodel.add(BatchNormalization())\n\nmodel.add(Dense(64,activation='relu'))\nmodel.add(BatchNormalization())\n\nmodel.add(Dense(32,activation='relu'))\nmodel.add(BatchNormalization())\n\nmodel.add(Dense(12,activation='relu'))\nmodel.add(BatchNormalization())\n\nmodel.add(Flatten())\n# TR = Derin öğrenme modelindeki çok boyutlu (2D, 3D vb.) verileri tek boyutlu bir vektöre dönüştürür.\n# EN = It converts multidimensional (2D, 3D, etc.) data in the deep learning model into a one-dimensional vector.\n\nmodel.add(Dense(1, activation='sigmoid'))\n# TR = Sigmoid fonksiyonu, çıktı değerini 0 ile 1 arasında sınırlayarak iki sınıflı (binary) sınıflandırma problemleri için kullanılır.\n# EN = The sigmoid function is used for binary classification problems, limiting the output value between 0 and 1.\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n# TR = EarlyStopping ekleyin: Eğitim sırasında model performansı iyileşmediğinde erken durması için kullanıyoruz.\n# EN = Add EarlyStopping: We use it to stop early when model performance does not improve during training.\n\n# TR = val_loss 10 epoch boyunca iyileşmezse eğitimi durduruyor ve en iyi ağırlıkları geri yüklüyor.\n# EN = If val_loss does not improve for 10 epochs, it stops training and restores the best weights.\n\nmodel.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n# TR = İkili sınıflandırma (çıktı katmanı sigmoid aktivasyon kullanır).\n# TR = Tahmin edilen olasılık ile gerçek ikili etiketler arasındaki farkı ölçer.\n# TR = optimizer='adam': Adam (Adaptive Moment Estimation) optimizasyon algoritması, modelin ağırlıklarını güncellerken kullanılan bir yöntemdir.\n# TR = metrics=['accuracy']: Modelin performansını değerlendirmek için kullanılan bir ölçümdür. Doğru sınıflandırılan örneklerin toplam örneklere oranını hesaplar.\n\n# EN = Binary classification (output layer uses a sigmoid activation).\n# EN = Measures the difference between the predicted probability and the actual binary labels.\n# EN = optimizer='adam': Adam (Adaptive Moment Estimation) optimization algorithm is a method used when updating the weights of the model.\n# EN = metrics=['accuracy']: A metric used to evaluate the performance of the model. It calculates the ratio of correctly classified examples to total examples.\n\nhistory=model.fit(x_train, y_train, validation_split=0.2, batch_size=128, epochs=100, callbacks=[early_stopping])\n# TR = Modeli 100 epoch boyunca eğitiyoruz, fakat EarlyStopping ile durdurulabilir. Batch boyutu 128 olarak belirlenmiş.\n# EN = We train the model for 100 epochs, but it can be stopped with EarlyStopping. Batch size is set to 128.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T15:29:37.233368Z","iopub.execute_input":"2024-09-30T15:29:37.233679Z","iopub.status.idle":"2024-09-30T17:09:13.688018Z","shell.execute_reply.started":"2024-09-30T15:29:37.233647Z","shell.execute_reply":"2024-09-30T17:09:13.687200Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Epoch 1/100\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1727710182.542311     112 service.cc:145] XLA service 0x7ea28000ab20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1727710182.542365     112 service.cc:153]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\nI0000 00:00:1727710182.542368     112 service.cc:153]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m   64/36114\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:29\u001b[0m 2ms/step - accuracy: 0.6508 - loss: 0.6690","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1727710186.823513     112 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 3ms/step - accuracy: 0.8663 - loss: 0.2828 - val_accuracy: 0.8704 - val_loss: 0.2692\nEpoch 2/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8705 - loss: 0.2698 - val_accuracy: 0.8706 - val_loss: 0.2686\nEpoch 3/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.2689 - val_accuracy: 0.8706 - val_loss: 0.2687\nEpoch 4/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8704 - loss: 0.2687 - val_accuracy: 0.8706 - val_loss: 0.2681\nEpoch 5/100\n\u001b[1m36114/36114\u001b[0m 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0.2673\nEpoch 10/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2675 - val_accuracy: 0.8706 - val_loss: 0.2674\nEpoch 11/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8705 - loss: 0.2677 - val_accuracy: 0.8707 - val_loss: 0.2671\nEpoch 12/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.2675 - val_accuracy: 0.8708 - val_loss: 0.2673\nEpoch 13/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.2669 - val_accuracy: 0.8707 - val_loss: 0.2671\nEpoch 14/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2670 - val_accuracy: 0.8708 - val_loss: 0.2670\nEpoch 15/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.2671 - val_accuracy: 0.8706 - val_loss: 0.2675\nEpoch 16/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8709 - loss: 0.2673 - val_accuracy: 0.8708 - val_loss: 0.2671\nEpoch 17/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2671 - val_accuracy: 0.8708 - val_loss: 0.2668\nEpoch 18/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2668 - val_accuracy: 0.8706 - val_loss: 0.2672\nEpoch 19/100\n\u001b[1m36114/36114\u001b[0m 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val_loss: 0.2664\nEpoch 24/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8709 - loss: 0.2668 - val_accuracy: 0.8707 - val_loss: 0.2666\nEpoch 25/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2665 - val_accuracy: 