{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":4,"hidden":false,"row":0,"width":4},"report_default":{}}}}},"cell_type":"markdown","source":"# 심층 탐구 분석 및 시각화 - Avito\n\nAvito는 러시아에서 가장 큰 분류 광고 웹 사이트입니다. 자신의 플랫폼에있는 판매자는 너무 적은 요구 (제품 또는 제품 목록에 이상이 있음을 나타냄) 또는 너무 많은 요구 (좋은 설명이있는 항목이 비싼 가격임을 나타냄)로 인해 때때로 좌절감을 느낍니다.\n\n이 [경쟁] (https://www.kaggle.com/c/avito-demand-prediction)에서 목표는 전체 설명 (제목, 설명, 이미지 등)을 기반으로 온라인 광고에 대한 수요를 예측하는 것입니다. , 문맥 (게시 된 지리적 위치, 이미 게시 된 유사한 광고) 및 비슷한 맥락에서 유사 광고에 대한 과거 수요에 대한 정보를 제공합니다. 이 정보를 통해 Avito는 판매자에게 리스팅을 최적화하는 방법을 알리고 현실적으로 얼마나 많은 이익이 기대되는지 알려줍니다.\n\n이 노트에서는 Avito가 공유하는 데이터 세트를 분석하고 더 잘 이해할 수 있도록 시각화를 준비했습니다.\n\n## Contents\n\n[1. 데이터 세트 준비] (# 1)\n[2. 기능 공학] (# 2)\n[3. 번역 열] (# 3)\n[4. 데이터 세트 스냅 샷] (# 4)\n[4.1. 교육 자료 스냅 샷] (# 4-1)\n[4.2 교육 기간 데이터의 스냅 샷] (# 4-2)\n[5. 변수 분포 이해하기 (# 5)\n[5.1 거래 확률 분포 란 무엇입니까 (# 5-1)\n[5.2 거래 확률 분포] (# 5-2)\n[5.3 부모 카테고리 분포] (# 5-3)\n[5.4 카테고리의 분포] (# 5-4)\n[5.5 지역 및 품목 수] (# 5-5)\n[5.6 지도상의 지역 시각화] (# 5-6)\n[5.7 지도상의 도시 시각화] (# 5-7)\n[5.8 도시와 그 품목 수] (# 5-8)\n[5.9 Param_1, Param_2, Param_3 분포] (# 5-9)\n[5.10 월간 요일 및 일수 - 항목 수] (# 5-10)\n[5.11 제목 단어 수, 설명 단어 수 및 분포] (# 5-11)\n[5.12 이미지 Top1 및 사용자 ID] (5-12)\n[5.13 사용자 유형 분포] (# 5-13)\n[5.14 광고 게재 일수] (# 5-14)\n[5.15 제목에 상위 브랜드 표시] (# 5-15)\n[6. 다중 변이 분석] (# 6)\n[6.1 변수의 상관 관계] (# 6-1)\n[6.2 부모 카테고리 및 사용자 유형별 거래 확률] (# 6-2)\n[6.3 지역 및 사용자 유형별 거래 확률] (# 6-3)\n[6.4 거래 확률 등급에 대한 가격의 이해] (# 6-4)\n[6.5 상위 카테고리 및 다른 거래 클래스에 따라 몇 개의 아이템이 있는지] (# 6-5)\n[6.6 다른 지역 및 다른 거래 확률의 항목] (# 6-6)\n[6.7 다른 지역 및 거래 가능성이 다른 품목의 평균 가격] (# 6-7)\n[6.8 거래 확률 및 상위 카테고리가 다른 품목의 평균 가격은 얼마입니까?] (# 6-8)\n[6.9 일주일 간의 거래 클래스 값과 그 평균 가격] (# 6-9)\n[6.10 다른 지역, 다른 요일 및 품목의 평균 가격 값] (# 6-10)\n[6.11 주 일 및 품목 최대 가격의 지역 바이스 값] (# 6-11)\n[6.12 다른 지역 및 주중의 거래 확률의 최대 값] (# 6-12)\n[7. 고가 또는 저가 평균 가격 품목의 특성] (# 7)\n[7.1 주간 요일별 품목의 평균 가격] (# 7-1)\n[7.2 지역 및 품목별 평균 품목 가격] (# 7-2)\n[7.3 카테고리와 부모 카테고리 별 아이템의 평균 가격] (# 7-3)\n[7.4 제목 및 설명에 포함 된 단어의 개수에 따른 평균 가격] (# 7-4)\n[8. 매우 높음 (또는 매우 낮음) 거래 비율을 갖는 품목의 특성] (# 8)\n[8.1 단어 표제어] (# 8-1)\n[8.2 품목의 모든 다른 특징을 저가 및 높은 비율로 비교] (# 8-2)\n[8.3 항목 설명에 사용 된 상위 N- 그램] (# 8-3)\n[9. 아이템 관련 이미지] (# 9)\n[9.1 높은 거래 확률을 갖는 이미지] (# 9-1)\n[9.2 낮은 거래 확률을 가진 이미지] (# 9-2)\n\n\n## 1. 데이터 세트 준비\n\n이 섹션에서는 필수 라이브러리를 포함하고 팬더를 사용하여 데이터 세트를 메모리에로드했습니다."},{"metadata":{"_cell_guid":"3d05a18e-9814-4629-bed7-89029311dada","_kg_hide-input":true,"_uuid":"a02288c8cbaadf46f7b83cca03567f92c8c34b72","trusted":true,"collapsed":true},"cell_type":"code","source":"from plotly.offline import init_notebook_mode, iplot\nfrom wordcloud import WordCloud\nimport plotly.graph_objs as go\nimport matplotlib.pyplot as plt\nimport plotly.plotly as py\nfrom plotly import tools\nfrom datetime import date\nimport pandas as pd\nimport numpy as np \nimport seaborn as sns\nimport random \nimport warnings\nwarnings.filterwarnings(\"ignore\")\ninit_notebook_mode(connected=True)\n\ntrain_df = pd.read_csv(\"../input/train.csv\", parse_dates=[\"activation_date\"])\ntest_df = pd.read_csv(\"../input/test.csv\", parse_dates=[\"activation_date\"])\npr_train = pd.read_csv(\"../input/periods_train.csv\", parse_dates=[\"activation_date\", \"date_from\", \"date_to\"])","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"70e02c5f-2b18-4828-a05d-8af1642e7974","_uuid":"f0c705c2e477ad6b098f6b9a131ce1bc42cb5970"},"cell_type":"markdown","source":"\n## 2. 피쳐링 공학\n\n이 섹션에서는 다음과 같은 기존 데이터 세트를 사용하여 추가 기능을 만들었습니다.\n\n1. WeekDay - 광고가 활성화 된 요일\n2. Month - 광고가 활성화 된 달\n3. Month Day - 광고가 활성화 된 날짜\n4. Week of the year - 올해의 주\n5. Description_len - 설명의 총 단어 수, 즉 기술 길이\n6. Title_len - 제목의 총 단어, 즉. 제목 길이\n7. Total Period - 광고가 운영 된 총 일수\n8. Deal Class - 거래 확률이 0.5보다 큰 경우 1 인 이진 변수 else 0\n10. Param Combined (param1 + param2 + param3) - params와 len의 값을 연결합니다."},{"metadata":{"_cell_guid":"2b4f7060-321a-4486-81af-405e66242045","_uuid":"a533d5bee62d0859f16b0ff07213a6dc73675d7c","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"## 2. feature engineering \n\n# date time features\ntrain_df['weekday'] = train_df.activation_date.dt.weekday\ntrain_df['month'] = train_df.activation_date.dt.month\ntrain_df['day'] = train_df.activation_date.dt.day\ntrain_df['week'] = train_df.activation_date.dt.week \n\n# length of description\ntrain_df['description'] = train_df['description'].fillna(\" \")\ntrain_df['description_len'] = train_df['description'].apply(lambda x : len(x.split()))\n\n# length of title\ntrain_df['title'] = train_df['title'].fillna(\" \")\ntrain_df['title_len'] = train_df['title'].apply(lambda x : len(x.split()))\n\n# param_combined and its length\ntrain_df['param_combined'] = train_df.apply(lambda row: ' '.join([str(row['param_1']), str(row['param_2']), str(row['param_3'])]), axis=1)\ntrain_df['param_combined'] = train_df['param_combined'].fillna(\" \")\ntrain_df['param_combined_len'] = train_df['param_combined'].apply(lambda x : len(x.split()))\n\n# charater len of text columns\ntrain_df['description_char'] = train_df['description'].apply(len)\ntrain_df['title_char'] = train_df['title'].apply(len)\ntrain_df['param_char'] = train_df['param_combined'].apply(len)\n\n# total period for which ads were run \npr_train['total_period'] = pr_train['date_to'] - pr_train['date_from']\n\n# english mapped of weekday\ndaymap = {0:'Sun', 1:'Mon', 2:'Tue', 3:'Wed', 4:'Thu', 5:'Fri', 6:'Sat'}\ntrain_df['weekday_en'] = train_df['weekday'].apply(lambda x : daymap[x])\n\n# bins of deal probability\ninterval = (-0.99, .10, .20, .30, .40, .50, .60, .70, .80, .90, 1.1)\ncats = ['0-0.1', '0.1-0.2', '0.2-0.3', '0.3-0.4', '0.4-0.5', '0.5-0.6', '0.6-0.7', '0.7-0.8', '0.8-0.9','0.9-1.0']\ntrain_df['deal_class'] = train_df['deal_probability'].apply(lambda x: \">=0.5\" if x >=0.5 else \"<0.5\")\ntrain_df[\"deal_class_2\"] = pd.cut(train_df.deal_probability, interval, labels=cats)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"9e8a1935-0830-4e28-a1cb-4753941f8cfb","_uuid":"ed4fb156900bccad4f95d44d4316787894e5706e"},"cell_type":"markdown","source":"\n## 3. 번역 열\n\n러시아어에서 영어로 데이터 집합의 일부 핵심 열을 번역합니다. 사용 준비가 된 사전은 yandex 및 SRK의 커널을 사용하여 준비됩니다."},{"metadata":{"_cell_guid":"4cdd8aa0-6846-416e-8f77-a67549e3b914","_kg_hide-input":true,"_uuid":"cbb7a9b6c7ce0dad1cc3de3d5aa8d4538c4b302c","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"parent_category_name_map = {\"Личные вещи\" : \"Personal belongings\",\n \"Для дома и дачи\" : \"For the home and garden\",\n \"Бытовая электроника\" : \"Consumer electronics\",\n \"Недвижимость\" : \"Real estate\",\n \"Хобби и отдых\" : \"Hobbies & leisure\",\n \"Транспорт\" : \"Transport\",\n \"Услуги\" : \"Services\",\n \"Животные\" : \"Animals\",\n \"Для бизнеса\" : \"For business\"}\n\nregion_map = {\"Свердловская область\" : \"Sverdlovsk oblast\",\n \"Самарская область\" : \"Samara oblast\",\n \"Ростовская область\" : \"Rostov oblast\",\n \"Татарстан\" : \"Tatarstan\",\n \"Волгоградская область\" : \"Volgograd oblast\",\n \"Нижегородская область\" : \"Nizhny Novgorod oblast\",\n \"Пермский край\" : \"Perm Krai\",\n \"Оренбургская область\" : \"Orenburg oblast\",\n \"Ханты-Мансийский АО\" : \"Khanty-Mansi Autonomous Okrug\",\n \"Тюменская область\" : \"Tyumen oblast\",\n \"Башкортостан\" : \"Bashkortostan\",\n \"Краснодарский край\" : \"Krasnodar Krai\",\n \"Новосибирская область\" : \"Novosibirsk oblast\",\n \"Омская область\" : \"Omsk oblast\",\n \"Белгородская область\" : \"Belgorod oblast\",\n \"Челябинская область\" : \"Chelyabinsk oblast\",\n \"Воронежская область\" : \"Voronezh oblast\",\n \"Кемеровская область\" : \"Kemerovo oblast\",\n \"Саратовская область\" : \"Saratov oblast\",\n \"Владимирская область\" : \"Vladimir oblast\",\n \"Калининградская область\" : \"Kaliningrad oblast\",\n \"Красноярский край\" : \"Krasnoyarsk Krai\",\n \"Ярославская область\" : \"Yaroslavl oblast\",\n \"Удмуртия\" : \"Udmurtia\",\n \"Алтайский край\" : \"Altai Krai\",\n \"Иркутская область\" : \"Irkutsk oblast\",\n \"Ставропольский край\" : \"Stavropol Krai\",\n \"Тульская область\" : \"Tula oblast\"}\n\n\ncategory_map = {\"Одежда, обувь, аксессуары\":\"Clothing, shoes, accessories\",\n\"Детская одежда и обувь\":\"Children's clothing and shoes\",\n\"Товары для детей и игрушки\":\"Children's products and toys\",\n\"Квартиры\":\"Apartments\",\n\"Телефоны\":\"Phones\",\n\"Мебель и интерьер\":\"Furniture and interior\",\n\"Предложение услуг\":\"Offer services\",\n\"Автомобили\":\"Cars\",\n\"Ремонт и строительство\":\"Repair and construction\",\n\"Бытовая техника\":\"Appliances\",\n\"Товары для компьютера\":\"Products for computer\",\n\"Дома, дачи, коттеджи\":\"Houses, villas, cottages\",\n\"Красота и здоровье\":\"Health and beauty\",\n\"Аудио и видео\":\"Audio and video\",\n\"Спорт и отдых\":\"Sports and recreation\",\n\"Коллекционирование\":\"Collecting\",\n\"Оборудование для бизнеса\":\"Equipment for business\",\n\"Земельные участки\":\"Land\",\n\"Часы и украшения\":\"Watches and jewelry\",\n\"Книги и журналы\":\"Books and magazines\",\n\"Собаки\":\"Dogs\",\n\"Игры, приставки и программы\":\"Games, consoles and software\",\n\"Другие животные\":\"Other animals\",\n\"Велосипеды\":\"Bikes\",\n\"Ноутбуки\":\"Laptops\",\n\"Кошки\":\"Cats\",\n\"Грузовики и спецтехника\":\"Trucks and buses\",\n\"Посуда и товары для кухни\":\"Tableware and goods for kitchen\",\n\"Растения\":\"Plants\",\n\"Планшеты и электронные книги\":\"Tablets and e-books\",\n\"Товары для животных\":\"Pet products\",\n\"Комнаты\":\"Room\",\n\"Фототехника\":\"Photo\",\n\"Коммерческая недвижимость\":\"Commercial property\",\n\"Гаражи и машиноместа\":\"Garages and Parking spaces\",\n\"Музыкальные инструменты\":\"Musical instruments\",\n\"Оргтехника и расходники\":\"Office equipment and consumables\",\n\"Птицы\":\"Birds\",\n\"Продукты питания\":\"Food\",\n\"Мотоциклы и мототехника\":\"Motorcycles and bikes\",\n\"Настольные компьютеры\":\"Desktop computers\",\n\"Аквариум\":\"Aquarium\",\n\"Охота и рыбалка\":\"Hunting and fishing\",\n\"Билеты и путешествия\":\"Tickets and travel\",\n\"Водный транспорт\":\"Water transport\",\n\"Готовый бизнес\":\"Ready business\",\n\"Недвижимость за рубежом\":\"Property abroad\"}\n\n# train_df['city_en'] = train_df['city'].apply(lambda x : region_map[x])\n# train_df['param_1_en'] = train_df['param_1'].apply(lambda x : region_map[x])\n# train_df['param_2_en'] = train_df['param_2'].apply(lambda x : region_map[x])\n# train_df['title_en'] = train_df['title'].apply(lambda x : region_map[x])","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"55a2348d-913b-4d99-badf-84cc6c857298","_uuid":"7f5c01ec689575d63a24e02dfe07197e0083e429","collapsed":true,"trusted":true},"cell_type":"code","source":"train_df['region_en'] = train_df['region'].apply(lambda x : region_map[x])\ntrain_df['parent_category_name_en'] = train_df['parent_category_name'].apply(lambda x : parent_category_name_map[x])\ntrain_df['category_name_en'] = train_df['category_name'].apply(lambda x : category_map[x])","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"77fa055d-d824-4b67-8ba3-2aefa7c84fc1","_uuid":"4a4286be31ac99fa291ca7ead5eed6cbdf83d357"},"cell_type":"markdown","source":"\n## 4. 데이터 스냅 샷\n\n### 4.1 Train 데이터의 스냅 샷\n"},{"metadata":{"_cell_guid":"4c79bc77-6de6-4d31-ac95-cf6a93af6769","_kg_hide-input":true,"_uuid":"e5d58ac44bcb3f1bb06dbd48ef2be4818e1b8030","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":11,"hidden":false,"row":4,"width":12},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"train_df.head()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"c81b4bc4-2ed7-4b39-b173-f91460b40838","_uuid":"eb7181644bc4c709d5fe5d9e27d2d8542c78548b"},"cell_type":"markdown","source":"\n### 4.2 Train Periods 스냅 샷 데이터 세트"},{"metadata":{"_cell_guid":"76180604-8ecb-4bd6-a2e5-9e120e4987d8","_kg_hide-input":true,"_uuid":"f7e11a9619397f68f0d959e4c43b8e43a9069a9e","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":9,"hidden":false,"row":15,"width":5},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"pr_train.head()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"f7dd7370-b3a9-4a6a-8c0a-d6cd40e592f0","_uuid":"4f94e0ed766e6fcb9d286b2accaa1474c1391ff6"},"cell_type":"markdown","source":"\n## 5. 변수 분포 이해하기\n\n### 5.1 거래 확률 분포 란?"},{"metadata":{"_cell_guid":"bde10dc4-3f2e-4a30-9e87-f9641c2d726d","_kg_hide-input":true,"_uuid":"f461c5e7323ee09ba2a63f7f5719c89e05627ecd","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":5,"height":6,"hidden":false,"row":15,"width":4},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"## 1. Deal Probability Distribution\ndef generate_histogram_plot(df, col, title):\n trace = go.Histogram(x = df[col]) \n layout = go.Layout(title=title, legend=dict(orientation=\"h\"), height=400)\n fig = go.Figure(data=[trace], layout=layout)\n iplot(fig)\n\n\n# generate_histogram_plot(train_df, 'deal_probability', 'Distribution of Deal Probability')\nplt.figure(figsize=(15,5))\nsns.distplot(train_df[\"deal_probability\"].values, bins=120, color=\"#ff002e\")\nplt.xlabel('Deal Probility', fontsize=14);\nplt.title(\"Distribution of Deal Probability\", fontsize=14);\nplt.show();","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"d8a4b1e1-7688-483d-8aa3-912b93448b69","_uuid":"d7b95d6d8f64c1800ef2743e0a038fb11593db1b"},"cell_type":"markdown","source":"\n****추론**\n> - 거래 확률 분포 그래프에서 대다수의 상품이 거래 확률이 매우 낮다는 것이 분명합니다. 약 78 %이며, 거래 확률이 0.7 이상인 값은 거의 없습니다.\n> - 매우 작은 타워가 거래 확률 1.0 근처에서 관찰되며 거래 확률이 매우 높은 데이터 집합에 일부 항목이 있음을 나타냅니다.\n\n** 거래 확률 = 1.0 인 상위 10 항목은 **\n> - 92013ca1fe79 | 문, 개구부, 경사면, 아치 설치\n> - c6239fc67a6f | 손톱 확장, 교정\n> - 44aa121e4559 | 화물 운송 (장거리), 선상, 열림\n> - b16d1b27c975 | 주택의 부상\n> - fe03dbc60ccf | 남부 연방 지구 North Caucasian Federal District Crimea를 가로 지르는 교통 수단\n\n\n\n### 5.2 거래 가능성 방식!\n"},{"metadata":{"_cell_guid":"30ed3620-b22d-4b4b-8721-3ee533f47fa3","_kg_hide-input":true,"_uuid":"9c364948a0c95e6dd5886c9c7a332e617085f07e","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":9,"height":15,"hidden":false,"row":15,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"def _generate_bar_plot_hor(df, col, title, color, w=None, h=None, lm=0, limit=100):\n cnt_srs = df[col].value_counts()[:limit]\n trace = go.Bar(y=cnt_srs.index[::-1], x=cnt_srs.values[::-1], orientation = 'h',\n marker=dict(color=color))\n\n layout = dict(title=title, margin=dict(l=lm), width=w, height=h)\n data = [trace]\n fig = go.Figure(data=data, layout=layout)\n iplot(fig)\n\ndef _generate_bar_plot_ver(df, col, title, color, w=None, h=None, lm=0, limit=100, need_trace = False):\n cnt_srs = df[col].value_counts()[:limit]\n trace = go.Bar(x=list(cnt_srs.index), y=list(cnt_srs.values),\n marker=dict(color = color))\n if need_trace:\n return trace\n if w != None and h != None:\n layout = dict(title=title, margin=dict(l=lm), width=w, height=h)\n else:\n layout = dict(title=title, margin=dict(l=lm))\n data = [trace]\n fig = go.Figure(data=data, layout=layout)\n iplot(fig)\n\ntrace1 = _generate_bar_plot_ver(train_df, 'deal_class_2', \"Deal Probability Bins\", '#7a8aa3', lm=0, limit=30, need_trace = True)\ntrace2 = _generate_bar_plot_ver(train_df, 'deal_class', \"Deal Proabibilities (>0.5 or <= 0.5)\", ['#f25771','#93ef51'], 200, limit=30, need_trace = True)\n\nfig = tools.make_subplots(rows=1, cols=3, specs=[[{'colspan': 2}, {},{}]], print_grid=False, subplot_titles = ['Deal Probability Bins','','Deal Proabibilities (>0.5 or <= 0.5)'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 3);\n\nfig['layout'].update(height=400, title='',showlegend=False)\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"04a68004-031a-413a-b036-35152ebc4a64","_uuid":"43cdea3332f801dea781f0481a3555f52152f6d6"},"cell_type":"markdown","source":"\n> - 거래 확률이 0.5보다 작은 약 130,000 개의 항목이 있고 거래 확률이 0.5보다 큰 약 182,000 개의 항목이 있습니다\n> - 대부분의 지배적 인 카테고리는 거래 확률이있는 항목입니다 : 0-0.2, 0.8-0.9\n\n\n### 5.3 상위 카테고리의 배포"},{"metadata":{"_cell_guid":"a1a4bbe8-8849-49df-8896-31afc6c3c635","_kg_hide-input":true,"_uuid":"3373b97d978dc6b9e895170e7ce6762818c60c05","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":15,"hidden":false,"row":30,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"## univariate analysis of different categorical columns \n\ncols = ['parent_category_name_en', 'category_name_en', 'region_en', 'city', 'param_1', 'param_2', 'param_3', 'weekday', 'day','title_len', 'description_len', 'image_top_1', 'user_id']\n_generate_bar_plot_hor(train_df, cols[0], \"Distribution of Parent Category\", '#f2b5bc', 600, 400, 200)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"58358561-eae4-4854-9044-272f842dd016","_uuid":"af317d81b9d30212e326d7ea8e27a46aa7a2acdd"},"cell_type":"markdown","source":"> -이 데이터 세트에서 최대 항목 수는 약 700,000 개의 항목 인 \"개인 소지품\"상위 카테고리에 있습니다.\n> - 가정 및 정원 및 소비자 전자 제품도 Avito에 거주합니다.\n> - 기업과 관련된 항목 수가 적지 만 (18K)\n\n\n### 5.4 카테고리 분포를 살펴 봅니다."