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This project analyzes customer subscription and listening
behavior data from a music streaming platform to identify patterns
related to customer churn.
The workflow covers the complete data analytics process,
including data collection, cleaning, transformation, exploratory data
analysis (EDA), feature engineering, and preparation of datasets for
predictive modeling.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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👋 Hi everyone!
My name is Anastasiia, though most people know me as Rada.
I’m a Product Designer from Ukraine who enjoys turning complex problems into simple, meaningful experiences.
While design is my profession, it’s not the only thing that shapes the way I think. I also teach DJing, and both worlds have taught me the same lesson: great experiences are never accidental. They are carefully designed.
I’m fascinated by user behavior, product strategy, research, and the decisions that turn ideas into products people genuinely want to use.
I joined this challenge to share my thoughts, learn from others, and become more consistent in creating content.
Here you'll find insights about UX, product thinking, research, design processes, lessons learned, and sometimes a few stories from the music world as well.
Looking forward to connecting with you all 🚀
Welcome! ✨ I love the connection between product design and DJing—both are really about understanding people, reading the room, and creating experiences that feel effortless. 🚀
MURMUR
https://murmur.figma.site
Little messages of positivity, set loose on Earth's real wind.
We send more words than ever, and almost none of them are held anywhere. They scroll past and vanish.
Murmur takes a positive message from a real person and releases it into Earth's actual jet stream, carried across the planet on live wind data, kept in a shared archive anyone can add to.
It's guided by a single constraint: everything is typography.
The wind is type.
The messages are type.
The atmosphere itself is made of language.
Live jet-stream data from Open-Meteo drives the movement of every message;
Even the sound is generated from the same atmospheric data.
Supabase holds the growing archive contributed by people around the world.
Built with Figma Make, Figma Agent, Supabase, Open-Meteo, and Weave.
One idea, one constraint, and a place for meaningful words to flow.
Looks cool
Data Cleaning and Sales Analysis
## Project Overview
This project demonstrates an end-to-end data cleaning and exploratory data analysis (EDA) workflow using Python.
The dataset was intentionally generated with multiple data quality issues to simulate real-world business scenarios commonly encountered by Data Analysts and Data Scientists.
---
## Objectives
- Identify data quality issues.
- Handle missing values.
- Remove duplicate records.
- Standardize mixed date formats.
- Perform exploratory data analysis.
- Generate business insights.
- Create visualizations for decision-making.
---
## Dataset Issues
The raw dataset contained several intentional problems:
- Missing values in `Qty`
- Missing values in `Harga`
- Duplicate transactions
- Mixed date formats
- Inconsistent category naming
---
## Data Cleaning Process
The following steps were performed:
1. Loaded and profiled the raw dataset.
2. Identified missing values and duplicate records.
3. Removed duplicate transactions.
4. Filled missing values using median imputation.
5. Investigated mixed date formats.
6. Built a custom date parser to standardize dates.
7. Saved the cleaned dataset.
---
## Results
### Before Cleaning
| Metric | Value |
|----------|---------|
| Total Records | 1009 |
| Missing Qty | 8 |
| Missing Harga | 5 |
| Duplicate Records | 10 |
# After Cleaning
| Metric | Value |
|----------|---------|
| Total Records | 999 |
| Missing Qty | 0 |
| Missing Harga | 0 |
| Duplicate Records | 0 |
| Failed Date Parsing | 0 |
---
## Business Insights
### Best-Selling Products
Kopi Arabica was the top-selling product, followed by Teh Hijau and Mouse.
### Sales by City
Bandung generated the highest sales volume, indicating strong market potential compared to Surabaya and Jakarta.
### Category Performance
Electronics dominated sales performance.
An inconsistency between `Makanan` and `makanan` was discovered, highlighting the importance of data standardization before analysis.
### Revenue
The total revenue generated was:
Rp 13,593,130,000
## Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
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