0.8707 - val_loss: 0.2670\nEpoch 26/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8705 - loss: 0.2672 - val_accuracy: 0.8709 - val_loss: 0.2665\nEpoch 27/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.2667 - val_accuracy: 0.8708 - val_loss: 0.2665\nEpoch 28/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.2669 - val_accuracy: 0.8709 - val_loss: 0.2666\nEpoch 29/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.2666 - val_accuracy: 0.8709 - val_loss: 0.2663\nEpoch 30/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2663 - val_accuracy: 0.8709 - val_loss: 0.2664\nEpoch 31/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.2669 - val_accuracy: 0.8709 - val_loss: 0.2665\nEpoch 32/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8706 - loss: 0.2669 - val_accuracy: 0.8709 - val_loss: 0.2669\nEpoch 33/100\n\u001b[1m36114/36114\u001b[0m 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val_loss: 0.2662\nEpoch 38/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2663 - val_accuracy: 0.8710 - val_loss: 0.2661\nEpoch 39/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2661 - val_accuracy: 0.8709 - val_loss: 0.2664\nEpoch 40/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2664 - val_accuracy: 0.8709 - val_loss: 0.2663\nEpoch 41/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2663 - val_accuracy: 0.8710 - val_loss: 0.2661\nEpoch 42/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8713 - loss: 0.2660 - val_accuracy: 0.8710 - val_loss: 0.2663\nEpoch 43/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2665 - val_accuracy: 0.8710 - val_loss: 0.2661\nEpoch 44/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2661 - val_accuracy: 0.8710 - val_loss: 0.2660\nEpoch 45/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2659 - val_accuracy: 0.8710 - val_loss: 0.2661\nEpoch 46/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2661 - val_accuracy: 0.8710 - val_loss: 0.2659\nEpoch 47/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2659 - val_accuracy: 0.8710 - val_loss: 0.2659\nEpoch 48/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8709 - loss: 0.2665 - val_accuracy: 0.8710 - val_loss: 0.2660\nEpoch 49/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2660 - val_accuracy: 0.8711 - val_loss: 0.2661\nEpoch 50/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2657 - val_accuracy: 0.8710 - val_loss: 0.2659\nEpoch 51/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2661 - val_accuracy: 0.8711 - val_loss: 0.2659\nEpoch 52/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2661 - val_accuracy: 0.8709 - val_loss: 0.2660\nEpoch 53/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2659 - val_accuracy: 0.8711 - val_loss: 0.2659\nEpoch 54/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8709 - loss: 0.2663 - val_accuracy: 0.8711 - val_loss: 0.2662\nEpoch 55/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8713 - loss: 0.2658 - val_accuracy: 0.8710 - val_loss: 0.2662\nEpoch 56/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2660 - val_accuracy: 0.8711 - val_loss: 0.2661\nEpoch 57/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8713 - loss: 0.2657 - val_accuracy: 0.8711 - val_loss: 0.2659\nEpoch 58/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.2660 - val_accuracy: 0.8711 - val_loss: 0.2659\nEpoch 59/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2659 - val_accuracy: 0.8710 - val_loss: 0.2659\nEpoch 60/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2660 - val_accuracy: 0.8709 - val_loss: 0.2661\nEpoch 61/100\n\u001b[1m36114/36114\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.2657 - val_accuracy: 0.8710 - val_loss: 0.2660\n","output_type":"stream"}]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:09:13.689712Z","iopub.execute_input":"2024-09-30T17:09:13.690205Z","iopub.status.idle":"2024-09-30T17:09:13.722584Z","shell.execute_reply.started":"2024-09-30T17:09:13.690141Z","shell.execute_reply":"2024-09-30T17:09:13.721658Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2560\u001b[0m)           │        \u001b[38;5;34m25,600\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2560\u001b[0m)           │        \u001b[38;5;34m10,240\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2560\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m327,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │           \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m2,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │           \u001b[38;5;34m396\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │            \u001b[38;5;34m48\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m13\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2560</span>)           │        <span style=\"color: #00af00; text-decoration-color: #00af00\">25,600</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2560</span>)           │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,240</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2560</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">327,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">396</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,114,829\u001b[0m (4.25 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,114,829</span> (4.25 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m369,745\u001b[0m (1.41 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">369,745</span> (1.41 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,592\u001b[0m (21.84 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">5,592</span> (21.84 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m739,492\u001b[0m (2.82 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">739,492</span> (2.82 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"test_loss, test_acc = model.evaluate(x_test, y_test)\n# TR = test_loss değişkeni, test verileri üzerinde hesaplanan kayıp değerini içerir. test_acc değişkeni, test verileri üzerinde hesaplanan doğruluk değerini içerir.