| Feature Name | Data Type | Description | PK (Yes/No) | |
|---|---|---|---|---|
| 0 | order_id | int64 | Unique identifier for each order | Yes |
| 1 | customer_id | int64 | Unique identifier for each customer | No |
| 2 | order_date | object | The timestamp when the order was placed | No |
| 3 | promised_delivery_time | object | The time informed to the customer for completi... | No |
| 4 | actual_delivery_time | object | The actual time when the order was delivered | No |
| 5 | delivery_status | object | The delivery status of the order | No |
| 6 | order_total | float64 | The total price of the order (in Rupees) | No |
| 7 | payment_method | object | The payment method used by the customer | No |
| 8 | delivery_partner_id | int64 | Unique identifier representing the delivery pa... | No |
| 9 | store_id | int64 | Unique identifier for the store that fulfilled... | No |
Phase 1
Segmenting Blinkit customers based on their spending behaviour and delivery experience to identify distinct customer clusters (high, medium, and low spending) for targeted marketing strategies and increasing sales
Proposal
Project Statement
Across customer-centric businesses, one of the major goals for the organization is being able to predict the needs of its customers and how to best cater to their needs while maintaining high profitability. However, to make the solution feasible, the way forward would be to create groups of consumers that share similar spending patterns, allowing the businesses to create targeted marketing campaigns, improve customer satisfaction, demand-oriented product development, and therefore improve profitability.Business Goal
The objective of this project is to develop a machine learning model that can create suitable and usable clusters/segments given the profile and purchases of the customer. The model should then be able to predict, within bounds of acceptable error, which segment a customer is likely to belong to. The model should allow stakeholders to make informed and data-backed decisions on product demand, campaign successes and customer retention.Data Source
We will use the “Blinkit Sales Dataset” for this project. This dataset provides detailed information on product sales, visibility, item types, and outlet performance, making it ideal for performing sales data analysis and gaining insights into business trends. The dataset is well-structured and suitable for data preprocessing, exploratory data analysis, and predictive modeling tasks.- Source Platform: Kaggle
- Dataset: Blinkit Sales Data (Vaghasiya 2025)
Tools and Technology
Following languages and libraries are planned to be used for this project. More libraries may be used and every non trivial library shall be updated.- Python
- Core Libraries
- Data Manipulation: Pandas, NumPy
- ML: scikit-learn
- Data Visualization & Storytelling: Matplotlib, Seaborn
- Data Manipulation: Pandas, NumPy
- Development Environment: VS Code, Google Colab, GitHub
- Core Libraries
- Dashboard: Power BI
- Python
Project Workflow
The project is to follow a standard data science development lifecycle,- Data Acquisition: Fetch the dataset from Kaggle using its API.
- Preprocessing: Handle missing values (if any), encode categorical variables, and
check for data inconsistencies.
- EDA: Analyze features to understand their relationship with attrition using statistical summaries and visualizations.
- Feature Engineering: Create new features from existing ones if necessary to improve model performance.
- Modeling: Train several clustering models (e.g., K Means Clustering, PCA, Decision Trees).
- Evaluation: Assess model performance using metrics like Accuracy, Precision, Recall, and F1-Score. Select the best-performing model.
- Visualization: Create an interactive dashboard in Power BI to present the key findings and predictions to stakeholders.
- Data Acquisition: Fetch the dataset from Kaggle using its API.
Data Extraction
The “Blinkit Sales Dataset” is acquired directly from the Kaggle repository. To ensure a professional and reproducible workflow, manually downloading the files is not done.
Instead, we will perform the following steps:- Automate the Process: We will write a Python script that utilizes the official Kaggle API to connect to the source and download the dataset.
- Ensure Reproducibility: This scripted approach guarantees that the data extraction process is consistent and can be easily re-run by any team member or reviewer.
- Prepare for Analysis: The script will handle the unzipping of the downloaded files and load the data directly into a Pandas DataFrame, making it immediately available for the next phase of our project.
- Notebook: Customer Segmentation Notebook
- Automate the Process: We will write a Python script that utilizes the official Kaggle API to connect to the source and download the dataset.
Schema / Data Dictionary
This data dictionary was created after inspecting the dataset.
