{"id":18244331,"url":"https://github.com/manikantasanjay/data_analysis_using_python_libraries_series","last_synced_at":"2026-04-12T13:45:02.412Z","repository":{"id":130816696,"uuid":"381718160","full_name":"ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series","owner":"ManikantaSanjay","description":"This Series contains Data Analysis projects performed on different Kaggle datasets and providing valuable insights into the data by making use of  Python libraries.","archived":false,"fork":false,"pushed_at":"2021-08-18T11:47:35.000Z","size":5080,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-14T14:51:45.472Z","etag":null,"topics":["exploratory-data-analysis","kaggle-datasets","machine-learning","matplotlib","numpy","opendatasets","pandas","python","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ManikantaSanjay.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-06-30T13:49:49.000Z","updated_at":"2024-04-25T05:18:12.000Z","dependencies_parsed_at":"2023-05-18T01:46:19.928Z","dependency_job_id":null,"html_url":"https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FData_Analysis_Using_Python_Libraries_Series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FData_Analysis_Using_Python_Libraries_Series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FData_Analysis_Using_Python_Libraries_Series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ManikantaSanjay%2FData_Analysis_Using_Python_Libraries_Series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ManikantaSanjay","download_url":"https://codeload.github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247908951,"owners_count":21016474,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["exploratory-data-analysis","kaggle-datasets","machine-learning","matplotlib","numpy","opendatasets","pandas","python","visualization"],"created_at":"2024-11-05T09:16:17.201Z","updated_at":"2026-04-12T13:45:02.330Z","avatar_url":"https://github.com/ManikantaSanjay.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data_Analysis_Using_Python_Libraries_Series\nThis Repo contains various Data Analysis on different Kaggle datasets and providing insights into the dataset by making use of Python libraries.\n## 1\u003e Facebook Data Analytics\n### :one: Dataset :\n#### https://www.kaggle.com/sheenabatra/facebook-data :link:\n### 2️⃣ Processes Involved :\n#### -\u003eData Preparation and Cleaning\n#### -\u003eExploratory Analysis and Visualization\n#### -\u003eQuestions and Answers\n#### -\u003eInferences and Conclusion\n### :three: Libraries Used :\n#### -\u003eopendatasets - It is a Python library for downloading datasets from online sources like Kaggle and Google Drive.\n#### -\u003enumpy \n#### -\u003epandas\n#### -\u003ematplotlib\n### :four: Google Collab Notebook File :\nNavigate to the Below Link: :point_down:\n#### https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/Facebook_Data_Analysis.ipynb :link:\n\nClick on  \u003cb\u003eOpen in Collab\u003c/b\u003e  at the top and fork it.\n## 2\u003e Data Analytics On Zomato Restaurants\n### 1️⃣ Content in Dataset :\n* Restaurant Id\n* Restaurant Name\n* Country Code\n* City\n* Address\n* Locality\n* Locality Verbose\n* Longitude\n* Latitude\n* Cuisines\n* Average Cost for two\n* Currency\n* Has Table booking\n* Has Online delivery\n* Is delivering\n* Switch to order menu\n* Price Range\n* Aggregate Rating\n* Rating color\n* Rating text\n* Votes\n\n### 2️⃣ Downloading the Dataset :\n#### By making use of the opendatasets library we download the dataset required for our EDA(Exploratory Data Analysis)\n#### Dataset URL : https://www.kaggle.com/shrutimehta/zomato-restaurants-data :link:\n\n### 3️⃣ Importing Dataset : \n#### By making use of the pandas library we import the dataset into a pandas dataframe.\n\n### 4️⃣ Exploratory Analysis and Visualization :\nIt deals with  the following aspects : \n#### -\u003e Understanding the Geographical Spread \n#### -\u003e Understanding the Rating Aggregate, Color and Text\n#### -\u003e Understanding the Country and their Respective Currency\n#### -\u003e Understanding the Online Delivery Distribution\n#### -\u003e Understanding the Coverage of the City\n\n### 5️⃣ Asking and Answering Questions :\n\n#### After understanding the several insights about the restaurants present in the survey, we try to answer them using data frame operations and visualizations.\n#### We ask the following questions:\n\nQ1: From which Locality Maximum hotels are listed in Zomato ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-1.png?raw=true)\n\nQ2: What kind of Cuisine these highly rates restaurants offer ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-2.png?raw=true)\n\nQ3: How many of such restaurants accept online delivery ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-3.png?raw=true)\n\nQ4: Understanding the Restaurants Rating locality wise ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-4.png?raw=true)\n\nQ5: Understanding the Relation between Rating VS Cost of Dining ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-5.png?raw=true)\n\nQ6: Location of Highly Rated restaurants across New Delhi ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-6.png?raw=true)\n\nQ7: Understanding about Common Eateries ?\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-7(1).png?raw=true)\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-7(2).png?raw=true)\n\n![alt-text](https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/answers-zomato/answer-7(3).png?raw=true)\n\n### 6️⃣ Inferences and Conclusions :\n\n#### We've drawn many inferences from the survey and we discuss them in this part of the project.\n\n### 7️⃣ Steps to Use :\nNavigate to the Below Link: :point_down:\n#### https://github.com/ManikantaSanjay/Data_Analysis_Using_Python_Libraries_Series/blob/main/Data_Analysis_on_Restaurants_Listed_In_Zomato.ipynb :link:\n\nClick on  \u003cb\u003eOpen in Collab\u003c/b\u003e  at the top and fork it.\n\n## Add a star 🌟 to the repo if u like it.:smiley: Thank You :v:\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanikantasanjay%2Fdata_analysis_using_python_libraries_series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmanikantasanjay%2Fdata_analysis_using_python_libraries_series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanikantasanjay%2Fdata_analysis_using_python_libraries_series/lists"}