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.

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## 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.

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## 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

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## 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.

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## 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 |

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## 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