https://github.com/iankitnegi/python_projects

Data Analyst Toolkit: A comprehensive collection of Python scripts and notebooks designed for data analysis tasks. Features data cleaning, visualization, statistical analysis, and machine learning models. Ideal for analysts seeking efficient, reproducible workflows.
https://github.com/iankitnegi/python_projects

matplotlib numpy pandas python seaborn

Last synced: 13 days ago
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Data Analyst Toolkit: A comprehensive collection of Python scripts and notebooks designed for data analysis tasks. Features data cleaning, visualization, statistical analysis, and machine learning models. Ideal for analysts seeking efficient, reproducible workflows.

• Host: GitHub
• URL: https://github.com/iankitnegi/python_projects
• Owner: iankitnegi
• Created: 2024-06-19T17:52:03.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-31T06:34:29.000Z (5 months ago)
• Last Synced: 2024-08-01T00:15:11.431Z (5 months ago)
• Topics: matplotlib, numpy, pandas, python, seaborn
• Language: HTML
• Homepage:
• Size: 722 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Portfolio Projects

### 1. AtliQ Hotels Data Analysis  

Datasets: 1. dim_date.csv, 2. dim_hotels.csv, 3. dim_rooms.csv, 4. fact_aggregated_bookings & 5. fact_bookings.csv

-  Data Import & Data Exploration:

    - Read bookings data in a dataframe

    - Explore bookings data

    - Read rest of the files

      - _Exercise-1. Find out unique property ids in aggregate bookings dataset_

      - _Exercise-2. Find out total bookings per property_id_

      - _Exercise-3. Find out days on which bookings are greater than capacity_

      - _Exercise-4. Find out properties that have highest capacity_

-  Data Cleaning:

    - Clean invalid guests

    - Outlier removal in revenue generated

      - _Exercise-1. In aggregate bookings find columns that have null values. Fill these null values with whatever you think is the appropriate subtitute (possible ways is to use mean or median)_

      - _Exercise-2. In aggregate bookings find out records that have successful_bookings value greater than capacity. Filter those records_

-  Data Transformation:

    - Create occupancy percentage column

    - Convert it to a percentage value

    - There are various types of data transformations that you may have to perform based on the need. Few examples of data transformations are Creating new columns, Normalization, Merging data & Aggregation

-  Insights Generation:

   - What is an average occupancy rate in each of the room categories?

   - Print average occupancy rate per city

   - When was the occupancy better? Weekday or Weekend?

   - In the month of June, what is the occupancy for different cities

   - We got new data for the month of august. Append that to existing data

   - Print revenue realized per city

   - Print month by month revenue

     - _Exercise-1. Print revenue realized per hotel type_

     - _Exercise-2. Print average rating per city_

     - _Exercise-3. Print a pie chart of revenue realized per booking platform_