},{"metadata":{"_cell_guid":"6d4e9692-94fe-4952-9097-3fdc329198d1","_kg_hide-input":true,"_uuid":"c82dddcdf5d18f803bafa817a6fe4c218c1dad6c","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":18,"hidden":false,"row":45,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"_generate_bar_plot_hor(train_df, cols[1], \"Distribution of Category\", '#66f992', 600, 500, 200, limit=30)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"64e45288-414a-4955-9059-8e096f30b84f","_uuid":"6ecd5523774385ed1a1c11c37b78d2ad3fd63c94"},"cell_type":"markdown","source":"\n> - 이전 그래프에서 개인 소지품과 관련된 항목이 대부분 데이터 세트에 있음을 알 수 있습니다. 이러한 항목 아래에서 의류, 액세서리, 아동복은 매우 일반적입니다. 전체적으로 약 550,000 개의 항목이이 상위 범주에 있습니다.\n> - 의류 카테고리와 마찬가지로 Shoes는 어린이 신발 및 성인용 신발 아이템의 수가 많기 때문에 데이터 세트에있는 다른 인기 항목 카테고리입니다.\n> - Avito는 장난감과 같은 아동 관련 아이템과 관련된 많은 광고를 보여줍니다.\n> - Avito는 아파트 및 가구, 인테리어 디자인과 같은 필수 요소에 대한 광고를 다수 볼 수 있습니다.\n> - 식물, 정제 및 전자 서적과 같은 카테고리는 Avito에있는 광고의 수가 상대적으로 적습니다. 이것들은 Avito에서 잘 수행되지 않을 수도 있습니다.\n\n\n### 5.5 항목 수가 가장 많은 지역"},{"metadata":{"_cell_guid":"496d4b6a-3572-40ed-9c4e-13f7078a337f","_kg_hide-input":true,"_uuid":"2c41ffc37ee0ddafc7ebcc653793e41e6888fb0d","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":21,"hidden":false,"row":63,"width":null},"report_default":{}}}},"scrolled":true,"trusted":true},"cell_type":"code","source":"_generate_bar_plot_hor(train_df, cols[2], \"Distribution of Region\", '#71c8e8', 600, 600, 200, limit=30)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"bdd84184-5f69-4375-8f14-d7303dfbea37","_uuid":"6562081a9e406a703e9945ea025cad43cb809922"},"cell_type":"markdown","source":"> - 지역 - 크라 스노 다르 크라이 (Krasnodar Krai)는 141K와 동일한 최대 품목 수를 가진 테이블을 상위에 둡니다. 이 지역에서 상당히 많은 수의 광고 / 항목이 홍보됩니다.\n> - 스 베르들 로브 스크 주 (Sverdlovsk oblast)와 로스토프 주 (Rostov Oblast)는 각각 94K와 89K와 같은 많은 수의 아이템\n\n\n### 5.6 지도에서 지역 시각화하기\n\nd3.js를 사용하여 러시아 지역의 차트를 항목 빈도로 생성했습니다.\n\n\n\n\n### 5.7 지도에서 도시 시각화하기"},{"metadata":{"_cell_guid":"05188250-4845-473f-96a8-e9735d28fd65","_kg_hide-input":true,"_uuid":"32d7ab900f821ee234c89297f54613602f79f6ad","collapsed":true,"trusted":true},"cell_type":"code","source":"from io import StringIO\n\ncitynames = StringIO(\"\"\"City,Lat,Long,Ads,color,size\nKrasnodar ,45.0392674,38.9872209,63638,#0061ff,10\nYekaterinburg ,56.8389261,60.6057025,63602,#0061ff,10\nNovosibirsk ,55.0083526,82.9357327,56929,#0061ff,10\nRostov-on-don ,47.2357137,39.701505,52323,#0061ff,10\nNizhny Novgorod ,56.2965039,43.936059,52010,#0061ff,10\nChelyabinsk ,55.1644419,61.4368432,48380,#0061ff,10\nPerm ,58.0296813,56.2667916,46720,#0061ff,10\nKazan ,55.8304307,49.0660806,46663,#0061ff,10\nSamara ,53.2415041,50.2212463,41875,#0061ff,10\nOmsk ,54.9884804,73.3242361,41412,#0061ff,10\nUfa ,54.7387621,55.9720554,41355,#0061ff,10\nKrasnoyarsk ,56.0152834,92.8932476,37932,#0061ff,10\nVoronezh ,51.6754966,39.2088823,36404,#0061ff,10\nVolgograd ,48.708048,44.5133035,33463,#0061ff,10\nSaratov ,51.5923654,45.9608031,31977,#0061ff,10\nTyumen ,57.1612975,65.5250172,30451,#0061ff,10\nKaliningrad ,54.7104264,20.4522144,28490,#0061ff,10\nBarnaul ,53.3547792,83.7697833,27460,#0061ff,10\nYaroslavl ,57.6260744,39.8844709,25098,#0061ff,10\nIrkutsk ,52.2869741,104.3050183,24659,#0061ff,10\nOrenburg ,51.7666482,55.1004538,22561,#0061ff,10\nSochi ,43.6028079,39.7341543,22289,#0061ff,10\nIzhevsk ,56.8618601,53.2324284,21972,#0061ff,10\nTolyatti ,53.5086002,49.4198344,20831,#0061ff,10\nKemerovo ,55.3450231,86.0623044,18216,#0061ff,10\nBelgorod ,50.5997134,36.5982621,17747,#0061ff,10\nTula ,54.204836,37.6184915,16136,#0061ff,10\nStavropol ,45.0454764,41.9683431,16135,#0061ff,10\nNaberezhnye Chelny ,55.7185054,52.3721038,15395,#0061ff,10\nNovokuznetsk ,53.7595935,87.1215705,13769,#0061ff,10\nVladimir ,56.1445956,40.4178687,13377,#0061ff,10\nSurgut ,61.2559503,73.3845471,11684,#0061ff,10\nMagnitogorsk ,53.4129429,59.0016233,11562,#0061ff,10\nNizhni Tagil ,57.9214912,59.9816185,9213,#0061ff,10\nNovorossiysk ,44.7154014,37.7619669,8601,#0061ff,10\nTaganrog ,47.2416334,38.8676013,8591,#0061ff,10\nSterlitamak ,53.6554353,55.9438931,7490,#0061ff,10\nVolzhsky ,48.8176494,44.7707294,7364,#0061ff,10\nDzerzhinsk ,56.2440992,43.4351805,7164,#0061ff,10\nEngels ,51.4753297,46.1136773,6950,#0061ff,10\nBiysk ,52.5072747,85.1472004,6313,#0061ff,10\nAngarsk ,52.5155702,103.91716,6192,#0061ff,10\nAnapa ,44.8857008,37.3199192,5789,#0061ff,10\nBratsk ,56.1737661,101.6038977,5704,#0061ff,10\nNizhnevartovsk ,60.9431185,76.5433724,5353,#0061ff,10\nPyatigorsk ,44.0498933,43.039636,5127,#0061ff,10\nStary Oskol ,51.2980824,37.8379593,5033,#0061ff,10\nNizhnekamsk ,55.613196,51.8469974,4951,#0061ff,10\nBalakovo ,52.0245587,47.7806627,4914,#0061ff,10\nMines ,66.7868346,169.5566368,4729,#34ef6c,8\nAlmetyevsk ,54.8937128,52.317293,4570,#34ef6c,8\nRybinsk ,58.057486,38.8116968,4447,#34ef6c,8\nVolgodonsk ,47.5060474,42.1794336,4354,#34ef6c,8\nSyzran ,53.1504504,48.397896,4332,#34ef6c,8\nNovocherkassk ,47.4177687,40.0726784,3981,#34ef6c,8\nBerezniki ,59.4131827,56.7849318,3941,#34ef6c,8\nMiass ,55.0506795,60.1034961,3687,#34ef6c,8\nKamensk-Uralsky ,56.4253389,61.9222979,3656,#34ef6c,8\nAchinsk ,56.2360841,90.4903153,3647,#34ef6c,8\nArmavir ,44.9873603,41.1111326,3632,#34ef6c,8\nNeftekamsk ,56.1026081,54.2867394,3563,#34ef6c,8\nKovrov ,56.356769,41.3226311,3452,#34ef6c,8\nNovomoskovsk ,54.0109034,38.2963063,3354,#34ef6c,8\nChrysostom ,42.964591,-85.686077,3266,#34ef6c,8\nGelendzhik ,44.5918615,38.0241663,3097,#34ef6c,8\nMurom ,55.5673975,42.0165852,2938,#34ef6c,8\nNorilsk ,69.35579,88.1892939,2844,#34ef6c,8\nArzamas ,55.3964609,43.8299175,2754,#34ef6c,8\nPervouralsk ,56.8999158,59.9521128,2696,#34ef6c,8\nSalavat ,53.3860437,55.9259472,2669,#34ef6c,8\nNevinnomyssk ,44.638015,41.9504639,2581,#34ef6c,8\nKhanty-Mansiysk ,61.0090919,69.0374596,2547,#34ef6c,8\nRubtsovsk ,51.5140399,81.2317683,2546,#34ef6c,8\nBerdsk ,54.7750638,83.0800315,2506,#34ef6c,8\nBataisk ,47.139761,39.7378477,2491,#34ef6c,8\nYeysk. ,46.6825784,38.2702941,2446,#34ef6c,8\nSolikamsk ,59.6720331,56.7557669,2387,#34ef6c,8\nKamyshin ,50.0946344,45.3939821,2344,#34ef6c,8\nNefteyugansk ,61.0980641,72.5816466,2312,#34ef6c,8\nSlavyansk-on-Kuban ,45.2588008,38.124859,2186,#34ef6c,8\nProkopevsk ,53.888753,86.7591829,2136,#34ef6c,8\nBuzuluk ,52.7732825,52.2613248,2119,#34ef6c,8\nOktyabrsky ,54.4772015,53.4895394,2116,#34ef6c,8\nKropotkin ,45.4296049,40.5540352,2076,#34ef6c,8\nKansk ,56.2168141,95.7198146,2004,#34ef6c,8\nEssentuki ,44.0455121,42.8575232,1999,#34ef6c,8\nTobolsk ,58.200024,68.2635228,1961,#34ef6c,8\nSarapul ,56.4521462,53.7833628,1952,#34ef6c,8\nTuapse ,44.106518,39.0806454,1912,#34ef6c,8\nKamensk-Shakhtinsky ,48.3141177,40.2689543,1902,#34ef6c,8\nBelorechensk ,44.7591972,39.8817157,1856,#34ef6c,8\nmineralnye vody ,44.211675,43.1238527,1842,#34ef6c,8\nKopeisk ,55.1339695,61.6457637,1820,#34ef6c,8\nTchaikovsky ,56.766116,54.1276713,1809,#34ef6c,8\nBelovo ,54.4102969,86.2936947,1796,#34ef6c,8\nZelenodolsk ,55.8516037,48.5371529,1776,#34ef6c,8\nGeorgievsk ,44.1497667,43.4577689,1750,#34ef6c,8\nKislovodsk ,43.9056014,42.7280949,1736,#34ef6c,8\nKungur ,57.431641,56.9397122,1719,#34ef6c,8\nLeninsk-Kuznetsk ,54.6699128,86.1734538,1699,#34ef6c,8\nTikhoretsk ,45.8676515,40.105095,1667,#34ef6c,8\nMinusinsk ,53.6978211,91.6963172,1655,#34ef6c,8\nAzov ,47.0947446,39.4136552,1644,#34ef6c,8\nNovokujbyshevsk ,53.0960666,49.8912888,1619,#34ef6c,8\nTuimazy ,54.598534,53.6921837,1613,#34ef6c,8\nUsolye-Sibirskoye ,52.7664236,103.6381711,1600,#34ef6c,8\nBalashov ,51.543181,43.1770959,1595,#34ef6c,8\nSerov ,59.6104645,60.6177432,1558,#34ef6c,8\nGlazov ,58.1368837,52.6548341,1534,#34ef6c,8\nVotkinsk ,57.0409442,53.9769303,1532,#34ef6c,8\nNovouralsk ,57.2575239,60.0834487,1466,#34ef6c,8\nKstovo town ,56.1328851,44.1740097,1462,#34ef6c,8\nOrsk ,51.2145242,58.5440565,1457,#34ef6c,8\nGubkin ,51.2824173,37.5434951,1455,#34ef6c,8\nRossosh. ,50.2023315,39.5884892,1447,#34ef6c,8\nBugulma ,54.5220314,52.8260805,1440,#34ef6c,8\nKrymsk ,44.9277854,37.9932126,1438,#34ef6c,8\nBudennovsk ,44.7925204,44.1536162,1403,#34ef6c,8\nVyksa ,55.3262146,42.17017,1382,#34ef6c,8\nAlexandrov ,56.3947309,38.712037,1362,#34ef6c,8\nBohr ,57.0497406,65.7129411,1343,#34ef6c,8\nYurga ,55.7297643,84.8944519,1337,#34ef6c,8\nIshim ,56.1146308,69.4771245,1327,#34ef6c,8\nTroitsk ,54.0747574,61.5670509,1301,#34ef6c,8\nBerezovsky ,56.9088786,60.7950057,1287,#34ef6c,8\nGus ' -Khrustal'nyy ,43.120086,131.8774404,1282,#34ef6c,8\nNovoaltajsk ,53.4415721,83.9208351,1228,#34ef6c,8\nBorisoglebsk ,51.3760702,42.0735155,1221,#34ef6c,8\nMikhaylovsk ,45.128744,42.0092318,1217,#34ef6c,8\nShchekino ,53.9883155,37.6290103,1214,#34ef6c,8\nTimashevsk ,45.6093387,38.9547015,1211,#34ef6c,8\nMikhajlovka ,50.0564141,43.2285897,1205,#34ef6c,8\nBald head ,61.2286702,-149.7154282,1204,#34ef6c,8\nMillerovo ,48.9182677,40.4050284,1197,#34ef6c,8\nLabinsk ,44.6236929,40.7471371,1191,#34ef6c,8\nElabuga ,55.763166,52.0254936,1185,#34ef6c,8\nPavlovo ,55.962069,43.0911692,1112,#34ef6c,8\nverkhnyaya pyshma ,56.9664827,60.5864162,1110,#34ef6c,8\nZheleznogorsk ,56.2343557,93.4888156,1104,#34ef6c,8\nNyagan ,62.129277,65.3745138,1094,#34ef6c,8\nTemryuk ,45.2710009,37.382298,1085,#34ef6c,8\nLiski ,50.979677,39.4941294,1083,#34ef6c,8\nUst ' -Labinsk ,66.787086,136.6596069,1082,#34ef6c,8\nUst ' -Ilimsk ,59.6533827,28.2696934,1054,#34ef6c,8\nIskitim ,54.642619,83.3083003,1036,#34ef6c,8\nKrasnokamsk ,58.0789548,55.7671866,1034,#34ef6c,8\nLeninogorsk ,54.6013024,52.4607127,1001,#34ef6c,8\nKumertau ,52.7637387,55.8115682,1000,#34ef6c,8\nKogalym ,62.2665501,74.5446849,983,#34ef6c,8\nMeleuz ,52.9610909,55.9283536,982,#34ef6c,8\nVol ,,,977,#f2ae93,6\nKrasnoturyinsk ,59.7650618,60.2158834,965,#f2ae93,6\nKaniv ,,,959,#f2ae93,6\nSalsk ,46.4806506,41.537242,950,#f2ae93,6\nNovoshakhtinsk ,47.7568793,39.9357686,939,#f2ae93,6\nAleksin ,54.5072013,37.0013226,937,#f2ae93,6\nAsbestos ,57.0052568,61.4580959,932,#f2ae93,6\nBeloretsk ,53.9673992,58.4060639,927,#f2ae93,6\nBelebey. ,54.1103461,54.1046266,917,#f2ae93,6\nNazarovo ,56.0131955,90.402565,910,#f2ae93,6\nBalakhna ,56.5170457,43.5819467,907,#f2ae93,6\nIshimbay ,53.4263581,56.0507671,906,#f2ae93,6\nhot spring ,35.9397365,-106.6433564,898,#f2ae93,6\nChistopol ,55.3634734,50.6404638,891,#f2ae93,6\nKurganinsk ,44.885976,40.5862301,887,#f2ae93,6\nbelaya kalitva ,48.1773148,40.7795085,878,#f2ae93,6\nChapaevsk ,52.9696663,49.7067317,872,#f2ae93,6\nAbinsk ,44.8715341,38.1644617,842,#f2ae93,6\nKorenovsk ,45.4623876,39.4481096,840,#f2ae93,6\nKiselevsk ,54.0055243,86.649758,839,#f2ae93,6\nYuzhnoural'sk ,54.4420646,61.2606289,832,#f2ae93,6\nPereslavl-Zalessky ,56.747048,38.8902603,826,#f2ae93,6\nOzersk ,55.71381,60.7009592,826,#f2ae93,6\nUzlovaya ,53.9789964,38.1666833,824,#f2ae93,6\nSovetsk ,55.0788538,21.8787067,817,#f2ae93,6\nMezhdurechensk ,53.6836301,88.0813924,812,#f2ae93,6\nGukovo ,48.0513115,39.9377047,803,#f2ae93,6\nAksai ,47.2651252,39.8705857,803,#f2ae93,6\nRostov ,57.195623,39.4131527,798,#f2ae93,6\nAnzhero-Sudzhensk ,56.0954566,85.9955088,789,#f2ae93,6\nUryupinsk ,50.7974183,42.0056002,784,#f2ae93,6\nChebarkul ,54.9810702,60.3856171,771,#f2ae93,6\nOtradny ,53.3769898,51.3402104,751,#f2ae93,6\nShebekino ,50.4118663,36.8947618,729,#f2ae93,6\nGusev ,54.5890504,22.2032977,721,#f2ae93,6\nAlekseevka ,50.6256541,38.6978113,720,#f2ae93,6\nSlavgorod ,52.9930044,78.6386564,697,#f2ae93,6\nGulkevichi ,45.3605521,40.7019416,695,#f2ae93,6\nKorkino ,54.9009507,61.3679909,688,#f2ae93,6\nBuguruslan ,53.6234055,52.4332215,688,#f2ae93,6\nValuiki ,50.2025736,38.1180528,688,#f2ae93,6\nShelekhov ,52.2075403,104.0988309,682,#f2ae93,6\nApsheronsk ,44.4654077,39.726798,679,#f2ae93,6\nZhigulevsk ,53.3907252,49.4722695,676,#f2ae93,6\nMarx ,51.7106121,46.7484815,667,#f2ae93,6\nRevda ,56.8188497,59.9036397,660,#f2ae93,6\nVyazniki ,56.2398425,42.1024424,658,#f2ae93,6\nChernyakhovsk ,54.6312721,21.8311353,648,#f2ae93,6\nSatka ,55.0467482,59.0082549,644,#f2ae93,6\nMozhga ,56.4380444,52.2116602,620,#f2ae93,6\nSayansk ,54.1065069,102.1888502,618,#f2ae93,6\nFrolovo ,49.7590354,43.6591507,610,#f2ae93,6\nLeningrad ,59.9342802,30.3350986,603,#f2ae93,6\nMegion ,61.0374313,76.0997981,599,#f2ae93,6\nKolchugino ,56.2983854,39.3701415,590,#f2ae93,6\nKushchevskaya ,46.5545352,39.6116637,589,#f2ae93,6\nLangepas ,61.2703184,75.1700858,586,#f2ae93,6\nUraj ,60.1254336,64.8066207,580,#f2ae93,6\nZavolzhye ,56.6415848,43.4029059,576,#f2ae93,6\nYugorsk ,61.3138277,63.3429553,563,#f2ae93,6\nCheremkhovo ,53.1400556,103.0939762,562,#f2ae93,6\nBirsk ,55.4251309,55.5442933,561,#f2ae93,6\nChusovoj ,58.2754326,57.8314153,557,#f2ae93,6\nSharypovo ,55.5222543,89.2205323,557,#f2ae93,6\nUglich ,57.5247896,38.3308361,556,#f2ae93,6\nKinel ,53.2210128,50.6343922,556,#f2ae93,6\nGorodets ,56.6499552,43.4800896,552,#f2ae93,6\nEfremov ,53.1494491,38.0937333,549,#f2ae93,6\nKirzhach ,56.1592176,38.8635727,543,#f2ae93,6\nDinskaya ,45.2206108,39.2308111,542,#f2ae93,6\nGur'yevsk ,54.7801208,20.6099047,540,#f2ae93,6\nBelokurikha ,51.9948245,84.9935561,527,#f2ae93,6\nAbundant ,48.3009359,40.2782294,522,#f2ae93,6\nBogorodsk ,56.1037285,43.5174444,520,#f2ae93,6\nUst ' -Kut ,66.787086,136.6596069,520,#f2ae93,6\nKamen-na-Obi ,53.7811403,81.3134611,512,#f2ae93,6\nForest ,41.124444,44.281944,510,#f2ae93,6\nSibay ,52.7133586,58.6681358,504,#f2ae93,6\nDonetsk ,48.3321569,39.9653797,495,#f2ae93,6\nLyantor ,61.6213111,72.1645209,491,#f2ae93,6\nSarov ,54.9342792,43.3252503,487,#f2ae93,6\nRtischevo ,51.6726793,45.5732029,485,#f2ae93,6\nNigella ,,,472,#f2ae93,6\nBuilder ,50.6127627,36.5815369,470,#f2ae93,6\nYalutorovsk ,56.6588674,66.3058533,464,#f2ae93,6\nZarinsk ,53.7273796,84.9265074,463,#f2ae93,6\nSosnovoborsk ,56.123153,93.3400796,460,#f2ae93,6\nKulebaki ,55.4203609,42.5233651,447,#f2ae93,6\nPyt-Yah ,60.7273587,72.8211905,445,#f2ae93,6\nAznakaevo ,54.8586973,53.0719188,439,#f2ae93,6\nSvetlograd ,45.3302224,42.863667,438,#f2ae93,6\nZelenokumsk ,44.4140877,43.8736157,437,#f2ae93,6\nPugachev ,52.0274013,48.7992943,437,#f2ae93,6\nAspen forests ,,,433,#f2ae93,6\nPavlovskaya ,46.1359683,39.7878804,432,#f2ae93,6\nTutaev ,57.8675746,39.5333393,427,#f2ae93,6\nKyshtym ,55.7174713,60.5521268,427,#f2ae93,6\nMyski ,53.7113802,87.8022023,417,#f2ae93,6\nSnezhinsk ,56.0571732,60.7543988,405,#f2ae93,6\nSorochinsk ,52.4243319,53.1557011,403,#f2ae93,6\nDyurtyuli ,55.4787765,54.8619269,403,#f2ae93,6\nNev ,,,398,#f2ae93,6\nDonskoy ,53.9674786,38.352923,394,#f2ae93,6\nPrimorsko-Akhtarsk ,46.0473137,38.190411,394,#f2ae93,6\nKalach-na-Donu ,48.6857119,43.5271494,392,#f2ae93,6\nOstrogozhsk ,50.8548419,39.0632827,389,#f2ae93,6\nNizhneudinsk ,54.9019282,99.024836,386,#f2ae93,6\nMariinsk ,56.1936066,87.7130977,385,#f2ae93,6\nAsha ,54.9890934,57.2733878,384,#f2ae93,6\nTulun. ,54.5699077,100.5817565,377,#f2ae93,6\nIrbit ,57.6680135,63.0632053,375,#f2ae93,6\nPolevskoy ,56.4824818,60.2446478,373,#f2ae93,6\nNovovoronezh ,51.3080701,39.2200189,373,#f2ae93,6\nsukhoy log ,56.9116454,62.0386461,373,#f2ae93,6\nBryukhovetskaya ,45.8066975,38.9933586,371,#f2ae93,6\nZainsk ,55.2898095,52.0131212,364,#f2ae93,6\nSeverskaya ,44.8528767,38.6770065,362,#f2ae93,6\nZavodoukovsk ,56.4982712,66.5488424,358,#f2ae93,6\nStarominskaya ,46.5333073,39.0405159,355,#f2ae93,6\nBogoroditsk ,53.7709957,38.1276312,353,#f2ae93,6\nBogdanovich ,56.7743358,62.0522923,352,#f2ae93,6\nKuibyshev ,53.2415041,50.2212463,350,#f2ae93,6\nKarpinsk ,59.7653982,60.0134853,347,#f2ae93,6\nMasty ,,,344,#f2ae93,6\nPavlovsk ,59.6811976,30.4443764,340,#f2ae93,6\nKartaly ,53.0553987,60.6373781,339,#f2ae93,6\nKalach ,48.6857119,43.5271494,336,#f2ae93,6\nRed Sulin ,,,336,#f2ae93,6\nMorozovsk ,48.3450513,41.8264746,333,#f2ae93,6\nSoviet ,52.2810596,104.3497995,332,#f2ae93,6\nKudymkar ,59.0069977,54.6643867,329,#f2ae93,6\nNew Oskol ,51.3093338,37.8733839,327,#f2ae93,6\nNovotroitsk ,51.2011047,58.2987542,325,#f2ae93,6\nVyselki ,45.5790083,39.6543073,322,#f2ae93,6\nTbilisi ,41.7151377,44.827096,318,#f2ae93,6\nZernograd ,46.8490381,40.3078719,315,#f2ae93,6\nRezh ,57.3665832,61.4221758,302,#f2ae93,6\nPokhvistnevo ,53.6482058,52.1229541,301,#f2ae93,6\nWinter ,61.5788242,-161.3904356,301,#f2ae93,6\nRainbow ,59.837799,30.3342009,300,#f2ae93,6\nBaltijsk ,54.6591222,19.9228947,295,#f2ae93,6\nUpper Ufaley ,56.0589027,60.2238679,295,#f2ae93,6\nKarasuk ,53.7342942,78.0024101,286,#f2ae93,6\nUchaly ,54.3089584,59.408949,285,#f2ae93,6\nNovokubansk ,45.111673,41.0182824,285,#f2ae93,6\nNeftekumsk ,44.7503541,44.9884168,283,#f2ae93,6\nUst ' -Katav ,66.787086,136.6596069,280,#f2ae93,6\nNovoaleksandrovsk ,45.4970265,41.2181899,280,#f2ae93,6\nErshov ,51.3493959,48.27618,279,#f2ae93,6\nNovaya Usman' ,51.626324,39.4129061,276,#f2ae93,6\nBobrov ,51.1051797,40.0217185,275,#f2ae93,6\nAramil. ,56.6964517,60.8330254,274,#f2ae93,6\nHoly Protection ,42.0198624,-87.8955378,272,#f2ae93,6\nZheleznovodsk ,44.1404266,43.0070123,272,#f2ae93,6\nLesosibirsk ,58.2276178,92.4960319,271,#f2ae93,6\nPetushki ,55.9251242,39.4489055,271,#f2ae93,6\nSysert town ,56.5103018,60.8247375,269,#f2ae93,6\nSemenov ,56.7889481,44.5018318,268,#f2ae93,6\nAtkarsk ,51.8740291,45,265,#f2ae93,6\nSemikarakorsk ,47.5155739,40.8140313,265,#f2ae93,6\nGrateful ,54.779012,32.0472391,265,#f2ae93,6\nSemiluki ,51.6815784,39.0303492,264,#f2ae93,6\nAlapaevsk ,57.8483493,61.6880302,264,#f2ae93,6\nIpatovo ,45.720543,42.9035145,263,#f2ae93,6\nYanaul ,56.2674828,54.9252541,263,#f2ae93,6\nFurnaces ,55.094262,61.386056,263,#f2ae93,6\nLyskovo ,56.0135442,45.0342011,262,#f2ae93,6\nPoltava ,49.588267,34.5514169,262,#f2ae93,6\nPetrovsk ,52.3144031,45.3882551,261,#f2ae93,6\nSol-Iletsk ,51.1645902,54.9884567,258,#f2ae93,6\nZarechny ,53.1855799,45.1685087,256,#f2ae93,6\nTalnakh ,69.5,88.4,254,#f2ae93,6\nDobryanka ,58.4704782,56.4232263,253,#f2ae93,6\nKireevsk ,53.9354648,37.9256207,253,#f2ae93,6\nOtradnaya ,44.3907775,41.5199929,252,#f2ae93,6\nSeveroural'sk ,60.1553083,59.9646358,250,#f2ae93,6\nArtyomovsk ,54.3477488,93.4134703,250,#f2ae93,6\nContainer ,55.752968,37.606156,248,#f2ae93,6\nKamyshlov ,56.8522639,62.7082352,247,#f2ae93,6\nKachkanar ,58.6978588,59.4929037,245,#f2ae93,6\nKimovsk ,53.9704765,38.5401265,240,#f2ae93,6\nAleksandrovskoe ,59.1562328,57.5942158,240,#f2ae93,6\nAfipsky ,44.9056131,38.8423874,239,#f2ae93,6\nNovoanninsky ,50.52546,42.6645851,236,#f2ae93,6\nBlagoveshchensk ,50.2727763,127.5404017,234,#f2ae93,6\nMiddle Akhtuba ,,,230,#f2ae93,6\nGubakha ,58.8389476,57.5544262,227,#f2ae93,6\nYuriev-Polish ,,,224,#f2ae93,6\nKuvandyk ,51.479495,57.3641027,223,#f2ae93,6\nTaishet ,55.9321466,98.0105748,222,#f2ae93,6\nBezenchuk ,52.9830748,49.4405959,221,#f2ae93,6\nLight ,55.7221576,37.6327633,220,#f2ae93,6\nZelenogradsk ,54.9562342,20.4747022,217,#f2ae93,6\nKotovo ,50.3154621,44.8019549,216,#f2ae93,6\nBoguchar ,49.9378835,40.5529044,215,#f2ae93,6\nLakinsk ,56.0175401,39.9610322,212,#f2ae93,6\nIl ,41.8793553,-87.6250403,211,#f2ae93,6\nPolysaevo ,54.6034533,86.2760157,209,#f2ae93,6\nZhirnovsk ,50.9745069,44.7853422,208,#f2ae93,6\nKalachinsk ,55.0323877,74.5697422,205,#bab7b6,4\nKasli ,55.889724,60.7444752,203,#bab7b6,4\nNurlat ,54.4192188,50.8188982,200,#bab7b6,4\nButurlinovka ,50.8330077,40.6023866,199,#bab7b6,4\nTrigeminal ,,,199,#bab7b6,4\nAleysk ,52.4934588,82.7806142,198,#bab7b6,4\nBeloyarsk ,53.459444,83.891389,195,#bab7b6,4\nChernyanka ,50.9339891,37.820799,195,#bab7b6,4\nKochubeyevskoye ,44.6795654,41.8204102,195,#bab7b6,4\nBarabinsk ,55.3463597,78.3466855,194,#bab7b6,4\nEmanzhelinsk ,54.7569133,61.3211493,194,#bab7b6,4\nSredneural'sk ,56.98592,60.470107,190,#bab7b6,4\nSuzdal ,56.4191591,40.4536153,190,#bab7b6,4\nDivnogorsk ,55.960443,92.3710776,186,#bab7b6,4\nNovopavlovsk ,43.9588581,43.6237301,185,#bab7b6,4\nSergach ,55.5218645,45.4665721,184,#bab7b6,4\nIglino ,54.8180482,56.3943691,183,#bab7b6,4\nKrasnoufimsk ,56.6153071,57.7536754,181,#bab7b6,4\nSuvorov ,54.1254579,36.485623,180,#bab7b6,4\nUva ,54.7387621,55.9720554,179,#bab7b6,4\nWasp ,55.604953,38.4232749,179,#bab7b6,4\nSobinka ,55.9886284,40.0182063,179,#bab7b6,4\nKotelnikovo ,47.630995,43.1423502,177,#bab7b6,4\nPallasovka ,50.0429768,46.8886722,175,#bab7b6,4\nBuinsk ,54.9756301,48.2787037,174,#bab7b6,4\nDavlekanovo ,54.2161646,55.0290702,173,#bab7b6,4\nSvetlogorsk ,54.9394562,20.1584706,173,#bab7b6,4\nLermontov ,44.1109665,42.9684183,170,#bab7b6,4\nKaltan ,53.5216252,87.2691571,167,#bab7b6,4\nKalininsk ,51.5006306,44.4835786,166,#bab7b6,4\nAnna ,29.5381022,-98.5817617,166,#bab7b6,4\nHillfort ,54.0755458,36.035116,166,#bab7b6,4\nKorocha ,50.8145765,37.1949424,165,#bab7b6,4\nSudogda ,55.951057,40.8600241,165,#bab7b6,4\nPoykovsky ,60.9935917,71.9003744,165,#bab7b6,4\nZaoksky ,54.658657,37.4694222,165,#bab7b6,4\nKhadyzhensk ,44.418695,39.5314274,164,#bab7b6,4\nNytva ,57.9331563,55.34672,164,#bab7b6,4\nYasnogorsk ,54.4808396,37.6941279,164,#bab7b6,4\nKukmor ,56.1886432,50.8963361,161,#bab7b6,4\nKatav-Ivanovsk ,54.7640685,58.2152026,160,#bab7b6,4\nFedorov ,53.1914992,50.0910744,160,#bab7b6,4\nKameshkovo ,56.3509682,41.0002049,160,#bab7b6,4\nNovotitarovskaya ,45.2366074,38.9712108,160,#bab7b6,4\nGvardeysk ,54.6521344,21.0670357,160,#bab7b6,4\nKushva ,58.2857349,59.7776308,159,#bab7b6,4\nSurovikino ,48.6085692,42.8455406,158,#bab7b6,4\nCherepanovo ,54.2190656,83.3670945,157,#bab7b6,4\nKirovgrad ,57.4160506,60.0705801,156,#bab7b6,4\nInozemtsevo KP ,,,155,#bab7b6,4\nMendeleevsk ,55.8991889,52.2938156,155,#bab7b6,4\nVereshchagino ,58.0704157,54.6538595,154,#bab7b6,4\nBavly ,54.3894536,53.2773473,153,#bab7b6,4\nMelenki ,55.3365242,41.6303343,151,#bab7b6,4\nVenev ,54.3494655,38.2582586,151,#bab7b6,4\nZverevo ,48.0219241,40.1207574,149,#bab7b6,4\nverkhnyaya salda ,58.0411092,60.5592467,148,#bab7b6,4\nShakhun'ya ,57.6734511,46.6107277,148,#bab7b6,4\nPioneer ,79.8511797,92.3411571,148,#bab7b6,4\nSpring ,30.662292,-96.370204,148,#bab7b6,4\nBorisovka ,50.601711,36.0089388,147,#bab7b6,4\nArsk ,56.08891,49.8754019,147,#bab7b6,4\nNovopokrovskaya ,45.9587987,40.6896191,147,#bab7b6,4\nVortex ,58.539018,31.270909,146,#bab7b6,4\nKrasnouralsk ,58.3302967,60.0713325,145,#bab7b6,4\nwhite clay ,,,143,#bab7b6,4\nKonstantinovsk ,47.5872228,41.0913182,142,#bab7b6,4\nTaman ,45.2120206,36.7102871,141,#bab7b6,4\nLayer ,56.9664654,41.0251717,141,#bab7b6,4\nChishmy ,54.5924024,55.3759905,140,#bab7b6,4\nnizhnyaya tura ,58.6277042,59.8558905,139,#bab7b6,4\nRaevskij ,,,139,#bab7b6,4\nKrasnoarmiisk ,56.1060907,38.1392419,136,#bab7b6,4\nRamon. ,51.9053053,39.3331623,136,#bab7b6,4\nGavrilov-Yam ,57.2992579,39.8548496,134,#bab7b6,4\nShelter ,42.099552,-87.882158,134,#bab7b6,4\nTal'menka ,53.8194388,83.5573031,134,#bab7b6,4\nChertkovo ,49.3887058,40.1467506,131,#bab7b6,4\nMamadysh ,55.7121866,51.3964842,131,#bab7b6,4\nBerezovka ,67.1294439,156.586111,130,#bab7b6,4\nOb ,55.0005568,82.6824259,130,#bab7b6,4\nStaroshcherbinovskaya ,46.6285357,38.6771227,128,#bab7b6,4\nUrin ,,,128,#bab7b6,4\nOcher ,57.8721328,54.7288281,127,#bab7b6,4\nTavda ,58.0442892,65.2561088,127,#bab7b6,4\nLcps ,,,127,#bab7b6,4\nOryol ,52.9668468,36.0624898,127,#bab7b6,4\nBiryuch ,50.6499174,38.4017418,126,#bab7b6,4\nDegtyarsk ,56.6980031,60.1020737,126,#bab7b6,4\nYuryuzan ,54.8585618,58.4244316,126,#bab7b6,4\nMednogorsk ,51.3968703,57.6084658,125,#bab7b6,4\nOktyabrsk ,53.1835334,48.7690418,123,#bab7b6,4\nZav'yalovo ,52.8382084,80.9159204,123,#bab7b6,4\nArgayash ,55.483791,60.8445137,123,#bab7b6,4\nAbdulino ,53.6819539,53.6474802,123,#bab7b6,4\nLinevo ,54.4555308,83.3748613,122,#bab7b6,4\nChernomorsky ,44.8526321,38.4927165,122,#bab7b6,4\nZelenogorsk ,56.1103549,94.613901,122,#bab7b6,4\nkrasny yar ,50.6148323,45.7713456,121,#bab7b6,4\nTashtagol ,52.7643345,87.8895009,120,#bab7b6,4\nEssentuki ,44.0455121,42.8575232,120,#bab7b6,4\nProletarsk ,46.7023654,41.7285328,117,#bab7b6,4\nNikolaevsk ,53.1423901,140.7314708,117,#bab7b6,4\nGorokhovets ,56.1961541,42.6953497,117,#bab7b6,4\nKalinin ,56.8587214,35.9175965,116,#bab7b6,4\nBaymak. ,52.5865508,58.3195186,116,#bab7b6,4\nNavashino ,55.5448316,42.1990405,115,#bab7b6,4\nSlyudyanka ,51.6628823,103.7040772,114,#bab7b6,4\nKrylovskaya ,46.315192,39.9700196,114,#bab7b6,4\nRed Cut ,,,113,#bab7b6,4\nUzhur ,55.3253488,89.8785426,113,#bab7b6,4\nRovenky ,49.9222068,38.8955685,112,#bab7b6,4\nZimovniki ,47.1284352,42.4543661,112,#bab7b6,4\nShushenskoe ,53.337199,91.9334697,112,#bab7b6,4\nTsimlyansk ,47.6451517,42.0889476,112,#bab7b6,4\nKuragino ,53.8970951,92.6812124,111,#bab7b6,4\nSaraktash ,51.7862865,56.3553676,111,#bab7b6,4\nTaiga ,,,110,#bab7b6,4\nKrasnoslobodsk ,56.0152834,92.8932476,110,#bab7b6,4\nPlavsk ,53.7111077,37.29209,109,#bab7b6,4\nKholmskaya ,44.8483621,38.3881198,109,#bab7b6,4\nDubovka ,49.0518384,44.8206815,108,#bab7b6,4\nKinel ' -Cherkassy ,53.2210128,50.6343922,108,#bab7b6,4\nZheleznogorsk-Ilimskiy ,56.5847536,104.1324423,108,#bab7b6,4\nBogotol ,56.2138488,89.5382038,107,#bab7b6,4\nMatveev-Kurgan ,47.5689833,38.8697014,107,#bab7b6,4\nDanilov ,58.184571,40.18021,107,#bab7b6,4\nMesyagutovo ,55.5360244,58.2457779,106,#bab7b6,4\nLeninsk ,48.6979193,45.2032172,106,#bab7b6,4\nWondrous ,,,105,#bab7b6,4\nKrasnoobsk ,54.9191126,82.9903163,104,#bab7b6,4\nVolokonovka ,50.4827685,37.859508,104,#bab7b6,4\nhighest mountain ,43.0524999,43.135,104,#bab7b6,4\nArkhipo-Osipovka ,44.3716397,38.5346737,103,#bab7b6,4\nKrasnaya Polyana ,43.6805415,40.2100333,103,#bab7b6,4\nVarenikovskaya ,45.1158643,37.648293,101,#bab7b6,4\nLadoga ,60.8664587,31.5103505,101,#bab7b6,4\nDzhubga KP ,,,100,#bab7b6,4\nAkhtyrsky ,44.8527171,38.2948067,99,#bab7b6,4\nCherlak ,54.1576836,74.7964453,99,#bab7b6,4\nElan. ,50.9527133,43.7395811,98,#bab7b6,4\nPromyshlennaya ,54.914389,85.646652,98,#bab7b6,4\nBalezino ,57.9701389,53.004901,98,#bab7b6,4\nYemelyanovo ,56.1767877,92.4813965,98,#bab7b6,4\nUst ' -Donetskiy ,56.6484793,161.6490624,96,#bab7b6,4\nTalitsa ,57.012712,63.7191476,96,#bab7b6,4\nPovorino ,51.1954067,42.2485575,96,#bab7b6,4\nGrayvoron. ,50.4779822,35.6792432,95,#bab7b6,4\nNizhnesortymsky ,60.9431185,76.5433724,95,#bab7b6,4\nKizel ,59.0548727,57.6238318,94,#bab7b6,4\nArkadak ,51.9348861,43.5027751,94,#bab7b6,4\nLukoyanov ,55.0314911,44.4799464,94,#bab7b6,4\nNovosergievka ,52.169683,53.834592,94,#bab7b6,4\nIsilkul ,54.9013089,71.2661449,94,#bab7b6,4\nKursk ,51.7091957,36.1562241,92,#bab7b6,4\nTalovaya ,51.1158426,40.7333578,92,#bab7b6,4\nTetyushi ,54.9479269,48.8247416,91,#bab7b6,4\nBye. ,59.945702,30.264974,90,#bab7b6,4\nEgorlykskaya ,46.5630276,40.6551962,89,#bab7b6,4\nNeman ,55.0276261,22.0268891,89,#bab7b6,4\nMoskalenki ,54.9389045,71.9437465,89,#bab7b6,4\nIlovlya ,49.3027047,43.9796985,89,#bab7b6,4\nProkhorovka ,51.03701,36.7302535,89,#bab7b6,4\nBelev ,53.8124145,36.1225732,88,#bab7b6,4\nGame ,40.576388,-73.966134,88,#bab7b6,4\nVeshenskaya ,49.6315639,41.7147171,88,#bab7b6,4\nUrussu ,54.5993239,53.4577423,87,#bab7b6,4\nStarovelichkovskaya ,45.4352571,38.720639,86,#bab7b6,4\nSolnechnodol'sk ,45.2906433,41.4881533,86,#bab7b6,4\nMenzelinsk ,55.7246469,53.1063186,85,#bab7b6,4\nVeydelevka ,50.1566384,38.4538939,85,#bab7b6,4\nOktyabrskoye ,62.4627068,66.0407137,84,#bab7b6,4\nVirgin ,37.7803187,-122.4861766,84,#bab7b6,4\nPervomaisk ,54.8718913,43.8014476,84,#bab7b6,4\nStrunino ,56.3690033,38.5829669,83,#bab7b6,4\nBagaevskaya ,45.552084,41.489765,83,#bab7b6,4\nIlansky ,56.2437046,96.0954395,83,#bab7b6,4\nMedvedovskaya ,45.453304,39.0117226,83,#bab7b6,4\nBagrationovsk ,54.384104,20.6466037,83,#bab7b6,4\nNeftegorsk ,52.803304,51.1735574,83,#bab7b6,4\nYeniseisk ,58.4501,92.1867687,82,#bab7b6,4\nBaikalsk ,51.5119541,104.1323949,82,#bab7b6,4\nGornozavodsk ,58.3777621,58.3243274,81,#bab7b6,4\nBodaibo ,57.854034,114.2011971,80,#bab7b6,4\nBorskoe ,,,80,#bab7b6,4\nUporovo ,56.3122423,66.2732555,80,#bab7b6,4\nGolyshmanovo ,56.4661404,68.6021557,80,#bab7b6,4\nKursavka ,44.4597842,42.5034801,79,#bab7b6,4\nKarmaskaly ,54.3673984,56.1746906,79,#bab7b6,4\nIsetskoe ,56.4575679,61.5231042,79,#bab7b6,4\nAgryz ,56.5193468,52.9405392,78,#bab7b6,4\nArdatov ,54.8469491,46.242137,78,#bab7b6,4\nAltai ,50.6181924,86.2199308,78,#bab7b6,4\nNovomikhaylovskiy KP ,,,78,#bab7b6,4\nChkalovsk ,56.7671329,43.2419118,77,#bab7b6,4\nFerry ,53.0231425,106.9312695,77,#bab7b6,4\nRokytne ,,,77,#bab7b6,4\nMiner ,44.9111,37.3244,77,#bab7b6,4\nBuzdyak ,54.5725346,54.521361,77,#bab7b6,4\nKuyeda ,56.4321722,55.5999722,77,#bab7b6,4\nOrdynsk ,54.3259196,81.7253415,76,#bab7b6,4\nVerkhneyarkeyevo ,55.4504546,54.3074796,76,#bab7b6,4\nReftinsky ,57.0948735,61.6717527,76,#bab7b6,4\nBohr ,57.0497406,65.7129411,76,#bab7b6,4\nAgidel. ,55.9005444,53.9334354,75,#bab7b6,4\nKarabanovo ,56.309014,38.7007564,75,#bab7b6,4\nStepnoe ,46.6443048,39.668826,75,#bab7b6,4\nVerkhneuralsk ,53.8764136,59.2207871,74,#bab7b6,4\nUst ' -Ordynskiy ,57.9561897,102.762782,74,#bab7b6,4\nRepairs ,40.2799938,-84.3993097,73,#bab7b6,4\nVinasse ,,,73,#bab7b6,4\nPolazna ,58.3005959,56.4173346,73,#bab7b6,4\nDudinka ,69.4041954,86.2002911,73,#bab7b6,4\nVasilevo ,55.8305946,48.7172177,72,#bab7b6,4\nVorsma. ,55.9864112,43.2706711,71,#bab7b6,4\nNyazepetrovsk ,56.0507525,59.5980666,71,#bab7b6,4\nLyubinsky ,55.2110673,72.5031212,71,#bab7b6,4\nTotskoe ,52.5220074,52.744454,70,#bab7b6,4\nKhvalynsk ,52.5010192,48.0923303,70,#bab7b6,4\nChaltyr ,47.284461,39.4991783,70,#bab7b6,4\nDiveevo ,55.0375016,43.2464427,69,#bab7b6,4\nKhomutovo ,52.4725764,104.4039141,68,#bab7b6,4\nTatsinskaya ,48.1661111,41.2777778,68,#bab7b6,4\nIzluchinsk ,60.952778,76.8888889,68,#bab7b6,4\nTulgan. ,,,68,#bab7b6,4\nNesterov ,54.6305473,22.5691252,67,#bab7b6,4\nTotskoye Vtoroye ,,,67,#bab7b6,4\nYetkul' ,54.8277933,61.5860445,67,#bab7b6,4\nZalari ,53.5656508,102.5101577,67,#bab7b6,4\nReasonable ,50.5938019,36.5905131,67,#bab7b6,4\nNovonikolaevskiy ,53.4855142,55.8584339,67,#bab7b6,4\nAzovo ,47.0947446,39.4136552,67,#bab7b6,4\nSuksun ,57.1392071,57.3961484,66,#bab7b6,4\nKumylzhenskaya ,49.8852321,42.5959232,66,#bab7b6,4\nDry land ,,,66,#bab7b6,4\nTyukalinsk ,55.8713349,72.2074289,66,#bab7b6,4\nKambarka ,56.2650223,54.2045733,66,#bab7b6,4\nVetluga ,57.8567021,45.7774009,65,#bab7b6,4\nCollarless ,,,65,#bab7b6,4\nGribanovsky ,51.3701748,41.7492319,65,#bab7b6,4\nChekmagush ,55.1393981,54.6406219,65,#bab7b6,4\nPsebay ,44.1264683,40.810896,65,#bab7b6,4\nDon ,50.5099618,41.2436177,65,#bab7b6,4\nKrasnovishersk ,60.4050484,57.0720287,65,#bab7b6,4\nUyskoe ,54.3749589,60.0069111,65,#bab7b6,4\nClear ,55.763319,37.551117,64,#bab7b6,4\nShackles ,,,64,#bab7b6,4\nLaishevo ,55.403071,49.5454122,64,#bab7b6,4\nTauride ,59.9478564,30.3758841,64,#bab7b6,4\nOlkhovatka ,50.2794069,39.2773996,64,#bab7b6,4\nNovokhopersk ,53.7595935,87.1215705,64,#bab7b6,4\nAlekseevskoe ,55.3070484,50.1189309,64,#bab7b6,4\nKushnarenkovo ,55.1065781,55.3377071,63,#bab7b6,4\nRipe ,60.268844,25.0198754,63,#bab7b6,4\nSamara ,53.2415041,50.2212463,62,#bab7b6,4\nToguchin ,55.2412704,84.3975441,62,#bab7b6,4\nSuvorov ,54.1254579,36.485623,62,#bab7b6,4\nJalil ,55.672012,37.767584,62,#bab7b6,4\nKazan ,55.8304307,49.0660806,62,#bab7b6,4\nDolgoderevenskoye ,55.3500609,61.3466506,61,#bab7b6,4\nLevokumskoe ,44.8183071,44.6508373,61,#bab7b6,4\nKrasnogvardeyskiy ,57.017555,60.6919552,61,#bab7b6,4\nTarasovsky ,48.6674446,40.3309112,61,#bab7b6,4\nVolodarsk ,56.2254693,43.1829627,61,#bab7b6,4\nPolessk ,54.8614558,21.1021296,61,#bab7b6,4\nSmolensk ,54.7903112,32.0503663,61,#bab7b6,4\nBorodino ,55.529267,35.8233939,61,#bab7b6,4\nSerafimovich ,49.5771478,42.7303561,61,#bab7b6,4\nSteeplechase ,,,61,#bab7b6,4\nKodinsk ,58.6076727,99.1779093,60,#bab7b6,4\nBol'sherech'ye ,56.0899964,74.6202867,60,#bab7b6,4\nTatarsk ,55.1936526,75.9684541,60,#bab7b6,4\nStavrovo ,56.1304267,40.0146544,60,#bab7b6,4\nBachata ,57.9917864,56.2066556,59,#bab7b6,4\nAleksandrovsk ,59.1562328,57.5942158,59,#bab7b6,4\nPlastunovskaya ,45.2934041,39.2634176,59,#bab7b6,4\nShigony. ,53.3886793,48.6747194,59,#bab7b6,4\nKrasnogorsk ,55.8263313,37.326297,58,#bab7b6,4\nKantemirovka ,49.7001031,39.8634775,58,#bab7b6,4\nBuraevo ,55.8487604,55.4056947,58,#bab7b6,4\nIvnya ,51.0587923,36.1359937,58,#bab7b6,4\nVarna ,,,58,#bab7b6,4\nSvirsk ,53.0907675,103.3369457,58,#bab7b6,4\nMob ,59.996208,30.3855309,58,#bab7b6,4\nZmeinogorsk ,51.1548347,82.1929542,58,#bab7b6,4\nVacha ,55.80303,42.7711491,57,#bab7b6,4\nPodgorensky ,50.4820676,39.6994072,57,#bab7b6,4\nTolbazy ,54.0114023,55.8925281,56,#bab7b6,4\nSP ,55.7976621,49.1114525,56,#bab7b6,4\nQuarries ,41.3147152,-82.257723,56,#bab7b6,4\nBolokhovo ,54.0906518,37.8250465,56,#bab7b6,4\nGuy ,,,55,#bab7b6,4\nUpper Mammoth ,43.168132,131.9342649,55,#bab7b6,4\nKarabash ,55.4777388,60.1995682,55,#bab7b6,4\nProletarian ,55.672203,37.657922,55,#bab7b6,4\nMaisky ,52.575894,103.898842,55,#bab7b6,4\nKrasnousolsky ,53.8907605,56.4698133,55,#bab7b6,4\nSarmanovo ,55.2519048,52.5882232,55,#bab7b6,4\nBakal. ,54.9524006,58.8262049,55,#bab7b6,4\nMrakovo ,64.671722,170.4226679,54,#bab7b6,4\nBakaly ,55.1740642,53.7936631,54,#bab7b6,4\nMar'yanovka ,44.7930796,133.7304295,54,#bab7b6,4\nVityazevo ,44.9869042,37.2580021,53,#bab7b6,4\nSergievsk ,44.1497667,43.4577689,53,#bab7b6,4\nKrasnoznamensk ,55.5929848,37.0422569,53,#bab7b6,4\nBorisoglebsky ,57.255917,39.1518325,53,#bab7b6,4\nBonfire ,51.7414926,39.3703242,52,#bab7b6,4\nKulunda ,52.5599728,78.9296701,52,#bab7b6,4\nBaltasi ,56.3423908,50.2135144,52,#bab7b6,4\nPestretsy ,55.7577075,49.648648,52,#bab7b6,4\nZaozernyj ,55.9742134,94.7096333,52,#bab7b6,4\nBol'shaya Martynovka ,47.273098,41.667194,51,#bab7b6,4\nKoltsovo airport ,56.7447746,60.8029485,51,#bab7b6,4\nMiass ,55.0506795,60.1034961,51,#bab7b6,4\nRed Yaruga ,,,51,#bab7b6,4\nVolchansk ,59.9302139,60.0862106,51,#bab7b6,4\nPerevolotsky ,51.7863445,54.4060187,51,#bab7b6,4\nKondratovo ,57.9781031,56.1061466,51,#bab7b6,4\nRed Tanks ,,,51,#bab7b6,4\nEgg ,55.762753,37.6488981,50,#bab7b6,4\nRed Humpback ,,,50,#bab7b6,4\nPeschanokopskoe ,46.1931842,41.0848939,50,#bab7b6,4\nAktanysh ,55.711589,54.076967,50,#bab7b6,4\nUyar ,55.8204208,94.3411473,50,#bab7b6,4\nSevero-Zadonsk ,54.0258379,38.3935947,49,#bab7b6,4\nAksubaevo ,54.8463748,50.7949589,49,#bab7b6,4\nSmall Blizzard ,,,49,#bab7b6,4\nDubna ,56.7320202,37.1668974,49,#bab7b6,4\nKaragay ,58.263232,54.9326774,49,#bab7b6,4\nSticky ,,,49,#bab7b6,4\nGrachevka ,46.2236169,39.2687227,48,#bab7b6,4\nVasyurinskaya ,45.1218907,39.4246946,48,#bab7b6,4\nCheremshan ,54.659677,51.5020158,48,#bab7b6,4\nUpper Tatyshly ,56.2721355,55.7995665,48,#bab7b6,4\nPervomaisky ,53.2449241,40.3456088,48,#bab7b6,4\nUspenka ,45.0920311,133.505696,48,#bab7b6,4\nNekhaevskaya ,,,47,#bab7b6,4\nIvanteevka ,55.975927,37.9195198,47,#bab7b6,4\nNekrasov ,,,47,#bab7b6,4\nKirgiz-Miyaki ,53.633696,54.807112,47,#bab7b6,4\nKamskie Polyany ,55.4281531,51.4125074,47,#bab7b6,4\nKagal'nitskaya ,46.8826759,40.1461595,46,#bab7b6,4\nArzgir ,45.3709971,44.2244998,46,#bab7b6,4\nMamontovo ,52.7117033,81.6118663,46,#bab7b6,4\nVoskresenskoe ,55.3238424,38.6815132,46,#bab7b6,4\nKazan ,55.8304307,49.0660806,46,#bab7b6,4\nShaky ,54.9914404,82.9087553,46,#bab7b6,4\nAlnashi ,56.1848048,52.4718954,46,#bab7b6,4\nCheerful ,56.311938,43.9905981,46,#bab7b6,4\nErtil. ,51.8427485,40.8070997,45,#bab7b6,4\nStaronizhesteblievskaya ,45.375295,38.4362196,45,#bab7b6,4\nPravdinsk ,54.4413138,21.0119817,45,#bab7b6,4\nOl'khovka ,49.8557775,44.556049,45,#bab7b6,4\nButurlino ,55.5721079,44.8718994,45,#bab7b6,4\nSmolensk ,54.7903112,32.0503663,45,#bab7b6,4\nVinzili. ,56.9606604,65.7702467,45,#bab7b6,4\nMezhdurechensky ,53.6836301,88.0813924,45,#bab7b6,4\nBredy ,52.4127836,60.3452815,45,#bab7b6,4\nNazyvaevsk ,55.5678779,71.3524589,44,#bab7b6,4\nPrincess ,51.6567789,39.1852796,44,#bab7b6,4\nSemibratovo ,57.3012552,39.5361496,44,#bab7b6,4\nUpper Tagil ,57.9214912,59.9816185,44,#bab7b6,4\nLobanovo ,55.8267504,37.1159871,44,#bab7b6,4\nNovominsk ,,,44,#bab7b6,4\nKormilovka ,54.9982721,74.0963633,44,#bab7b6,4\nTyazhinsky ,56.1080657,88.765165,44,#bab7b6,4\nKez ,57.903389,53.715125,44,#bab7b6,4\nRich ,55.757046,37.658277,43,#bab7b6,4\nNovodmitrievskaya ,44.8353965,38.8764177,43,#bab7b6,4\nAkbulak ,51.0018209,55.630588,43,#bab7b6,4\nRich Saba ,,,43,#bab7b6,4\nKhokholsky ,51.4270624,38.8170048,43,#bab7b6,4\nLoose. ,,,43,#bab7b6,4\nPoshekhonye ,58.5007075,39.1422317,43,#bab7b6,4\nKabardian ,,,43,#bab7b6,4\nNovoselytsia ,48.2211075,26.2712988,43,#bab7b6,4\nFish Settlement ,,,43,#bab7b6,4\nNovomyshastovskaya ,45.1974486,38.579875,43,#bab7b6,4\nDanilovka ,50.3631794,44.1143852,42,#bab7b6,4\nGolubitskaya ,45.3245901,37.2734832,42,#bab7b6,4\nMarianska ,54.1867775,16.1791438,42,#bab7b6,4\nKolyvan ,55.3034398,82.736478,42,#bab7b6,4\nYugo-Kamskiy ,57.7019215,55.5832569,42,#bab7b6,4\nBig Boldino. ,55.0020416,45.3102099,42,#bab7b6,4\nTomarovka ,50.6771785,36.2314146,41,#bab7b6,4\nMuromtsevo ,55.9318834,40.9077578,41,#bab7b6,4\nMoshkovo ,55.3081425,83.6138868,41,#bab7b6,4\nMikhailovsky ,59.937868,30.329148,41,#bab7b6,4\nOctober ,47.208574,38.9359088,41,#bab7b6,4\nSharlyk ,54.9013396,55.1421498,41,#bab7b6,4\nKuleshovka ,47.075975,39.5503769,41,#bab7b6,4\nKashary ,49.0371038,41.0124889,41,#bab7b6,4\nLight Yar ,,,41,#bab7b6,4\nNovosemeykino ,53.3741199,50.352605,41,#bab7b6,4\nNovouzensk ,50.4679581,48.1402832,40,#bab7b6,4\nUpper Uslon ,55.4667363,37.6446193,40,#bab7b6,4\nThe Arsen'yevo ,44.1580429,133.2645474,40,#bab7b6,4\nLeninist ,,,40,#bab7b6,4\nMyshkin ,57.7868036,38.4503218,40,#bab7b6,4\nChesma ,53.8050467,60.6530025,40,#bab7b6,4\nOb region ,,,40,#bab7b6,4\nStaromyshastovskaya ,45.3394939,39.0743226,40,#bab7b6,4\nTop Tour ,59.939848,30.327542,39,#bab7b6,4\nBolgar ,54.9670821,49.0342414,39,#bab7b6,4\nSosnovsky ,55.2073363,61.0851755,39,#bab7b6,4\nTurinsk ,58.041296,63.6850141,39,#bab7b6,4\nRudnya ,54.948864,31.0570797,39,#bab7b6,4\nThe ilinogorsk ,56.2285684,42.9834692,39,#bab7b6,4\nSechenovo ,55.2313222,45.8961809,39,#bab7b6,4\nAzov ,47.0947446,39.4136552,39,#bab7b6,4\nUpper Hava ,,,38,#bab7b6,4\nVikulovo ,56.8178163,70.6100679,38,#bab7b6,4\nShem ,43.0257879,131.8881605,38,#bab7b6,4\nBykovo ,55.6222772,38.0641286,38,#bab7b6,4\nRodionovo-Nesvetayskaya ,47.6124206,39.7100993,38,#bab7b6,4\nOkhansk ,57.7193113,55.3891197,38,#bab7b6,4\nIvdel. ,60.6923384,60.4316497,38,#bab7b6,4\nOmutinsky ,56.4253495,67.6027065,38,#bab7b6,4\nGagino ,55.2285836,45.0224032,38,#bab7b6,4\nKizilskoe ,,,38,#bab7b6,4\nAgapovka ,53.3028426,59.1413957,37,#bab7b6,4\nKuitun ,54.3352057,101.503572,37,#bab7b6,4\nBizhbulyak ,53.695696,54.276083,37,#bab7b6,4\nAskarovo ,53.3384127,58.5108708,37,#bab7b6,4\nMuslyumovo ,55.5939065,61.6171856,37,#bab7b6,4\nMulino. ,56.3095721,42.9560492,37,#bab7b6,4\nLower Tavda ,58.0442892,65.2561088,37,#bab7b6,4\nKaraidel ,55.8429698,56.8948472,37,#bab7b6,4\nLower Salda ,41.9746606,46.5109337,37,#bab7b6,4\nDeep ,69.3961111,30.608889,36,#bab7b6,4\nSummer Rate ,,,36,#bab7b6,4\nTroitskoe ,54.8732242,37.1170793,36,#bab7b6,4\nSharan ,54.815259,53.9992392,36,#bab7b6,4\nBogandinsky ,56.8881364,65.8835448,36,#bab7b6,4\nOblivskaya ,48.5363069,42.4950474,36,#bab7b6,4\nChunsky ,56.9289834,99.3955168,35,#bab7b6,4\nAlekseevskaya ,55.807779,37.638693,35,#bab7b6,4\nBol'shaya Glushitsa ,52.3838645,50.4848061,35,#bab7b6,4\nDal'neye Konstantinovo ,55.8071267,44.086347,35,#bab7b6,4\nIgrim ,63.1908332,64.4183685,35,#bab7b6,4\nPyshma ,56.9623902,63.2586345,35,#bab7b6,4\nPiatigorsky ,44.0420899,43.0611289,35,#bab7b6,4\nWad ,61.52401,105.318756,35,#f477d7,2\nSuzun ,53.7842471,82.3148366,35,#f477d7,2\nSheregesh ,52.9269506,87.9926205,35,#f477d7,2\nIsyangulovo ,52.1929515,56.5806021,35,#f477d7,2\nIl ,41.8793553,-87.6250403,34,#f477d7,2\nKavkazskaya ,45.4414893,40.6703827,34,#f477d7,2\nDawn ,55.7443997,37.5659085,34,#f477d7,2\nSwamp ,58.6287557,78.55714,34,#f477d7,2\nPetrov Val ,50.1372882,45.2103405,34,#f477d7,2\nAbat ,56.1017308,47.2417613,34,#f477d7,2\nNovovelichkovskaya ,45.2762888,38.8489494,34,#f477d7,2\nCherdyn ,60.4053202,56.4779816,34,#f477d7,2\nAnnunciation ,40.757823,-73.816195,34,#f477d7,2\nMignard ,55.0797695,57.5558728,34,#f477d7,2\nErmakovka ,,,34,#f477d7,2\nArchangel ,64.5472507,40.5601553,33,#f477d7,2\nDear ,57.188226,39.427407,33,#f477d7,2\nNorth ,66.7613451,124.123753,33,#f477d7,2\nRoshchino ,60.2503016,29.5993134,33,#f477d7,2\nKamenka ,53.1862593,44.0123063,33,#f477d7,2\nYazykovo ,54.2907459,47.3722118,33,#f477d7,2\nDivnomorskoe ,44.5047212,38.1300171,33,#f477d7,2\nAmber ,54.7222558,20.5229887,33,#f477d7,2\nKochenevo ,55.0093845,82.2017554,33,#f477d7,2\nAnastasievskaya ,45.2188292,37.8876474,32,#f477d7,2\nBetween kultayevo ,,,32,#f477d7,2\nAban ,56.6773409,96.0790961,32,#f477d7,2\nNovosheshminsk ,55.064236,51.227402,32,#f477d7,2\nAktobe ,50.2839339,57.166978,32,#f477d7,2\nCornflower ,52.2913991,104.2495579,32,#f477d7,2\nVincent. ,59.9247596,30.2973555,32,#f477d7,2\nBerezansky ,,,31,#f477d7,2\nAkyar ,51.8592453,58.2096844,31,#f477d7,2\nAscension ,38.582301,-121.4881264,31,#f477d7,2\nYaya ,56.2086152,86.4520659,31,#f477d7,2\nKupino ,54.368612,77.295878,31,#f477d7,2\nLadushkin ,54.5728766,20.1764433,31,#f477d7,2\nApastovo ,55.2017565,48.5069993,31,#f477d7,2\nArtie ,60.3952227,25.6985732,31,#f477d7,2\nMaloyaz ,55.1750475,58.1501193,31,#f477d7,2\nBoguchany ,58.366667,97.45,31,#f477d7,2\nBaykalovo ,57.3989951,63.7622111,31,#f477d7,2\nSlavsk ,55.045091,21.6727057,31,#f477d7,2\nVolginsky ,55.9489379,39.2399756,30,#f477d7,2\nNew Lyalya ,,,30,#f477d7,2\nSukhobuzimskoe ,56.495472,93.2782749,30,#f477d7,2\nBig Sosnova ,,,30,#f477d7,2\nIlek ,51.5258062,53.3898536,30,#f477d7,2\nOlginskaya ,45.9430527,38.545404,30,#f477d7,2\nOak ,40.2277929,-84.4119716,30,#f477d7,2\nOdoev ,53.9384912,36.6848996,30,#f477d7,2\nNatukhaevskaya ,44.9090279,37.5673666,30,#f477d7,2\nKamskoye Ust'ye ,71.133333,149.2666669,30,#f477d7,2\nElizabethan ,51.7550308,55.1070387,30,#f477d7,2\nSyumsi ,57.10251,51.6082227,29,#f477d7,2\nVoronezh ,51.6754966,39.2088823,29,#f477d7,2\nMotley ,,,29,#f477d7,2\nYashkino ,55.8672657,85.4135688,29,#f477d7,2\nLvov ,,,29,#f477d7,2\nBokovskaya ,49.2235697,41.8366361,29,#f477d7,2\nBelorechensky ,44.7955684,39.7153444,29,#f477d7,2\nTopchiha ,52.824354,83.119824,29,#f477d7,2\nNovoorsk ,51.3833046,58.9769955,29,#f477d7,2\nKizner ,56.281072,51.506689,29,#f477d7,2\nYakshur-BoD'ya ,57.1858597,53.1638723,29,#f477d7,2\nUst ' -Kinel'skiy ,70.6582101,138.1764434,29,#f477d7,2\nSladkovo ,55.5296435,70.3519715,28,#f477d7,2\nSeraphim ,32.8194812,-96.8038278,28,#f477d7,2\nCats ,56.8442085,53.2235168,28,#f477d7,2\nMilyutinskaya ,48.6222146,41.6788415,28,#f477d7,2\nGolovchino ,50.5414043,35.810403,28,#f477d7,2\nStaroderevyankovskaya ,46.1327339,38.9644692,28,#f477d7,2\nPlatnirovskaya ,45.3900384,39.3842097,28,#f477d7,2\nBogolyubovo ,56.1969204,40.5361274,28,#f477d7,2\nGiant ,55.825944,37.431593,28,#f477d7,2\nGostagayevskaya ,45.0206335,37.5017499,28,#f477d7,2\nPilna ,55.5571972,45.9189433,27,#f477d7,2\nErzovka ,48.930935,44.636009,27,#f477d7,2\nUpper Dubrovo ,54.15777,86.2408347,27,#f477d7,2\nCelts. ,,,27,#f477d7,2\nSharanga ,57.1784965,46.5418305,27,#f477d7,2\nDavydovka ,51.1558928,39.4299604,27,#f477d7,2\nBol'shoye Murashkino ,55.7795248,44.7726992,27,#f477d7,2\nPelagiada ,45.2107299,42.0094203,27,#f477d7,2\nSochi ,43.6028079,39.7341543,27,#f477d7,2\nRebrikha ,53.0852774,82.3454181,27,#f477d7,2\nSherbakul' ,54.6335629,72.4016361,26,#f477d7,2\nPonds ,44.1407755,-73.5796034,26,#f477d7,2\nFrequent ,55.1489618,61.3631771,26,#f477d7,2\nSpassky ,55.0220571,49.5151668,26,#f477d7,2\nNovovarshavka ,54.1694522,74.69325,26,#f477d7,2\nSolar ,40.2463544,-84.4242106,26,#f477d7,2\nAskino ,56.9862292,86.1623243,26,#f477d7,2\nPetrovskoe ,57.0096489,39.2690257,26,#f477d7,2\nLower Sergi ,,,26,#f477d7,2\nKurumoch ,53.4880872,50.0350563,26,#f477d7,2\nAgronomist ,59.287215,39.6849209,25,#f477d7,2\nNew Nekouz ,,,25,#f477d7,2\nGreat Source ,,,25,#f477d7,2\nGamovo ,57.8766593,56.0908466,25,#f477d7,2\nPokrovskoe ,57.243727,66.7871708,25,#f477d7,2\nSiva ,,,25,#f477d7,2\nWarm ,59.9272335,30.3017308,25,#f477d7,2\nDolzhanskaya ,46.6446145,37.8054336,25,#f477d7,2\nSokol'skoye ,57.1447448,43.1648753,25,#f477d7,2\nBead ,41.2913167,-82.2184884,25,#f477d7,2\nKrasnoarmeysk ,56.1060907,38.1392419,25,#f477d7,2\nVolga region ,55.7254755,48.8732886,24,#f477d7,2\nPavlogradka ,54.2048528,73.5658901,24,#f477d7,2\nShihans ,,,24,#f477d7,2\nUsolye ,59.4254685,56.6803807,24,#f477d7,2\nBeguile ,,,24,#f477d7,2\nShipunovo ,52.2255171,82.2690835,24,#f477d7,2\nKrasnobrodsky ,54.1578292,86.4452611,24,#f477d7,2\nBalakirevo ,56.50372,38.8375707,24,#f477d7,2\nKunashak ,55.7040901,61.5382494,24,#f477d7,2\nSoldato-Aleksandrovskoe ,44.263401,43.7629729,24,#f477d7,2\nMeget. ,52.4265719,104.0469053,24,#f477d7,2\nAsekeyevo ,53.574846,52.8129,23,#f477d7,2\nMost pure ,,,23,#f477d7,2\nKrivodanovka ,55.0868852,82.6436105,23,#f477d7,2\nPoletaevo ,55.029888,61.109618,23,#f477d7,2\nAromashevo ,56.8621905,68.643792,23,#f477d7,2\nSwag ,43.5025757,43.6213212,23,#f477d7,2\nThe Kukushtan ,57.6442367,56.4727932,23,#f477d7,2\nKurlovo ,55.4563422,40.6134152,23,#f477d7,2\nUpper Kigi ,55.4120248,58.5983581,23,#f477d7,2\nKrasnoshchekovo ,51.6661372,82.756927,23,#f477d7,2\nThe chernyshkovskiy ,48.4151033,42.2247624,23,#f477d7,2\nMingrelian ,,,23,#f477d7,2\nPanino ,55.7553966,42.925377,23,#f477d7,2\nPowerman ,55.819527,37.7803129,23,#f477d7,2\nTugulym ,57.056336,64.6282995,23,#f477d7,2\nBaranchinsky ,58.1837654,59.7208223,23,#f477d7,2\nStarotitarovskaya ,45.207153,37.1544117,23,#f477d7,2\nSalair ,54.2357698,85.8098785,23,#f477d7,2\nNizhnebakanskaya ,44.8649744,37.8649618,23,#f477d7,2\nHorde ,57.1986909,56.9152545,22,#f477d7,2\nUral ,60.2784931,59.1619065,22,#f477d7,2\nKrapivinsky ,54.9282136,87.165889,22,#f477d7,2\nIrbeyskoye ,55.641817,95.45565,22,#f477d7,2\nUvat ,59.1413589,68.8927572,22,#f477d7,2\nMamonovo ,54.4658152,19.9348453,22,#f477d7,2\nInskoy ,54.4221392,86.4139246,22,#f477d7,2\nNovoleushkovskaya ,45.998601,39.994959,22,#f477d7,2\nZnamensk ,48.588212,45.7220737,22,#f477d7,2\nYarkovo ,54.8096369,82.6009979,22,#f477d7,2\nOld Poltava ,,,22,#f477d7,2\nVolovo ,53.5583495,38.0075807,22,#f477d7,2\nKletskaya ,49.3069277,43.0542141,22,#f477d7,2\nArkhangelsk ,64.5472507,40.5601553,22,#f477d7,2\nSoviet ,52.2810596,104.3497995,22,#f477d7,2\nTashla ,52.4154845,56.2368222,22,#f477d7,2\nAnapa ,44.8857008,37.3199192,22,#f477d7,2\nThe city ,40.71541,-74.0370795,21,#f477d7,2\nSevernoe ,56.344586,78.3453225,21,#f477d7,2\nNew grateful ,,,21,#f477d7,2\nAlexandria ,52.2643886,104.3144521,21,#f477d7,2\nIzhmorskiy ,56.1966377,86.6235938,21,#f477d7,2\nFirry ,51.7070329,39.1715256,21,#f477d7,2\nPodstepki ,51.300833,42.067222,21,#f477d7,2\n'ikinskoe ,54.5036874,22.5144631,21,#f477d7,2\nShelaboliha ,53.4066765,82.6293575,21,#f477d7,2\nNovoderevyankovskaya ,46.3187542,38.7465307,21,#f477d7,2\nMaslova Pristan' ,50.4533349,36.7412805,21,#f477d7,2\nTrudobelikovskiy ,45.2652481,38.1479791,21,#f477d7,2\nKovernino ,57.1260445,43.8059949,21,#f477d7,2\nTemirgoyevskaya ,45.1099574,40.2950699,20,#f477d7,2\nFalconers ,,,20,#f477d7,2\nKashirskoe ,55.5853254,37.7247072,20,#f477d7,2\nBalakhta ,55.3863814,91.6383629,20,#f477d7,2\nPreobrazhenskaya ,,,20,#f477d7,2\nKrasnokholmsky ,58.136622,37.2483841,20,#f477d7,2\nlarge village ,,,20,#f477d7,2\nReshetiha ,56.213277,43.29085,20,#f477d7,2\nBazarnye Mataki ,54.9031163,49.930424,20,#f477d7,2\nKrasnozerskoe ,54.002725,79.253276,20,#f477d7,2\nPereyaslovskaya ,45.8404226,39.023038,20,#f477d7,2\nItem ,55.7903633,37.5308055,20,#f477d7,2\nGremyachinsk ,58.5483546,57.8344472,20,#f477d7,2\nSharkan. ,57.3014699,53.86763,20,#f477d7,2\nSoviet ,52.2810596,104.3497995,20,#f477d7,2\nUst ' -Kachka ,57.9561897,102.762782,20,#f477d7,2\nIvanovskaya ,57.1056854,41.4830084,20,#f477d7,2\nLove ,34.9884995,-84.3710444,20,#f477d7,2\nRodino ,52.5018246,80.2178471,20,#f477d7,2\nSholokhov ,55.7469941,37.5998128,19,#f477d7,2\nThe between novobataysk ,53.4415721,83.9208351,19,#f477d7,2\nNovobelokatay ,55.7052745,58.9601678,19,#f477d7,2\nSterlibashevo ,53.4404849,55.2588033,19,#f477d7,2\nUpper Russian ,61.52401,105.318756,19,#f477d7,2\nWhite Yar ,,,19,#f477d7,2\nYaroslavl ,57.6260744,39.8844709,19,#f477d7,2\nKurchanskaya ,45.2241466,37.5715291,19,#f477d7,2\nKirov ,58.6035321,49.6667983,19,#f477d7,2\nSalym ,60.0612676,71.453642,19,#f477d7,2\nBulanash. ,57.2805918,61.9923455,19,#f477d7,2\nKarakulino ,56.0116996,53.701144,19,#f477d7,2\nCascara ,57.1754794,65.9319011,19,#f477d7,2\nKalmanka ,52.892624,83.5334281,19,#f477d7,2\nDvurechensk ,56.5959665,61.1001623,19,#f477d7,2\nBorodino ,55.529267,35.8233939,19,#f477d7,2\nVavozh ,56.7780157,51.9234687,19,#f477d7,2\nMatveevka ,53.8655861,55.5429814,18,#f477d7,2\nDubovskoe ,47.3314331,43.229142,18,#f477d7,2\nHillforts ,54.0755458,36.035116,18,#f477d7,2\nNovoyegor'yevskoye ,51.7589361,80.894499,18,#f477d7,2\nPervomaisk ,54.8718913,43.8014476,18,#f477d7,2\nKrasnoturansk ,54.3204104,91.5481134,18,#f477d7,2\nBorovikha ,53.5037857,83.8402581,18,#f477d7,2\nKiyasovo ,56.340018,53.119622,18,#f477d7,2\nZilair. ,52.2387653,57.4475033,18,#f477d7,2\nRepair ,40.2799938,-84.3993097,18,#f477d7,2\nRyazan ,54.6095418,39.7125857,18,#f477d7,2\nShentala. ,54.4315419,51.471088,18,#f477d7,2\nOtradny ,53.3769898,51.3402104,18,#f477d7,2\nVishnevogorsk ,55.9995195,60.6619317,18,#f477d7,2\nAchikulak ,44.5479719,44.8328236,18,#f477d7,2\nGrigoropolisskaya ,45.293877,41.0546109,18,#f477d7,2\nSakmara ,51.983941,55.3417939,18,#f477d7,2\nBol'shaya Chernigovka ,52.0971139,50.8660236,18,#f477d7,2\nREP'yevka ,51.0828584,38.6406083,18,#f477d7,2\nBetween novokorsunskaya ,,,18,#f477d7,2\nMar ,63.523889,118.9922219,18,#f477d7,2\nBrinkovskaya ,46.0329941,38.5854964,18,#f477d7,2\nNebug ,44.1728118,39.003195,18,#f477d7,2\nFershampenuaz ,53.5199891,59.8158991,17,#f477d7,2\nPlate ,55.767444,37.559533,17,#f477d7,2\nKaluga ,54.5518584,36.2850973,17,#f477d7,2\nKrasnogorsk ,55.8263313,37.326297,17,#f477d7,2\nOzinki ,51.1978497,49.7335082,17,#f477d7,2\nGorkov ,56.433558,40.429514,17,#f477d7,2\nZelenogorsk ,56.1103549,94.613901,17,#f477d7,2\nPetrovskaya ,45.4248831,37.9475755,17,#f477d7,2\nUpper Sergi ,,,17,#f477d7,2\nKrasnoe ,55.7966349,40.7055587,17,#f477d7,2\nKislyakovskaya ,46.4482967,39.6777217,17,#f477d7,2\nKurkino ,55.8919173,37.3888238,17,#f477d7,2\nElias ,58.5754261,55.686194,17,#f477d7,2\nBig Sorokino ,,,17,#f477d7,2\nSargat ,,,17,#f477d7,2\nErmolaevo ,55.0352426,73.2878262,17,#f477d7,2\nUst-Kishert ,57.3646912,57.2443716,17,#f477d7,2\nChulym ,56.4249414,87.7065414,17,#f477d7,2\nYutsa. ,43.9582417,43.0153218,17,#f477d7,2\nUinskoe ,56.8813564,56.570105,17,#f477d7,2\nCoastal ,33.7949658,-79.01173,17,#f477d7,2\nAndreevo ,55.2859981,31.3164636,17,#f477d7,2\nLivery ,,,16,#f477d7,2\nDebesies ,,,16,#f477d7,2\nPit ,,,16,#f477d7,2\nSafe ,37.7804745,-122.4819218,16,#f477d7,2\nPersianovsky ,,,16,#f477d7,2\nOdessa ,46.8274593,-100.8105206,16,#f477d7,2\nMuscovite ,54.342206,48.3794911,16,#f477d7,2\nAspen ,57.7529184,58.8085176,16,#f477d7,2\nEmanzhelinka ,54.7569133,61.3211493,16,#f477d7,2\nRaevskaya ,43.540834,39.9718239,16,#f477d7,2\nKaratuzskoe ,53.6049572,92.8725162,16,#f477d7,2\nTrunovskoe ,45.478943,42.1414011,16,#f477d7,2\nSouth ,61.52401,105.318756,16,#f477d7,2\nTernovka ,52.0588292,39.7623556,16,#f477d7,2\nBreitovo ,58.2932614,37.8709306,16,#f477d7,2\nSu-psekh ,44.8608464,37.3556845,16,#f477d7,2\nChik ,54.9938789,82.4381478,16,#f477d7,2\nNovotroitskaya ,51.2011047,58.2987542,16,#f477d7,2\nBig Ului ,,,16,#f477d7,2\nVolchikha ,52.0176911,80.3315766,16,#f477d7,2\nBohan. ,53.153491,103.7773727,16,#f477d7,2\nGrachevka ,46.2236169,39.2687227,16,#f477d7,2\nTurinskaya Sloboda ,57.6127467,64.38753,16,#f477d7,2\nTonshaevo ,51.8516394,45.6579869,16,#f477d7,2\nMishkino ,55.5344665,55.9575687,16,#f477d7,2\nAha ,,,15,#f477d7,2\nTRANS-Ural ,60.2828203,59.1636239,15,#f477d7,2\nBilimbay ,56.964999,59.822966,15,#f477d7,2\nVorob'evka ,51.743182,36.2559731,15,#f477d7,2\nShuttle-Tops ,,,15,#f477d7,2\nWeight ,55.761826,37.574839,15,#f477d7,2\nShvartsevsky ,,,15,#f477d7,2\nSprout ,64.493627,40.706974,15,#f477d7,2\nKropachevo ,55.0123473,57.9832945,15,#f477d7,2\nReeds ,55.7537759,37.6621929,15,#f477d7,2\nKosikha ,53.360667,84.574352,15,#f477d7,2\nNovoukrainka ,52.0872235,58.5928529,15,#f477d7,2\nNikologory ,56.1387941,41.9940184,15,#f477d7,2\nKrutiha ,53.968556,81.198764,15,#f477d7,2\nAlzamay ,55.5579234,98.6569345,15,#f477d7,2\nKuibyshev ,53.2415041,50.2212463,15,#f477d7,2\nShalit ,,,15,#f477d7,2\nSiberian ,61.0137097,99.1966559,15,#f477d7,2\nBig Eagle ,,,15,#f477d7,2\nHope ,55.7207921,37.6301239,15,#f477d7,2\nPraskoveya ,44.7350391,44.2010499,15,#f477d7,2\n. ,61.52401,105.318756,15,#f477d7,2\nIvanovskaya ,57.1056854,41.4830084,15,#f477d7,2\nSubkhankulovo ,54.5582368,53.8112799,15,#f477d7,2\nElhovka ,,,15,#f477d7,2\nTevriz. ,57.512722,72.40078,15,#f477d7,2\nMelikhovskaya ,47.4776885,40.4910299,15,#f477d7,2\nThe ponomarevka ,56.1239181,86.4956723,15,#f477d7,2\nShali ,43.1434684,45.9042912,15,#f477d7,2\nFree ,59.9152594,30.3270455,15,#f477d7,2\nYar ,58.250278,52.1444441,14,#f477d7,2\nTumbotino ,55.9991834,43.0186121,14,#f477d7,2\nErmekeevo ,54.0534735,53.6866106,14,#f477d7,2\nGritsovskiy ,54.135148,38.157559,14,#f477d7,2\nBereslavka ,48.6205943,44.0491785,14,#f477d7,2\nSagittarius ,55.680013,37.75736,14,#f477d7,2\nPleshanovo ,52.8416063,53.4831411,14,#f477d7,2\nRed Weavers ,,,14,#f477d7,2\nMizhgirya ,54.05,57.816667,14,#f477d7,2\nROE deer ,,,14,#f477d7,2\nNew Rogachik ,,,14,#f477d7,2\nLysogorskaya ,44.1034877,43.2768364,14,#f477d7,2\nKlyavlino ,54.256028,52.026029,14,#f477d7,2\nKhvorostyanka ,52.6052298,48.9659955,14,#f477d7,2\nBurmakino ,57.4341878,40.3148687,14,#f477d7,2\nForelock ,,,14,#f477d7,2\nNovogurovsky ,54.4676703,37.3384052,14,#f477d7,2\nAleksandrov Guy ,,,14,#f477d7,2\nVagai. ,57.5410626,69.1489536,14,#f477d7,2\nBig Murta. ,,,14,#f477d7,2\nPetra Dubrava ,53.2947856,50.363171,14,#f477d7,2\nIdrinsky ,54.4968342,92.4600881,14,#f477d7,2\nPiterka ,50.6760886,47.4371845,14,#f477d7,2\nRomanovskaya ,47.537724,42.031986,14,#f477d7,2\nAdamovka ,51.5217605,59.9418677,14,#f477d7,2\nOkoneshnikovo ,54.8334935,75.0872903,14,#f477d7,2\nKrutinka ,56.0058358,71.5005566,13,#f477d7,2\nKrasnoselsky ,55.7777853,37.6539574,13,#f477d7,2\nRevyakino ,54.3656635,37.6501819,13,#f477d7,2\nRoschinsky ,,,13,#f477d7,2\nZol ,43.9077082,43.3072726,13,#f477d7,2\nFree ,59.9152594,30.3270455,13,#f477d7,2\nBurla ,53.3347847,78.3407084,13,#f477d7,2\nKazminskoye ,44.5873331,41.6772201,13,#f477d7,2\nBeloyarsk ,53.459444,83.891389,13,#f477d7,2\nYukamenskoye ,57.8908782,52.2419322,13,#f477d7,2\nAfonino. ,56.2631896,44.09055,13,#f477d7,2\nChelbasskaya ,45.9773921,39.3668159,13,#f477d7,2\nYelan ' -Koleno ,50.9527133,43.7395811,13,#f477d7,2\nRobin ,55.4175332,37.5210786,13,#f477d7,2\nNovodzherelievskaya ,45.7761807,38.6709133,13,#f477d7,2\nElansky. ,50.8933287,43.7146274,13,#f477d7,2\nOf Solun Demetrius ,,,13,#f477d7,2\nSands ,55.7495092,37.5882891,13,#f477d7,2\nFastovetskaya ,45.9184314,40.1587542,13,#f477d7,2\nVerkh-Chebula ,56.029946,87.6191013,13,#f477d7,2\nMountain ,44.1661723,-73.6983436,13,#f477d7,2\nTimashevo ,45.6093387,38.9547015,13,#f477d7,2\nUst ' -Kalmanka ,70.6582101,138.1764434,13,#f477d7,2\nPetropavlovka ,50.610973,105.3319159,13,#f477d7,2\nBobrovsky ,51.0804478,40.0516046,13,#f477d7,2\nKytmanovo ,53.456763,85.432515,13,#f477d7,2\nVarnavino ,57.3987392,45.0870547,13,#f477d7,2\nOld Ales ,,,12,#f477d7,2\nRozhdestveno ,59.321861,29.947245,12,#f477d7,2\nZhirnov ,48.175694,41.1332358,12,#f477d7,2\nBetween kuzedeyevo ,,,12,#f477d7,2\nLate ,55.1848299,61.3895381,12,#f477d7,2\nMundybash ,53.2140133,87.2799316,12,#f477d7,2\nKhrenova ,,,12,#f477d7,2\nKrasnoyarsk ,56.0152834,92.8932476,12,#f477d7,2\nRose ,45.5405469,-122.6098499,12,#f477d7,2\nNovogorny ,44.2675354,42.8809384,12,#f477d7,2\nNizhnyaya Omka ,55.4330626,74.9385554,12,#f477d7,2\nUst '-Charyshskaya Pristan' ,52.400875,83.656612,12,#f477d7,2\nOld Yeast ,,,12,#f477d7,2\nThe Grand ,59.9551956,30.3626677,12,#f477d7,2\nZonal ,54.7597869,83.1267793,12,#f477d7,2\nInedible ,,,12,#f477d7,2\nBerdyaush. ,55.1542769,59.1516053,12,#f477d7,2\nBessonovka ,53.2770501,45.0300183,12,#f477d7,2\nVyshesteblievskaya ,45.1920953,36.9938916,12,#f477d7,2\nIsakli ,,,12,#f477d7,2\nBol'shoye Kozino ,56.4043603,43.7134852,12,#f477d7,2\nZudilovo ,53.4851134,83.8885124,12,#f477d7,2\nUrazovo ,50.0860607,38.0456662,11,#f477d7,2\nAleksandrovka ,55.4805694,59.8307577,11,#f477d7,2\nOEK ,55.743227,37.6109601,11,#f477d7,2\nUglovskoe ,60.0730685,30.7338451,11,#f477d7,2\nDyad'kovskaya ,45.5546575,39.1920043,11,#f477d7,2\nRussian Glade ,53.2659412,50.1983682,11,#f477d7,2\nThe city ,40.71541,-74.0370795,11,#f477d7,2\nStreletsky ,,,11,#f477d7,2\nMole ,54.933876,20.6954061,11,#f477d7,2\nYurga ,55.7297643,84.8944519,11,#f477d7,2\nSennaya ,59.9270631,30.3182699,11,#f477d7,2\nBuilding ceramics ,,,11,#f477d7,2\nLadovskaya Balka ,45.6293491,41.4073998,11,#f477d7,2\nThe Novoutkinsk ,56.9960455,59.5549752,11,#f477d7,2\nTisul ,55.762923,88.3101532,11,#f477d7,2\nZalesovo ,53.9984408,84.7699905,11,#f9e84a,1\nStar ,59.9557644,30.3005904,11,#f9e84a,1\nKalinin ,56.8587214,35.9175965,11,#f9e84a,1\nMoscow ,55.755826,37.6172999,11,#f9e84a,1\nTemizhbekskaya ,45.4443415,40.8464744,11,#f9e84a,1\nKochevo ,59.6185698,54.3310196,11,#f9e84a,1\nAdagum ,45.095705,37.721554,11,#f9e84a,1\nTonkino ,57.3700122,46.4550252,11,#f9e84a,1\nRomanovo ,54.9742292,35.7524586,11,#f9e84a,1\nLuzino ,54.9451576,73.0392654,11,#f9e84a,1\nOveryata ,58.0819675,55.8727412,11,#f9e84a,1\nSmyshlyaevka ,53.2415041,50.2212463,11,#f9e84a,1\nDzhiginka ,45.1329273,37.3425322,11,#f9e84a,1\nFilimonovo ,55.3309517,31.8424663,11,#f9e84a,1\nDemino. ,58.043235,39.1140726,11,#f9e84a,1\nIP ,53.6202282,55.9172076,11,#f9e84a,1\nDombarovsky ,51.0943657,59.8424007,11,#f9e84a,1\nAlkyne-2 ,,,11,#f9e84a,1\nLobva. ,59.18034,60.4906237,11,#f9e84a,1\nBorgustanskaya ,44.054356,42.528294,11,#f9e84a,1\nPrimorsk ,60.3584066,28.6307178,10,#f9e84a,1\nNovonikolaevskaya ,,,10,#f9e84a,1\nBekeshevskaya ,44.1136239,42.4319562,10,#f9e84a,1\nPeaceful ,55.7316419,37.6361735,10,#f9e84a,1\nTabuns ,,,10,#f9e84a,1\nKonstantinovsky ,47.6615036,41.2331101,10,#f9e84a,1\nSokolovo ,55.9355616,38.4325223,10,#f9e84a,1\nConvenient ,43.1784514,131.9112994,10,#f9e84a,1\nMarkov ,,,10,#f9e84a,1\nGeorge ,40.6279647,-111.9295163,10,#f9e84a,1\nPodbelski ,46.6422493,38.2894472,10,#f9e84a,1\nNewcomer ,,,10,#f9e84a,1\nKagal'nik ,47.0766326,39.324479,10,#f9e84a,1\nMukhtolovo ,55.4634082,43.1817051,10,#f9e84a,1\nYasnaya Polyana ,54.0695041,37.523205,10,#f9e84a,1\nShkurinskaya ,46.5912443,39.3582598,10,#f9e84a,1\nSaratov ,51.5923654,45.9608031,10,#f9e84a,1\nZnamenskoe ,48.588212,45.7220737,10,#f9e84a,1\nLarge Meadow ,41.2925851,-82.1939739,10,#f9e84a,1\nTayturka ,52.864821,103.45689,10,#f9e84a,1\nKiev ,50.4669977,30.5099178,10,#f9e84a,1\nMaiden ,53.2686475,50.2583982,10,#f9e84a,1\nFedorovka ,47.339085,46.9469451,10,#f9e84a,1\nBayevo ,53.2654957,80.7651834,10,#f9e84a,1\nYumaguzino ,52.903028,56.3849319,10,#f9e84a,1\nSosva ,59.3054609,61.6456536,10,#f9e84a,1\nPoltavka ,54.364736,71.7538684,10,#f9e84a,1\nSuburban ,54.590123,38.216453,10,#f9e84a,1\nVerkh-Neyvinskiy ,57.2725269,60.1289278,10,#f9e84a,1\nLarge Chapurniki ,,,10,#f9e84a,1\nKutulik ,53.3493259,102.7860751,10,#f9e84a,1\nAbundant ,48.3009359,40.2782294,10,#f9e84a,1\nTselinny ,54.4629301,63.6775794,10,#f9e84a,1\nCoal ,55.762479,37.6190029,10,#f9e84a,1\nCharyshskoe ,,,10,#f9e84a,1\nGlebovka ,46.6397916,39.9922948,9,#f9e84a,1\nA: ,61.52401,105.318756,9,#f9e84a,1\nKicinska ,,,9,#f9e84a,1\nArmison ,,,9,#f9e84a,1\nKabakovo ,54.5329246,56.1495367,9,#f9e84a,1\nGorbatov ,56.1276639,43.0729987,9,#f9e84a,1\nPavlovka ,52.6871762,47.1342578,9,#f9e84a,1\nThe Chernoistochinsk ,57.7388133,59.8716793,9,#f9e84a,1\nNovoshcherbinovskaya ,46.4740922,38.6396826,9,#f9e84a,1\nNikolaevka ,50.4510664,38.2880695,9,#f9e84a,1\nShepsi. ,44.0412912,39.1520452,9,#f9e84a,1\nKaltasy ,55.970262,54.808792,9,#f9e84a,1\nRodnikovskaya ,44.7618941,40.6624772,9,#f9e84a,1\nTube ,55.5766284,37.6088602,9,#f9e84a,1\nKrasnokamensk ,50.0966137,118.0361307,9,#f9e84a,1\nSolton ,52.84143,86.472988,9,#f9e84a,1\nforest glade ,43.25472,132.03288,9,#f9e84a,1\nFertile ,59.8745762,30.335882,9,#f9e84a,1\nNew Dawns ,55.7443997,37.5659085,9,#f9e84a,1\nTartar ,55.1802364,50.7263945,9,#f9e84a,1\nTrinity ,39.292411,-76.592545,9,#f9e84a,1\nGrivenskaya ,45.6446494,38.1651985,9,#f9e84a,1\nKomsomolets ,80.557931,94.485398,9,#f9e84a,1\nMstera. ,56.3742945,41.9211733,9,#f9e84a,1\nSochi ,43.6028079,39.7341543,9,#f9e84a,1\nFloating ,61.52401,105.318756,9,#f9e84a,1\nBerezovo ,63.9365323,65.0479028,9,#f9e84a,1\nAbdon ,,,9,#f9e84a,1\nTayzhina ,53.6729935,87.4389355,9,#f9e84a,1\nSylva. ,58.032417,56.7418097,9,#f9e84a,1\nLarge Log ,,,9,#f9e84a,1\nTufts ,42.4074843,-71.1190232,9,#f9e84a,1\nPlotnikovo ,47.0370115,42.8505374,9,#f9e84a,1\nTsibanobalka ,44.9792173,37.3348077,9,#f9e84a,1\nYedesia ,,,8,#f9e84a,1\nSayan ,53.220426,95.2669339,8,#f9e84a,1\nMitrofanovka ,51.6398648,35.8420408,8,#f9e84a,1\nSandata ,46.277611,41.756863,8,#f9e84a,1\nStaromar'ivka ,,,8,#f9e84a,1\nSplices ,,,8,#f9e84a,1\nGramoteino ,54.519499,86.358927,8,#f9e84a,1\nBarsukovskaya ,44.7604905,41.814484,8,#f9e84a,1\nAbrau-Durso ,44.7013725,37.5999358,8,#f9e84a,1\nRaspberry Lake ,,,8,#f9e84a,1\nMelekhovo ,56.2567474,41.3094424,8,#f9e84a,1\nCement ,53.6621098,55.9681347,8,#f9e84a,1\nZaykovo ,57.5463931,62.7495718,8,#f9e84a,1\nCartilage ,,,8,#f9e84a,1\nTurtas ,58.935923,69.1354308,8,#f9e84a,1\nMonetary ,55.6307923,37.6249244,8,#f9e84a,1\nBiliaivka ,,,8,#f9e84a,1\nTansy ,,,8,#f9e84a,1\nBig Irba ,,,8,#f9e84a,1\nBagan ,54.0984453,77.6793844,8,#f9e84a,1\nAginskoye ,51.1028317,114.5262839,8,#f9e84a,1\nKeys ,53.0512443,158.6410794,8,#f9e84a,1\nEkaterinovka ,53.970278,53.992778,8,#f9e84a,1\nSernovodsk ,43.3155928,45.16252,8,#f9e84a,1\nOak Umet ,52.9697328,50.2892949,8,#f9e84a,1\nVelichaevskoe ,55.7535485,37.6792315,8,#f9e84a,1\nMyskhako ,44.6602909,37.7480908,8,#f9e84a,1\nUspenka ,45.0920311,133.505696,8,#f9e84a,1\nKachug ,53.9407018,105.8742023,8,#f9e84a,1\nProlific ,,,8,#f9e84a,1\nElantsy. ,52.8078301,106.4110693,8,#f9e84a,1\nBalakhonovskoe ,,,8,#f9e84a,1\nDashing ,,,8,#f9e84a,1\nTimiryazevskiy ,55.8232761,37.5541853,8,#f9e84a,1\nZhdanov ,,,8,#f9e84a,1\nNekrasovskaya ,45.1457868,39.7542189,8,#f9e84a,1\nSowing campaign ,,,8,#f9e84a,1\nTrinity ,39.292411,-76.592545,8,#f9e84a,1\nAchit ,56.7938509,57.8930941,8,#f9e84a,1\nsteppe lake ,,,8,#f9e84a,1\nNikolo-Pavlovskoye ,57.7842303,60.0577694,8,#f9e84a,1\nGeorgievka ,55.216111,53.719722,8,#f9e84a,1\nDemetrius ,56.129257,40.4109426,8,#f9e84a,1\nNalobikha ,53.202532,84.5983931,8,#f9e84a,1\nVetluzhsky ,57.8594243,45.2564822,8,#f9e84a,1\nAbramovka ,55.5104992,39.0057664,8,#f9e84a,1\nMal'chevskaya ,49.06118,40.378346,8,#f9e84a,1\nButka ,56.7243666,63.7958076,7,#f9e84a,1\nGrouting ,45.0619198,38.9883981,7,#f9e84a,1\nDruzhinino ,56.7872856,59.5189448,7,#f9e84a,1\nKonstantinovka ,56.688751,50.8764989,7,#f9e84a,1\nKargat ,55.1987331,80.2835225,7,#f9e84a,1\nNovonukutskiy ,53.70149,102.698419,7,#f9e84a,1\nKultuk ,51.7241507,103.7082244,7,#f9e84a,1\nVsevolodo-Vil'va ,59.218361,57.4404589,7,#f9e84a,1\nSokur ,55.2147146,83.3206188,7,#f9e84a,1\nBetween Primorka ,,,7,#f9e84a,1\nYurovka ,45.1159196,37.4153937,7,#f9e84a,1\nChervishevo ,56.9418648,65.4266428,7,#f9e84a,1\nAvian ,56.74586,37.186464,7,#f9e84a,1\nDonkeys ,55.8591078,37.3953044,7,#f9e84a,1\nMosque ,55.7788518,37.62694,7,#f9e84a,1\nAkhtanizovskaya ,45.3208316,37.1045531,7,#f9e84a,1\nMaslyanino ,54.3453192,84.2097658,7,#f9e84a,1\nNovoagansk ,61.9431337,76.6623635,7,#f9e84a,1\nAtaman ,47.153222,39.5483669,7,#f9e84a,1\nSoloneshnoe ,60.1493501,29.9356673,7,#f9e84a,1\nLarge Atnya ,,,7,#f9e84a,1\nKayasula ,44.3239335,44.994265,7,#f9e84a,1\nZirgan. ,53.2274321,55.9174632,7,#f9e84a,1\nNew Town ,41.3092693,-82.2252987,7,#f9e84a,1\nKrasnokumskoye ,44.17893,43.4910283,7,#f9e84a,1\nYuganets ,56.2511959,43.2315804,7,#f9e84a,1\nFree ,59.9152594,30.3270455,7,#f9e84a,1\nBavleny ,56.3917032,39.5833285,7,#f9e84a,1\nThe village of stolbische ,,,7,#f9e84a,1\nNewborn baby ,,,7,#f9e84a,1\nNovomalorossiyskaya ,45.6352936,39.8952382,7,#f9e84a,1\nTelma. ,52.6938018,103.7227886,7,#f9e84a,1\nNovolabinskaya ,45.1105593,39.898427,7,#f9e84a,1\nDorogino. ,54.3619002,83.3209736,7,#f9e84a,1\nGremyache ,56.005557,92.8194106,7,#f9e84a,1\nAksakovo ,53.8628876,52.6370656,7,#f9e84a,1\nUst ' -Tarka ,57.9845406,102.7395955,7,#f9e84a,1\nRogovskaya ,45.7303833,38.7367363,7,#f9e84a,1\nBershet' ,57.7297471,56.3724781,7,#f9e84a,1\nKudryashovsky ,55.0962193,82.7741577,7,#f9e84a,1\nZyukayka ,58.2084997,54.707343,7,#f9e84a,1\nCedar ,59.98901,30.3275199,7,#f9e84a,1\nUspenka ,45.0920311,133.505696,7,#f9e84a,1\nNew Borrowing ,,,7,#f9e84a,1\nCalm ,,,7,#f9e84a,1\nLower Ingash ,56.1937056,96.5250799,7,#f9e84a,1\nNew Egorlyk ,43.2859075,-75.0844757,7,#f9e84a,1\nBalagansk ,54.014015,103.0579229,7,#f9e84a,1\nMalysheva ,57.1101387,61.3976419,7,#f9e84a,1\nRailway ,52.0287875,113.4946194,7,#f9e84a,1\nNewfound ,41.0309396,-74.5350952,7,#f9e84a,1\nBrodokalmak. ,55.5747494,62.0725298,7,#f9e84a,1\nMagnitka ,55.3377048,59.6818456,7,#f9e84a,1\nObsharovka ,53.1183502,48.8618445,6,#f9e84a,1\nYasnogorsky ,54.5208139,37.8476447,6,#f9e84a,1\nVengerovo ,55.6839213,76.7586505,6,#f9e84a,1\nGofitskoye ,48.733333,135.7166669,6,#f9e84a,1\nGrape ,55.7277506,37.6069442,6,#f9e84a,1\nBetween novoplatnirovskaya ,,,6,#f9e84a,1\nSolomenskiy ,61.850172,34.3507461,6,#f9e84a,1\nAverage ,47.549685,42.0075185,6,#f9e84a,1\nNew Igirma ,,,6,#f9e84a,1\nKonstantinovka ,56.688751,50.8764989,6,#f9e84a,1\nKaragayly ,58.263232,54.9326774,6,#f9e84a,1\nTovarkovskiy ,53.6803594,38.2079717,6,#f9e84a,1\nTolmachevo ,55.0113541,82.652163,6,#f9e84a,1\nVolga ,51.9460656,41.3847096,6,#f9e84a,1\nRazvilnoe ,46.2385845,41.2963939,6,#f9e84a,1\nQuick Source ,,,6,#f9e84a,1\nMountain Shield ,,,6,#f9e84a,1\nBadgers ,54.2627035,37.4855581,6,#f9e84a,1\nUrban ,42.8981709,47.6213795,6,#f9e84a,1\nSeptember ,55.744812,37.5552639,6,#f9e84a,1\nKonokovo ,56.6965089,36.7733099,6,#f9e84a,1\nStaroutkinsk ,57.2287633,59.3184961,6,#f9e84a,1\nPankrushiha ,,,6,#f9e84a,1\nVolition ,,,6,#f9e84a,1\nPriupskaya ,53.9357874,36.7081468,6,#f9e84a,1\nTrademake ,59.9061375,30.2764642,6,#f9e84a,1\nBetween novochernorechenskiy ,,,6,#f9e84a,1\nPetrokamenskoye ,57.7121761,60.6397029,6,#f9e84a,1\nThe Old Village ,34.1332589,-118.2574213,6,#f9e84a,1\nStarosubhangulovo ,53.1069946,57.431456,6,#f9e84a,1\nCedar ,59.98901,30.3275199,6,#f9e84a,1\nCrossing ,54.7317938,20.5309126,6,#f9e84a,1\nBottom Sour ,,,6,#f9e84a,1\nOaklets ,55.8235081,37.4980014,6,#f9e84a,1\nNovolugovoye ,54.9787385,83.1136054,6,#f9e84a,1\nAlexandrovskaya ,59.7333531,30.3412111,6,#f9e84a,1\nSuburban Pokrovka ,,,6,#f9e84a,1\nMezhevoy ,55.1662846,58.7926322,6,#f9e84a,1\nKrivyanskaya ,47.422036,40.1614326,6,#f9e84a,1\nUpper Sinyachikha ,,,6,#f9e84a,1\nKomsomol ,62.904167,117.6225001,6,#f9e84a,1\nVysotskoe ,60.6251856,28.568911,6,#f9e84a,1\nNinas ,44.791433,-93.2405232,6,#f9e84a,1\nDrakino. ,54.8584223,37.2751833,6,#f9e84a,1\nAtig. ,56.6874738,59.4255664,6,#f9e84a,1\nHappy ,55.0388165,82.9601062,6,#f9e84a,1\nPavlovsky ,46.0793187,39.84244,6,#f9e84a,1\nPodgorny ,56.1237623,93.4352157,6,#f9e84a,1\nVerkh-Tula ,54.8813557,82.7713182,6,#f9e84a,1\nBacks ,43.1841324,-76.3478796,6,#f9e84a,1\nArrow ,55.7661977,37.5987642,5,#f9e84a,1\nITAT ,56.0737815,89.0121637,5,#f9e84a,1\nGornopravdinsk ,60.06798,69.9156299,5,#f9e84a,1\nThoroughbred ,,,5,#f9e84a,1\nUlkan ,55.9,107.8,5,#f9e84a,1\nUst-UDA ,54.17157,103.027249,5,#f9e84a,1\nGrigor ,,,5,#f9e84a,1\nGolovin ,59.9529481,31.0357048,5,#f9e84a,1\nKrasyukovskaya ,47.5264242,40.0847764,5,#f9e84a,1\nSemiletka ,55.3587891,54.6140328,5,#f9e84a,1\nAndryuk ,44.121889,40.834147,5,#f9e84a,1\nTyukhtet ,56.532166,89.3167499,5,#f9e84a,1\nAisha. ,56.3309296,43.8451531,5,#f9e84a,1\nNikolo-Berezovka ,,,5,#f9e84a,1\nChermoz ,58.785736,56.149805,5,#f9e84a,1\nTransient ,,,5,#f9e84a,1\nDuvan. ,55.6943705,57.894923,5,#f9e84a,1\nBiryusinsk ,55.9645954,97.8115312,5,#f9e84a,1\nSevere ,46.9665443,142.7442787,5,#f9e84a,1\nLinda ,,,5,#f9e84a,1\nCherry ,43.116916,131.8787103,5,#f9e84a,1\nBagovskaya ,44.1737073,40.6122396,5,#f9e84a,1\nTernovskaya ,45.8491367,40.4157239,5,#f9e84a,1\nNizhnetroitskiy ,54.3395273,53.6888296,5,#f9e84a,1\nKanelovskaya ,46.5920508,39.1892132,5,#f9e84a,1\nLeviha. ,,,5,#f9e84a,1\nShedok. ,44.2167852,40.8422117,5,#f9e84a,1\nNew Buyan ,,,5,#f9e84a,1\nVesele ,59.667495,29.876485,5,#f9e84a,1\nIncidental ,,,5,#f9e84a,1\nUst ' -Ishim ,57.9561897,102.762782,5,#f9e84a,1\nBarrier ,59.8501966,30.2444145,5,#f9e84a,1\nBaryshevo ,54.9563863,83.1787903,5,#f9e84a,1\nAlexandria ,52.2643886,104.3144521,5,#f9e84a,1\nKirensk ,57.7746006,108.1240863,5,#f9e84a,1\nYUS'va ,58.9632514,54.9578479,5,#f9e84a,1\nYasenskaya ,46.3611841,38.2725015,5,#f9e84a,1\nZykovo ,55.953056,93.160556,5,#f9e84a,1\nKorzhevsky ,,,5,#f9e84a,1\nVerkhnebakansky ,,,5,#f9e84a,1\nTaiga ,,,5,#f9e84a,1\nMiddle Icon ,,,5,#f9e84a,1\nKopans ,,,5,#f9e84a,1\nKugulta ,45.3637991,42.3804842,5,#f9e84a,1\nNovoselovo ,54.4612569,20.134925,5,#f9e84a,1\nBig Fat ,,,5,#f9e84a,1\nSelfless ,,,5,#f9e84a,1\nLocomotive ,56.2835208,43.8447859,5,#f9e84a,1\nKurya ,51.6050473,82.2976276,5,#f9e84a,1\nChernukha ,55.9010068,44.7111581,5,#f9e84a,1\nPetropavlovsk ,53.0409109,158.677726,5,#f9e84a,1\nEl'tsovka ,55.0788478,82.9079376,5,#f9e84a,1\nKrasnaya Gorka ,55.1955903,56.6783675,5,#f9e84a,1\nLower Maktama ,54.8603009,52.4223979,5,#f9e84a,1\nThe sedel'nikovo ,56.944416,75.301193,5,#f9e84a,1\nBetween pereleshino ,,,5,#f9e84a,1\nBig Ducks ,,,5,#f9e84a,1\nMichael. ,59.933931,30.29392,5,#f9e84a,1\nTalinka ,61.5501963,66.4508649,5,#f9e84a,1\nTirlyanskiy ,54.210181,58.5900559,5,#f9e84a,1\nSavasleyka ,55.4566847,42.3106841,5,#f9e84a,1\nYurla. ,59.3313673,54.337379,4,#f9e84a,1\nThe. ,61.52401,105.318756,4,#f9e84a,1\nNovoomskiy ,54.8406466,73.2999336,4,#f9e84a,1\nCrowns ,55.6500636,37.7703916,4,#f9e84a,1\nNew ,41.3286881,-82.2257298,4,#f9e84a,1\nPribelskaya ,55.1024702,55.3744713,4,#f9e84a,1\nNovobeysugskaya ,45.4744072,39.8789122,4,#f9e84a,1\nClick beetle ,,,4,#f9e84a,1\nNezhinsky ,,,4,#f9e84a,1\nLenin's ,55.7537117,37.6198845,4,#f9e84a,1\nNovobessergenevka ,47.1846827,38.845193,4,#f9e84a,1\nStarodubskoe ,47.4052239,142.813522,4,#f9e84a,1\nZhigalovo ,54.8138199,105.1572145,4,#f9e84a,1\nStaropavlovskaya ,43.8427177,43.6387314,4,#f9e84a,1\nNovoseleznevo ,55.6673937,69.1992458,4,#f9e84a,1\nYurts ,43.0925967,46.377133,4,#f9e84a,1\nKolosovka ,56.4699384,73.6043718,4,#f9e84a,1\nNezhinka ,51.7668517,55.362449,4,#f9e84a,1\nArya. ,55.0602008,44.6505593,4,#f9e84a,1\nTaseevo ,57.1919803,94.9017113,4,#f9e84a,1\nTogul ,53.462097,85.909836,4,#f9e84a,1\nGrahovo ,56.041542,51.9645381,4,#f9e84a,1\nRussian Aktash ,50.31111,87.59917,4,#f9e84a,1\nMemory 13 Fighters ,,,4,#f9e84a,1\nRed guard ,54.938057,73.3826962,4,#f9e84a,1\nMining ,66.4085,112.2989001,4,#f9e84a,1\nForesails ,,,4,#f9e84a,1\nCentral ,53.7266683,37.6476205,4,#f9e84a,1\nStarobachaty ,54.2407853,86.2093804,4,#f9e84a,1\nAntipovka ,49.8273887,45.3131636,4,#f9e84a,1\nDvubratsky ,,,4,#f9e84a,1\nAscension ,38.582301,-121.4881264,4,#f9e84a,1\nShemordan. ,56.1896598,50.3989574,4,#f9e84a,1\nKuzino. ,60.731389,46.375278,4,#f9e84a,1\nPirovskoe ,57.0096489,39.2690257,4,#f9e84a,1\nBelozerny ,,,4,#f9e84a,1\nLower Chir ,48.4873376,43.1770892,4,#f9e84a,1\nPouch ,,,4,#f9e84a,1\nIset ,56.975553,60.3645218,4,#f9e84a,1\nSatis. ,55.0219227,43.8116138,4,#f9e84a,1\nHigh ,40.2325484,-84.4083627,4,#f9e84a,1\nGubskaya ,44.3204868,40.6203439,4,#f9e84a,1\nKuyanovo ,56.9514926,86.4664438,4,#f9e84a,1\nNovoalekseevskaya ,55.8050607,37.6366383,4,#f9e84a,1\nNagutskoye ,44.4412922,42.8839636,4,#f9e84a,1\nTunoshna ,57.56232,40.1702562,4,#f9e84a,1\nPodtesovo ,58.594334,92.10675,4,#f9e84a,1\nPeshkovo ,55.8151618,37.1051564,4,#f9e84a,1\nBurlatsky ,,,4,#f9e84a,1\nAntipino ,53.307222,85.841389,3,#f9e84a,1\nNovobirilyussy ,56.9586226,90.6849872,3,#f9e84a,1\nBarsovo ,61.253056,73.1880561,3,#f9e84a,1\nBaturinskaya ,45.7893558,39.3676261,3,#f9e84a,1\nSumkino ,58.1105755,68.3324493,3,#f9e84a,1\nGayna. ,53.4612379,54.9583544,3,#f9e84a,1\nTyumentsevo ,53.3202173,81.4954332,3,#f9e84a,1\nZolotkovo ,55.4055752,36.1153056,3,#f9e84a,1\nMountain Loo ,43.679982,39.599683,3,#f9e84a,1\nCivil ,62.753841,40.365013,3,#f9e84a,1\nPodgornoe ,56.1237623,93.4352157,3,#f9e84a,1\nNyrob ,60.7412843,56.7240294,3,#f9e84a,1\nGoryachevodsk ,44.0325815,43.1237842,3,#f9e84a,1\nArtyshta ,54.120041,86.275291,3,#f9e84a,1\nCheben'ki ,51.9434449,55.707125,3,#f9e84a,1\nBekhteyevka ,50.6256541,38.6978113,3,#f9e84a,1\nNikolaev ,,,3,#f9e84a,1\nKyshtovka ,56.5465443,76.6460943,3,#f9e84a,1\nBig Jalga ,,,3,#f9e84a,1\nGremyachevo ,55.3904432,43.0427094,3,#f9e84a,1\nLukino ,53.8122263,37.3835881,3,#f9e84a,1\nNezamayevskaya ,46.1642633,40.2745019,3,#f9e84a,1\nSARS. ,,,3,#f9e84a,1\nKAZ. ,53.109084,87.546299,3,#f9e84a,1\nTemyasovo ,52.9850842,58.1242712,3,#f9e84a,1\nSokolowski ,,,3,#f9e84a,1\nThe Verkhne-Katunskoye ,,,3,#f9e84a,1\nGuerrilla ,54.6926077,158.6259863,3,#f9e84a,1\nKozlovka ,55.8407362,48.2478277,3,#f9e84a,1\nLog ,57.820556,29.033889,3,#f9e84a,1\nDry-water ,,,3,#f9e84a,1\nAtoka ,,,3,#f9e84a,1\nUlu-Telyak ,54.9143517,56.9764798,3,#f9e84a,1\nWatch ,41.579842,-72.348371,3,#f9e84a,1\nPodgorny ,56.1237623,93.4352157,3,#f9e84a,1\nNovokhopersky ,51.1013439,41.6225907,3,#f9e84a,1\nKrasnoarmeysky ,56.1060907,38.1392419,3,#f9e84a,1\nOffsite ,,,3,#f9e84a,1\nMikhailovskaya ,59.9547672,30.3522581,3,#f9e84a,1\nHaiduk ,,,3,#f9e84a,1\nLosevo ,60.6794024,29.9951179,3,#f9e84a,1\nFront line ,51.662303,39.1355361,3,#f9e84a,1\nLugovskoy ,56.9576144,64.5209275,3,#f9e84a,1\nLesogorsk ,50.919895,128.4833834,3,#f9e84a,1\nUnyugan ,61.9477104,64.929808,3,#f9e84a,1\nNovoukrainka ,52.0872235,58.5928529,3,#f9e84a,1\nDzerzhinskoe ,56.2440992,43.4351805,3,#f9e84a,1\nTatar Kargala ,,,3,#f9e84a,1\nBelogorsk ,50.919895,128.4833834,3,#f9e84a,1\nDmitrievka ,52.8782146,40.7819068,3,#f9e84a,1\nLocation ,61.52401,105.318756,3,#f9e84a,1\nPshekhskaya ,44.6962724,39.8024873,3,#f9e84a,1\nRussian ,61.52401,105.318756,3,#f9e84a,1\nPlekhanovo ,54.2440403,37.5677676,3,#f9e84a,1\nChistogorsky ,53.9643763,87.3765119,3,#f9e84a,1\nOtrado-Olginskoye ,45.3134082,40.9410833,3,#f9e84a,1\nDmitrievsky ,52.1497781,34.9583385,3,#f9e84a,1\nOrlovo ,55.5539213,37.8797048,3,#f9e84a,1\nBajkit ,61.6833669,96.3806203,3,#f9e84a,1\nVladimir ,56.1445956,40.4178687,3,#f9e84a,1\nKrasnooktyabr'skiy ,45.7769032,41.3210299,3,#f9e84a,1\nKazachinskoye ,57.6951501,93.2753817,3,#f9e84a,1\nBirch ,53.1847609,45.0114915,3,#f9e84a,1\nSinegorsky ,,,3,#f9e84a,1\nChernolesskoye ,44.7174507,43.7094904,3,#f9e84a,1\nShamary ,57.3458381,58.2236209,3,#f9e84a,1\nSpitsevka ,45.116904,42.503334,3,#f9e84a,1\nPyatnitskoe ,55.855876,37.3541811,3,#f9e84a,1\nSpiridonovka ,54.4609601,52.0682038,3,#f9e84a,1\nTishchenskoye ,45.454458,41.663958,3,#f9e84a,1\nKrasnoyarsk ,56.0152834,92.8932476,3,#f9e84a,1\nTyret ' 1-I ,53.666667,102.3,3,#f9e84a,1\n'shegrivskaya ,53.9166884,74.9056421,3,#f9e84a,1\nKanashevo ,55.2087523,62.0636608,3,#f9e84a,1\nTashara. ,55.5161284,83.4994196,3,#f9e84a,1\nState-farm ,37.7819137,-122.4829998,2,#f9e84a,1\nTurgoyak ,55.1532947,60.1182997,2,#f9e84a,1\nMan'kovo-Kalitvenskoe ,,,2,#f9e84a,1\nGarden ,41.5354131,-81.6301373,2,#f9e84a,1\nPelym ,58.0296813,56.2667916,2,#f9e84a,1\nInzer. ,54.3132483,57.521635,2,#f9e84a,1\nLower Mammoth ,43.168132,131.9342649,2,#f9e84a,1\nChekalin ,54.0924199,36.246824,2,#f9e84a,1\nSomovo ,51.940281,38.7999923,2,#f9e84a,1\nStantsionno-Oyashinskiy ,55.4660637,83.8213352,2,#f9e84a,1\nKangly ,44.2552306,43.0246539,2,#f9e84a,1\nMaikop ,44.5984115,40.1080869,2,#f9e84a,1\nFire ,40.2349703,-84.4073027,2,#f9e84a,1\nPrivolzhsky ,55.9620263,48.4181351,2,#f9e84a,1\nMotygino ,58.1842332,94.6766736,2,#f9e84a,1\nIrgakly ,44.3579583,44.7417186,2,#f9e84a,1\nNikolskaya ,55.7569443,37.6222431,2,#f9e84a,1\nArpachin ,47.2301268,40.1800864,2,#f9e84a,1\nKilmez. ,56.9438063,51.0642669,2,#f9e84a,1\nOrlivka ,54.7929425,20.5292624,2,#f9e84a,1\nTurukhansk ,65.79586,87.962418,2,#f9e84a,1\nFreedoms ,55.8242262,37.450658,2,#f9e84a,1\nLokosovo ,61.131389,74.855278,2,#f9e84a,1\nKurya ,51.6050473,82.2976276,2,#f9e84a,1\nMother ,43.2492477,-79.8472957,2,#f9e84a,1\nRefusal ,,,2,#f9e84a,1\nPravokumskoye ,44.7670019,44.6465776,2,#f9e84a,1\nOtrado-Kubanskoye ,45.2437132,40.8364137,2,#f9e84a,1\nVerkh-Irmen ,54.5753174,82.2443909,2,#f9e84a,1\nGalyugayevskaya ,43.6979535,44.9378128,2,#f9e84a,1\nVerkhnyaya Tishanka ,51.3242533,40.5357423,2,#f9e84a,1\nGidrotorf ,56.4729686,43.5413278,2,#f9e84a,1\nKrasnokholm ,51.5969624,54.1584621,2,#f9e84a,1\nPersistent ,55.746348,37.590016,2,#f9e84a,1\nRudnogorsk ,57.2749315,103.7420582,2,#f9e84a,1\nBetween chernoyerkovskaya ,,,2,#f9e84a,1\nNovosineglazovsky ,,,2,#f9e84a,1\nRed Communard ,,,2,#f9e84a,1\nAkhtar ,40.5704309,-74.5878482,2,#f9e84a,1\nBobrovka ,53.1802769,83.883399,2,#f9e84a,1\nSinyavsky ,47.2742374,39.2912186,2,#f9e84a,1\nZdvinsk ,55.874736,26.536179,2,#f9e84a,1\nTemirtau ,53.139652,87.4524,2,#f9e84a,1\nUbinskoe ,55.3025486,79.6827969,2,#f9e84a,1\nUgleural'skiy ,58.947362,57.5850671,2,#f9e84a,1\nKhrushchev ,,,2,#f9e84a,1\nIlovka ,50.7063285,38.6322049,2,#f9e84a,1\nBrusyanka ,53.981031,38.0798285,2,#f9e84a,1\nHrushevsky ,,,2,#f9e84a,1\nMemory Of The Paris Commune ,,,2,#f9e84a,1\nOatmeal ,,,2,#f9e84a,1\nScrapers ,,,2,#f9e84a,1\nKvarkeno ,52.081198,59.7309019,2,#f9e84a,1\nMishelevka ,52.852778,103.177555,2,#f9e84a,1\nSevero-Yeniseyskiy ,60.373528,93.0464481,2,#f9e84a,1\nNovobiryusinskiy ,56.9568013,97.7357959,2,#f9e84a,1\nHummocks ,,,2,#f9e84a,1\nRazdolnaya ,45.3775828,39.5559412,2,#f9e84a,1\nSeverka ,56.8674893,60.2882554,2,#f9e84a,1\nAnnuals ,,,2,#f9e84a,1\nMakhnevo ,,,2,#f9e84a,1\nUrshel'skiy ,55.6768605,40.2177807,2,#f9e84a,1\nZemlyansk ,51.9014159,38.7237155,2,#f9e84a,1\nChunoyar. ,57.4492483,97.3580243,2,#f9e84a,1\nNew Tavolzhanka ,50.3506793,36.8288173,2,#f9e84a,1\nNOVOIL'inskiy ,57.905903,55.475332,1,#f9e84a,1\nGarden ,41.5354131,-81.6301373,1,#f9e84a,1\nLyamino ,56.6147936,43.6936054,1,#f9e84a,1\nTemizhbekskaya ,45.4443415,40.8464744,1,#f9e84a,1\nIgarka ,67.4649215,86.5779764,1,#f9e84a,1\nBeth. ,40.2315741,-84.4044553,1,#f9e84a,1\nLeft-hand bag ,,,1,#f9e84a,1\nSyava ,58.0208259,46.3203563,1,#f9e84a,1\nWarm Mountain ,,,1,#f9e84a,1\nMortka ,59.332724,66.011516,1,#f9e84a,1\nWil ,55.894765,37.382695,1,#f9e84a,1\nMartyush. ,56.3996976,61.8799755,1,#f9e84a,1\nSource ,40.2340635,-84.4108954,1,#f9e84a,1\nDear ,57.188226,39.427407,1,#f9e84a,1\nKoltubanovskiy ,52.2524204,34.933525,1,#f9e84a,1\nParkland ,57.6509165,39.9326725,1,#f9e84a,1\nThe Alekseyevskaya ,45.7749889,40.1511278,1,#f9e84a,1\nLower Floodplain ,48.7097102,44.7605528,1,#f9e84a,1\nOrel-Izumrud ,43.4624361,39.926639,1,#f9e84a,1\nBirch ,53.1847609,45.0114915,1,#f9e84a,1\nFriendship ,55.6505192,37.5019844,1,#f9e84a,1\nCorneal ,,,1,#f9e84a,1\nPetrel ,55.6616974,37.528978,1,#f9e84a,1\nHigh ,40.2325484,-84.4083627,1,#f9e84a,1\nCherry ,43.116916,131.8787103,1,#f9e84a,1\nKoshurnikovo ,54.166667,93.3,1,#f9e84a,1\nBeshpagir ,45.023167,42.372986,1,#f9e84a,1\nDry Buffalo ,,,1,#f9e84a,1\nLebyazhye ,59.9606948,29.4164305,1,#f9e84a,1\nUfa ,54.7387621,55.9720554,1,#f9e84a,1\nKhatanga ,71.964027,102.4406129,1,#f9e84a,1\nKalya ,60.2403961,59.9852525,1,#f9e84a,1\nLomintsevsky ,,,1,#f9e84a,1\nIshnya ,57.1937981,39.3505077,1,#f9e84a,1\nBystrogorskiy ,48.2047809,41.1462905,1,#f9e84a,1\nKruglolesskoye ,44.6586161,42.809809,1,#f9e84a,1\nMarkov ,,,1,#f9e84a,1\nBetween zavodouspenskoye ,,,1,#f9e84a,1\nMain ,43.2391424,-75.0423727,1,#f9e84a,1\nKerch ,45.2414255,36.529626,1,#f9e84a,1\nKama ,57.8148148,53.0533256,1,#f9e84a,1\nPirogovo ,55.9830471,37.7317604,1,#f9e84a,1\nPashia. ,,,1,#f9e84a,1\nGusevsky ,54.6477364,22.1682691,1,#f9e84a,1\nGary ,55.7500546,37.6499561,1,#f9e84a,1\nOnokhino ,56.9218669,65.5392605,1,#f9e84a,1\nPrikubansky ,44.2224416,42.2539539,1,#f9e84a,1\nKrasnogvardeysky ,59.9749045,30.4715074,1,#f9e84a,1\nSavinka ,50.074459,47.09511,1,#f9e84a,1\nMatmas ,,,1,#f9e84a,1\nPridonskoy ,51.683333,39.083333,1,#f9e84a,1\nSotnikovskoye ,45.0030925,43.7825227,1,#f9e84a,1\n. Ogarevka ,53.9195147,37.5608857,1,#f9e84a,1\nTinskaya ,55.419808,95.0372801,1,#f9e84a,1\nAnjievskii ,,,1,#f9e84a,1\nSucked ,,,1,#f9e84a,1\nEastern ,61.52401,105.318756,1,#f9e84a,1\nPereleshinsky ,,,1,#f9e84a,1\nKovalevsky ,47.2307061,42.2980172,1,#f9e84a,1\nVorontsovka ,69.5755,147.556549,1,#f9e84a,1\"\"\")\n\n\nimport folium\n\ncities = pd.read_csv(citynames)\ncities = cities.dropna()\n\nmap_osm = folium.Map(location=[63.5907183,97.4981968], zoom_start=3)\n\nfor i, row in cities.iterrows():\n folium.CircleMarker([row['Lat'], row['Long']],\n radius=row['size'],\n color=row['color'],\n fill_color=row['color'],\n ).add_to(map_osm)\n\n\n \nmap_osm","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"72dc987f-0a60-47eb-b838-1fd294ffbfb7","_kg_hide-input":true,"_uuid":"3fc81699317c55ae4ad608b69c5a15aa7a84aa09"},"cell_type":"markdown","source":"\n### 5.8 도시 분포"},{"metadata":{"_cell_guid":"286b4541-c71b-480c-ad85-76dd821289d7","_kg_hide-input":true,"_uuid":"1e4d473a4b9a7a4cd102747c1db691dd006fc869","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":21,"hidden":false,"row":84,"width":null},"report_default":{}}}},"scrolled":true,"trusted":true},"cell_type":"code","source":"_generate_bar_plot_hor(train_df, cols[3], \"Distribution of City\", '#c2e2a3', 600, 600, 200, limit=30)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"e5c55e96-7f4e-4edd-ba78-81712e5bc412","_uuid":"b5a91c9b188e326f92488f0747ba6e33c120fc01"},"cell_type":"markdown","source":"> - 주요 도시 : 크라 스노 다르 (63638 품목), 예카 테 린 부르크 (63602 품목), 노보시비르스크 (56929 품목), 로스토프 돈 (52323 품목)\n\n\n### 5.9 데이터 세트의 Param_1 및 Param_2 분포 이해"},{"metadata":{"_cell_guid":"eeb25b5e-e9f3-4f5c-af38-919ae6f91f6e","_kg_hide-input":true,"_uuid":"949d306bd2441af0dd5d7e7864d98c0c093d9e29","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":15,"hidden":false,"row":105,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _generate_bar_plot_ver(train_df, cols[4], \"Param 1 Values\", '#bafcea', 700, 400, 200, limit=30, need_trace = True)\ntrace2 = _generate_bar_plot_ver(train_df, cols[5], \"Param 2 Values\", '#bafcea', 700, 400, 200, limit=30, need_trace = True)\ntrace3 = _generate_bar_plot_ver(train_df, cols[6], \"Param 3 Values\", '#bafcea', 700, 400, 200, limit=30, need_trace = True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Param 1 Values','Param 2 Values'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n# fig.append_trace(trace3, 1, 3);\n\nfig['layout'].update(height=400,title='Top Values in Param 1,2 columns', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"ab0329db-1551-469d-a88d-f077e38c5b62","_uuid":"43efd6cd689672de330e38be2d6aaafa8a972576"},"cell_type":"markdown","source":"\n> - Param_1에는 371 개의 고유 값이 있고, Param_2에는 271 개, Param_3에는 약 1200 개의 값이 있습니다.\n> - Param_3에는 센티미터 길이, 퀀 타이 트 등과 같은 항목에 대한 매우 낮은 수준의 분류가 들어 있습니다.\n\n\n\n### 5.10 어느 달의 요일과 주에 항목의 활성화"},{"metadata":{"_cell_guid":"7b72807a-1c1f-421e-aba5-a4f8e32e2325","_kg_hide-input":true,"_uuid":"3eea7e25302ca9704bc3ea6579da3a041a49a7ee","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":11,"hidden":false,"row":120,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _generate_bar_plot_ver(train_df, cols[7], \"WeekDays\", '#d38bed', 700, 400, 200, limit=30, need_trace = True)\ntrace2 = _generate_bar_plot_ver(train_df, cols[8], \"MonthDays\", '#d38bed', 700, 400, 200, limit=30, need_trace = True)\n\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Week Days','Month Days'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=300,title='Ads Posted in different Week/Month Days', showlegend=False);\niplot(fig); \n","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"4f844edf-e260-4888-a373-cc547e0705e2","_uuid":"85f74647a4bf7250770a8d4b4e8767399ca37ea5"},"cell_type":"markdown","source":"\n> - 주말에는 더 많은 수의 항목이 게시됩니다 (총 약 460K). 금요일에는 항목 수가 가장 적은 것으로 나타납니다.\n> - 주어진 데이터 세트에서, 항목의 최대 수는 19 일과 20 일에 관찰되었으며, 17 일과 18 일에는 가장 낮았습니다. 데이터베이스의 정확한 날짜는 2017 년 3 월 19 일과 20 일이며 3 월 17 일과 3 월 18 일 최저입니다.\n\n\n### 5.11 항목 제목 및 설명 길이"},{"metadata":{"_cell_guid":"1a5405f2-4a17-492a-bac6-d25d3024bfc6","_kg_hide-input":true,"_uuid":"44731cfc85092d893b74db37e351eeedb4790a64","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":15,"hidden":false,"row":131,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _generate_bar_plot_ver(train_df, cols[9], \"Title Length\", '#f7a92c', 700, 400, 200, limit=30, need_trace = True)\ntrace2 = _generate_bar_plot_ver(train_df, cols[10], \"Description Length\", '#f7a92c', 700, 400, 200, limit=30, need_trace = True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Title Word Count','Description Word Count'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400, title='', showlegend=False);\niplot(fig); \n","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"f57a06de-25a6-4be7-bf64-c399d95c0a0c","_uuid":"068a7d8e4b665cad3f32fa73d9fe638b818bb8b2"},"cell_type":"markdown","source":"> - 데이터 세트에있는 항목의 제목은 1 ~ 10 단어이며 제목 2 단어는 360K 항목과 가장 큰 숫자입니다\n> - 또한 항목에 대한 설명은 다른 값을 취합니다 : 0에서 최대 25 단어까지. 빈 설명이 포함 된 많은 수의 항목이 데이터 집합에 있습니다.\n> - 6 단어를 포함하는 설명이있는 항목이 가장 높고 1 단어를 포함하는 설명이있는 항목이 각각 66K 및 17K와 가장 낮습니다\n\n\n### 5.12 이미지 Top1 및 UserId"},{"metadata":{"_cell_guid":"0eedf79d-145a-4ed5-aaab-29adc12b35ea","_kg_hide-input":true,"_uuid":"789be024f19f011442afb55a155cebedcb3400ec","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":15,"hidden":false,"row":146,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _generate_bar_plot_ver(train_df, cols[11], \"Image Top 1\", '#e2dfd9', 700, 400, 200, limit=30, need_trace = True)\ntrace2 = _generate_bar_plot_ver(train_df, cols[12], \"User Id\", '#e2dfd9', 700, 400, 200, limit=30, need_trace = True)\n\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Image Top 1','User Id'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400,title='Ads having different ImageTop1 and User Id', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"28a53565-f228-4b71-b374-42178b8d2f0e","_uuid":"d8eebf668c333e6abeb77c343ff0d60face5874b"},"cell_type":"markdown","source":"> - user_id = 45ba3f23bf25의 사용자는 최대 항목 수 (108)를 게시하고 user_id = ee74bccca74f (980 항목) 및 user_id = 60dfed1efb6e (907 항목)을 게시했습니다.