Phase 2
Customer Segmentation Modelling
| order_id | customer_id | order_date | promised_delivery_time | actual_delivery_time | delivery_status | order_total | payment_method | delivery_partner_id | store_id | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1961864118 | 30065862 | 2024-07-17 08:34:01 | 2024-07-17 08:52:01 | 2024-07-17 08:47:01 | On Time | 3197.07 | Cash | 63230 | 4771 |
| 1 | 1549769649 | 9573071 | 2024-05-28 13:14:29 | 2024-05-28 13:25:29 | 2024-05-28 13:27:29 | On Time | 976.55 | Cash | 14983 | 7534 |
| 2 | 9185164487 | 45477575 | 2024-09-23 13:07:12 | 2024-09-23 13:25:12 | 2024-09-23 13:29:12 | On Time | 839.05 | UPI | 39859 | 9886 |
| 3 | 9644738826 | 88067569 | 2023-11-24 16:16:56 | 2023-11-24 16:34:56 | 2023-11-24 16:33:56 | On Time | 440.23 | Card | 61497 | 7917 |
| 4 | 5427684290 | 83298567 | 2023-11-20 05:00:39 | 2023-11-20 05:17:39 | 2023-11-20 05:18:39 | On Time | 2526.68 | Cash | 84315 | 2741 |
| 5 | 3265154092 | 43367112 | 2023-03-18 16:29:51 | 2023-03-18 16:49:51 | 2023-03-18 16:48:51 | On Time | 3161.43 | UPI | 554 | 3442 |
| 6 | 4898355547 | 13284996 | 2023-04-16 18:50:37 | 2023-04-16 19:01:37 | 2023-04-16 19:02:37 | On Time | 956.40 | Card | 14630 | 1318 |
| 7 | 6568151549 | 88866835 | 2024-03-31 06:26:48 | 2024-03-31 06:37:48 | 2024-03-31 06:39:48 | On Time | 905.47 | Cash | 67714 | 115 |
| 8 | 6006693867 | 24496983 | 2023-07-13 23:49:36 | 2023-07-14 00:02:36 | 2023-07-14 00:05:36 | On Time | 1371.17 | Card | 91362 | 9021 |
| 9 | 374186990 | 52215833 | 2023-08-09 01:17:30 | 2023-08-09 01:37:30 | 2023-08-09 01:44:30 | Slightly Delayed | 1601.19 | Wallet | 77203 | 7955 |
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 order_id 5000 non-null int64
1 customer_id 5000 non-null int64
2 order_date 5000 non-null object
3 promised_delivery_time 5000 non-null object
4 actual_delivery_time 5000 non-null object
5 delivery_status 5000 non-null object
6 order_total 5000 non-null float64
7 payment_method 5000 non-null object
8 delivery_partner_id 5000 non-null int64
9 store_id 5000 non-null int64
dtypes: float64(1), int64(4), object(5)
memory usage: 390.8+ KBorder_id 0
customer_id 0
order_date 0
promised_delivery_time 0
actual_delivery_time 0
delivery_status 0
order_total 0
payment_method 0
delivery_partner_id 0
store_id 0
dtype: int64| order_id | customer_id | order_total | delivery_partner_id | store_id | |
|---|---|---|---|---|---|
| count | 5.000000e+03 | 5.000000e+03 | 5000.00000 | 5000.000000 | 5000.000000 |
| mean | 5.029129e+09 | 5.009685e+07 | 2201.86170 | 50050.318200 | 4999.689000 |
| std | 2.863533e+09 | 2.919082e+07 | 1303.02438 | 28802.276922 | 2886.089242 |
| min | 6.046500e+04 | 3.181300e+04 | 13.25000 | 43.000000 | 1.000000 |
| 25% | 2.531421e+09 | 2.404314e+07 | 1086.21500 | 24928.500000 | 2509.250000 |
| 50% | 5.074378e+09 | 4.997808e+07 | 2100.69000 | 50262.500000 | 4987.000000 |
| 75% | 7.488579e+09 | 7.621215e+07 | 3156.88250 | 74478.250000 | 7500.750000 |
| max | 9.998298e+09 | 9.989390e+07 | 6721.46000 | 99968.000000 | 9995.000000 |
| 0 | |
|---|---|
| order_id | int64 |
| customer_id | int64 |
| order_date | object |
| promised_delivery_time | object |
| actual_delivery_time | object |
| delivery_status | object |
| order_total | float64 |
| payment_method | object |
| delivery_partner_id | int64 |
| store_id | int64 |
'Shape of DataFrame:'(5000, 10)'Data Types:'order_id int64
customer_id int64
order_date object
promised_delivery_time object
actual_delivery_time object
delivery_status object
order_total float64
payment_method object
delivery_partner_id int64
store_id int64
dtype: object'First 5 Rows:'| order_id | customer_id | order_date | promised_delivery_time | actual_delivery_time | delivery_status | order_total | payment_method | delivery_partner_id | store_id | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1961864118 | 30065862 | 2024-07-17 08:34:01 | 2024-07-17 08:52:01 | 2024-07-17 08:47:01 | On Time | 3197.07 | Cash | 63230 | 4771 |
