Minor Project -Used Cars Price Detection

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":4897786,"sourceType":"datasetVersion","datasetId":2840264}],"dockerImageVersionId":30017,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# MINOR PROJECT \n\n***\n\n## Used Car Price Prediction  \n\nGiven *data about used cars*, let's try to predict the **price** of a given car.  \n  \nWe will use linear regression and gradient boosting (LightGBM) to make our predictions.","metadata":{}},{"cell_type":"markdown","source":"# Getting Started","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.linear_model import LinearRegression\nimport lightgbm as lgb\n\nfrom sklearn.metrics import mean_squared_error","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2024-05-03T04:03:07.302905Z","iopub.execute_input":"2024-05-03T04:03:07.303398Z","iopub.status.idle":"2024-05-03T04:03:07.311113Z","shell.execute_reply.started":"2024-05-03T04:03:07.303268Z","shell.execute_reply":"2024-05-03T04:03:07.309376Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/used-car-price-dataset/used_car_dataset.csv')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:06:22.615895Z","iopub.execute_input":"2024-05-03T05:06:22.616572Z","iopub.status.idle":"2024-05-03T05:06:22.635839Z","shell.execute_reply.started":"2024-05-03T05:06:22.616501Z","shell.execute_reply":"2024-05-03T05:06:22.634971Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:06:26.902622Z","iopub.execute_input":"2024-05-03T05:06:26.903402Z","iopub.status.idle":"2024-05-03T05:06:26.931065Z","shell.execute_reply.started":"2024-05-03T05:06:26.903348Z","shell.execute_reply":"2024-05-03T05:06:26.929976Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"                                               car_name car_price_in_rupees  \\\n0     Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...         ₹ 4.45 Lakh   \n1                            Maruti Suzuki Alto 800 Lxi         ₹ 2.93 Lakh   \n2                               Tata Safari XZ Plus New        ₹ 22.49 Lakh   \n3                               Maruti Suzuki Ciaz ZXI+         ₹ 6.95 Lakh   \n4        Jeep Compass Sport Plus 1.4 Petrol [2019-2020]           ₹ 12 Lakh   \n...                                                 ...                 ...   \n2100                         Ford Figo Titanium1.5 TDCi          ₹ 3.6 Lakh   \n2101                    MINI Cooper Countryman Cooper D           ₹ 22 Lakh   \n2102                          Hyundai Verna 1.6 VTVT SX         ₹ 8.38 Lakh   \n2103                         Maruti Suzuki Ciaz VXi+ AT         ₹ 6.75 Lakh   \n2104                          Hyundai Verna 1.6 VTVT SX         ₹ 8.76 Lakh   \n\n     kms_driven fuel_type       city  year_of_manufacture  \n0     22,402 km    Petrol     Mumbai                 2016  \n1     10,344 km    Petrol    Kolkata                 2019  \n2     12,999 km    Diesel  Bangalore                 2021  \n3     45,000 km    Petrol      Thane                 2016  \n4     11,193 km    Petrol    Kolkata                 2019  \n...         ...       ...        ...                  ...  \n2100  42,158 km    Diesel    Kolkata                 2015  \n2101  68,862 km    Diesel  Hyderabad                 2013  \n2102  37,622 km    Petrol    Chennai                 2018  \n2103  64,726 km    Petrol     Mumbai                 2017  \n2104  29,150 km    Petrol       Pune                 2017  \n\n[2105 rows x 6 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>car_name</th>\n      <th>car_price_in_rupees</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n      <th>city</th>\n      <th>year_of_manufacture</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...</td>\n      <td>₹ 4.45 Lakh</td>\n      <td>22,402 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Maruti Suzuki Alto 800 Lxi</td>\n      <td>₹ 2.93 Lakh</td>\n      <td>10,344 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Tata Safari XZ Plus New</td>\n      <td>₹ 22.49 Lakh</td>\n      <td>12,999 km</td>\n      <td>Diesel</td>\n      <td>Bangalore</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Maruti Suzuki Ciaz ZXI+</td>\n      <td>₹ 6.95 Lakh</td>\n      <td>45,000 km</td>\n      <td>Petrol</td>\n      <td>Thane</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Jeep Compass Sport Plus 1.4 Petrol [2019-2020]</td>\n      <td>₹ 12 Lakh</td>\n      <td>11,193 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2100</th>\n      <td>Ford Figo Titanium1.5 TDCi</td>\n      <td>₹ 3.6 Lakh</td>\n      <td>42,158 km</td>\n      <td>Diesel</td>\n      <td>Kolkata</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>2101</th>\n      <td>MINI Cooper Countryman Cooper D</td>\n      <td>₹ 22 Lakh</td>\n      <td>68,862 km</td>\n      <td>Diesel</td>\n      <td>Hyderabad</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>2102</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.38 Lakh</td>\n      <td>37,622 km</td>\n      <td>Petrol</td>\n      <td>Chennai</td>\n      <td>2018</td>\n    </tr>\n    <tr>\n      <th>2103</th>\n      <td>Maruti Suzuki Ciaz VXi+ AT</td>\n      <td>₹ 6.75 Lakh</td>\n      <td>64,726 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2017</td>\n    </tr>\n    <tr>\n      <th>2104</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.76 Lakh</td>\n      <td>29,150 km</td>\n      <td>Petrol</td>\n      <td>Pune</td>\n      <td>2017</td>\n    </tr>\n  </tbody>\n</table>\n<p>2105 rows × 6 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:06:59.742792Z","iopub.execute_input":"2024-05-03T05:06:59.743266Z","iopub.status.idle":"2024-05-03T05:06:59.758056Z","shell.execute_reply.started":"2024-05-03T05:06:59.743212Z","shell.execute_reply":"2024-05-03T05:06:59.756337Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2105 entries, 0 to 2104\nData columns (total 6 columns):\n #   Column               Non-Null Count  Dtype \n---  ------               --------------  ----- \n 0   car_name             2105 non-null   object\n 1   car_price_in_rupees  2105 non-null   object\n 2   kms_driven           2105 non-null   object\n 3   fuel_type            2105 non-null   object\n 4   city                 2105 non-null   object\n 5   year_of_manufacture  2105 non-null   int64 \ndtypes: int64(1), object(5)\nmemory usage: 98.8+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Preprocessing","metadata":{}},{"cell_type":"code","source":"data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:07:13.456019Z","iopub.execute_input":"2024-05-03T05:07:13.456475Z","iopub.status.idle":"2024-05-03T05:07:13.468115Z","shell.execute_reply.started":"2024-05-03T05:07:13.456423Z","shell.execute_reply":"2024-05-03T05:07:13.466990Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"car_name               0\ncar_price_in_rupees    0\nkms_driven             0\nfuel_type              0\ncity                   0\nyear_of_manufacture    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"null_columns = data.columns[data.isna().mean() > 0.25]\n\ndata = data.drop(null_columns, axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:04:07.419621Z","iopub.execute_input":"2024-05-03T04:04:07.420057Z","iopub.status.idle":"2024-05-03T04:04:08.398050Z","shell.execute_reply.started":"2024-05-03T04:04:07.420017Z","shell.execute_reply":"2024-05-03T04:04:08.396674Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:07:27.657839Z","iopub.execute_input":"2024-05-03T05:07:27.658235Z","iopub.status.idle":"2024-05-03T05:07:27.681608Z","shell.execute_reply.started":"2024-05-03T05:07:27.658200Z","shell.execute_reply":"2024-05-03T05:07:27.679874Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"                                               car_name car_price_in_rupees  \\\n0     Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...         ₹ 4.45 Lakh   \n1                            Maruti Suzuki Alto 800 Lxi         ₹ 2.93 Lakh   \n2                               Tata Safari XZ Plus New        ₹ 22.49 Lakh   \n3                               Maruti Suzuki Ciaz ZXI+         ₹ 6.95 Lakh   \n4        Jeep Compass Sport Plus 1.4 Petrol [2019-2020]           ₹ 12 Lakh   \n...                                                 ...                 ...   \n2100                         Ford Figo Titanium1.5 TDCi          ₹ 3.6 Lakh   \n2101                    MINI Cooper Countryman Cooper D           ₹ 22 Lakh   \n2102                          Hyundai Verna 1.6 VTVT SX         ₹ 8.38 Lakh   \n2103                         Maruti Suzuki Ciaz VXi+ AT         ₹ 6.75 Lakh   \n2104                          Hyundai Verna 1.6 VTVT SX         ₹ 8.76 Lakh   \n\n     kms_driven fuel_type       city  year_of_manufacture  \n0     22,402 km    Petrol     Mumbai                 2016  \n1     10,344 km    Petrol    Kolkata                 2019  \n2     12,999 km    Diesel  Bangalore                 2021  \n3     45,000 km    Petrol      Thane                 2016  \n4     11,193 km    Petrol    Kolkata                 2019  \n...         ...       ...        ...                  ...  \n2100  42,158 km    Diesel    Kolkata                 2015  \n2101  68,862 km    Diesel  Hyderabad                 2013  \n2102  37,622 km    Petrol    Chennai                 2018  \n2103  64,726 km    Petrol     Mumbai                 2017  \n2104  29,150 km    Petrol       Pune                 2017  \n\n[2105 rows x 6 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>car_name</th>\n      <th>car_price_in_rupees</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n      <th>city</th>\n      <th>year_of_manufacture</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...</td>\n      <td>₹ 4.45 Lakh</td>\n      <td>22,402 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Maruti Suzuki Alto 800 Lxi</td>\n      <td>₹ 2.93 Lakh</td>\n      <td>10,344 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Tata Safari XZ Plus New</td>\n      <td>₹ 22.49 Lakh</td>\n      <td>12,999 km</td>\n      <td>Diesel</td>\n      <td>Bangalore</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Maruti Suzuki Ciaz ZXI+</td>\n      <td>₹ 6.95 Lakh</td>\n      <td>45,000 km</td>\n      <td>Petrol</td>\n      <td>Thane</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Jeep Compass Sport Plus 1.4 Petrol [2019-2020]</td>\n      <td>₹ 12 Lakh</td>\n      <td>11,193 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2100</th>\n      <td>Ford Figo Titanium1.5 TDCi</td>\n      <td>₹ 3.6 Lakh</td>\n      <td>42,158 km</td>\n      <td>Diesel</td>\n      <td>Kolkata</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>2101</th>\n      <td>MINI Cooper Countryman Cooper D</td>\n      <td>₹ 22 Lakh</td>\n      <td>68,862 km</td>\n      <td>Diesel</td>\n      <td>Hyderabad</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>2102</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.38 Lakh</td>\n      <td>37,622 km</td>\n      <td>Petrol</td>\n      <td>Chennai</td>\n      <td>2018</td>\n    </tr>\n    <tr>\n      <th>2103</th>\n      <td>Maruti Suzuki Ciaz VXi+ AT</td>\n      <td>₹ 6.75 Lakh</td>\n      <td>64,726 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2017</td>\n    </tr>\n    <tr>\n      <th>2104</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.76 Lakh</td>\n      <td>29,150 km</td>\n      <td>Petrol</td>\n      <td>Pune</td>\n      <td>2017</td>\n    </tr>\n  </tbody>\n</table>\n<p>2105 rows × 6 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:07:48.949218Z","iopub.execute_input":"2024-05-03T05:07:48.949642Z","iopub.status.idle":"2024-05-03T05:07:48.970343Z","shell.execute_reply.started":"2024-05-03T05:07:48.949604Z","shell.execute_reply":"2024-05-03T05:07:48.969318Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"                                               car_name car_price_in_rupees  \\\n0     Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...         ₹ 4.45 Lakh   \n1                            Maruti Suzuki Alto 800 Lxi         ₹ 2.93 Lakh   \n2                               Tata Safari XZ Plus New        ₹ 22.49 Lakh   \n3                               Maruti Suzuki Ciaz ZXI+         ₹ 6.95 Lakh   \n4        Jeep Compass Sport Plus 1.4 Petrol [2019-2020]           ₹ 12 Lakh   \n...                                                 ...                 ...   \n2100                         Ford Figo Titanium1.5 TDCi          ₹ 3.6 Lakh   \n2101                    MINI Cooper Countryman Cooper D           ₹ 22 Lakh   \n2102                          Hyundai Verna 1.6 VTVT SX         ₹ 8.38 Lakh   \n2103                         Maruti Suzuki Ciaz VXi+ AT         ₹ 6.75 Lakh   \n2104                          Hyundai Verna 1.6 VTVT SX         ₹ 8.76 Lakh   \n\n     kms_driven fuel_type       city  year_of_manufacture  \n0     22,402 km    Petrol     Mumbai                 2016  \n1     10,344 km    Petrol    Kolkata                 2019  \n2     12,999 km    Diesel  Bangalore                 2021  \n3     45,000 km    Petrol      Thane                 2016  \n4     11,193 km    Petrol    Kolkata                 2019  \n...         ...       ...        ...                  ...  \n2100  42,158 km    Diesel    Kolkata                 2015  \n2101  68,862 km    Diesel  Hyderabad                 2013  \n2102  37,622 km    Petrol    Chennai                 2018  \n2103  64,726 km    Petrol     Mumbai                 2017  \n2104  29,150 km    Petrol       Pune                 2017  \n\n[2105 rows x 6 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>car_name</th>\n      <th>car_price_in_rupees</th>\n      <th>kms_driven</th>\n      <th>fuel_type</th>\n      <th>city</th>\n      <th>year_of_manufacture</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT [2017-2...</td>\n      <td>₹ 4.45 Lakh</td>\n      <td>22,402 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Maruti Suzuki Alto 800 Lxi</td>\n      <td>₹ 2.93 Lakh</td>\n      <td>10,344 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Tata Safari XZ Plus New</td>\n      <td>₹ 22.49 Lakh</td>\n      <td>12,999 km</td>\n      <td>Diesel</td>\n      <td>Bangalore</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Maruti Suzuki Ciaz ZXI+</td>\n      <td>₹ 6.95 Lakh</td>\n      <td>45,000 km</td>\n      <td>Petrol</td>\n      <td>Thane</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Jeep Compass Sport Plus 1.4 Petrol [2019-2020]</td>\n      <td>₹ 12 Lakh</td>\n      <td>11,193 km</td>\n      <td>Petrol</td>\n      <td>Kolkata</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2100</th>\n      <td>Ford Figo Titanium1.5 TDCi</td>\n      <td>₹ 3.6 Lakh</td>\n      <td>42,158 km</td>\n      <td>Diesel</td>\n      <td>Kolkata</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>2101</th>\n      <td>MINI Cooper Countryman Cooper D</td>\n      <td>₹ 22 Lakh</td>\n      <td>68,862 km</td>\n      <td>Diesel</td>\n      <td>Hyderabad</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>2102</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.38 Lakh</td>\n      <td>37,622 km</td>\n      <td>Petrol</td>\n      <td>Chennai</td>\n      <td>2018</td>\n    </tr>\n    <tr>\n      <th>2103</th>\n      <td>Maruti Suzuki Ciaz VXi+ AT</td>\n      <td>₹ 6.75 Lakh</td>\n      <td>64,726 km</td>\n      <td>Petrol</td>\n      <td>Mumbai</td>\n      <td>2017</td>\n    </tr>\n    <tr>\n      <th>2104</th>\n      <td>Hyundai Verna 1.6 VTVT SX</td>\n      <td>₹ 8.76 Lakh</td>\n      <td>29,150 km</td>\n      <td>Petrol</td>\n      <td>Pune</td>\n      <td>2017</td>\n    </tr>\n  </tbody>\n</table>\n<p>2105 rows × 6 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"{column: len(data[column].unique()) for column in data.columns if data.dtypes[column] == 