\n> -이 항목을 게시 한 771769 명의 사용자가 있습니다. 이때 고유 한 이미지 상위 1 개 코드 3062 개가 있습니다.\n> - 상단 이미지 상위 1 개 코드는 2219 (18739 개 항목), 1002 개 (18646 개 항목), 2918 (15407 개 항목)\n \n\n\n### 5.13 데이터 집합에 사용자 유형 배포"},{"metadata":{"_cell_guid":"d38ed5d1-12d3-4c82-98d3-0981d0ec9b91","_kg_hide-input":true,"_uuid":"afbb41ce710a8d36fbbe34bd50713615e54d7414","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":15,"hidden":false,"row":161,"width":null},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"def _create_pie_chart(df, col):\n tm = df[col].value_counts()\n labels = list(tm.index)\n values = list(tm.values)\n trace = go.Pie(labels=labels, values=values, marker=dict(colors=['#f9c968', '#75e575', '#d693b3']))\n return trace\ntrace1 = _create_pie_chart(train_df, 'user_type')\nlayout = go.Layout(title='Distribution of User Type', width=600, height=400, margin=dict(l=100))\ndata = [trace1]\nfig = go.Figure(data=data, layout=layout)\niplot(fig)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"1dcbe65f-0689-4930-b26f-9f01f2006303","_uuid":"df8c51b48806f6d27efb88dd5b161927d44f40a4"},"cell_type":"markdown","source":"> - 대부분의 항목은 개인 사용자 유형에 속하는 사용자가 게시하지만 항목의 소수는 \"Shop\"사용자 유형에 속한 사용자가 게시합니다.\n> - 사용자 중 약 72 %는 사설 유형이며 약 5 %는 Shop 사용자 유형입니다.\n\n\n### 5.14 실행중인 일일 광고 수"},{"metadata":{"_cell_guid":"e77f3806-b642-4a71-b96e-4b2e26050336","_kg_hide-input":true,"_uuid":"620135f53802ddf1668fb3c37adf0269c0fc283e","collapsed":true,"trusted":true},"cell_type":"code","source":"t = pr_train['total_period'].value_counts()\n\nlabels = list(t.index)\nlabels = [str(x).replace(\"00:00:00\",\"\").strip() for x in labels]\nvalues = list(t.values)\n\nlayout = go.Layout(title='For How Much Days Ads are Run', width=600, height=400, margin=dict(l=100), xaxis=dict(tickangle=-65))\ntrace1 = go.Bar(x=labels, y=values, marker=dict(color=\"#FF7441\"))\n\ndata = [trace1]\nfig = go.Figure(data=data, layout=layout)\niplot(fig)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"0e8ccff2-d83e-4adc-a271-5d402d50dd84","_uuid":"f5063fe1cb6c0db799fa9a0af6ffde26a91a2147"},"cell_type":"markdown","source":"> - Avito의 광고 / 항목 중 약 65 %가 약 2 주 (즉, 13 일 가까이) 동안 활성화됩니다. 13 일 동안 활성화 된 데이터 세트에는 약 6.9M의 광고가 있습니다.\n> - 데이터 세트에는 매우 짧은 기간 동안 활성화 된 광고가 많이 있습니다. 즉, 몇 시간 동안 (약 546,000 개의 광고), 거래 확률이 매우 낮은 광고 일 것입니다\n> - 데이터 세트의 광고 중 약 20 %가 1 주일 이내에 활성화됩니다 (즉, 7 일 미만)\n\n\n## 5.15 타이틀에 가장 많이 나오는 브랜드 / 제품"},{"metadata":{"_cell_guid":"0a388dde-9a25-446c-a562-9d626bc1d0b8","_kg_hide-input":true,"_uuid":"4b2dd8a0158c341828dcfcd0d291fb2d4cfa621c","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":5,"height":8,"hidden":false,"row":21,"width":4},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"# txt = \" \".join(train_df.title)\n# def blue_color(word, font_size, position, orientation, random_state=None, **kwargs):\n# return 'hsl({:d}, 80%, {:d}%)'.format(random.randint(180, 200), random.randint(60, 80))\n# wordcloud1 = WordCloud(blue_color='white', max_font_size=50, width=500, height=300).generate(txt)\n# plt.figure(figsize=(16,8))\n# plt.imshow(wordcloud1.recolor(color_func=green_color, random_state=3),interpolation=\"bilinear\")\n# plt.axis(\"off\")\n# plt.show() ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"524b6fcb-471c-4a3a-b1ff-23f9fffbe62f","_uuid":"2a3ce18144efcc4d4c005928ff6d0aa276140f87"},"cell_type":"markdown","source":"\n\n> - 스마트 폰 및 전자 제품에 관한 항목이 많습니다.\n> - 주목할만한 스마트 폰 : ** iphone 5s, iphone 6s, Samsung Galaxy, Sony Xperia, Xiaomi, Redmi, Nokia **\n> - 사용 된 일부 전자 제품 조항 : ** Xbox, PS3, Lenovo, Asus **\n> - 주목할만한 의류 브랜드 : ** Addidas, Reebok, Zara **"},{"metadata":{"_cell_guid":"a2b1cb90-6ba9-416a-b671-a966b3968536","_uuid":"4ff56ae0646ff2cbd916ca27c5517ccc3434a62b"},"cell_type":"markdown","source":"\n## 6. 다변량 분석\n\n\n### 6.1 데이터 세트의 상관 관계"},{"metadata":{"_cell_guid":"3ea0dcd1-d39b-40b4-a4ac-db9846cafe34","_kg_hide-input":true,"_uuid":"ba0b0397975ab31107ebbbdb3f5856fe156538ab","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"col":0,"height":12,"hidden":false,"row":176,"width":4},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"corr = train_df.corr()\nmask = np.zeros_like(corr, dtype=np.bool)\nmask[np.triu_indices_from(mask)] = True\nsns.set(style=\"white\")\n\nf, ax = plt.subplots(figsize=(10, 8))\ncmap = sns.diverging_palette(30, 10, as_cmap=True)\nsns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.1, cbar_kws={\"shrink\": .5});","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"9b148c2c-fb05-4f94-b855-6292c1701ab7","_uuid":"67e69629e5bd45887c67c2a1951f1b886241caed","extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}}},"cell_type":"markdown","source":"> - 상관 관계 그래프에서 거래 확률은 설명 len 및 제목 len과 좋은 상관 관계를 보여줍니다. 모델링하는 동안 중요한 기능을 통해 이러한 기능을 수행 할 수 있습니다.\n> - Item Seq Number는 title len, description len 및 Image Top 1과 약간 상관 관계가 있습니다.\n> - 이미지 상단 1 코드는 거래 확률 및 제목 및 설명의 길이와 좋은 상관 관계를 보여줍니다."},{"metadata":{"_cell_guid":"47ccae2c-7bde-428a-9293-9df2e91dc44f","_uuid":"285880b46dcf2e655987b73fc55b3af2e1f577f7","extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}}},"cell_type":"markdown","source":"\n### 6.2 상위 카테고리 및 사용자 유형별 거래 확률"},{"metadata":{"_cell_guid":"12e47cde-2331-41d9-9de5-02436cdfd9c9","_kg_hide-input":true,"_uuid":"eab22aa6565f923316a6d1874480f52b20c160d8","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"sns.set(rc={'figure.figsize':(15, 8)})\nsns.boxplot(x=\"parent_category_name_en\", y=\"deal_probability\", hue=\"user_type\", palette=\"PRGn\", data=train_df)\nplt.title(\"Deal probability by parent category and User Type\")\nplt.xticks(rotation='vertical')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"6b2480c1-3dca-4037-a436-7d623c2e64fb","_uuid":"61bf2a4d0d387449a58c70f56113d2cc6115040e"},"cell_type":"markdown","source":"> - 위의 상자 그래프 그래프에서 \"상점\"사용자 유형을 가진 사용자는 부동산, 운송 및 서비스 범주의 항목과 만 관련됩니다. 서비스 범주는 거래 확률이 가장 높은 반면 부동산은 가장 낮습니다.\n> - 사용자 유형이 \"회사\"인 사용자는 서비스 범주에서 가장 높은 거래 확률을 보이고 다음으로 운송 및 동물이옵니다. Shop 사용자 유형과 달리 회사 사용자 유형은 거의 모든 상위 카테고리와 관련됩니다\n> - 사용자 유형이 '비공개'인 사용자는 서비스 카테고리에서 0.1에서 0.6 사이의 범위에 흩어져 있습니다.\n\n### 6.3 지역 및 사용자 유형별 거래 확률"},{"metadata":{"_cell_guid":"0e03fc74-442e-44d9-bce4-2c30563b0b91","_kg_hide-input":true,"_uuid":"7d03236e6051cfdb934e11d54dd0b78b02bd654f","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"sns.set(rc={'figure.figsize':(15,8)})\nsns.boxplot(x=\"region_en\", y=\"deal_probability\", hue=\"user_type\", palette=\"coolwarm\", data=train_df)\nplt.title(\"Deal probability by Region and User Type\")\nplt.xticks(rotation='vertical')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"9ce4b4cf-50be-47d6-9759-80287a75d4b4","_uuid":"b229feffc8dfae6c68517d0d030742b99092ffc1"},"cell_type":"markdown","source":"> - 위의 상자 플롯은 Orenburg Oblast 및 Saratov Oblast에 게시되고 Shop 사용자 유형에 속하는 품목이 가장 높은 거래 확률 범위를 갖는 것을 보여줍니다.\n> - 회사 사용자 유형 사용자의 항목의 경우 Stavropol Krai 및 Orenburg Oblast에 최대 유사 콘텐츠가 표시됩니다. \n\n\n### 6.4 거래 확률 클래스와 관련된 가격 로그 이해"},{"metadata":{"_cell_guid":"b740cefa-4edb-4755-b6af-388cd1bded67","_kg_hide-input":true,"_uuid":"d7100f65bfba770f8f257ca7f451b3bcd848729c","collapsed":true,"trusted":true},"cell_type":"code","source":"train_df['price_log'] = np.log(train_df['price'] + 1)\nsns.set_style(\"whitegrid\", {'axes.grid' : False})\ng = sns.boxplot(x='deal_class_2', y='price_log', data=train_df, palette=\"RdBu\")\ng.set_xlabel('The Deal Probability Class',fontsize=12)\ng.set_ylabel('Log of Price',fontsize=12)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"0e9f054e-f8fb-47b4-bac0-1e5dea491115","_uuid":"f4373114694cb2592c8150fd202c025d64308ee6"},"cell_type":"markdown","source":"> - 거래 확률이 낮은 항목은 로그 가격이 낮지 만 거래 확률이 약 0.5 인 항목의 경우 약간 높습니다.\n\n다른 변수 그룹에 의한 아이템의 평균 가격을 알 수 있습니다.\n\n\n### 6.5 서로 다른 학부모 카테고리와 다른 거래 클래스에 몇 개의 아이템이 들어 있습니까?"},{"metadata":{"_cell_guid":"aace59f3-28cc-4ade-adba-ac975cc92c18","_kg_hide-input":true,"_uuid":"8203f560353c85177df6da7d6344e94209e349c7","collapsed":true,"trusted":true},"cell_type":"code","source":"cols = ['parent_category_name_en','deal_class_2']\ncolmap = sns.light_palette(\"#ff4284\", as_cmap=True)\npd.crosstab(train_df[cols[0]], train_df[cols[1]]).style.background_gradient(cmap = colmap)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"75b7953a-f3e1-4fd3-b7da-fae91265c40b","_uuid":"82b4c1423bb4ed11940b47392caab86898a129e8"},"cell_type":"markdown","source":"> - 많은 소비자 전자 제품이 높은 거래 가능성 (0.7 - 0.8)을 가지고 있으며, 데이터 세트를 지배하는 개인 소지품 카테고리는 거래 확률이 1.0에 가까운 항목이 더 많습니다\n> - 가정 및 정원 관련 카테고리에는 높은 거래 확률을 갖는 항목도 포함됩니다.\n> - 서비스 카테고리에만 0.9보다 큰 거래 확률을 갖는 항목이 있습니다.\n\n\n### 6.6 다른 지역 및 다른 거래 확률 항목"},{"metadata":{"_cell_guid":"89c17fb3-01be-4649-a1c5-88eca7e3a0bb","_kg_hide-input":true,"_uuid":"85ec903eaa0d8570be7db825bcef0f9ff812ef0b","collapsed":true,"trusted":true},"cell_type":"code","source":"cols = ['region_en','deal_class_2']\ncolmap = sns.light_palette(\"#7cc8ff\", as_cmap=True)\npd.crosstab(train_df[cols[0]], train_df[cols[1]]).style.background_gradient(cmap = colmap)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"8be5b396-a081-4ae8-b4f0-af92bab29f55","_uuid":"6086c3afbab7054b336a1be91a938643bcb56ec9"},"cell_type":"markdown","source":"\n\n### 6.7 다른 지역 및 다른 거래 확률을 갖는 품목의 평균 가격"},{"metadata":{"_cell_guid":"1bbec77e-53d6-4971-9e0b-5ea52dc01a0f","_kg_hide-input":true,"_uuid":"21a5676f8ec4d4853e80ab18f9808928c9b4d4ba","collapsed":true,"trusted":true},"cell_type":"code","source":"def _create_bubble_plots(col1, col2, aggcol, func, title, cs):\n tempdf = train_df.groupby([col1, col2]).agg({aggcol : func}).reset_index()\n tempdf[aggcol] = tempdf[aggcol].apply(lambda x : int(x))\n tempdf = tempdf.sort_values(aggcol, ascending=False)\n\n sizes = list(reversed([i for i in range(10,31)]))\n intervals = int(len(tempdf) / len(sizes))\n size_array = [9]*len(tempdf)\n \n st = 0\n for i, size in enumerate(sizes):\n for j in range(st, st+intervals):\n size_array[j] = size \n st = st+intervals\n tempdf['size_n'] = size_array\n # tempdf = tempdf.sample(frac=1).reset_index(drop=True)\n\n cols = list(tempdf['size_n'])\n\n trace1 = go.Scatter( x=tempdf[col1], y=tempdf[col2], mode='markers', text=tempdf[aggcol],\n marker=dict( size=tempdf.size_n, color=cols, colorscale=cs ))\n data = [trace1]\n layout = go.Layout(title=title)\n fig = go.Figure(data=data, layout=layout)\n iplot(fig)\n\n_create_bubble_plots('region_en', 'deal_class_2', 'price', 'mean', '', 'Picnic')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"f6e2d256-c626-4a3e-96a5-a14fd3765cad","_uuid":"9afa0d440b944a4462ead62186274a81fa45231c"},"cell_type":"markdown","source":"> - Kransnodar Krai Region, Irkutsk Oblast 및 Nizhny Novgorod Oblast의 거래 가능성이 낮은 품목 (0.2 미만)은 다른 어떤 지역보다 평균 가격이 높습니다.\n> - 평균 가격이 높고 거래 비율이 높은 품목 (0.7 - 0.8)은 Tyumen Oblast 및 Stavropoi Krai 지역에 속합니다.\n> - Kransnodar Krai (품목 수가 가장 많음)는 평균 가격이 높은 품목의 거래 비율이 서로 다릅니다\n\n### 6.8 거래 확률 및 상위 카테고리가 다른 품목의 평균 가격은 얼마입니까?"},{"metadata":{"_cell_guid":"16ca9dd9-9b31-44b1-b0d8-6078b77d8577","_kg_hide-input":true,"_uuid":"4cecf59089a7d6a5222604daaa6579105c6e59b5","collapsed":true,"trusted":true},"cell_type":"code","source":"_create_bubble_plots('parent_category_name_en', 'deal_class_2', 'price', 'mean', '', 'Electric')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"b1dedcb0-d5e3-43cd-8f88-9be135999f2c","_uuid":"7b21333264e32b32b9b9edf26aff9f8483456108"},"cell_type":"markdown","source":"> - 부동산 및 운송 카테고리에는 일반적으로 평균 가격이 더 높은 항목이 포함됩니다\n> - 개인 보험 범주에는 거래 확률의 어떤 값이든 평균 가격이 더 높은 항목이 포함되어 있지 않습니다\n> - 거래 확률이 높고 가격이 높은 항목은 가정과 정원 및 부동산 카테고리에 속합니다.\n\n\n### 6.9 다른 주중 날짜와 그들의 평균 가격에 거래 클래스 값"},{"metadata":{"_cell_guid":"ecd260d2-1988-42da-9e58-4274292bd70a","_kg_hide-input":true,"_uuid":"a4fef8b3e48cbb6bae5287d9b1010b644decf06d","collapsed":true,"trusted":true},"cell_type":"code","source":"_create_bubble_plots('deal_class_2', 'weekday_en', 'price', 'mean', '', 'Jet')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"9e30da8b-6d12-4089-8e6b-e90d49b9257e","_uuid":"e79abff864113d3349f60f321e0a555346907da8"},"cell_type":"markdown","source":"> - 높은 거래 비율 (0.9+)을 갖고 토요일과 월요일에 게시 된 항목의 평균 가격이 다른 어느 날보다 높습니다.\n> - 거래 비율이 낮은 품목의 최대 가격 (<0.1)이 화요일에 게시되었습니다.\n\n### 6.10 다른 지역, 다른 요일 및 항목의 평균 가격 값"},{"metadata":{"_cell_guid":"4a2b31f7-2300-4886-8043-daa82fe1c876","_kg_hide-input":true,"_uuid":"4f997e60ce79d097135b4a35e85bc5d1ed2ea2c3","collapsed":true,"trusted":true},"cell_type":"code","source":"_create_bubble_plots('region_en', 'weekday_en', 'price', 'mean', 'Mean Price of Items by Regions and Deal Probability Class', 'Portland')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"0def40f3-bcd9-48fa-a5ea-9da1a4c88b8f","_uuid":"17ddce3f30b95aaf2aba8af7d4c598a316191a71"},"cell_type":"markdown","source":"> - 화요일과 수요일에는 토요일이 가장 낮지 만 다른 지역보다 가격이 높은 항목이 대부분 있습니다.\n\n\n### 6.11 주중 주간 및 지역 최대 가치 항목의 최대 가격"},{"metadata":{"_cell_guid":"53203767-8a23-472d-be7d-e02164b10e9d","_kg_hide-input":true,"_uuid":"8932bce62a28ebb125404649894f7d5e4ea389c2","collapsed":true,"trusted":true},"cell_type":"code","source":"_create_bubble_plots('region_en', 'weekday_en', 'price', 'max', '', 'Hot')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"42b2bb40-3d26-4d74-bf99-3084b05b9332","_uuid":"274d68fe43d11d04bb2e868ad848fcdd333fc674"},"cell_type":"markdown","source":"\n\n### 6.12 다른 지역 및 요일별 거래 확률의 최대 값"},{"metadata":{"_cell_guid":"2c5f4af5-4cc8-4320-8393-52fd54896003","_kg_hide-input":true,"_uuid":"b76f0965dec55eec4f345ab08adc585fe5f038a3","collapsed":true,"trusted":true},"cell_type":"code","source":"_create_bubble_plots('parent_category_name_en', 'weekday_en', 'deal_probability', 'max', '', 'Earth')","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"1503c0d4-6af3-47b7-bc78-95f92e3c0662","_uuid":"938ffa49f0b0fd53afd64f5527deb6b3450dd245"},"cell_type":"markdown","source":"\n## 7. 고가 또는 저가 평균 가격을 가진 품목의 특성은 무엇입니까\n\n\n### 7.1 주별 주 평균 요율 일 및 월 요일"},{"metadata":{"_cell_guid":"b5de5fe6-32f2-4a3c-ad9e-900c857d99e4","_kg_hide-input":true,"_uuid":"a0bbc41a0981b61b001871c6ac726f04cf2b38e0","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"def _aggregate(base_col, agg_col, agg_func, return_trace = False):\n tempdf = train_df.groupby(base_col).agg({agg_col : agg_func})\n \n trace = go.Bar(x=tempdf.index, y=tempdf.price, name='', marker=dict(color='#e7e4ff'))\n if return_trace:\n return trace\n \n data = [trace]\n layout = go.Layout(title='', width=500, legend=dict(orientation=\"h\"), margin=dict(b=200))\n fig = go.Figure(data=data, layout=layout)\n iplot(fig)\n\ntrace1 = _aggregate('weekday', 'price', 'mean', return_trace=True)\ntrace2 = _aggregate('day', 'price', 'mean', return_trace=True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Mean Price on WeekDays','Mean Price of Month Days'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400, paper_bgcolor='#f4f3f9', plot_bgcolor='#f4f3f9', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"a8a40414-b1ed-4c31-9e51-ca64c247f22a","_uuid":"94040c4b63c33248105b1ace5bdda06562eccf49"},"cell_type":"markdown","source":"> - 최대 가격 품목은 해당 월 22 일에 즉흥적으로 판매되었습니다. 2017 년 3 월 22 일 가장 저렴한 품목이 2017 년 3 월 3 일에 승격되었습니다.\n\n\n### 7.2 지역 및 도시 별 품목의 평균 가격"},{"metadata":{"_cell_guid":"7112ac6b-fea2-40fe-bdca-2c996fa3efa0","_kg_hide-input":true,"_uuid":"324e71c92299ec7872c1d1b04b66b5b0a82ae615","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _aggregate('region_en', 'price', 'mean', return_trace=True)\ntrace2 = _aggregate('city', 'price', 'mean', return_trace=True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Mean Price of Regions','Mean Price of Cities'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400, paper_bgcolor='#f4f3f9', plot_bgcolor='#f4f3f9', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"cdf93efa-99db-4deb-af63-dcb43a3f7037","_uuid":"d73585ca8a47679a77fa249ca4a088cb8fb1efe5"},"cell_type":"markdown","source":"> - Irutsk Oblast Region 및 Krasnoyarask Krai에서 최대 평균 가격 (2.14M)을 갖는 품목이 홍보되었습니다.\n\n\n### 7.3 카테고리 및 상위 카테고리 별 평균 가격"},{"metadata":{"_cell_guid":"45cae26b-32e5-4801-9be3-fe3145e5e62d","_kg_hide-input":true,"_uuid":"6c1d96c3447b107a63bcb21e865243a62798b315","collapsed":true,"extensions":{"jupyter_dashboards":{"version":1,"views":{"grid_default":{"hidden":true},"report_default":{}}}},"trusted":true},"cell_type":"code","source":"trace1 = _aggregate('category_name_en', 'price', 'mean', return_trace=True)\ntrace2 = _aggregate('parent_category_name_en', 'price', 'mean', return_trace=True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Mean Price on Categories','Mean Price of Parent Categories'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400, paper_bgcolor='#f4f3f9', plot_bgcolor='#f4f3f9', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"824757b2-1393-4394-9c51-6d4dc1c974c3","_uuid":"c4b236159cc6c19eea50825b3b1c138ee4c1d235"},"cell_type":"markdown","source":"> - 예상대로 부동산 항목은 가장 높은 평균 항목을, 비즈니스 및 교통\n> - 부동산 Abroa, Land 및 Commerical Property는 가장 높은 평균 가격을 가지고 있습니다.