\n# EN = The test_loss variable contains the loss value calculated on the test data. The test_acc variable contains the accuracy value calculated on the test data.\n\nprint(f\"Test doğruluğu: {test_acc:.4f}\")","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:09:13.723626Z","iopub.execute_input":"2024-09-30T17:09:13.723903Z","iopub.status.idle":"2024-09-30T17:10:19.204976Z","shell.execute_reply.started":"2024-09-30T17:09:13.723873Z","shell.execute_reply":"2024-09-30T17:10:19.204060Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"\u001b[1m45143/45143\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 1ms/step - accuracy: 0.8716 - loss: 0.2652\nTest doğruluğu: 0.8717\n","output_type":"stream"}]},{"cell_type":"code","source":"pred=model.predict(x_test)\n# TR = modeli x_test ile predict özelliği ile tahmin ettik. predict=tahmin demek. Dahmin edip pred eşitledik. \n# EN = We predicted the model with x_test and the predict feature. predict=means prediction. We guessed and equalized the pred.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:10:19.206124Z","iopub.execute_input":"2024-09-30T17:10:19.206473Z","iopub.status.idle":"2024-09-30T17:11:47.278332Z","shell.execute_reply.started":"2024-09-30T17:10:19.206440Z","shell.execute_reply":"2024-09-30T17:11:47.277433Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"\u001b[1m45143/45143\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 1ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"pred","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.279842Z","iopub.execute_input":"2024-09-30T17:11:47.280191Z","iopub.status.idle":"2024-09-30T17:11:47.286595Z","shell.execute_reply.started":"2024-09-30T17:11:47.280128Z","shell.execute_reply":"2024-09-30T17:11:47.285627Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"array([[4.2944436e-04],\n       [7.5001001e-02],\n       [1.5232167e-01],\n       ...,\n       [5.0186980e-01],\n       [6.6173941e-02],\n       [1.3798541e-04]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"pred = np.argmax(pred, axis=1)\npred","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.287760Z","iopub.execute_input":"2024-09-30T17:11:47.288108Z","iopub.status.idle":"2024-09-30T17:11:47.311629Z","shell.execute_reply.started":"2024-09-30T17:11:47.288074Z","shell.execute_reply":"2024-09-30T17:11:47.310702Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, ..., 0, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"y_test","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.312819Z","iopub.execute_input":"2024-09-30T17:11:47.313097Z","iopub.status.idle":"2024-09-30T17:11:47.322613Z","shell.execute_reply.started":"2024-09-30T17:11:47.313066Z","shell.execute_reply":"2024-09-30T17:11:47.321709Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"          Response\n7740248          0\n3233560          0\n1129640          0\n10450014         0\n5159235          0\n...            ...\n7613438          0\n2014883          0\n8309858          0\n60777            0\n10624298         0\n\n[1444552 rows x 1 columns]","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>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7740248</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3233560</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1129640</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10450014</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5159235</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>7613438</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2014883</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8309858</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>60777</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10624298</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1444552 rows × 1 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#y_test = np.argmax(y_test, axis=1)\ny_test","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.323943Z","iopub.execute_input":"2024-09-30T17:11:47.324340Z","iopub.status.idle":"2024-09-30T17:11:47.334991Z","shell.execute_reply.started":"2024-09-30T17:11:47.324297Z","shell.execute_reply":"2024-09-30T17:11:47.334142Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"          Response\n7740248          0\n3233560          0\n1129640          0\n10450014         0\n5159235          0\n...            ...\n7613438          0\n2014883          0\n8309858          0\n60777            0\n10624298         0\n\n[1444552 rows x 1 columns]","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>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7740248</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3233560</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1129640</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10450014</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5159235</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>7613438</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2014883</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8309858</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>60777</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10624298</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1444552 rows × 1 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"accuracy_score(y_test, pred)\n# TR = accuracy_score fonksiyonu ile y_test, pred kullanarak sonucumuzu bulduk.