{"id":25909187,"url":"https://github.com/abhinav330/instagram-influencers-analysis","last_synced_at":"2025-03-03T08:16:40.103Z","repository":{"id":254762323,"uuid":"847467805","full_name":"Abhinav330/Instagram-Influencers-Analysis","owner":"Abhinav330","description":"This Jupyter Notebook focuses on preprocessing and visualizing data from an Instagram profiles dataset. 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It includes data loading, inspection, visualization, and some data preprocessing steps.\n\n## Data Loading and Basic Inspection\n- The script loads a dataset from a CSV file named 'Instagram Profiles - Github Hashtag - instagram_profile.csv' into a Pandas DataFrame named `df`.\n- It displays the first few rows and provides information about the dataset using `df.head()` and `df.info()`.\n\n## Data Visualization Class\n- The script defines a Python class named `visual_preprocess` to encapsulate data visualization and preprocessing functions.\n\n## Data Exploration and Preprocessing\n- The class contains various methods for exploring and preprocessing the data:\n  - `_row_col`: Helper function to calculate the number of rows and columns in the DataFrame.\n  - `disp_tot_row_col`: Displays the total row and column count.\n  - `missingv`: Visualizes missing values using a heatmap.\n  - `_null_calculator`: Helper function to calculate the percentage of null values in columns.\n  - `null_percentage`: Calculates and displays columns with a specified percentage of null values.\n  - `get_col_empty`: Returns columns with null values above a specified threshold.\n\n## Data Cleaning\n- Columns with a high percentage of null values (above 50%) are dropped from the DataFrame.\n\n## Data Visualization\n- Various data visualizations are created using Seaborn and Matplotlib, including:\n  - Distribution of 'posts_count' using a histogram.\n  - Filtering and exploration of records with 'posts_count' greater than 2000.\n  - Scatterplots of various features ('followers', 'following', 'highlights_count', etc.) with respect to different account types and privacy settings.\n  - Bar plots showing relationships between 'is_business_account' and 'is_professional_account' with 'followers' and 'following'.\n  - Additional scatterplots exploring features related to 'following' and 'followers'.\n\n## Hashtag Analysis\n- The script defines a function (`hashtag_freq`) to extract and analyze hashtags from the 'post_hashtags' column.\n- The function counts the frequency of hashtags and displays the top 10 most frequently used hashtags in the dataset.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinav330%2Finstagram-influencers-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhinav330%2Finstagram-influencers-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinav330%2Finstagram-influencers-analysis/lists"}