'object'}","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:07:56.173453Z","iopub.execute_input":"2024-05-03T05:07:56.173891Z","iopub.status.idle":"2024-05-03T05:07:56.187624Z","shell.execute_reply.started":"2024-05-03T05:07:56.173852Z","shell.execute_reply":"2024-05-03T05:07:56.186334Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"{'car_name': 946,\n 'car_price_in_rupees': 811,\n 'kms_driven': 1628,\n 'fuel_type': 8,\n 'city': 16}"},"metadata":{}}]},{"cell_type":"code","source":"data = data.drop('kms_driven', axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:08:22.538805Z","iopub.execute_input":"2024-05-03T05:08:22.539248Z","iopub.status.idle":"2024-05-03T05:08:22.546784Z","shell.execute_reply.started":"2024-05-03T05:08:22.539211Z","shell.execute_reply":"2024-05-03T05:08:22.545218Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"def onehot_encode(df, columns, prefixes):\n    df = df.copy()\n    for column, prefix in zip(columns, prefixes):\n        dummies = pd.get_dummies(df[column], prefix=prefix)\n        df = pd.concat([df, dummies], axis=1)\n        df = df.drop(column, axis=1)\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:13:25.907315Z","iopub.execute_input":"2024-05-03T05:13:25.907813Z","iopub.status.idle":"2024-05-03T05:13:25.916266Z","shell.execute_reply.started":"2024-05-03T05:13:25.907771Z","shell.execute_reply":"2024-05-03T05:13:25.914769Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"data = onehot_encode(\n    data,\n    ['car_name', 'car_price_in_rupees', 'fuel_type', 'city'],\n    ['cname', 'price', 'fname', 'state']\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:15:55.179839Z","iopub.execute_input":"2024-05-03T05:15:55.180257Z","iopub.status.idle":"2024-05-03T05:15:55.235095Z","shell.execute_reply.started":"2024-05-03T05:15:55.180220Z","shell.execute_reply":"2024-05-03T05:15:55.233867Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:16:04.140269Z","iopub.execute_input":"2024-05-03T05:16:04.140692Z","iopub.status.idle":"2024-05-03T05:16:04.174522Z","shell.execute_reply.started":"2024-05-03T05:16:04.140649Z","shell.execute_reply":"2024-05-03T05:16:04.173353Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"      year_of_manufacture  cname_Audi A3 35 TDI Premium + Sunroof  \\\n0                    2016                                       0   \n1                    2019                                       0   \n2                    2021                                       0   \n3                    2016                                       0   \n4                    2019                                       0   \n...                   ...                                     ...   \n2100                 2015                                       0   \n2101                 2013                                       0   \n2102                 2018                                       0   \n2103                 2017                                       0   \n2104                 2017                                       0   \n\n      cname_Audi A3 35 TDI Premium Plus  \\\n0                                     0   \n1                                     0   \n2                                     0   \n3                                     0   \n4                                     0   \n...                                 ...   \n2100                                  0   \n2101                                  0   \n2102                                  0   \n2103                                  0   \n2104                                  0   \n\n      cname_Audi A3 35 TDI Premium Plus + Sunroof  \\\n0                                               0   \n1                                               0   \n2                                               0   \n3                                               0   \n4                                               0   \n...                                           ...   \n2100                                            0   \n2101                                            0   \n2102                                            0   \n2103                                            0   \n2104                                            0   \n\n      cname_Audi A3 35 TDI Technology + Sunroof  \\\n0                                             0   \n1                                             0   \n2                                             0   \n3                                             0   \n4                                             0   \n...                                         ...   \n2100                                          0   \n2101                                          0   \n2102                                          0   \n2103                                          0   \n2104                                          0   \n\n      cname_Audi A3 35 TFSI Premium Plus  cname_Audi A3 40 TFSI Premium  \\\n0                                      0                              0   \n1                                      0                              0   \n2                                      0                              0   \n3                                      0                              0   \n4                                      0                              0   \n...                                  ...                            ...   \n2100                                   0                              0   \n2101                                   0                              0   \n2102                                   0                              0   \n2103                                   0                              0   \n2104                                   0                              0   \n\n      cname_Audi A3 40 TFSI Premium Plus + sunroof  \\\n0                                                0   \n1                                                0   \n2                                                0   \n3                                                0   \n4                                                0   \n...                                            ...   \n2100                                             0   \n2101                                             0   \n2102                                             0   \n2103                                             0   \n2104                                             0   \n\n      cname_Audi A4 2.0 TDI (143 bhp)  cname_Audi A4 2.0 TDI (143bhp)  ...  \\\n0                                   0                               0  ...   \n1                                   0                               0  ...   \n2                                   0                               0  ...   \n3                                   0                               0  ...   \n4                                   0                               0  ...   \n...                               ...                             ...  ...   \n2100                                0                               0  ...   \n2101                                0                               0  ...   \n2102                                0                               0  ...   \n2103                                0                               0  ...   \n2104                                0                               0  ...   \n\n      state_Gurgaon  state_Hyderabad  state_Kolkata  state_Mumbai  \\\n0                 0                0              0             1   \n1                 0                0              1             0   \n2                 0                0              0             0   \n3                 0                0              0             0   \n4                 0                0              1             0   \n...             ...              ...            ...           ...   \n2100              0                0              1             0   \n2101              0                1              0             0   \n2102              0                0              0             0   \n2103              0                0              0             1   \n2104              0                0              0             0   \n\n      state_Noida  state_Pallikarnai  state_Poonamallee  state_Pune  \\\n0               0                  0                  0           0   \n1               0                  0                  0           0   \n2               0                  0                  0           0   \n3               0                  0                  0           0   \n4               0                  0                  0           0   \n...           ...                ...                ...         ...   \n2100            0                  0                  0           0   \n2101            0                  0                  0           0   \n2102            0                  0                  0           0   \n2103            0                  0                  0           0   \n2104            0                  0                  0           1   \n\n      state_Thane  state_Thiruvallur  \n0               0                  0  \n1               0                  0  \n2               0                  0  \n3               1                  0  \n4               0                  0  \n...           ...                ...  \n2100            0                  0  \n2101            0                  0  \n2102            0                  0  \n2103            0                  0  \n2104            0                  0  \n\n[2105 rows x 1782 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>year_of_manufacture</th>\n      <th>cname_Audi A3 35 TDI Premium + Sunroof</th>\n      <th>cname_Audi A3 35 TDI Premium Plus</th>\n      <th>cname_Audi A3 35 TDI Premium Plus + Sunroof</th>\n      <th>cname_Audi A3 35 TDI Technology + Sunroof</th>\n      <th>cname_Audi A3 35 TFSI Premium Plus</th>\n      <th>cname_Audi A3 40 TFSI Premium</th>\n      <th>cname_Audi A3 40 TFSI Premium Plus + sunroof</th>\n      <th>cname_Audi A4 2.0 TDI (143 bhp)</th>\n      <th>cname_Audi A4 2.0 TDI (143bhp)</th>\n      <th>...</th>\n      <th>state_Gurgaon</th>\n      <th>state_Hyderabad</th>\n      <th>state_Kolkata</th>\n      <th>state_Mumbai</th>\n      <th>state_Noida</th>\n      <th>state_Pallikarnai</th>\n      <th>state_Poonamallee</th>\n      <th>state_Pune</th>\n      <th>state_Thane</th>\n      <th>state_Thiruvallur</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2019</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2019</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2100</th>\n      <td>2015</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2101</th>\n      <td>2013</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2102</th>\n      <td>2018</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2103</th>\n      <td>2017</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2104</th>\n      <td>2017</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2105 rows × 1782 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.isna().sum().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:16:14.860446Z","iopub.execute_input":"2024-05-03T05:16:14.861328Z","iopub.status.idle":"2024-05-03T05:16:14.891065Z","shell.execute_reply.started":"2024-05-03T05:16:14.861259Z","shell.execute_reply":"2024-05-03T05:16:14.889925Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"# Splitting and Scaling","metadata":{}},{"cell_type":"code","source":"y = data.loc[:, 'year_of_manufacture']\nX = data.drop('year_of_manufacture', axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:21.349495Z","iopub.execute_input":"2024-05-03T05:17:21.349917Z","iopub.status.idle":"2024-05-03T05:17:21.359688Z","shell.execute_reply.started":"2024-05-03T05:17:21.349879Z","shell.execute_reply":"2024-05-03T05:17:21.358257Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:29:18.859033Z","iopub.execute_input":"2024-05-03T05:29:18.859478Z","iopub.status.idle":"2024-05-03T05:29:18.893583Z","shell.execute_reply.started":"2024-05-03T05:29:18.859428Z","shell.execute_reply":"2024-05-03T05:29:18.892402Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"      year_of_manufacture  cname_Audi A3 35 TDI Premium + Sunroof  \\\n0                    2016                                       0   \n1                    2019                                       0   \n2                    2021                                       0   \n3                    2016                                       0   \n4                    2019                                       0   \n...                   ...                                     ...   \n2100                 2015                                       0   \n2101                 2013                                       0   \n2102                 2018                                       0   \n2103                 2017                                       0   \n2104                 2017                                       0   \n\n      cname_Audi A3 35 TDI Premium Plus  \\\n0                                     0   \n1                                     0   \n2                                     0   \n3                                     0   \n4                                     0   \n...                                 ...   \n2100                                  0   \n2101                                  0   \n2102                                  0   \n2103                                  0   \n2104                                  0   \n\n      cname_Audi A3 35 TDI Premium Plus + Sunroof  \\\n0                                               0   \n1                                               0   \n2                                               0   \n3                                               0   \n4                                               0   \n...                                           ...   \n2100                                            0   \n2101                                            0   \n2102                                            0   \n2103                                            0   \n2104                                            0   \n\n      cname_Audi A3 35 TDI Technology + Sunroof  \\\n0                                             0   \n1                                             0   \n2                                             0   \n3                                             0   \n4                                             0   \n...                                         ...   \n2100                                          0   \n2101                                          0   \n2102                                          0   \n2103                                          0   \n2104                                          0   \n\n      cname_Audi A3 35 TFSI Premium Plus  cname_Audi A3 40 TFSI Premium  \\\n0                                      0                              0   \n1                                      0                              0   \n2                                      0                              0   \n3                                      0                              0   \n4                                      0                              0   \n...                                  ...                            ...   \n2100                                   0                              0   \n2101                                   0                              0   \n2102                                   0                              0   \n2103                                   0                              0   \n2104                                   0                              0   \n\n      cname_Audi A3 40 TFSI Premium Plus + sunroof  \\\n0                                                0   \n1                                                0   \n2                                                0   \n3                                                0   \n4                                                0   \n...                                            ...   \n2100                                             0   \n2101                                             0   \n2102                                             0   \n2103                                             0   \n2104                                             0   \n\n      cname_Audi A4 2.0 TDI (143 bhp)  cname_Audi A4 2.0 TDI (143bhp)  ...  \\\n0                                   0                               0  ...   \n1                                   0                               0  ...   \n2                                   0                               0  ...   \n3                                   0                               0  ...   \n4                                   0                               0  ...   \n...                               ...                             ...  ...   \n2100                                0                               0  ...   \n2101                                0                               0  ...   \n2102                                0                               0  ...   \n2103                                0                               0  ...   \n2104                                0                               0  ...   \n\n      state_Gurgaon  state_Hyderabad  state_Kolkata  state_Mumbai  \\\n0                 0                0              0             1   \n1                 0                0              1             0   \n2                 0                0              0             0   \n3                 0                0              0             0   \n4                 0                0              1             0   \n...             ...              ...            ...           ...   \n2100              0                0              1             0   \n2101              0                1              0             0   \n2102              0                0              0             0   \n2103              0                0              0             1   \n2104              0                0              0             0   \n\n      state_Noida  state_Pallikarnai  state_Poonamallee  state_Pune  \\\n0               0                  0                  0           0   \n1               0                  0                  0           0   \n2               0                  0                  0           0   \n3               0                  0                  0           0   \n4               0                  0                  0           0   \n...           ...                ...                ...         ...   \n2100            0                  0                  0           0   \n2101            0                  0                  0           0   \n2102            0                  0                  0           0   \n2103            0                  0                  0           0   \n2104            0                  0                  0           1   \n\n      state_Thane  state_Thiruvallur  \n0               0                  0  \n1               0                  0  \n2               0                  0  \n3               1                  0  \n4               0                  0  \n...           ...                ...  \n2100            0                  0  \n2101            0                  0  \n2102            0                  0  \n2103            0                  0  \n2104            0                  0  \n\n[2105 rows x 1782 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>year_of_manufacture</th>\n      <th>cname_Audi A3 35 TDI Premium + Sunroof</th>\n      <th>cname_Audi A3 35 TDI Premium Plus</th>\n      <th>cname_Audi A3 35 TDI Premium Plus + Sunroof</th>\n      <th>cname_Audi A3 35 TDI Technology + Sunroof</th>\n      <th>cname_Audi A3 35 TFSI Premium Plus</th>\n      <th>cname_Audi A3 40 TFSI Premium</th>\n      <th>cname_Audi A3 40 TFSI Premium Plus + sunroof</th>\n      <th>cname_Audi A4 2.0 TDI (143 bhp)</th>\n      <th>cname_Audi A4 2.0 TDI (143bhp)</th>\n      <th>...</th>\n      <th>state_Gurgaon</th>\n      <th>state_Hyderabad</th>\n      <th>state_Kolkata</th>\n      <th>state_Mumbai</th>\n      <th>state_Noida</th>\n      <th>state_Pallikarnai</th>\n      <th>state_Poonamallee</th>\n      <th>state_Pune</th>\n      <th>state_Thane</th>\n      <th>state_Thiruvallur</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2019</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2019</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2100</th>\n      <td>2015</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2101</th>\n      <td>2013</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2102</th>\n      <td>2018</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2103</th>\n      <td>2017</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2104</th>\n      <td>2017</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2105 rows × 1782 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"scaler = StandardScaler()\n\nX = scaler.fit_transform(X)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:25.089104Z","iopub.execute_input":"2024-05-03T05:17:25.089915Z","iopub.status.idle":"2024-05-03T05:17:25.245333Z","shell.execute_reply.started":"2024-05-03T05:17:25.089858Z","shell.execute_reply":"2024-05-03T05:17:25.244089Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=34)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:28.768661Z","iopub.execute_input":"2024-05-03T05:17:28.769281Z","iopub.status.idle":"2024-05-03T05:17:28.871602Z","shell.execute_reply.started":"2024-05-03T05:17:28.769240Z","shell.execute_reply":"2024-05-03T05:17:28.870580Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"markdown","source":"# Training","metadata":{}},{"cell_type":"code","source":"lin_model = LinearRegression()\n\nlin_model.fit(X_train, y_train)\n\nlin_y_preds = lin_model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:34.505167Z","iopub.execute_input":"2024-05-03T05:17:34.505613Z","iopub.status.idle":"2024-05-03T05:17:35.570164Z","shell.execute_reply.started":"2024-05-03T05:17:34.505574Z","shell.execute_reply":"2024-05-03T05:17:35.569088Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"lgb_model = lgb.LGBMRegressor(\n    boosting_type='gbdt',\n    num_leaves=31,\n    n_estimators=100,\n    reg_lambda=1.0\n)\n\nlgb_model.fit(X_train, y_train)\n\nlgb_y_preds = lgb_model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:37.855640Z","iopub.execute_input":"2024-05-03T05:17:37.856090Z","iopub.status.idle":"2024-05-03T05:17:38.162505Z","shell.execute_reply.started":"2024-05-03T05:17:37.856055Z","shell.execute_reply":"2024-05-03T05:17:38.161396Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"code","source":"lin_loss = np.sqrt(mean_squared_error(y_test, lin_y_preds))\nlgb_loss = np.sqrt(mean_squared_error(y_test, lgb_y_preds))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:44.273125Z","iopub.execute_input":"2024-05-03T05:17:44.273817Z","iopub.status.idle":"2024-05-03T05:17:44.280151Z","shell.execute_reply.started":"2024-05-03T05:17:44.273776Z","shell.execute_reply":"2024-05-03T05:17:44.279207Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"print(data.columns)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:31:31.406667Z","iopub.execute_input":"2024-05-03T05:31:31.407552Z","iopub.status.idle":"2024-05-03T05:31:31.415326Z","shell.execute_reply.started":"2024-05-03T05:31:31.407488Z","shell.execute_reply":"2024-05-03T05:31:31.413786Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","text":"Index(['year_of_manufacture', 'cname_Audi A3 35 TDI Premium + Sunroof',\n       'cname_Audi A3 35 TDI Premium Plus',\n       'cname_Audi A3 35 TDI Premium Plus + Sunroof',\n       'cname_Audi A3 35 TDI Technology + Sunroof',\n       'cname_Audi A3 35 TFSI Premium Plus', 'cname_Audi A3 40 TFSI Premium',\n       'cname_Audi A3 40 TFSI Premium Plus + sunroof',\n       'cname_Audi A4 2.0 TDI (143 bhp)', 'cname_Audi A4 2.0 TDI (143bhp)',\n       ...