| 1 | 1549769649 | 9573071 | 2024-05-28 13:14:29 | 2024-05-28 13:25:29 | 2024-05-28 13:27:29 | On Time | 976.55 | Cash | 14983 | 7534 |
| 2 | 9185164487 | 45477575 | 2024-09-23 13:07:12 | 2024-09-23 13:25:12 | 2024-09-23 13:29:12 | On Time | 839.05 | UPI | 39859 | 9886 |
| 3 | 9644738826 | 88067569 | 2023-11-24 16:16:56 | 2023-11-24 16:34:56 | 2023-11-24 16:33:56 | On Time | 440.23 | Card | 61497 | 7917 |
| 4 | 5427684290 | 83298567 | 2023-11-20 05:00:39 | 2023-11-20 05:17:39 | 2023-11-20 05:18:39 | On Time | 2526.68 | Cash | 84315 | 2741 |
Missing Values:
order_id 0
customer_id 0
order_date 0
promised_delivery_time 0
actual_delivery_time 0
delivery_status 0
order_total 0
payment_method 0
delivery_partner_id 0
store_id 0
dtype: int64order_id 0
customer_id 0
order_date 0
promised_delivery_time 0
actual_delivery_time 0
delivery_status 0
order_total 0
payment_method 0
delivery_partner_id 0
store_id 0
dtype: int64Number of Duplicated Rows: 0Descriptive Statistics:
| order_id | customer_id | order_date | promised_delivery_time | actual_delivery_time | delivery_status | order_total | payment_method | delivery_partner_id | store_id | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000000e+03 | 5.000000e+03 | 5000 | 5000 | 5000 | 5000 | 5000.00000 | 5000 | 5000.000000 | 5000.000000 |
| unique | NaN | NaN | 5000 | 4999 | 5000 | 3 | NaN | 4 | NaN | NaN |
| top | NaN | NaN | 2024-07-17 08:34:01 | 2024-10-13 14:06:50 | 2024-07-17 08:47:01 | On Time | NaN | Card | NaN | NaN |
| freq | NaN | NaN | 1 | 2 | 1 | 3470 | NaN | 1285 | NaN | NaN |
| mean | 5.029129e+09 | 5.009685e+07 | NaN | NaN | NaN | NaN | 2201.86170 | NaN | 50050.318200 | 4999.689000 |
| std | 2.863533e+09 | 2.919082e+07 | NaN | NaN | NaN | NaN | 1303.02438 | NaN | 28802.276922 | 2886.089242 |
| min | 6.046500e+04 | 3.181300e+04 | NaN | NaN | NaN | NaN | 13.25000 | NaN | 43.000000 | 1.000000 |
| 25% | 2.531421e+09 | 2.404314e+07 | NaN | NaN | NaN | NaN | 1086.21500 | NaN | 24928.500000 | 2509.250000 |
| 50% | 5.074378e+09 | 4.997808e+07 | NaN | NaN | NaN | NaN | 2100.69000 | NaN | 50262.500000 | 4987.000000 |
| 75% | 7.488579e+09 | 7.621215e+07 | NaN | NaN | NaN | NaN | 3156.88250 | NaN | 74478.250000 | 7500.750000 |
| max | 9.998298e+09 | 9.989390e+07 | NaN | NaN | NaN | NaN | 6721.46000 | NaN | 99968.000000 | 9995.000000 |
Categorical Column Summary:
| order_date | promised_delivery_time | actual_delivery_time | delivery_status | payment_method | |
|---|---|---|---|---|---|
| count | 5000 | 5000 | 5000 | 5000 | 5000 |
| unique | 5000 | 4999 | 5000 | 3 | 4 |
| top | 2024-07-17 08:34:01 | 2024-10-13 14:06:50 | 2024-07-17 08:47:01 | On Time | Card |
| freq | 1 | 2 | 1 | 3470 | 1285 |
Rows with Invalid (≤0) Order Total:
Empty DataFrame
Columns: [order_id, customer_id, order_date, promised_delivery_time, actual_delivery_time, delivery_status, order_total, payment_method, delivery_partner_id, store_id]
Index: []
Rows with Invalid Delivery Times (Before Order Date):
order_id customer_id order_date promised_delivery_time \
1281 2148242015 39093069 30-04-23 23:48 2023-01-05 00:04:00
1563 7821942520 84468124 12-02-24 23:57 2024-02-13 00:12:00
2933 3368317718 70867736 31-05-24 23:59 2024-01-06 00:17:00
actual_delivery_time delivery_status order_total payment_method \
1281 2023-01-05 00:33:00 Significantly Delayed 3239.66 UPI
1563 2024-02-13 00:13:00 On Time 2732.91 Wallet
2933 2024-01-06 00:12:00 On Time 914.88 UPI
delivery_partner_id store_id
1281 13676 9904
1563 11136 5805
2933 96055 522 Unique Value Counts per Column:
order_id 5000
customer_id 2172
order_date 5000
promised_delivery_time 4999
actual_delivery_time 5000
delivery_status 3
order_total 4550
payment_method 4
delivery_partner_id 5000
store_id 5000
dtype: int64References
Vaghasiya, Akshit. 2025. “Blinkit Sales Dataset.” https://www.kaggle.com/datasets/akxiit/blinkit-sales-dataset.