\n# EN = We found our result using the accuracy_score function and y_test, pred.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.336121Z","iopub.execute_input":"2024-09-30T17:11:47.336424Z","iopub.status.idle":"2024-09-30T17:11:47.533304Z","shell.execute_reply.started":"2024-09-30T17:11:47.336393Z","shell.execute_reply":"2024-09-30T17:11:47.532367Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"0.8709696847188609"},"metadata":{}}]},{"cell_type":"code","source":"confusion_matrix(y_test, pred)\n# TR = confusion_matrix fonksiyonu ile y_test, pred kullanarak ne kadarını yanlış tahmin ettiğimizi buluyoruz\n# EN = Using the confusion_matrix function and y_test, pred, we find out how much we guessed wrong.\n\n# TR = 4 sayı değeri veriyor bize. Sol üst ve sağ alt doğru tahmin, sağ üst ve sol alt yanlış tahmin. \n# EN = It gives us 4 number values. Upper left and lower right are correct guesses, upper right and lower left are incorrect guesses.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.534623Z","iopub.execute_input":"2024-09-30T17:11:47.535033Z","iopub.status.idle":"2024-09-30T17:11:47.865859Z","shell.execute_reply.started":"2024-09-30T17:11:47.534977Z","shell.execute_reply":"2024-09-30T17:11:47.864957Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"array([[1258161,       0],\n       [ 186391,       0]])"},"metadata":{}}]},{"cell_type":"code","source":"sns.heatmap(confusion_matrix(y_test, pred),annot=True);\n# TR = Yukarıdakinin görsel versiyonu.\n# EN = Visual version of the above.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:47.867003Z","iopub.execute_input":"2024-09-30T17:11:47.867318Z","iopub.status.idle":"2024-09-30T17:11:48.454900Z","shell.execute_reply.started":"2024-09-30T17:11:47.867285Z","shell.execute_reply":"2024-09-30T17:11:48.453995Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test, pred))\n# TR = print içinde yazdık yoksa sayıların sıralanması bozuluyor.\n# EN = We wrote it in print, otherwise the order of the numbers will be disrupted.\n\n# TR = classification_report ile y_test, pred kullanarak precision, recall, f1-score ve support ulaştık.\n# EN = We achieved precision, recall, f1-score and support using classification_report and y_test, pred.\n\n# TR = Precision (Kesinlik): Doğru olarak pozitif tahmin edilen örneklerin, toplam pozitif tahmin edilen örnekler içindeki oranını gösterir. Yani, modelin pozitif sınıfı ne kadar doğru tahmin ettiğini ölçer.\n# EN = Precision (Precision): It shows the ratio of correctly predicted positive samples among the total positive predicted samples. That is, it measures how accurately the model predicts the positive class.\n\n# TR = Recall (Duyarlılık): Gerçek pozitif örneklerin, toplam pozitif örnekler içindeki oranını gösterir. Modelin pozitif sınıfı ne kadar iyi bulduğunu ölçer.\n# EN = Recall (Sensitivity): Shows the ratio of true positive samples in total positive samples. It measures how well the model finds the positive class.\n\n# TR = F1-score: Precision ve recall'un harmonik ortalamasıdır. Hem precision hem de recall'u dikkate alarak modelin genel performansını özetler.\n# EN = F1-score: It is the harmonic mean of precision and recall. It summarizes the overall performance of the model, taking into account both precision and recall.\n\n# TR = Support: Her sınıftan kaç örneğin bulunduğunu gösterir. Yani, gerçek etiketlerde her bir sınıfa ait kaç örnek olduğunu ifade eder.\n# EN = Support: Shows how many examples of each class are available. That is, it expresses how many examples of each class there are in the real labels.","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:48.461690Z","iopub.execute_input":"2024-09-30T17:11:48.462000Z","iopub.status.idle":"2024-09-30T17:11:51.524088Z","shell.execute_reply.started":"2024-09-30T17:11:48.461967Z","shell.execute_reply":"2024-09-30T17:11:51.523021Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.87      1.00      0.93   1258161\n           1       0.00      0.00      0.00    186391\n\n    accuracy                           0.87   1444552\n   macro avg       0.44      0.50      0.47   1444552\nweighted avg       0.76      0.87      0.81   1444552\n\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'],label='Accuracy')\nplt.plot(history.history['val_accuracy'],label='Val_Accuracy')\nplt.legend();","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:51.525297Z","iopub.execute_input":"2024-09-30T17:11:51.525598Z","iopub.status.idle":"2024-09-30T17:11:51.764031Z","shell.execute_reply.started":"2024-09-30T17:11:51.525566Z","shell.execute_reply":"2024-09-30T17:11:51.763192Z"},"trusted":true},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"pickle.dump(model,open('Sigorta.pkl','wb'))","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:11:51.765028Z","iopub.execute_input":"2024-09-30T17:11:51.765315Z","iopub.status.idle":"2024-09-30T17:11:51.861469Z","shell.execute_reply.started":"2024-09-30T17:11:51.765284Z","shell.execute_reply":"2024-09-30T17:11:51.860663Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}