\n\n\n### 7.4 제목과 설명에있는 단어의 수에 의한 평균 가격"},{"metadata":{"_cell_guid":"9d063a21-5b18-4141-8e2e-75fce942938c","_kg_hide-input":true,"_uuid":"6a67554c5bf97fddb2f2108aa2c890e58b1ede42","collapsed":true,"trusted":true},"cell_type":"code","source":"trace1 = _aggregate('title_len', 'price', 'mean', return_trace=True)\ntrace2 = _aggregate('description_len', 'price', 'mean', return_trace=True)\n\nfig = tools.make_subplots(rows=1, cols=2, print_grid=False, subplot_titles = ['Mean Price by Title Length','Mean Price by Description Length'])\nfig.append_trace(trace1, 1, 1);\nfig.append_trace(trace2, 1, 2);\n\nfig['layout'].update(height=400, paper_bgcolor='#f4f3f9', plot_bgcolor='#f4f3f9', showlegend=False);\niplot(fig); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"176d5807-49f6-4ada-b492-8b8b00d35de1","_uuid":"ac249d8a7c5ad93da3a1551ce90ad24f866ecdaf"},"cell_type":"markdown","source":"> - 긴 제목은 가격이 높고 6-7 단어는 평균 가격이 매우 높습니다.\n> - 약 400 단어의 설명은 약 15 백만의 높은 평균 가격을 가짐\n\n\n## 8. 매우 높음 (또는 매우 낮음) 거래 비율을 갖는 품목의 특성은 무엇입니까\n\n\n### 8.1 제목에 나오는 단어들"},{"metadata":{"_cell_guid":"8c1ad01c-5b04-4d0f-b2c2-458f75a94ed5","_kg_hide-input":true,"_uuid":"1dffe7418dbe098066284a7f8a653707306274e7","collapsed":true,"trusted":true},"cell_type":"code","source":"good_performing_ads = train_df[train_df['deal_probability'] >= 0.90]\nbad_performing_ads = train_df[train_df['deal_probability'] <= 0.05]\n\n# pos_text_cln = \" \".join(good_performing_ads.title)\n# neg_text_cln = \" \".join(bad_performing_ads.title)\n# def green_color(word, font_size, position, orientation, random_state=None, **kwargs):\n# return 'hsl({:d}, 80%, {:d}%)'.format(random.randint(85, 140), random.randint(60, 80))\n\n# def red_color(word, font_size, position, orientation, random_state=None, **kwargs):\n# return 'hsl({:d}, 80%, {:d}%)'.format(random.randint(0, 35), random.randint(60, 80))\n \n# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[15, 8])\n# wordcloud1 = WordCloud(background_color='white', height=400).generate(pos_text_cln)\n\n# ax1.imshow(wordcloud1.recolor(color_func=green_color, random_state=3),interpolation=\"bilinear\")\n# ax1.axis('off');\n# ax1.set_title('Items having high deal percentage');\n\n# wordcloud2 = WordCloud(background_color='white', height=400).generate(neg_text_cln)\n# ax2.imshow(wordcloud2.recolor(color_func=red_color, random_state=3),interpolation=\"bilinear\")\n# ax2.axis('off');\n# ax2.set_title('Items having low deal percentage');","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"15b07f87-aa87-4ece-826e-7674d0ce8a70","_uuid":"ff6e6354c710c20abd38146f3a680de82069e77a"},"cell_type":"markdown","source":"\n\n> - Iphone, Hyuanday, JCB, Toyota 등 높은 거래 비율을 갖고 있으며 거래 비율이 낮은 항목의 제목에는 표시되지 않는 항목이 있다는 것을 흥미롭게 보았습니다.\n> - Samsung, Zara 및 Adiddas와 같은 브랜드는 거래 확률이 낮은 항목 세트에 속합니다.\n\n\n### 8.2 품목의 기타 모든 기능을 저 / 고수준 비율과 비교\n"},{"metadata":{"_cell_guid":"463b9e34-a6da-4298-ad1a-0d70d291e494","_kg_hide-input":true,"_uuid":"1ffe65642b9742d6628df1f80583d1bb442d2636","collapsed":true,"trusted":true},"cell_type":"code","source":"def get_trace(col, df, color):\n temp = df[col].value_counts().nlargest(3)\n x = list(reversed(list(temp.index)))\n y = list(reversed(list(temp.values)))\n trace = go.Bar(x = y, y = x, width = [0.9, 0.9, 0.9], orientation='h', marker=dict(color=color))\n return trace\n\n\ncompare_cols = ['parent_category_name_en', 'category_name', 'city', 'param_1', 'param_2', 'weekday', 'title_len', 'description_len']\n\n\ngoodtraces = []\ngoodtitles = []\nfor i,col in enumerate(compare_cols):\n goodtitles.append(col)\n goodtraces.append(get_trace(col, good_performing_ads, '#a3dd56'))\n\n \nbadtraces = []\nbadtitles = []\nfor i,col in enumerate(compare_cols):\n badtitles.append(col)\n badtraces.append(get_trace(col, bad_performing_ads, '#ef7067'))\n\ntitles = []\nfor each in compare_cols:\n titles.append(each)\n titles.append(each)\nfig = tools.make_subplots(rows=len(compare_cols), cols=2, print_grid=False, horizontal_spacing = 0.15, subplot_titles=titles)\ni = 0\nfor g,b in zip(goodtraces, badtraces):\n i += 1\n fig.append_trace(g, i, 1);\n fig.append_trace(b, i, 2);\n\nfig['layout'].update(height=1000, margin=dict(l=100), showlegend=False, title=\"Comapring Features of Ads with High and Low Deal Percentages\");\niplot(fig, filename='simple-subplot'); ","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"b9dbe738-5195-4bda-b3f8-3e5be30a296a","_uuid":"9cfeb6ea862d38f587c43d12225476ae6a0164dc"},"cell_type":"markdown","source":"\n### 8.3 설명에 사용 된 상위 N- 그램"},{"metadata":{"_cell_guid":"ca0a31be-20b7-4b88-87fd-1aed3a81bd7c","_kg_hide-input":true,"_uuid":"98a11457a3ce0034d9c86d469214806e257ca5b4","collapsed":true,"trusted":true},"cell_type":"code","source":"# stopwords = ['а', 'е', 'и', 'ж', 'м', 'о', 'на', 'не', 'ни', 'об', 'но', 'он', 'мне', 'мои', 'мож', 'она', 'они', 'оно', 'мной', 'много', 'многочисленное', 'многочисленная', 'многочисленные', 'многочисленный', 'мною', 'мой', 'мог', 'могут', 'можно', 'может', 'можхо', 'мор', 'моя', 'моё', 'мочь', 'над', 'нее', 'оба', 'нам', 'нем', 'нами', 'ними', 'мимо', 'немного', 'одной', 'одного', 'менее', 'однажды', 'однако', 'меня', 'нему', 'меньше', 'ней', 'наверху', 'него', 'ниже', 'мало', 'надо', 'один', 'одиннадцать', 'одиннадцатый', 'назад', 'наиболее', 'недавно', 'миллионов', 'недалеко', 'между', 'низко', 'меля', 'нельзя', 'нибудь', 'непрерывно', 'наконец', 'никогда', 'никуда', 'нас', 'наш', 'нет', 'нею', 'неё', 'них', 'мира', 'наша', 'наше', 'наши', 'ничего', 'начала', 'нередко', 'несколько', 'обычно', 'опять', 'около', 'мы', 'ну', 'нх', 'от', 'отовсюду', 'особенно', 'нужно', 'очень', 'отсюда', 'в', 'во', 'вон', 'вниз', 'внизу', 'вокруг', 'вот', 'восемнадцать', 'восемнадцатый', 'восемь', 'восьмой', 'вверх', 'вам', 'вами', 'важное', 'важная', 'важные', 'важный', 'вдали', 'везде', 'ведь', 'вас', 'ваш', 'ваша', 'ваше', 'ваши', 'впрочем', 'весь', 'вдруг', 'вы', 'все', 'второй', 'всем', 'всеми', 'времени', 'время', 'всему', 'всего', 'всегда', 'всех', 'всею', 'всю', 'вся', 'всё', 'всюду', 'г', 'год', 'говорил', 'говорит', 'года', 'году', 'где', 'да', 'ее', 'за', 'из', 'ли', 'же', 'им', 'до', 'по', 'ими', 'под', 'иногда', 'довольно', 'именно', 'долго', 'позже', 'более', 'должно', 'пожалуйста', 'значит', 'иметь', 'больше', 'пока', 'ему', 'имя', 'пор', 'пора', 'потом', 'потому', 'после', 'почему', 'почти', 'посреди', 'ей', 'два', 'две', 'двенадцать', 'двенадцатый', 'двадцать', 'двадцатый', 'двух', 'его', 'дел', 'или', 'без', 'день', 'занят', 'занята', 'занято', 'заняты', 'действительно', 'давно', 'девятнадцать', 'девятнадцатый', 'девять', 'девятый', 'даже', 'алло', 'жизнь', 'далеко', 'близко', 'здесь', 'дальше', 'для', 'лет', 'зато', 'даром', 'первый', 'перед', 'затем', 'зачем', 'лишь', 'десять', 'десятый', 'ею', 'её', 'их', 'бы', 'еще', 'при', 'был', 'про', 'процентов', 'против', 'просто', 'бывает', 'бывь', 'если', 'люди', 'была', 'были', 'было', 'будем', 'будет', 'будете', 'будешь', 'прекрасно', 'буду', 'будь', 'будто', 'будут', 'ещё', 'пятнадцать', 'пятнадцатый', 'друго', 'другое', 'другой', 'другие', 'другая', 'других', 'есть', 'пять', 'быть', 'лучше', 'пятый', 'к', 'ком', 'конечно', 'кому', 'кого', 'когда', 'которой', 'которого', 'которая', 'которые', 'который', 'которых', 'кем', 'каждое', 'каждая', 'каждые', 'каждый', 'кажется', 'как', 'какой', 'какая', 'кто', 'кроме', 'куда', 'кругом', 'с', 'т', 'у', 'я', 'та', 'те', 'уж', 'со', 'то', 'том', 'снова', 'тому', 'совсем', 'того', 'тогда', 'тоже', 'собой', 'тобой', 'собою', 'тобою', 'сначала', 'только', 'уметь', 'тот', 'тою', 'хорошо', 'хотеть', 'хочешь', 'хоть', 'хотя', 'свое', 'свои', 'твой', 'своей', 'своего', 'своих', 'свою', 'твоя', 'твоё', 'раз', 'уже', 'сам', 'там', 'тем', 'чем', 'сама', 'сами', 'теми', 'само', 'рано', 'самом', 'самому', 'самой', 'самого', 'семнадцать', 'семнадцатый', 'самим', 'самими', 'самих', 'саму', 'семь', 'чему', 'раньше', 'сейчас', 'чего', 'сегодня', 'себе', 'тебе', 'сеаой', 'человек', 'разве', 'теперь', 'себя', 'тебя', 'седьмой', 'спасибо', 'слишком', 'так', 'такое', 'такой', 'такие', 'также', 'такая', 'сих', 'тех', 'чаще', 'четвертый', 'через', 'часто', 'шестой', 'шестнадцать', 'шестнадцатый', 'шесть', 'четыре', 'четырнадцать', 'четырнадцатый', 'сколько', 'сказал', 'сказала', 'сказать', 'ту', 'ты', 'три', 'эта', 'эти', 'что', 'это', 'чтоб', 'этом', 'этому', 'этой', 'этого', 'чтобы', 'этот', 'стал', 'туда', 'этим', 'этими', 'рядом', 'тринадцать', 'тринадцатый', 'этих', 'третий', 'тут', 'эту', 'суть', 'чуть', 'тысяч']\n\n# from collections import Counter \n# import string \n# punc = string.punctuation \n\n# def clean_text(txt): \n# txt = txt.lower()\n# txt = \"\".join(x for x in txt if x not in punc)\n# words = txt.split()\n# words = [wrd for wrd in words if wrd not in stopwords]\n# words = [wrd for wrd in words if len(wrd) > 1]\n# txt = \" \".join(words)\n# return txt\n\n# def ngrams(txt, n):\n# txt = txt.split()\n# output = []\n# for i in range(len(txt)-n+1):\n# output.append(\" \".join(txt[i:i+n]))\n# return output\n\n# good_translated = ['any complexity', 'experience', 'type of work', 'Experience', 'All types', 'Offered service', 'The price is negotiable', 'quickly high quality', 'work of any', 'home appliances', 'questions telephone', 'approach to each', 'Rent rent', 'Quickly high quality', 'Extensive experience', 'experience', 'Individual approach', 'washing machine', 'construction waste', 'each client', 'the price is negotiable', 'city area', 'clearing settlement', 'Call any', 'short time', 'garbage disposal', 'long term', 'paving slab', '20 tons', 'finishing work', 'finishing work', 'Call agree', 'check out the house', 'All issues', 'welding work', '500 rubles', 'switch sockets', 'extensive experience', 'building work', 'high quality low price', 'Offered service', 'any convenient', 'bad habit', 'convenient for You', 'Departure is possible', 'ceiling walls', 'Our company', 'individual approach', 'Nail extension', '300 RUB', 'Work weekends', 'Garbage disposal', 'You can', 'interior door', 'types of construction', 'types of finishing', 'extensive experience', 'low price', 'any kind', '500 rubles', 'shortest time', 'dishwasher', '15 tons', 'range of services', 'cash non-cash', 'offered service', 'work 10', 'Without days', 'rubles per hour', 'qualitatively expensive', 'executed work', 'washing machine', 'Cash non-cash', 'construction work', '300 rubles', 'building material', 'installation of doors', 'eyelash extension', '10 tons', 'All work', 'the gel Polish', 'system of discounts', 'Provide services', '100 rubles', '1000 rubles', 'shower cabin', 'office moving', 'We work', 'wallpapering', 'Departure of the specialist', 'removal of construction', 'Cargo transportation to the city', 'Quality assurance', '200 rubles', 'apartment houses', 'the alignment of the walls', 'any area', 'Departure of the master', 'roofing work', 'quality repair']\n# bad_translated = ['As a unit', 'fit to size', 'Android smartphone', 'state of new', '400 rubles', 'land plot', 'room apartment', 'Will sell a new', 'GB slot', 'On growth', 'Sell dress', 'view of the move', 'In the photo', 'Operating time', 'battery charger', 'State of new', 'see In', 'genuine leather', 'small bargain', 'Very convenient', 'public transport', 'On a plot', 'Complete set', 'GB memory', '300 RUB', 'Non-negotiable', 'cm Weight', 'see Sell', 'appearance', '150 RUB', 'rest of russia', 'room apartment', 'availability order', 'trades Sell', 'At purchase', 'card slot', 'Will sell a jacket -', 'Whole range', 'able Sell', '200 rubles', 'We will be glad', 'Our address', 'The price is negotiable', 'bargaining is appropriate', 'condition size', 'Within walking distance', 'plastic Windows', 'state In', 'operational volume', 'cm length', 'condition Size', 'Boo good', 'Reason for sale', 'Good condition', 'wifi bluetooth', 'our shop', 'Sell new', 'On issues', 'On insole', 'storage card', 'Excellent condition', 'In the apartment', '100 cotton', 'All issues', 'Sell new', 'our shop', 'perfect condition', 'photo in sight', 'kindergarten', 'wide choice', '100 rubles', '500 rubles', 'Genuine leather', 'Delivery is possible', 'Perfect condition', 'The price shown', 'As a gift', 'our site', 'RAM', 'It is possible to bargain', 'genuine leather', 'You can', 'questions telephone', 'Bargaining is appropriate', 'working condition', 'able will Sell', 'walking distance', 'good condition', 'excellent condition', 'In a perfect', 'As a unit', 'In stock', 'Good condition', 'Excellent condition', 'In good', 'conditin is', 'In excellent', 'perfect condition', 'good condition', 'excellent condition']\n# bad_translated = list(reversed(bad_translated))\n\n# def get_bigrams_data(txt, tag, col, translated_list):\n# txt = clean_text(txt)\n# all_bigrams = ngrams(txt, 2)\n# topbigrams = Counter(all_bigrams).most_common(100)\n \n# xvals = [translated_list[i] for i in range(len(topbigrams))] \n# # xvals = list(reversed([_[0] for _ in topbigrams]))\n# yvals = list(reversed([_[1] for _ in topbigrams]))\n# trace = go.Bar(x=yvals, y=xvals, name=tag, marker=dict(color=col), xaxis=dict(linecolor='#fff',), opacity=0.7, orientation='h')\n# return trace\n\n# txt = \" \".join(good_performing_ads.description)\n# good_tr1 = get_bigrams_data(txt, 'Top Ngrams used in Items with High Deal Percentage', '#bbf783', good_translated)\n\n# txt = \" \".join(bad_performing_ads.description[:10000])\n# bad_tr1 = get_bigrams_data(txt, 'Top Ngrams used in Items with Low Deal Percentage', '#f78396', bad_translated)\n\n# fig = tools.make_subplots(rows=1, cols=2, print_grid=False);\n# fig.append_trace(good_tr1, 1, 1);\n# fig.append_trace(bad_tr1, 1, 2);\n\n# fig['layout'].update(height=1000, margin=dict(l=200), title='', \n# legend=dict(x=0.1, y=1.2));\n# iplot(fig, filename='simple-subplot');","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"17444584-0b36-4153-b7cf-12eefe2dc82a","_uuid":"8ef729dc5d4ab024dfd99016b7588480941df683"},"cell_type":"markdown","source":"\n\n\n> - 좋은 실적을 보이는 광고의 특징 중 하나는 (루비 일 가능성이 높음) 루핑 작업, 아파트, 품질, 시공 작업과 같은 항목에 대해 분명하게 언급하고 있다는 것입니다. 실적이 좋지 않은 광고는 '우수한 상태', '근무 조건', '흥정', '도보 거리'등과 같은 추가 세부 정보를 말하지만,\n\n## 9. 좋고 나쁜 이미지\n\n### 9.1 높은 거래 확률을 갖는 이미지를 살펴 봅니다."},{"metadata":{"_cell_guid":"e8e845a4-b1c8-4d0f-a627-668127ac67b7","_kg_hide-input":true,"_uuid":"84220d34c312744c622269242c6f00ef0806084d","collapsed":true,"trusted":true},"cell_type":"code","source":"import zipfile\nfrom PIL import Image \nimport numpy as np \nfrom IPython.display import display, HTML\n\n\ngood_images = train_df[train_df['deal_probability'] == 1]['image'][:10]\ngood_images = good_images.dropna()\n\nbad_images = train_df[train_df['deal_probability'] == 0.0]['image'][:10]\nbad_images = bad_images.dropna()\n\ngoodones = []\nbadones = []\nwith zipfile.ZipFile('../input/train_jpg.zip', 'r') as train_zip:\n files_in_zip = sorted(train_zip.namelist())\n for idx, file in enumerate(files_in_zip):\n if any(file.endswith(x+\".jpg\") for x in good_images): \n img = train_zip.extract(file, path=file.split('/')[3])\n goodones.append(img)\n elif any(file.endswith(x+\".jpg\") for x in bad_images): \n img = train_zip.extract(file, path=file.split('/')[3])\n badones.append(img)\n\nf, ax = plt.subplots(1,5)\nf.set_size_inches(80, 40)\nfor i in range(5):\n ax[i].imshow(Image.open(goodones[i]).resize((300, 300), Image.ANTIALIAS))\nplt.show()\n\nf, ax = plt.subplots(1,5)\nf.set_size_inches(80, 40)\nfor i in range(5):\n ax[i].imshow(Image.open(goodones[i+5]).resize((300, 300), Image.ANTIALIAS))\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"b5d26054-c602-4f8b-9574-96aae4552b85","_uuid":"2154ec147dee3bc720784b42e2a386bb066da2ef"},"cell_type":"markdown","source":"### 9.2 거래 확률이 낮은 이미지"},{"metadata":{"_cell_guid":"f9274cfc-6514-452a-9369-0e09783b830d","_kg_hide-input":true,"_uuid":"f374eefe2eaea0f67f7677f9b5dc8b09f0a87b31","collapsed":true,"trusted":true},"cell_type":"code","source":"f, ax = plt.subplots(1,5)\nf.set_size_inches(80, 40)\nfor i in range(5):\n ax[i].imshow(Image.open(badones[i]).resize((300, 300), Image.ANTIALIAS))\nplt.show()\n\nf, ax = plt.subplots(1,5)\nf.set_size_inches(80, 40)\nfor i in range(5):\n ax[i].imshow(Image.open(badones[i+5]).resize((300, 300), Image.ANTIALIAS))\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"65dd266a-7d18-41b2-b59b-31e4606a7816","_uuid":"cb8f03fa896c209e417c7354a640d229d72bc132"},"cell_type":"markdown","source":"Low vs. Low Deal 확률 이미지에 대한 간략한 설명보다 높은 품질의 이미지가 높은 확률의 항목과 연결되어 있음을 보여줍니다.\n\n"}],"metadata":{"extensions":{"jupyter_dashboards":{"activeView":"grid_default","version":1,"views":{"grid_default":{"cellMargin":10,"defaultCellHeight":20,"maxColumns":12,"name":"grid","type":"grid"},"report_default":{"name":"report","type":"report"}}}},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.5","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}