\n       'state_Gurgaon', 'state_Hyderabad', 'state_Kolkata', 'state_Mumbai',\n       'state_Noida', 'state_Pallikarnai', 'state_Poonamallee', 'state_Pune',\n       'state_Thane', 'state_Thiruvallur'],\n      dtype='object', length=1782)\n","output_type":"stream"}]},{"cell_type":"code","source":"These two lines of code calculate and print the Root Mean Squared Error (RMSE) for two different models:\na Linear Regression model (lin_loss) and a Gradient Boosted model (lgb_loss).\n    \nRMSE is a measure of the average magnitude of the errors between predicted and actual values.\nLower RMSE values indicate better model performance in terms of prediction accuracy.","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Linear Regression RMSE:\", lin_loss)\nprint(\"Gradient Boosted RMSE:\", lgb_loss)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:25:45.662574Z","iopub.execute_input":"2024-05-03T05:25:45.663099Z","iopub.status.idle":"2024-05-03T05:25:45.670017Z","shell.execute_reply.started":"2024-05-03T05:25:45.663047Z","shell.execute_reply":"2024-05-03T05:25:45.668419Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"Linear Regression RMSE: 340159668607913.44\nGradient Boosted RMSE: 2.86068559927202\n","output_type":"stream"}]},{"cell_type":"code","source":"These two lines of code calculate and print the R-squared scores for two different models (linear regression and gradient boosted) on a test dataset.\nThe R-squared score measures the proportion of the variance in the dependent variable that is predictable from the independent variables in a regression model.\nHigher R-squared scores indicate a better fit of the model to the data.","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Linear Regression R^2 Score:\", lin_model.score(X_test, y_test))\nprint(\"Gradient Boosted R^2 Score:\", lgb_model.score(X_test, y_test))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:17:49.704445Z","iopub.execute_input":"2024-05-03T05:17:49.704908Z","iopub.status.idle":"2024-05-03T05:17:49.724745Z","shell.execute_reply.started":"2024-05-03T05:17:49.704865Z","shell.execute_reply":"2024-05-03T05:17:49.723755Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"Linear Regression R^2 Score: -1.3682711338193156e+28\nGradient Boosted R^2 Score: 0.03228653313268315\n","output_type":"stream"}]},{"cell_type":"code","source":"EVALUATION OF THE MODEL","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\n\n# Assuming you have a DataFrame 'data' with columns 'kms_driven' and 'price'\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(data[['year_of_manufacture']], data['cname_Audi A3 35 TDI Premium + Sunroof'], test_size=0.2, random_state=42)\n\n# Train a linear regression model\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# Make predictions on the testing set\ny_pred = model.predict(X_test)\n\n# Visualize the results using a scatter plot\nplt.figure(figsize=(8, 6))\nplt.scatter(X_test, y_test, color='blue', label='Actual')\nplt.scatter(X_test, y_pred, color='red', label='Predicted')\nplt.xlabel('year_of_manufacture')\nplt.ylabel('cname_Audi A3 35 TDI Premium + Sunroof')\nplt.title('Actual vs Predicted Price')\nplt.legend()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:32:11.305537Z","iopub.execute_input":"2024-05-03T05:32:11.306139Z","iopub.status.idle":"2024-05-03T05:32:11.600866Z","shell.execute_reply.started":"2024-05-03T05:32:11.306100Z","shell.execute_reply":"2024-05-03T05:32:11.599508Z"},"trusted":true},"execution_count":80,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x432 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAg4AAAGECAYAAABeeKgaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZxU1Z3//9dbFAE1cUNHRYQkmKSJqNgumahRwTUaXMbE5RuXxCDzDVl/OjGTTKKZccb4nYlGzYiMMdGESBxHEzJqNOKWcYk0oAgSIm7YyiiiccMN/Pz+uKehKGq53dTtru5+Px+PelTdc+8595yqLurDPeeeo4jAzMzMLI8NeroCZmZm1ns4cDAzM7PcHDiYmZlZbg4czMzMLDcHDmZmZpabAwczMzPLzYGDWR8m6VxJv+jpenSVpJ9J+qf0ej9Ji7rpvCHpQw0qa4GkAxpRllkzcOBgViBJd0l6WdLGOY8/TdL/FF2vRpL0lKQ3Jb0u6XlJP5W0aaPPExF/iIgP56hPoe9h+kzfSu19UdINkrardnxEjI6Iu4qqj1l3c+BgVhBJI4D9gAA+3aOVKd5REbEpMBbYE/hO+QGSNuz2WhVncmrvzsDmwEXlB/Sx9pqt5sDBrDinAA8APwNOLd0hacf0P9VlkpZLukzSR4EpwMfT/2b/ko69S9IZJXnX+h+1pB9JekbSq5JmS9ovT+UkLZR0ZMn2hul/0GMlDZL0i1S3v0iaJWnbemVGxLPALcDHUpkh6UuSHgMeS2lHSnoolXufpDElddhd0hxJr0n6FTCoZN8Bktq7+B5uLOlfJS1JV0WmSBpcUtbZkpZKek7S5/O8f6m9LwH/VdLepyR9U9I84I30nj4laXzaP0DS30t6PLVxtqQd076PSPq9pJckLZL0mbz1MOtODhzMinMKMC09Du344ZU0APhv4GlgBLADMD0iFgKTgPsjYtOI2DzneWYBuwFbAr8E/lPSoNpZALgWOLFk+1DgxYiYQxbovB/YEdgq1evNegWmH8EjgLklyUcDewMtksYCVwFnpnKvAGakH/aBwK+Bn6e2/CdwXJXzdPY9/AHZ1YHdgA+l47+byjoMOAs4GBgFjK/XzpJ6bJ3qWNreE4FPAZtHxMqyLN9I+48A3gd8HlghaRPg92Sf3zbpmH+XNDpvXcy6iwMHswJI2hfYCbguImYDjwMnpd17AdsDZ0fEGxHxVkR0uU8+In4REcsjYmVE/BuwMVB3LADZj9SnJQ1J2yelNIB3yX7YPxQRqyJidkS8WqOsX6f/3f8PcDfwzyX7/iUiXoqIN4EvAldExB9TuVcDbwP7pMdGwMUR8W5EXE8WFFWS+z2UpHTer6d6vJbqd0I65DPATyNifkS8AZxbo50dLkntfRhYShYQrN4XEc+k9pY7A/hORCyKzMMRsRw4EngqIn6aPsc5ZFcy/iZHXcy6lfvgzIpxKnBbRLyYtn+Z0i4i+1/80xX+N9olkv4/sh+k7cnGU7wP2LpevohYLGkhcJSk35KNw9g97f55qud0SZsDvwC+HRHvVinu6Ii4vcq+Z0pe7wScKunLJWkDS+r+bKy98t7TVcrszHs4FBgCzM5iCAAEDEivtwdm5zhnqa9ExJVV9j1TJR2yej9eIX0nYO+OrpVkQ7LPwaypOHAwa7DUd/4ZYICk/03JGwObS9qV7IdluKQNK/zwVVqu9g2yH74Of1Vyrv2AbwLjgAUR8Z6kl8l+GPPo6K7YAHg0IhYDpADhPOC8NMjzZmAR8JOc5ZYqbdMzwPkRcX75QZI+CewgSSXBw3Aq/9B25j18kaybZXQag1FuKdkPeofh1ZuSS60lh58BPgjMr5B+d0QcvJ7nNiucuyrMGu9oYBXQQtanvhvwUeAPZOMeHiT7sbpA0iZpIOInUt7ngWGpv7/DQ8CxkoYom1vgCyX7NgNWAsuADSV9l+yKQ17TgUOAv2VNNwWSDpS0SxpL8CpZ18WqTpRbzX8AkyTtrcwmkj4laTPg/tSWr6RBhceSdUlUkvs9jIj30nkvkrRNat8Okg5Nx18HnCapJXXbfK8B7azmSuAfJY1K7R8jaSuy8Ro7S/qcpI3SY8802NOsqThwMGu8U8n6zJdExP92PIDLgJPJrgYcRTZIbwnQDnw25b0DWAD8r6SObo6LgHfIfhCvJhts2eFWsrsY/kx2if0tal8qX0tELCX7wf5r4Fclu/4KuJ4saFhINm5hvSeSiog2svEGlwEvA4uB09K+d4Bj0/bLZO/JDVXKWUXn3sNvpnM9IOlV4HbSOJCIuAW4OOVbnJ6L8kOyQOU2svf2J8DgNO7iELJxF88B/0s2oDPX/B9m3UlrdyeamZmZVecrDmZmZpabAwczMzPLzYGDmZmZ5ebAwczMzHJz4GBmZma5FT4BVJoH/kdks7RdGREXlO1X2n8EsAI4LU23WjWvpOPJpoX9KLBXusWro7wxZPPfvw94D9gzIt6qVr+tt946RowY0ZC2mpmZ9QazZ89+MSKGdiVvoYFDmjzmx2SLx7QDsyTNiIhHSw47nGxhmVFkC+FcTjb1aq2888nu976i7Hwbkt1r/rmIeDhNrFJtilwARowYQVtbW61DzMzM+hRJeaZWr6joroq9gMUR8USa3GU6MKHsmAnANWnBlwfIpuXdrlbeiFgYEYsqnO8QYF5EPJyOW54mijEzM7MGKDpw2IG1Z7FrT2l5jsmTt9zOQEi6VdIcSX9X6SBJEyW1SWpbtmxZjmaYmZkZFB84VFpop3yqymrH5MlbbkNgX7JpffcFjpE0bp1CIqZGRGtEtA4d2qUuHjMzs36p6MGR7ay96twwsnnY8xwzMEfeSue7u2MpY0k3A2OBmZ2uuZmZ9Zh3332X9vZ23nqr6th2y2HQoEEMGzaMjTbaqGFlFh04zAJGSRoJPEu2gMtJZcfMACZLmk42OPKViFgqaVmOvOVuBf4urXD3DvBJsgWCzMysF2lvb2ezzTZjxIgRZDffWWdFBMuXL6e9vZ2RI0c2rNxCuyoiYiUwmewHfSFwXUQskDRJ0qR02M3AE2Sr0v0H8H9r5QWQdIykduDjwE2Sbk15XiZbfW4W2VLEcyLipiLbaGZmjffWW2+x1VZbOWhYD5LYaqutGn7VpvB5HCLiZrLgoDRtSsnrAL6UN29KvxG4sUqeX9CA5X/NzKxnOWhYf0W8h5450szMrIobb7wRSfzpT3+qedzFF1/MihUrunyen/3sZ0yePLnL+buTAwczM7Mqrr32Wvbdd1+mT59e87j1DRx6EwcOZmbW602bBiNGwAYbZM/Tpq1/ma+//jr33nsvP/nJT1YHDqtWreKss85il112YcyYMVx66aVccsklPPfccxx44IEceOCBAGy66aary7n++us57bTTAPjtb3/L3nvvze6778748eN5/vnn17+i3cyBQyMV8ZdrZmY1TZsGEyfC009DRPY8ceL6/xP861//msMOO4ydd96ZLbfckjlz5jB16lSefPJJ5s6dy7x58zj55JP5yle+wvbbb8+dd97JnXfeWbPMfffdlwceeIC5c+dywgkncOGFF65fJXtA4YMj+42Ov9yOS1Udf7kAJ5/cc/UyM+vjvv3tNf/0dlixIktfn39+r732Wr72ta8BcMIJJ3DttdfyxBNPMGnSJDbcMPv53HLLLTtVZnt7O5/97GdZunQp77zzTkNvk+wuDhwapai/XDMzq2nJks6l57F8+XLuuOMO5s+fjyRWrVqFJPbYY49cdyqUHlN6O+SXv/xlvvGNb/DpT3+au+66i3PPPbfrlewh7qpolCL+cs3MrK7hwzuXnsf111/PKaecwtNPP81TTz3FM888w8iRIxk7dixTpkxh5cqVALz00ksAbLbZZrz22mur82+77bYsXLiQ9957jxtvXDN7wCuvvMIOO2TLLl199dVdr2APcuDQKEX85ZqZWV3nnw9DhqydNmRIlt5V1157Lcccc8xaaccddxzPPfccw4cPZ8yYMey666788pe/BGDixIkcfvjhqwdHXnDBBRx55JEcdNBBbLfddqvLOPfcczn++OPZb7/92HrrrbtewR6kbP6l/qu1tTXa2trWv6DyMQ6Q/eVOnequCjOzTlq4cCEf/ehHcx8/bVrWM7xkSfb/tfPP9z+9HSq9l5JmR0RrV8rzGIdG6fgL9V+umVm3O/lk/3PbXRw4NJL/cs3MrI/zGAczMzPLzYGDmZmZ5ebAwczMzHJz4GBmZma5OXAwMzOrYMCAAey222587GMf4/jjj1+v1S9PO+00rr/+egDOOOMMHn300arH3nXXXdx3332dPseIESN48cUXu1zHvBw4mJmZVTB48GAeeugh5s+fz8CBA5kyZcpa+1etWtWlcq+88kpaWlqq7u9q4NBdHDiYmVnvV/DqxPvttx+LFy/mrrvu4sADD+Skk05il112YdWqVZx99tnsueeejBkzhiuuuAKAiGDy5Mm0tLTwqU99ihdeeGF1WQcccAAdEw/+7ne/Y+zYsey6666MGzeOp556iilTpnDRRRex22678Yc//IFly5Zx3HHHseeee7Lnnnty7733Atl6Gocccgi77747Z555Jt01oaPncTAzs96t4NWJV65cyS233MJhhx0GwIMPPsj8+fMZOXIkU6dO5f3vfz+zZs3i7bff5hOf+ASHHHIIc+fOZdGiRTzyyCM8//zztLS08PnPf36tcpctW8YXv/hF7rnnHkaOHMlLL73ElltuyaRJk9h0000566yzADjppJP4+te/zr777suSJUs49NBDWbhwIeeddx777rsv3/3ud7npppuYOnXqerc1DwcOZmbWuxW0OvGbb77JbrvtBmRXHL7whS9w3333sddee61eDvu2225j3rx5q8cvvPLKKzz22GPcc889nHjiiQwYMIDtt9+egw46aJ3yH3jgAfbff//VZVVbovv2229fa0zEq6++ymuvvcY999zDDTfcAMCnPvUptthiiy63tTMcOJiZWe9W0OrEHWMcym2yySarX0cEl156KYceeuhax9x88811l9+OiFxLdL/33nvcf//9DB48eJ19efI3msc4mJlZ79aDqxMfeuihXH755bz77rsA/PnPf+aNN95g//33Z/r06axatYqlS5dy5513rpP34x//OHfffTdPPvkkUH2J7kMOOYTLLrts9XZHMLP//vszLY3luOWWW3j55ZeLaWQZBw5mZta7FbGudk5nnHEGLS0tjB07lo997GOceeaZrFy5kmOOOYZRo0axyy678Ld/+7d88pOfXCfv0KFDmTp1Ksceeyy77rorn/3sZwE46qijuPHGG1cPjrzkkktoa2tjzJgxtLS0rL6743vf+x733HMPY8eO5bbbbmN4NwRK4GW1G7estpmZNUxnl9X2utrVeVltMzOzcl6duNu4q8LMzMxyc+BgZmZmuTlwMDOzptTfx+A1QhHvoQMHMzNrOoMGDWL58uUOHtZDRLB8+XIGDRrU0HILHxwp6TDgR8AA4MqIuKBsv9L+I4AVwGkRMadWXknHA+cCHwX2ioi2sjKHA48C50bEvxbXOjMzK8KwYcNob29n2bJlPV2VXm3QoEEMGzasoWUWGjhIGgD8GDgYaAdmSZoREaXriR4OjEqPvYHLgb3r5J0PHAtcUeXUFwG3FNAkMzPrBhtttNHqqZituRR9xWEvYHFEPAEgaTowgexqQIcJwDWRXY96QNLmkrYDRlTLGxELU9o6J5R0NPAE8EZRjTIzM+uvih7jsAPwTMl2e0rLc0yevGuRtAnwTeC8LtbXzMzMaig6cKi0+kb5SJdqx+TJW+484KKIeL1mpaSJktoktbn/zMzMLL+iuyragR1LtocBz+U8ZmCOvOX2Bv5G0oXA5sB7kt6KiMtKD4qIqcBUyKacztcUMzMzKzpwmAWMkjQSeBY4ATip7JgZwOQ0hmFv4JWIWCppWY68a4mI/TpeSzoXeL08aDAzM7OuKzRwiIiVkiYDt5LdUnlVRCyQNCntnwLcTHYr5mKy2zFPr5UXQNIxwKXAUOAmSQ9FxNqLoZuZmVnDeXVMr45pZmb9zPqsjumZI83MzCw3Bw5mZmaWmwMHMzMzy82Bg5mZmeXmwMHMzMxyc+BgZmZmuTlwMDMzs9wcOFgxpk2DESNggw2y52nTerpGZmbWAEVPOW390bRpMHEirFiRbT/9dLYNcPLJPVcvMzNbbzWvOEjauLsqYn3It7+9JmjosGJFlm5mZr1ava6K+wEk/bwb6mJ9xZIlnUs3M7Neo15XxUBJpwJ/LenY8p0RcUMx1bJebfjwrHuiUrqZmfVq9a44TAL2ATYHjip7HFls1azXOv98GDJk7bQhQ7J0MzPr1WpecYiI/wH+R1JbRPykm+pkvV3HAMhvfzvrnhg+PAsaPDDSzKzXy7WstqSBZFcf9k9JdwNTIuLdAuvWLbystpmZ9Tfrs6x23tsx/x3YKD0DfA64HDijKyc1MzOz3ilv4LBnROxasn2HpIeLqJCZmZk1r7wzR66S9MGODUkfAFYVUyUzMzNrVnmvOJwN3CnpCUDATsDphdXKzMzMmlKuwCEiZkoaBXyYLHD4U0S8XWjNzMzMrOnkChwkbQScyZq7Ku6SdEVfuKvCzMzM8ss7xuFyYA+yuyr+Pb2+vKhKmdXklTfNzHqM76qw3sUrb5qZ9SjfVWG9i1feNDPrUXmvOJyF76qwZuCVN83MelTdwEHSAGBXwHdVWM/zyptmZj2qbldFRKwCPh0Rb0fEvIh42EGD9RivvGlm1qPyjnG4T9JlkvaTNLbjUWjNzCo5+WSYOhV22gmk7HnqVA+MNDPrJnlXx7yzQnJExEGNr1L38uqYZmbW3xS+OmZEHNiVws3MzKxvydVVIWljSSdJ+ntJ3+145Mx7mKRFkhZLOqfCfkm6JO2fV9oFUi2vpOMlLZD0nqTWkvSDJc2W9Eh67vVXRMzMzJpJ3tsxfwO8AswGcg+MTHdk/Bg4GGgHZkmaERGPlhx2ONkdG6OAvclmpNy7Tt75wLHAFWWnfBE4KiKek/Qx4FZgh7z1NTMzs9ryBg7DIuKwLpS/F7A4Ip4AkDQdmACUBg4TgGsiG2zxgKTNJW0HjKiWNyIWprS1ThYRc0s2FwCDJG3su0DMzMwaozN3VezShfJ3AJ4p2W5n3SsA1Y7Jk7eW44C5lYIGSRMltUlqW7ZsWSeKNDMz69/yBg77ArPTeIN5aQzBvBz5VCGt/DaOasfkyVv5pNJo4AdkK3quW0jE1IhojYjWoUOH5inSzMzMyN9VcXgXy28HdizZHgY8l/OYgTnyrkPSMOBG4JSIeLwLdbb+avx4mDlzzfa4cXD77T1XHzOzJpT3ikNUedQzCxglaaSkgcAJwIyyY2YAp6S7K/YBXomIpTnzrkXS5sBNwLci4t6cbTNbN2iAbHv8+J6pj5lZk8p7xeEm1nQfDAJGAouA0bUyRcRKSZPJ7m4YAFwVEQskTUr7pwA3A0cAi4EVpMWzquUFkHQMcCkwFLhJ0kMRcSgwGfgQ8A+S/iFV45CIeCFnO62/Kg8a6qWbmfVTuWaOXCdTNtfCmRFRcQxBb+KZIw3Ipq+upgvfETOzZrY+M0fm7apYS0TMAfbsSl4zMzPrvXJ1VUj6RsnmBsBYwPcxWt8xblzlbolx47q/LmZmTSzvFYfNSh4bk415mFBUpcy63e23rxsk+K4KM7N15F3k6ryO15K2AP4SXRkcYdbMHCSYmdVV84pDWszqI+n1xpLuAB4Hnpfk+9TMzMz6mXpdFZ8lu+0S4NR0/FDgk8A/F1gvMzMza0L1Aod3SrokDgWujYhVaZGpvHNAmJmZWR9RL3B4W9LHJA0FDgRuK9k3pLhqmZmZWTOqd9Xgq8D1ZN0TF0XEkwCSjgDm1spoZmZmfU/NwCEi/gh8pEL6zWRTRZuZmVk/0qWZI82sk0aPzqa17niMrrnMi5lZ03LgYFa00aPh0UfXTnv0UQcPZtYrOXAwK1p50FAv3cysiXUqcJDk/yKZmZn1Y5294vDzQmphZmZmvUJnAwcVUguzvqylpXPpZmZNrO7sj5K+BwRZ0LCtpO927IuI7xdYN7O+YcGCdQdItrRk6WZmvUyeaaOfKnn9LvB0MVUx68McJJhZH1E3cIiIqzteS/pq6baZmZn1Lx7jYGZmZrl1NnAYV0gtzMzMrFfoVOAQES8VVREzMzNrfp450szMzHJz4GBmZma5OXAw683Gj1971c3x43u6RmbWx+UKHCQdKWmupJckvSrpNUmvFl05M6th/HiYOXPttJkzHTyYWaHyTAAFcDFwLPBIRESB9TGzvMqDhnrpZmYNkLer4hlgvoMGMzOz/i3vFYe/A26WdDfwdkdiRPywkFqZmZlZU8p7xeF8YAUwCNis5FGXpMMkLZK0WNI5FfZL0iVp/zxJY+vllXS8pAWS3pPUWlbet9LxiyQdmrN9Zr3PuCrzsVVLNzNrgLxXHLaMiEM6W7ikAcCPgYOBdmCWpBkRUbJMIIcDo9Jjb+ByYO86eeeTjbm4oux8LcAJwGhge+B2STtHxKrO1t2s6d1++7oDJMeNy9LNzAqSN3C4XdIhEXFbJ8vfC1gcEU8ASJoOTABKA4cJwDVp/MQDkjaXtB0wolreiFiY0srPNwGYHhFvA09KWpzqcH8n623WOzhIMLNulrer4kvA7yS92cnbMXcgG1jZoT2l5TkmT96unM/MzMy6KNcVh4jINZ6hgkqraZbfmVHtmDx5u3I+JE0EJgIMHz68TpFmZmbWIVfgIGn/SukRcU+drO3AjiXbw4Dnch4zMEferpyPiJgKTAVobW31LaZmZmY55R3jcHbJ60Fk4wZmAwfVyTcLGCVpJPAs2cDFk8qOmQFMTmMY9gZeiYilkpblyFtuBvBLST8kGxw5CniwXuPMzMwsn7xdFUeVbkvaEbgwR76VkiYDtwIDgKsiYoGkSWn/FOBm4AhgMdktn6fXypvOfwxwKTAUuEnSQxFxaCr7OrLBlyuBL/mOCjMzs8ZRVyaDVHY7w7yI2KXxVepera2t0dbW1tPVMDMz6zaSZkdEa/0j15V3jMOlrBlkuAGwG/BwV05oZr3EkCHw5ptrtgcPhhUreq4+ZtYU8o5xKP0v+Urg2oi4t4D6mFkzKA8aINseMsTBg1k/l3eMw9VFV8TMmkh50FAv3cz6jZqBg6TrIuIzkh5h7fkQBEREjCm0dmZmZtZU6l1x+Gp6PrLoipiZmVnzqxk4RMTS9Pw0gKT31ctjZn3A4MGVuyUGD+7+uphZU8l7V8WZwPeBN1nTZRHABwqql5n1pBUrfFeFmVWU9+rBWcDoiHixyMqYWRNxkGBmFeRdHfNxslkdzczMrB/Le8XhW8B9kv4IvN2RGBFfKaRWZmZm1pTyBg5XAHcAjwDvFVcdMzMza2Z5A4eVEfGNQmtiZmZmTS/vGIc7JU2UtJ2kLTsehdbMzMzMmk7eKw4npedvlaT5dkwzM7N+Ju9aFSOLroiZ9RPSumkR66aZWVPK1VUhaYik70iamrZHSfI01GbWOZWChlrpZtZ08o5x+CnwDvDXabsd+KdCamRmZmZNK2/g8MGIuBB4FyAi3iRbIdPMzMz6kbyBwzuSBpPWqZD0QUomgjIzM7P+Ie9dFd8DfgfsKGka8AngtKIqZWZmZs2pbuAgaQNgC+BYYB+yLoqvesErM+u0CN9VYdbL1Q0cIuI9SZMj4jrgpm6ok5n1ZQ4SzHq1vGMcfi/pLEk7euZIMzOz/ivvGIfPp+cvlaR55kgzM7N+xjNHmpmZWW41uyok7S3pYUmvS7pf0ke7q2JmZmbWfOqNcfgxcBawFfBD4OLCa2RmZmZNq17gsEFE/D4i3o6I/wSGdkelzMzMrDnVG+OwuaRjq21HxA3FVMvMrAs8R4RZ4eoFDncDR1XZDsCBg5k1h1orbzp4MGuYmoFDRJy+vieQdBjwI2AAcGVEXFC2X2n/EcAK4LSImFMrb5pD4lfACOAp4DMR8bKkjYArgbGpbddExL+sbxvMzMwsk3cCqC6RNIBsgOXhQAtwoqSWssMOB0alx0Tg8hx5zwFmRsQoYGbaBjge2DgidgH2AM6UNKKQxpmZmfVDhQYOwF7A4oh4IiLeAaYDE8qOmUB2ZSAi4gGycRTb1ck7Abg6vb4aODq9DmATSRsCg4F3gFcLapuZmVm/U3TgsAPwTMl2e0rLc0ytvNtGxFKA9LxNSr8eeANYCiwB/jUiXiqvlKSJktoktS1btqwr7TIzM+uXao5xKLujYh057qqoNFqpfJRStWPy5C23F7AK2J5sRc8/SLo9Ip5Yq5CIqcBUgNbWVo+aMusLvPKmWbeod1fFUTX25bmroh3YsWR7GPBczmMG1sj7vKTtImJp6tZ4IaWfBPwuIt4FXpB0L9AKrBU4mFkf5SDBrHBF31UxCxglaSTwLHAC2Y97qRnAZEnTgb2BV1JAsKxG3hnAqcAF6fk3KX0JcJCkXwBDgH3wbJdmZmYNU3eRK0kfJrvb4SMpaSEwNSL+XC9vRKyUNBm4leyWyqsiYoGkSWn/FOBmslsxF5Pdjnl6rbyp6AuA6yR9gSxYOD6l/xj4KTCfrKvjpxExr149zczMLB9FjUt7kj5O1h0xFZhD9mO8O/BF4Nh0F0Sv1traGm1tbT1dDTMzs24jaXZEtHYlb70rDt8FToyIu0rSfi3pDuB7ZHMsmJmZWT9R73bMD5YFDQBExN3ABwqpkZmZmTWteoHDazX2vdHIipiZmVnzq9dVsaOkSyqki3UncjIz67s8R4QZUD9wOLvGPo8oNLP+wStvmq1WL3D4cET8fbfUxMzMzJpevTEOh3VLLczMzKxXqHfFYYCkLai8bgSVFpAyMzOzvqte4PARYDbVF5zyLZlmZmb9SL3A4dGI2L1bamJm1qy88qbZanXXqjAzMxwkmCX1Bkf+qFtqYWZmZr1CzcAhIn7WTfUwMzOzXqDeFQczMzOz1Rw4mJmZWW41AwdJW5dt/x9Jl0iaKFWbg9XMzMz6qnpXHG7reCHpO8DnyOZ1OBj4YYH1MjMzsyZU73bM0qsKxwL7RcQbkn4JzCmuWmZm/YTnh7Bepl7gMFMnnOYAABlvSURBVFjS7mRXJgZExBsAEfGupFWF187MrC/zqpvWC9ULHJaypkviJUnbRcRSSVsBK4utmpmZmTWbmoFDRBxYZddfgP0bXx0zMzNrZnVvx5S0gaQN0uuBksYC74+IFYXXzszMzJpKvdsxjybrrnhW0gTgD8C/AvMkHdUN9TMzM7MmUm+Mw/eAXYHBwMPAnhGxSNJOwH8Bvy24fmZmfZdX3bReqO7qmBHxvwCSlkTEopT2dEf3hZmZrQcHCdbL5BrjkF5+viRtADCwqEqZmZlZc6oXOEwkBQgR8WBJ+o7ABUVVyszMzJpTvdsxZ1VJfwp4qoD6mJmZWRPzOAUzMzPLrfDAQdJhkhZJWizpnAr7lVbcXCxpXponomZeSVtK+r2kx9LzFiX7xki6X9ICSY9IGlR0G83MzPqLQgOHNIjyx8DhQAtwoqSWssMOB0alx0Tg8hx5zwFmRsQoYGbaRtKGwC+ASRExGjgAeLeo9pmZmfU39SaAGiLp7ySdLWmQpNMkzZB0oaRNc5S/F7A4Ip6IiHeA6cCEsmMmANdE5gFgc0nb1ck7Abg6vb4aODq9PgSYFxEPA0TE8ojwYlxm1j9J6z7M1lO9Kw4/A7YFRgI3Aa1kM0eKdGWgjh2AZ0q221NanmNq5d02IpYCpOdtUvrOQEi6VdIcSX+Xo45mZn1PrZU3zdZDvQmgdo6Iz0gS2dTT4yMiJP2BbCbJeir9hZbPdlLtmDx5y20I7AvsCawAZkqaHREz1zqhNJGsW4Thw4fXKdLMzMw65BrjEBEB3JyeO7bzTHfWTjbnQ4dhwHM5j6mV9/nUnUF6fqGkrLsj4sW0CNfNwFjKRMTUiGiNiNahQ4fmaIaZmZlB/cChrWMsQ0SUzhz5QeC1HOXPAkZJGilpIHACMKPsmBnAKenuin2AV1L3Q628M4BT0+tTgd+k17cCY9LYjA2BTwKP5qinmZmZ5VBvAqgzqqQ/LunAeoVHxEpJk8l+0AcAV0XEAkmT0v4pZFcFjgAWk3UvnF4rbyr6AuA6SV8AlgDHpzwvS/ohWdDRcZXkpnr1NDMzs3wUnVhgJY11OBA4CTgqIrYtqmLdpbW1Ndra2nq6GmZmjeeVN62KNP6vtSt5c41xkLS3pB8BT5N1E/wB+EhXTmhmZt0kYt2H2XqqN4/D+ZIeA/4ZeATYHVgWEVdHxMvdUUEzMzNrHvVux5wILCKbs+G/I+ItSQ5ZzczM+ql6XRV/BZwPfBpYLOnnwOB0x4KZmZn1M/XuqlgF3ALckhaLOhIYAjwraWZEnNQNdTQzM7MmkfvKQUS8BVwPXC/pfcAxhdXKzMzMmlKXuhwi4lXWLDJlZmZm/YTHKpiZWed4foh+Ldc8DmZmZoBX3bTaVxwkHRQRd0g6ttL+iLihmGqZmZlZM6rXVfFJ4A7gqAr7AnDgYGZm1o/Uux3ze+n59O6pjpmZmTWzel0V36i1PyJ+2NjqmJmZWTOr11WxWXr+MLAn2QJXkHVd3FNUpczMrElF+K6Kfq5eV8V5AJJuA8ZGxGtp+1zgPwuvnZmZNR8HCf1a3tsxhwPvlGy/A4xoeG3MzMysqeWdAOrnwIOSbiS7m+IY4JrCamVmZmZNKVfgEBHnS7oF2C8lnR4Rc4urlpmZmTWjXIGDpOHAi8CNpWkRsaSoipmZmVnzydtVcRNZFwXAYGAksAgYXUSlzMzMrDnl7arYpXRb0ljgzEJqZGZmZk2rq8tqz5G0Z6MrY2Zm/ZzniGh6ecc4lM4guQEwFlhWSI3MzKx/qrXypoOHppH3isNmJa9Xko15+K/GV8fMzMyaWd4xDueVbksaRDbttGePNDMz60fyzhyJpAGSDpd0DfA08NniqmVmZmbNqO4VB0n7AycBnwIeBD4BjIyIFQXXzczMzJpMvWW124ElwOXA2RHxmqQnHTSYmVnDeeXNXqFeV8V/ATuQdUscJWkT1kwEZWZm1lgR6z6sqdQMHCLiq2SrYP4QOBD4MzBU0mckbVp89czMzKyZ1B0cGZk7IuKLZEHEScDRwFN5TiDpMEmLJC2WdE6F/ZJ0Sdo/L81KWTOvpC0l/V7SY+l5i7Iyh0t6XdJZeepoZmZm+eS+qwIgIt6NiN9GxEnAjh3pkirO6SBpAPBj4HCgBThRUkvZYYcDo9JjItl4inp5zwFmRsQoYGbaLnURcEtn2mZmZmb1dSpwKBURb5ZsfqDKYXsBiyPiiYh4B5gOTCg7ZgJwTbqy8QCwuaTt6uSdAFydXl9NdgUEAElHA08AC7raNjMzM6usy4FDmWqjV3YAninZbk9peY6plXfbiFgKkJ63AUiDN78JrDVhVTlJEyW1SWpbtswzZ5uZmeXVqMChmkoTj5cHGdWOyZO33HnARRHxeq2DImJqRLRGROvQoUPrFGlmZmYdurQ6ZgVVViahnZKxEMAw4Lmcxwyskfd5SdtFxNLUrfFCSt8b+BtJFwKbA+9JeisiLutsg8zMrA/x/BAN05kppwdL+nCV3d+skj4LGCVppKSBwAnAjLJjZgCnpLsr9gFeSd0PtfLOAE5Nr08FfgMQEftFxIiIGAFcDPyzgwYzs36u1qqb1mm5AgdJRwEPAb9L27tJWh0ARMRtlfJFxEpgMnArsBC4LiIWSJokaVI67GaywYyLgf8A/m+tvCnPBcDBkh4DDk7bZmZmVjBFjks1kmYDBwF3RcTuKW1eRIwpuH6Fa21tjba2tp6uhpmZFaXWlYV+2l0haXZEtHYlb96uipUR8UpXTmBmZmZ9R97BkfMlnQQMkDQK+ApwX3HVMjMzs2aU94rDl4HRwNvAtcCrwNeKqpSZmVnDVOuO6KfdFOsr1xWHtIz2t9PDzMysd3GQ0DC5AgdJrcDfky1ytTpPXxgcaWZmZvnlHeMwDTgbeAR4r7jqmJmZWTPLGzgsi4jyiZvMzMysn8kbOHxP0pVkS1i/3ZEYETcUUiszMzNrSnkDh9OBjwAbsaarIgAHDmZmZv1I3sBh14jYpdCamJmZWdPLO4/DA5JaCq2JmZlZbySt++jD8l5x2Bc4VdKTZGMcBIRvxzQzs36t1sqbfXTuiLyBw2GF1sLMzMx6hbwzRz4NIGkbYFChNTIzM7OmlWuMg6RPS3oMeBK4G3gKuKXAepmZmVkTyjs48h+BfYA/R8RIYBxwb2G1MjMzs6aUN3B4NyKWAxtI2iAi7gR2K7BeZmZmza8frryZd3DkXyRtCtwDTJP0ArCyuGqZmZn1En04SKgk7xWHCcCbwNeB3wGPA0cVVSkzMzNrTnnvqnijZPPqgupiZmZmTS7vXRXHSnpM0iuSXpX0mqRXi66cmZmZNZe8YxwuBI6KiIVFVsbMzMyaW94xDs87aDAzM7O8VxzaJP0K+DXZWhUARISX1TYzM+tH8gYO7wNWAIeUpAXgwMHMzKwIlRbQaoJbP/PeVXF60RUxMzOzpIlX3cx7V8XVkjYv2d5C0lXFVcvMzMyaUd7BkWMi4i8dGxHxMrB7MVUyMzOzZpU3cNhA0hYdG5K2JP/4CDMzM+sj8gYO/wbcJ+kfJX0fuI9sboe6JB0maZGkxZLOqbBfki5J++dJGlsvr6QtJf0+TUr1+46gRtLBkmZLeiQ9H5SzfWZmZpZDrsAhIq4BjgOeB5YBx0bEz+vlkzQA+DFwONACnCippeyww4FR6TERuDxH3nOAmRExCpiZtgFeJJuoahfgVKBuHc3MzJpOE6+6mbu7ISIeBR7tZPl7AYsj4gkASdPJFswqLWcCcE1EBPCApM0lbQeMqJF3AnBAyn81cBfwzYiYW1LuAmCQpI0j4m3MzMx6kyYIEirJ21XRVTsAz5Rst6e0PMfUyrttRCwFSM/bVDj3ccBcBw1mZmaNU/QAx0o3opaHUNWOyZO38kml0cAPWHvCqtL9E8m6RRg+fHieIs3MzIzirzi0AzuWbA8Dnst5TK28z6fuDNLzCx0HSRoG3AicEhGPV6pUREyNiNaIaB06dGinG2VmZtZfFR04zAJGSRopaSBwAjCj7JgZwCnp7op9gFdS90OtvDPIBj+Snn8DkCapugn4VkTcW2TDzMzM+qNCuyoiYqWkycCtwADgqohYIGlS2j8FuBk4AlhMth7G6bXypqIvAK6T9AVgCXB8Sp8MfAj4B0n/kNIOiYjVVyTMzMys6xRNOmqzu7S2tkZbW1tPV8PMzKzbSJodEa1dyVt0V4WZmZn1IQ4czMzMLDcHDmZmZpabAwczMzPLzYGDmZmZ5ebAwczMzHJz4GBmZma5OXAwMzOz3Bw4mJmZWW4OHMzMzCw3Bw5mZmaWmwMHMzMzy82Bg5mZmeXmwMHMzMxyc+BgZmZmuTlwMDMzs9wcOJiZmVluDhzMzMwsNwcOZmZmlpsDBzMzM8vNgYOZmZnl5sDBzMzMcnPgYGZmZrk5cDAzM7PcHDiYmZlZbg4czMzMLDcHDmZmZpabAwczMzPLzYGDmZmZ5ebAwczMzHIrPHCQdJikRZIWSzqnwn5JuiTtnydpbL28kraU9HtJj6XnLUr2fSsdv0jSoUW3r9S0aTBiBGywQfY8bVp3nr25FPleFFX2+PEgrXmMH9+YcgFGj1677NGjG1NukXUeMmTtsocMaUy5pWV2PBqlN5btOndP2a5zA0VEYQ9gAPA48AFgIPAw0FJ2zBHALYCAfYA/1ssLXAick16fA/wgvW5Jx20MjEz5B9Sq4x577BGN8ItfRAwZEgFrHkOGZOn9TZHvRVFljxu3dpkdj3Hj1r/OLS2Vy25pad46Dx5cuezBg9ev3EpldjzWV28s23V2nXuizln5tEUXf9uV5S+GpI8D50bEoWn7WylY+ZeSY64A7oqIa9P2IuAAYES1vB3HRMRSSdul/B8uL1/SramM+6vVsbW1Ndra2ta7rSNGwNNPr5u+007w1FPrXXyvUuR7UVTZtSL59f2KFFW269z7y3adu6ds17lS+ZodEa1dyVt0V8UOwDMl2+0pLc8xtfJuGxFLAdLzNp04H5ImSmqT1LZs2bJONaiaJUs6l96XFfle+H02M+tZRQcOlWKm8lip2jF58nblfETE1IhojYjWoUOH1ikyn+HDO5felxX5Xvh9NjPrWUUHDu3AjiXbw4Dnch5TK+/zqYuC9PxCJ85XiPPPX3fw2JAhWXp/U+R7UVTZ48Z1Lr0zWlo6l55XkXUePLhz6WbWj3R1cESeB7Ah8ATZQMWOAY6jy475FGsPjnywXl7g/7H24MgL0+vRrD048gm6aXBkRDZAb6edIqTsuT8OjOxQ5HtRVNnlgw0bMciwQ/kAyfUdGNmhyDqXD5Bc34GRHYoa7NVby3adu6ds17m87CYdHAkg6QjgYrK7JK6KiPMlTQKIiCmSBFwGHAasAE6PiLZqeVP6VsB1wHBgCXB8RLyU9n0b+DywEvhaRNxSq36NGhxpZmbWW6zP4MjCA4dm58DBzMz6m2a+q8LMzMz6EAcOZmZmlpsDBzMzM8vNgYOZmZnl5sDBzMzMcnPgYGZmZrk5cDAzM7PcHDiYmZlZbg4czMzMLLd+P3OkpGXA0z1dj5y2Bl7s6UoUqK+3D/p+G/t6+6Dvt9Ht6/3ytHGniOjS8tD9PnDoTSS1dXWK0N6gr7cP+n4b+3r7oO+30e3r/Ypuo7sqzMzMLDcHDmZmZpabA4feZWpPV6Bgfb190Pfb2NfbB32/jW5f71doGz3GwczMzHLzFQczMzPLzYFDN5G0o6Q7JS2UtEDSV1P6lpJ+L+mx9LxFSZ5vSVosaZGkQ0vS70ppD6XHNlXOWTF/s7dR0mYlbXtI0ouSLq5wvhGS3iw5bkoztU/SVun41yVdVlbWHpIeSW2/RJKqnLPbPsNGtU/SEEk3SfpTKueCKufr1s+vkW1M+5rue9jAz7CvfAcPljQ7fddmSzqopKym+w42so2Ffg8jwo9ueADbAWPT682APwMtwIXAOSn9HOAH6XUL8DCwMTASeBwYkPbdBbTWOV/V/L2hjWXlzgb2r5A+ApjfxJ/hJsC+wCTgsrKyHgQ+Dgi4BTi8pz/DRrUPGAIcmF4PBP5QpX3d+vkV8Bk23fewke0rK7e3fgd3B7ZPrz8GPFtSVtN9BxvZxiK/h77i0E0iYmlEzEmvXwMWAjsAE4Cr02FXA0en1xOA6RHxdkQ8CSwG9urEKdc3f6cV0UZJo4BtyP7oe1Rn2xcRb0TE/wBvlZYjaTvgfRFxf2Tf3GtY856U6tbPsFHti4gVEXFnev0OMAcYVlS9O6NRbeyEXvkZlurl38G5EfFcSl8ADJK0cbN+B1OdG9LGIr+HDhx6gKQRZFHiH4FtI2IpZH8wZF9QyP5QninJ1p7SOvw0XVb6hyqX2OrlL1SD2ghwIvCr9OWuZKSkuZLulrRfg6pfV872VbMDWVs7VPtseuwzXM/2lZazOXAUMLPKIT3y+UHD2ti038NGfYb0ne/gccDciHibXvAdhPVuY2k5Df0ebpi7BdYQkjYF/gv4WkS8WqVbDbLLZ+U6vrgnR8SzkjZLZX2OLGLOm79QDWpjhxPI2lfJUmB4RCyXtAfwa0mjI+LVrtQ7r060r2oRFdIqfTY98hk2oH0d5WwIXAtcEhFPVDikRz6/VLdGtLFpv4eN+gyTXv8dlDQa+AFwSEdShcOa5jsIDWljR3rDv4e+4tCNJG1E9ocwLSJuSMnPp8tmHZewX0jp7cCOJdmHAc8BRMSz6fk14JdUvnRWNX+RGtXGdOyuwIYRMbvSudLlw+Xp9Wyy/sedG9icdXSyfdW0s/Ylw2qfTbd/hg1qX4epwGMRsc6gOuiZzw8a18Zm/R428jPsC99BScOAG4FTIuLxlNy030FoWBs7NPx76MChm6TLmD8BFkbED0t2zQBOTa9PBX5Tkn5C6o8bCYwCHpS0oaStU5kbAUcC8yucsmL+RrerVKPaWJLvRLJIudr5hkoakF5/IOWvFFE3RBfaV1G6zPiapH1SmadUydOtn2Gj2pfK+ifg/cDXahzTrZ9fOk9D2tis38NGfoZJr/4Opkv0NwHfioh7Ow5u1u9gqnND2pj2FfM9jG4aDdvfH2QjlwOYBzyUHkcAW5H1Oz2WnrcsyfNtsuhvEWk0LNko6NmpnAXAj1hzt8Wnge/Xyt8b2liy7wngI2Vpq9tI1p+3gGzU8xzgqCZs31PAS8DrZP97aUnprWQ/NI8Dl7FmMrYe+wwb1T6y/5UF2aCujnLO6OnPr8FtbMrvYSP/RvvCdxD4DvBGybEPAds063ewkW2kwO+hZ440MzOz3NxVYWZmZrk5cDAzM7PcHDiYmZlZbg4czMzMLDcHDmZmZpabAwczMzPLzYGDmeUmaT9lS/Q+JGlwT9cHQNJHUn3mSvpgJ/MeIOmvi6qbWV/kwMGsH+uYMa4TTgb+NSJ2i4g3i6hTFxwN/CYido91p9ut5wCgU4FDF94zsz7FgYNZLyHpHyV9tWT7fElfkXS2pFmS5kk6r2T/ryXNTlcIJpakvy7p+5L+CHy8yrnGpf/BPyLpqjTl7hnAZ4DvSppWJd8BylbYu07SnyVdIOlkSQ+msj6YjjtK0h/TOW6XtG1KPzed7y5JT0j6SkofIWl+yXnOSsceQTad7hmS7qzT7sMkzZH0sKSZylYenAR8PV2x2E/SzyT9Tel7VdKuOyX9EnhE0gBJ/6/kfT8z36do1gcUPX2mH3740ZgHMAKYk15vQDYN7mfJFrFRSvtvYP90TMeUtIPJptbdKm0H8Jka5xlEtpTwzmn7GrIV+gB+BvxNjbwHAH8BtgM2Bp4Fzkv7vgpcnF5vwZopfs8A/i29Phe4L+XdGlgObJTaPr/kPGcB55bkOatk3zrtBoamNo0sO6Y871rtA14vadcbJfknAt9JrzcG2jr2+eFHX394WW2zXiIinpK0XNLuwLbAXGBPsmV056bDNiVbpOYe4CuSjknpO6b05cAqspX3qvkw8GRE/DltXw18Cai4ul4FsyJbRAhJjwO3pfRHgAPT62HAr5St8jcQeLIk/00R8TbwtqQXUls7o1K7hwL3RMSTABHxUifLBHiwIz/Zez6m5OrE+9N5nqyY06wPceBg1rtcCZwG/BVwFTAO+JeIuKL0IEkHAOOBj0fECkl3kV1JAHgrIlbVOIfWs45vl7x+r2T7Pdb8m3Mp8MOImJHqem6V/KtSnpWs3bU6iApqtFtkV1rqWX2etErhwJJ9b5SeCvhyRNyao0yzPsVjHMx6lxuBw8iuNNyaHp+XtCmApB0kbUP2P+CX04/nR4B9OnGOPwEjJH0obX8OuLtRDUjeT9aNAWuWCq7leWAbSVtJ2phsGetq5VZq9/3AJ9PSyEjaMqW/BmxWkv8pYI/0egJZN0kltwJ/q2xJbSTtLGmTHO0w6/V8xcGsF4mId9IgwL+kqwa3SfoocH/2H2ReB/4P8DtgkqR5ZMsBP9CJc7wl6XTgPyVtCMwCpjS4Keem8p9NdRtZp07vSvo+8Eey7oA/VTm0YrsjYlkaKHmDpA2AF4CDgd8C10uaAHwZ+A/gN5IeJFu6+I0K54Dsys8IYE66MrGM7O4Osz7Py2qb9SLpR28OcHxEPNbT9TGz/sddFWa9hKQWYDEw00GDmfUUX3Ew68ck3ci63QTfrDfoT9IuwM/Lkt+OiL0bWT8zaz4OHMzMzCw3d1WYmZlZbg4czMzMLDcHDmZmZpabAwczMzPLzYGDmZmZ5fb/A0lobZvYJQVdAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]}]}